From 212b5f1d94341850e44e1caf6b317c96a0feee55 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Tue, 16 Sep 2025 14:59:29 +0200 Subject: [PATCH 001/139] Minor changes, issue still not fixed --- .../databases/hybrid/falkordb/FalkorDBAdapter.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py index cb6899925..64da07324 100644 --- a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py +++ b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py @@ -4,7 +4,6 @@ import asyncio import json from textwrap import dedent from uuid import UUID -from webbrowser import Error from typing import List, Dict, Any, Optional, Tuple, Type, Union from falkordb import FalkorDB @@ -427,7 +426,7 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): for index in indices.result_set ] ) - except Error as e: + except Exception as e: print(e) return False From c391be41d6234b94fb59e5de3463fff480383bd0 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Wed, 17 Sep 2025 12:09:57 +0200 Subject: [PATCH 002/139] refactor: Remove falkordb adapter from main repo, since we have it on community --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 +- .../databases/graph/get_graph_engine.py | 25 +- .../hybrid/falkordb/FalkorDBAdapter.py | 1226 ----------------- .../databases/vector/create_vector_engine.py | 15 +- cognee/shared/data_models.py | 1 - cognee/tests/test_falkordb.py | 174 --- distributed/pyproject.toml | 1 - docker-compose.yml | 11 - .../database_examples/falkordb_example.py | 87 -- pyproject.toml | 1 - 10 files changed, 5 insertions(+), 1538 deletions(-) delete mode 100644 cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py delete mode 100755 cognee/tests/test_falkordb.py delete mode 100644 examples/database_examples/falkordb_example.py diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 7705a51eb..5a6d7b124 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -58,7 +58,7 @@ body: - Python version: [e.g. 3.9.0] - Cognee version: [e.g. 0.1.0] - LLM Provider: [e.g. OpenAI, Ollama] - - Database: [e.g. Neo4j, FalkorDB] + - Database: [e.g. Neo4j] validations: required: true diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index 4ec0eb483..594744983 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -32,7 +32,6 @@ def create_graph_engine( graph_database_name="", graph_database_username="", graph_database_password="", - graph_database_port="", ): """ Create a graph engine based on the specified provider type. @@ -45,15 +44,12 @@ def create_graph_engine( ----------- - graph_database_provider: The type of graph database provider to use (e.g., neo4j, - falkordb, kuzu). - - graph_database_url: The URL for the graph database instance. Required for neo4j - and falkordb providers. + kuzu). + - graph_database_url: The URL for the graph database instance. Required for neo4j. - graph_database_username: The username for authentication with the graph database. Required for neo4j provider. - graph_database_password: The password for authentication with the graph database. Required for neo4j provider. - - graph_database_port: The port number for the graph database connection. Required - for the falkordb provider. - graph_file_path: The filesystem path to the graph file. Required for the kuzu provider. @@ -86,21 +82,6 @@ def create_graph_engine( graph_database_name=graph_database_name or None, ) - elif graph_database_provider == "falkordb": - if not (graph_database_url and graph_database_port): - raise EnvironmentError("Missing required FalkorDB credentials.") - - from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine - from cognee.infrastructure.databases.hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter - - embedding_engine = get_embedding_engine() - - return FalkorDBAdapter( - database_url=graph_database_url, - database_port=graph_database_port, - embedding_engine=embedding_engine, - ) - elif graph_database_provider == "kuzu": if not graph_file_path: raise EnvironmentError("Missing required Kuzu database path.") @@ -179,5 +160,5 @@ def create_graph_engine( raise EnvironmentError( f"Unsupported graph database provider: {graph_database_provider}. " - f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'falkordb', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}" + f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}" ) diff --git a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py deleted file mode 100644 index 64da07324..000000000 --- a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py +++ /dev/null @@ -1,1226 +0,0 @@ -import asyncio - -# from datetime import datetime -import json -from textwrap import dedent -from uuid import UUID -from typing import List, Dict, Any, Optional, Tuple, Type, Union - -from falkordb import FalkorDB - -from cognee.infrastructure.databases.exceptions import MissingQueryParameterError -from cognee.infrastructure.databases.graph.graph_db_interface import ( - GraphDBInterface, - record_graph_changes, - NodeData, - EdgeData, - Node, -) -from cognee.infrastructure.databases.vector.embeddings import EmbeddingEngine -from cognee.infrastructure.databases.vector.vector_db_interface import VectorDBInterface -from cognee.infrastructure.engine import DataPoint - - -class IndexSchema(DataPoint): - """ - Define a schema for indexing that includes text data and associated metadata. - - This class inherits from the DataPoint class. It contains a string attribute 'text' and - a dictionary 'metadata' that specifies the index fields for this schema. - """ - - text: str - - metadata: dict = {"index_fields": ["text"]} - - -class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): - """ - Manage and interact with a graph database using vector embeddings. - - Public methods include: - - query - - embed_data - - stringify_properties - - create_data_point_query - - create_edge_query - - create_collection - - has_collection - - create_data_points - - create_vector_index - - has_vector_index - - index_data_points - - add_node - - add_nodes - - add_edge - - add_edges - - has_edges - - retrieve - - extract_node - - extract_nodes - - get_connections - - search - - batch_search - - get_graph_data - - delete_data_points - - delete_node - - delete_nodes - - delete_graph - - prune - - get_node - - get_nodes - - get_neighbors - - get_graph_metrics - - get_document_subgraph - - get_degree_one_nodes - """ - - def __init__( - self, - database_url: str, - database_port: int, - embedding_engine=EmbeddingEngine, - ): - self.driver = FalkorDB( - host=database_url, - port=database_port, - ) - self.embedding_engine = embedding_engine - self.graph_name = "cognee_graph" - - def query(self, query: str, params: dict = {}): - """ - Execute a query against the graph database. - - Handles exceptions during the query execution by logging errors and re-raising the - exception. - - The method can be called only if a valid query string and parameters are provided. - - Parameters: - ----------- - - - query (str): The query string to be executed against the graph database. - - params (dict): A dictionary of parameters to be used in the query. (default {}) - - Returns: - -------- - - The result of the query execution, returned by the graph database. - """ - graph = self.driver.select_graph(self.graph_name) - - try: - result = graph.query(query, params) - return result - except Exception as e: - print(f"Error executing query: {e}") - raise e - - async def embed_data(self, data: list[str]) -> list[list[float]]: - """ - Embed a list of text data into vector representations using the embedding engine. - - Parameters: - ----------- - - - data (list[str]): A list of strings that should be embedded into vectors. - - Returns: - -------- - - - list[list[float]]: A list of lists, where each inner list contains float values - representing the embedded vectors. - """ - return await self.embedding_engine.embed_text(data) - - async def stringify_properties(self, properties: dict) -> str: - """ - Convert properties dictionary to a string format suitable for database queries. - - Parameters: - ----------- - - - properties (dict): A dictionary containing properties to be converted to string - format. - - Returns: - -------- - - - str: A string representation of the properties in the appropriate format. - """ - - def parse_value(value): - """ - Convert a value to its string representation based on type for database queries. - - Parameters: - ----------- - - - value: The value to parse into a string representation. - - Returns: - -------- - - Returns the string representation of the value in the appropriate format. - """ - if type(value) is UUID: - return f"'{str(value)}'" - if type(value) is int or type(value) is float: - return value - if ( - type(value) is list - and len(value) > 0 - and type(value[0]) is float - and len(value) == self.embedding_engine.get_vector_size() - ): - return f"'vecf32({value})'" - # if type(value) is datetime: - # return datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%f%z") - if type(value) is dict: - return f"'{json.dumps(value).replace(chr(39), chr(34))}'" - if type(value) is str: - # Escape single quotes and handle special characters - escaped_value = ( - str(value) - .replace("'", "\\'") - .replace('"', '\\"') - .replace("\n", "\\n") - .replace("\r", "\\r") - .replace("\t", "\\t") - ) - return f"'{escaped_value}'" - return f"'{str(value)}'" - - return ",".join([f"{key}:{parse_value(value)}" for key, value in properties.items()]) - - async def create_data_point_query(self, data_point: DataPoint, vectorized_values: dict): - """ - Compose a query to create or update a data point in the database. - - Parameters: - ----------- - - - data_point (DataPoint): An instance of DataPoint containing information about the - entity. - - vectorized_values (dict): A dictionary of vectorized values related to the data - point. - - Returns: - -------- - - A tuple containing the query string and parameters dictionary. - """ - node_label = type(data_point).__name__ - property_names = DataPoint.get_embeddable_property_names(data_point) - - properties = { - **data_point.model_dump(), - **( - { - property_names[index]: ( - vectorized_values[index] - if index < len(vectorized_values) - else getattr(data_point, property_name, None) - ) - for index, property_name in enumerate(property_names) - } - ), - } - - # Clean the properties - remove None values and handle special types - clean_properties = {} - for key, value in properties.items(): - if value is not None: - if isinstance(value, UUID): - clean_properties[key] = str(value) - elif isinstance(value, dict): - clean_properties[key] = json.dumps(value) - elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float): - # This is likely a vector - convert to string representation - clean_properties[key] = f"vecf32({value})" - else: - clean_properties[key] = value - - query = dedent( - f""" - MERGE (node:{node_label} {{id: $node_id}}) - SET node += $properties, node.updated_at = timestamp() - """ - ).strip() - - params = {"node_id": str(data_point.id), "properties": clean_properties} - - return query, params - - def sanitize_relationship_name(self, relationship_name: str) -> str: - """ - Sanitize relationship name to be valid for Cypher queries. - - Parameters: - ----------- - - relationship_name (str): The original relationship name - - Returns: - -------- - - str: A sanitized relationship name valid for Cypher - """ - # Replace hyphens, spaces, and other special characters with underscores - import re - - sanitized = re.sub(r"[^\w]", "_", relationship_name) - # Remove consecutive underscores - sanitized = re.sub(r"_+", "_", sanitized) - # Remove leading/trailing underscores - sanitized = sanitized.strip("_") - # Ensure it starts with a letter or underscore - if sanitized and not sanitized[0].isalpha() and sanitized[0] != "_": - sanitized = "_" + sanitized - return sanitized or "RELATIONSHIP" - - async def create_edge_query(self, edge: tuple[str, str, str, dict]) -> str: - """ - Generate a query to create or update an edge between two nodes in the graph. - - Parameters: - ----------- - - - edge (tuple[str, str, str, dict]): A tuple consisting of source and target node - IDs, edge type, and edge properties. - - Returns: - -------- - - - str: A string containing the query to be executed for creating the edge. - """ - # Sanitize the relationship name for Cypher compatibility - sanitized_relationship = self.sanitize_relationship_name(edge[2]) - - # Add the original relationship name to properties - edge_properties = {**edge[3], "relationship_name": edge[2]} - properties = await self.stringify_properties(edge_properties) - properties = f"{{{properties}}}" - - return dedent( - f""" - MERGE (source {{id:'{edge[0]}'}}) - MERGE (target {{id: '{edge[1]}'}}) - MERGE (source)-[edge:{sanitized_relationship} {properties}]->(target) - ON MATCH SET edge.updated_at = timestamp() - ON CREATE SET edge.updated_at = timestamp() - """ - ).strip() - - async def create_collection(self, collection_name: str): - """ - Create a collection in the graph database with the specified name. - - Parameters: - ----------- - - - collection_name (str): The name of the collection to be created. - """ - pass - - async def has_collection(self, collection_name: str) -> bool: - """ - Check if a collection with the specified name exists in the graph database. - - Parameters: - ----------- - - - collection_name (str): The name of the collection to check for existence. - - Returns: - -------- - - - bool: Returns true if the collection exists, otherwise false. - """ - collections = self.driver.list_graphs() - - return collection_name in collections - - async def create_data_points(self, data_points: list[DataPoint]): - """ - Add a list of data points to the graph database via batching. - - Can raise exceptions if there are issues during the database operations. - - Parameters: - ----------- - - - data_points (list[DataPoint]): A list of DataPoint instances to be inserted into - the database. - """ - embeddable_values = [] - vector_map = {} - - for data_point in data_points: - property_names = DataPoint.get_embeddable_property_names(data_point) - key = str(data_point.id) - vector_map[key] = {} - - for property_name in property_names: - property_value = getattr(data_point, property_name, None) - - if property_value is not None: - vector_map[key][property_name] = len(embeddable_values) - embeddable_values.append(property_value) - else: - vector_map[key][property_name] = None - - vectorized_values = await self.embed_data(embeddable_values) - - for data_point in data_points: - vectorized_data = [ - vectorized_values[vector_map[str(data_point.id)][property_name]] - if vector_map[str(data_point.id)][property_name] is not None - else None - for property_name in DataPoint.get_embeddable_property_names(data_point) - ] - - query, params = await self.create_data_point_query(data_point, vectorized_data) - self.query(query, params) - - async def create_vector_index(self, index_name: str, index_property_name: str): - """ - Create a vector index in the specified graph for a given property if it does not already - exist. - - Parameters: - ----------- - - - index_name (str): The name of the vector index to be created. - - index_property_name (str): The name of the property on which the vector index will - be created. - """ - graph = self.driver.select_graph(self.graph_name) - - if not self.has_vector_index(graph, index_name, index_property_name): - graph.create_node_vector_index( - index_name, index_property_name, dim=self.embedding_engine.get_vector_size() - ) - - def has_vector_index(self, graph, index_name: str, index_property_name: str) -> bool: - """ - Determine if a vector index exists on the specified property of the given graph. - - Parameters: - ----------- - - - graph: The graph instance to check for the vector index. - - index_name (str): The name of the index to check for existence. - - index_property_name (str): The property name associated with the index. - - Returns: - -------- - - - bool: Returns true if the vector index exists, otherwise false. - """ - try: - indices = graph.list_indices() - - return any( - [ - (index[0] == index_name and index_property_name in index[1]) - for index in indices.result_set - ] - ) - except Exception as e: - print(e) - return False - - async def index_data_points( - self, index_name: str, index_property_name: str, data_points: list[DataPoint] - ): - """ - Index a list of data points in the specified graph database based on properties. - - To be implemented: does not yet have a defined behavior. - - Parameters: - ----------- - - - index_name (str): The name of the index to be created for the data points. - - index_property_name (str): The property name on which to index the data points. - - data_points (list[DataPoint]): A list of DataPoint instances to be indexed. - """ - pass - - async def add_node(self, node_id: str, properties: Dict[str, Any]) -> None: - """ - Add a single node with specified properties to the graph. - - Parameters: - ----------- - - - node_id (str): Unique identifier for the node being added. - - properties (Dict[str, Any]): A dictionary of properties associated with the node. - """ - # Clean the properties - remove None values and handle special types - clean_properties = {"id": node_id} - for key, value in properties.items(): - if value is not None: - if isinstance(value, UUID): - clean_properties[key] = str(value) - elif isinstance(value, dict): - clean_properties[key] = json.dumps(value) - elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float): - # This is likely a vector - convert to string representation - clean_properties[key] = f"vecf32({value})" - else: - clean_properties[key] = value - - query = "MERGE (node {id: $node_id}) SET node += $properties, node.updated_at = timestamp()" - params = {"node_id": node_id, "properties": clean_properties} - - self.query(query, params) - - # Helper methods for DataPoint compatibility - async def add_data_point_node(self, node: DataPoint): - """ - Add a single data point as a node in the graph. - - Parameters: - ----------- - - - node (DataPoint): An instance of DataPoint to be added to the graph. - """ - await self.create_data_points([node]) - - async def add_data_point_nodes(self, nodes: list[DataPoint]): - """ - Add multiple data points as nodes in the graph. - - Parameters: - ----------- - - - nodes (list[DataPoint]): A list of DataPoint instances to be added to the graph. - """ - await self.create_data_points(nodes) - - @record_graph_changes - async def add_nodes(self, nodes: Union[List[Node], List[DataPoint]]) -> None: - """ - Add multiple nodes to the graph in a single operation. - - Parameters: - ----------- - - - nodes (Union[List[Node], List[DataPoint]]): A list of Node tuples or DataPoint objects to be added to the graph. - """ - for node in nodes: - if isinstance(node, tuple) and len(node) == 2: - # Node is in (node_id, properties) format - node_id, properties = node - await self.add_node(node_id, properties) - elif hasattr(node, "id") and hasattr(node, "model_dump"): - # Node is a DataPoint object - await self.add_node(str(node.id), node.model_dump()) - else: - raise ValueError( - f"Invalid node format: {node}. Expected tuple (node_id, properties) or DataPoint object." - ) - - async def add_edge( - self, - source_id: str, - target_id: str, - relationship_name: str, - properties: Optional[Dict[str, Any]] = None, - ) -> None: - """ - Create a new edge between two nodes in the graph. - - Parameters: - ----------- - - - source_id (str): The unique identifier of the source node. - - target_id (str): The unique identifier of the target node. - - relationship_name (str): The name of the relationship to be established by the - edge. - - properties (Optional[Dict[str, Any]]): Optional dictionary of properties - associated with the edge. (default None) - """ - if properties is None: - properties = {} - - edge_tuple = (source_id, target_id, relationship_name, properties) - query = await self.create_edge_query(edge_tuple) - self.query(query) - - @record_graph_changes - async def add_edges(self, edges: List[EdgeData]) -> None: - """ - Add multiple edges to the graph in a single operation. - - Parameters: - ----------- - - - edges (List[EdgeData]): A list of EdgeData objects representing edges to be added. - """ - for edge in edges: - if isinstance(edge, tuple) and len(edge) == 4: - # Edge is in (source_id, target_id, relationship_name, properties) format - source_id, target_id, relationship_name, properties = edge - await self.add_edge(source_id, target_id, relationship_name, properties) - else: - raise ValueError( - f"Invalid edge format: {edge}. Expected tuple (source_id, target_id, relationship_name, properties)." - ) - - async def has_edges(self, edges): - """ - Check if the specified edges exist in the graph based on their attributes. - - Parameters: - ----------- - - - edges: A list of edges to check for existence in the graph. - - Returns: - -------- - - Returns a list of edge tuples that exist in the graph. - """ - existing_edges = [] - for edge in edges: - exists = await self.has_edge(str(edge[0]), str(edge[1]), edge[2]) - if exists: - existing_edges.append(edge) - return existing_edges - - async def retrieve(self, data_point_ids: list[UUID]): - """ - Retrieve data points from the graph based on their IDs. - - Parameters: - ----------- - - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - retrieve. - - Returns: - -------- - - Returns the result set containing the retrieved nodes or an empty list if not found. - """ - result = self.query( - "MATCH (node) WHERE node.id IN $node_ids RETURN node", - { - "node_ids": [str(data_point) for data_point in data_point_ids], - }, - ) - return result.result_set - - async def extract_node(self, data_point_id: UUID): - """ - Extract the properties of a single node identified by its data point ID. - - Parameters: - ----------- - - - data_point_id (UUID): The UUID of the data point to extract. - - Returns: - -------- - - Returns the properties of the node if found, otherwise None. - """ - result = await self.retrieve([data_point_id]) - result = result[0][0] if len(result[0]) > 0 else None - return result.properties if result else None - - async def extract_nodes(self, data_point_ids: list[UUID]): - """ - Extract properties of multiple nodes identified by their data point IDs. - - Parameters: - ----------- - - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - extract. - - Returns: - -------- - - Returns the properties of the nodes in a list. - """ - return await self.retrieve(data_point_ids) - - async def get_connections(self, node_id: UUID) -> list: - """ - Retrieve connection details (predecessors and successors) for a given node ID. - - Parameters: - ----------- - - - node_id (UUID): The UUID of the node whose connections are to be retrieved. - - Returns: - -------- - - - list: Returns a list of tuples representing the connections of the node. - """ - predecessors_query = """ - MATCH (node)<-[relation]-(neighbour) - WHERE node.id = $node_id - RETURN neighbour, relation, node - """ - successors_query = """ - MATCH (node)-[relation]->(neighbour) - WHERE node.id = $node_id - RETURN node, relation, neighbour - """ - - predecessors, successors = await asyncio.gather( - self.query(predecessors_query, dict(node_id=node_id)), - self.query(successors_query, dict(node_id=node_id)), - ) - - connections = [] - - for neighbour in predecessors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - for neighbour in successors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - return connections - - async def search( - self, - collection_name: str, - query_text: str = None, - query_vector: list[float] = None, - limit: int = 10, - with_vector: bool = False, - ): - """ - Search for nodes in a collection based on text or vector query, with optional limitation - on results. - - Parameters: - ----------- - - - collection_name (str): The name of the collection in which to search. - - query_text (str): The text to search for (if using text-based query). (default - None) - - query_vector (list[float]): The vector representation of the query if using - vector-based search. (default None) - - limit (int): Maximum number of results to return from the search. (default 10) - - with_vector (bool): Flag indicating whether to return vectors with the search - results. (default False) - - Returns: - -------- - - Returns the search results as a result set from the graph database. - """ - if query_text is None and query_vector is None: - raise MissingQueryParameterError() - - if query_text and not query_vector: - query_vector = (await self.embed_data([query_text]))[0] - - # For FalkorDB, let's do a simple property-based search instead of vector search for now - # since the vector index might not be set up correctly - if "." in collection_name: - [label, attribute_name] = collection_name.split(".") - else: - # If no dot, treat the whole thing as a property search - label = "" - attribute_name = collection_name - - # Simple text-based search if we have query_text - if query_text: - if label: - query = f""" - MATCH (n:{label}) - WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text) - RETURN n, 1.0 as score - LIMIT $limit - """ - else: - query = f""" - MATCH (n) - WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text) - RETURN n, 1.0 as score - LIMIT $limit - """ - - params = {"query_text": query_text, "limit": limit} - result = self.query(query, params) - return result.result_set - else: - # For vector search, return empty for now since vector indexing needs proper setup - return [] - - async def batch_search( - self, - collection_name: str, - query_texts: list[str], - limit: int = None, - with_vectors: bool = False, - ): - """ - Perform batch search across multiple queries based on text inputs and return results - asynchronously. - - Parameters: - ----------- - - - collection_name (str): The name of the collection in which to perform the - searches. - - query_texts (list[str]): A list of text queries to search for. - - limit (int): Optional limit for the search results for each query. (default None) - - with_vectors (bool): Flag indicating whether to return vectors with the results. - (default False) - - Returns: - -------- - - Returns a list of results for each search query executed in parallel. - """ - query_vectors = await self.embedding_engine.embed_text(query_texts) - - return await asyncio.gather( - *[ - self.search( - collection_name=collection_name, - query_vector=query_vector, - limit=limit, - with_vector=with_vectors, - ) - for query_vector in query_vectors - ] - ) - - async def get_graph_data(self): - """ - Retrieve all nodes and edges from the graph along with their properties. - - Returns: - -------- - - Returns a tuple containing lists of nodes and edges data retrieved from the graph. - """ - query = "MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties" - - result = self.query(query) - - nodes = [ - ( - record[2]["id"], - record[2], - ) - for record in result.result_set - ] - - query = """ - MATCH (n)-[r]->(m) - RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties - """ - result = self.query(query) - edges = [ - ( - record[3]["source_node_id"], - record[3]["target_node_id"], - record[2], - record[3], - ) - for record in result.result_set - ] - - return (nodes, edges) - - async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]): - """ - Remove specified data points from the graph database based on their IDs. - - Parameters: - ----------- - - - collection_name (str): The name of the collection from which to delete the data - points. - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - delete. - - Returns: - -------- - - Returns the result of the deletion operation from the database. - """ - return self.query( - "MATCH (node) WHERE node.id IN $node_ids DETACH DELETE node", - { - "node_ids": [str(data_point) for data_point in data_point_ids], - }, - ) - - async def delete_node(self, node_id: str) -> None: - """ - Delete a specified node from the graph by its ID. - - Parameters: - ----------- - - - node_id (str): Unique identifier for the node to delete. - """ - query = f"MATCH (node {{id: '{node_id}'}}) DETACH DELETE node" - self.query(query) - - async def delete_nodes(self, node_ids: List[str]) -> None: - """ - Delete multiple nodes from the graph by their identifiers. - - Parameters: - ----------- - - - node_ids (List[str]): A list of unique identifiers for the nodes to delete. - """ - for node_id in node_ids: - await self.delete_node(node_id) - - async def delete_graph(self): - """ - Delete the entire graph along with all its indices and nodes. - """ - try: - graph = self.driver.select_graph(self.graph_name) - - indices = graph.list_indices() - for index in indices.result_set: - for field in index[1]: - graph.drop_node_vector_index(index[0], field) - - graph.delete() - except Exception as e: - print(f"Error deleting graph: {e}") - - async def get_node(self, node_id: str) -> Optional[NodeData]: - """ - Retrieve a single node from the graph using its ID. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node to retrieve. - """ - result = self.query( - "MATCH (node) WHERE node.id = $node_id RETURN node", - {"node_id": node_id}, - ) - - if result.result_set and len(result.result_set) > 0: - # FalkorDB returns node objects as first element in the result list - return result.result_set[0][0].properties - return None - - async def get_nodes(self, node_ids: List[str]) -> List[NodeData]: - """ - Retrieve multiple nodes from the graph using their IDs. - - Parameters: - ----------- - - - node_ids (List[str]): A list of unique identifiers for the nodes to retrieve. - """ - result = self.query( - "MATCH (node) WHERE node.id IN $node_ids RETURN node", - {"node_ids": node_ids}, - ) - - nodes = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns node objects as first element in each record - nodes.append(record[0].properties) - return nodes - - async def get_neighbors(self, node_id: str) -> List[NodeData]: - """ - Get all neighboring nodes connected to the specified node. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node for which to retrieve neighbors. - """ - result = self.query( - "MATCH (node)-[]-(neighbor) WHERE node.id = $node_id RETURN DISTINCT neighbor", - {"node_id": node_id}, - ) - - neighbors = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns neighbor objects as first element in each record - neighbors.append(record[0].properties) - return neighbors - - async def get_edges(self, node_id: str) -> List[EdgeData]: - """ - Retrieve all edges that are connected to the specified node. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node whose edges are to be retrieved. - """ - result = self.query( - """ - MATCH (n)-[r]-(m) - WHERE n.id = $node_id - RETURN n.id AS source_id, m.id AS target_id, type(r) AS relationship_name, properties(r) AS properties - """, - {"node_id": node_id}, - ) - - edges = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns values by index: source_id, target_id, relationship_name, properties - edges.append( - ( - record[0], # source_id - record[1], # target_id - record[2], # relationship_name - record[3], # properties - ) - ) - return edges - - async def has_edge(self, source_id: str, target_id: str, relationship_name: str) -> bool: - """ - Verify if an edge exists between two specified nodes. - - Parameters: - ----------- - - - source_id (str): Unique identifier of the source node. - - target_id (str): Unique identifier of the target node. - - relationship_name (str): Name of the relationship to verify. - """ - # Check both the sanitized relationship type and the original name in properties - sanitized_relationship = self.sanitize_relationship_name(relationship_name) - - result = self.query( - f""" - MATCH (source)-[r:{sanitized_relationship}]->(target) - WHERE source.id = $source_id AND target.id = $target_id - AND (r.relationship_name = $relationship_name OR NOT EXISTS(r.relationship_name)) - RETURN COUNT(r) > 0 AS edge_exists - """, - { - "source_id": source_id, - "target_id": target_id, - "relationship_name": relationship_name, - }, - ) - - if result.result_set and len(result.result_set) > 0: - # FalkorDB returns scalar results as a list, access by index instead of key - return result.result_set[0][0] - return False - - async def get_graph_metrics(self, include_optional: bool = False) -> Dict[str, Any]: - """ - Fetch metrics and statistics of the graph, possibly including optional details. - - Parameters: - ----------- - - - include_optional (bool): Flag indicating whether to include optional metrics or - not. (default False) - """ - # Get basic node and edge counts - node_result = self.query("MATCH (n) RETURN count(n) AS node_count") - edge_result = self.query("MATCH ()-[r]->() RETURN count(r) AS edge_count") - - # FalkorDB returns scalar results as a list, access by index instead of key - num_nodes = node_result.result_set[0][0] if node_result.result_set else 0 - num_edges = edge_result.result_set[0][0] if edge_result.result_set else 0 - - metrics = { - "num_nodes": num_nodes, - "num_edges": num_edges, - "mean_degree": (2 * num_edges) / num_nodes if num_nodes > 0 else 0, - "edge_density": num_edges / (num_nodes * (num_nodes - 1)) if num_nodes > 1 else 0, - "num_connected_components": 1, # Simplified for now - "sizes_of_connected_components": [num_nodes] if num_nodes > 0 else [], - } - - if include_optional: - # Add optional metrics - simplified implementation - metrics.update( - { - "num_selfloops": 0, # Simplified - "diameter": -1, # Not implemented - "avg_shortest_path_length": -1, # Not implemented - "avg_clustering": -1, # Not implemented - } - ) - else: - metrics.update( - { - "num_selfloops": -1, - "diameter": -1, - "avg_shortest_path_length": -1, - "avg_clustering": -1, - } - ) - - return metrics - - async def get_document_subgraph(self, content_hash: str): - """ - Get a subgraph related to a specific document by content hash. - - Parameters: - ----------- - - - content_hash (str): The content hash of the document to find. - """ - query = """ - MATCH (d) WHERE d.id CONTAINS $content_hash - OPTIONAL MATCH (d)<-[:CHUNK_OF]-(c) - OPTIONAL MATCH (c)-[:HAS_ENTITY]->(e) - OPTIONAL MATCH (e)-[:IS_INSTANCE_OF]->(et) - RETURN d AS document, - COLLECT(DISTINCT c) AS chunks, - COLLECT(DISTINCT e) AS orphan_entities, - COLLECT(DISTINCT c) AS made_from_nodes, - COLLECT(DISTINCT et) AS orphan_types - """ - - result = self.query(query, {"content_hash": f"text_{content_hash}"}) - - if not result.result_set or not result.result_set[0]: - return None - - # Convert result to dictionary format - # FalkorDB returns values by index: document, chunks, orphan_entities, made_from_nodes, orphan_types - record = result.result_set[0] - return { - "document": record[0], - "chunks": record[1], - "orphan_entities": record[2], - "made_from_nodes": record[3], - "orphan_types": record[4], - } - - async def get_degree_one_nodes(self, node_type: str): - """ - Get all nodes that have only one connection. - - Parameters: - ----------- - - - node_type (str): The type of nodes to filter by, must be 'Entity' or 'EntityType'. - """ - if not node_type or node_type not in ["Entity", "EntityType"]: - raise ValueError("node_type must be either 'Entity' or 'EntityType'") - - result = self.query( - f""" - MATCH (n:{node_type}) - WITH n, COUNT {{ MATCH (n)--() }} as degree - WHERE degree = 1 - RETURN n - """ - ) - - # FalkorDB returns node objects as first element in each record - return [record[0] for record in result.result_set] if result.result_set else [] - - async def get_nodeset_subgraph( - self, node_type: Type[Any], node_name: List[str] - ) -> Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]]: - """ - Fetch a subgraph consisting of a specific set of nodes and their relationships. - - Parameters: - ----------- - - - node_type (Type[Any]): The type of nodes to include in the subgraph. - - node_name (List[str]): A list of names of the nodes to include in the subgraph. - """ - label = node_type.__name__ - - # Find primary nodes of the specified type and names - primary_query = f""" - UNWIND $names AS wantedName - MATCH (n:{label}) - WHERE n.name = wantedName - RETURN DISTINCT n.id, properties(n) AS properties - """ - - primary_result = self.query(primary_query, {"names": node_name}) - if not primary_result.result_set: - return [], [] - - # FalkorDB returns values by index: id, properties - primary_ids = [record[0] for record in primary_result.result_set] - - # Find neighbors of primary nodes - neighbor_query = """ - MATCH (n)-[]-(neighbor) - WHERE n.id IN $ids - RETURN DISTINCT neighbor.id, properties(neighbor) AS properties - """ - - neighbor_result = self.query(neighbor_query, {"ids": primary_ids}) - # FalkorDB returns values by index: id, properties - neighbor_ids = ( - [record[0] for record in neighbor_result.result_set] - if neighbor_result.result_set - else [] - ) - - all_ids = list(set(primary_ids + neighbor_ids)) - - # Get all nodes in the subgraph - nodes_query = """ - MATCH (n) - WHERE n.id IN $ids - RETURN n.id, properties(n) AS properties - """ - - nodes_result = self.query(nodes_query, {"ids": all_ids}) - nodes = [] - if nodes_result.result_set: - for record in nodes_result.result_set: - # FalkorDB returns values by index: id, properties - nodes.append((record[0], record[1])) - - # Get edges between these nodes - edges_query = """ - MATCH (a)-[r]->(b) - WHERE a.id IN $ids AND b.id IN $ids - RETURN a.id AS source_id, b.id AS target_id, type(r) AS relationship_name, properties(r) AS properties - """ - - edges_result = self.query(edges_query, {"ids": all_ids}) - edges = [] - if edges_result.result_set: - for record in edges_result.result_set: - # FalkorDB returns values by index: source_id, target_id, relationship_name, properties - edges.append( - ( - record[0], # source_id - record[1], # target_id - record[2], # relationship_name - record[3], # properties - ) - ) - - return nodes, edges - - async def prune(self): - """ - Prune the graph by deleting the entire graph structure. - """ - await self.delete_graph() diff --git a/cognee/infrastructure/databases/vector/create_vector_engine.py b/cognee/infrastructure/databases/vector/create_vector_engine.py index 77bf7d83f..b4378841d 100644 --- a/cognee/infrastructure/databases/vector/create_vector_engine.py +++ b/cognee/infrastructure/databases/vector/create_vector_engine.py @@ -19,8 +19,7 @@ def create_vector_engine( for each provider, raising an EnvironmentError if any are missing, or ImportError if the ChromaDB package is not installed. - Supported providers include: pgvector, FalkorDB, ChromaDB, and - LanceDB. + Supported providers include: pgvector, ChromaDB, and LanceDB. Parameters: ----------- @@ -74,18 +73,6 @@ def create_vector_engine( embedding_engine, ) - elif vector_db_provider == "falkordb": - if not (vector_db_url and vector_db_port): - raise EnvironmentError("Missing requred FalkorDB credentials!") - - from ..hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter - - return FalkorDBAdapter( - database_url=vector_db_url, - database_port=vector_db_port, - embedding_engine=embedding_engine, - ) - elif vector_db_provider == "chromadb": try: import chromadb diff --git a/cognee/shared/data_models.py b/cognee/shared/data_models.py index 4e80ed81a..dcf036b09 100644 --- a/cognee/shared/data_models.py +++ b/cognee/shared/data_models.py @@ -288,7 +288,6 @@ class SummarizedCode(BaseModel): class GraphDBType(Enum): NETWORKX = auto() NEO4J = auto() - FALKORDB = auto() KUZU = auto() diff --git a/cognee/tests/test_falkordb.py b/cognee/tests/test_falkordb.py deleted file mode 100755 index a42470e54..000000000 --- a/cognee/tests/test_falkordb.py +++ /dev/null @@ -1,174 +0,0 @@ -import os -import cognee -import pathlib -from cognee.infrastructure.files.storage import get_storage_config -from cognee.modules.search.operations import get_history -from cognee.modules.users.methods import get_default_user -from cognee.shared.logging_utils import get_logger -from cognee.modules.search.types import SearchType - -logger = get_logger() - - -async def check_falkordb_connection(): - """Check if FalkorDB is available at localhost:6379""" - try: - from falkordb import FalkorDB - - client = FalkorDB(host="localhost", port=6379) - # Try to list graphs to check connection - client.list_graphs() - return True - except Exception as e: - logger.warning(f"FalkorDB not available at localhost:6379: {e}") - return False - - -async def main(): - # Check if FalkorDB is available - if not await check_falkordb_connection(): - print("⚠️ FalkorDB is not available at localhost:6379") - print(" To run this test, start FalkorDB server:") - print(" docker run -p 6379:6379 falkordb/falkordb:latest") - print(" Skipping FalkorDB test...") - return - - print("✅ FalkorDB connection successful, running test...") - - # Configure FalkorDB as the graph database provider - cognee.config.set_graph_db_config( - { - "graph_database_url": "localhost", # FalkorDB URL (using Redis protocol) - "graph_database_port": 6379, - "graph_database_provider": "falkordb", - } - ) - - # Configure FalkorDB as the vector database provider too since it's a hybrid adapter - cognee.config.set_vector_db_config( - { - "vector_db_url": "localhost", - "vector_db_port": 6379, - "vector_db_provider": "falkordb", - } - ) - - data_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_falkordb") - ).resolve() - ) - cognee.config.data_root_directory(data_directory_path) - cognee_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_falkordb") - ).resolve() - ) - cognee.config.system_root_directory(cognee_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - dataset_name = "artificial_intelligence" - - ai_text_file_path = os.path.join( - pathlib.Path(__file__).parent, "test_data/artificial-intelligence.pdf" - ) - await cognee.add([ai_text_file_path], dataset_name) - - text = """A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word. - LLMs are artificial neural networks. The largest and most capable, as of March 2024, are built with a decoder-only transformer-based architecture while some recent implementations are based on other architectures, such as recurrent neural network variants and Mamba (a state space model). - Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results.[6] They are thought to acquire knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora. - Some notable LLMs are OpenAI's GPT series of models (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's PaLM and Gemini (the latter of which is currently used in the chatbot of the same name), xAI's Grok, Meta's LLaMA family of open-source models, Anthropic's Claude models, Mistral AI's open source models, and Databricks' open source DBRX. - """ - - await cognee.add([text], dataset_name) - - await cognee.cognify([dataset_name]) - - from cognee.infrastructure.databases.vector import get_vector_engine - - vector_engine = get_vector_engine() - random_node = (await vector_engine.search("entity.name", "AI"))[0] - random_node_name = random_node.payload["text"] - - search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name - ) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted sentences are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=random_node_name) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted chunks are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search( - query_type=SearchType.SUMMARIES, query_text=random_node_name - ) - assert len(search_results) != 0, "Query related summaries don't exist." - print("\nExtracted summaries are:\n") - for result in search_results: - print(f"{result}\n") - - user = await get_default_user() - history = await get_history(user.id) - - assert len(history) == 6, "Search history is not correct." - - # Assert local data files are cleaned properly - await cognee.prune.prune_data() - data_root_directory = get_storage_config()["data_root_directory"] - assert not os.path.isdir(data_root_directory), "Local data files are not deleted" - - # Assert relational, vector and graph databases have been cleaned properly - await cognee.prune.prune_system(metadata=True) - - # For FalkorDB vector engine, check if collections are empty - # Since FalkorDB is a hybrid adapter, we can check if the graph is empty - # as the vector data is stored in the same graph - if hasattr(vector_engine, "driver"): - # This is FalkorDB - check if graphs exist - collections = vector_engine.driver.list_graphs() - # The graph should be deleted, so either no graphs or empty graph - if vector_engine.graph_name in collections: - # Graph exists but should be empty - vector_graph_data = await vector_engine.get_graph_data() - vector_nodes, vector_edges = vector_graph_data - assert len(vector_nodes) == 0 and len(vector_edges) == 0, ( - "FalkorDB vector database is not empty" - ) - else: - # Fallback for other vector engines like LanceDB - connection = await vector_engine.get_connection() - collection_names = await connection.table_names() - assert len(collection_names) == 0, "Vector database is not empty" - - from cognee.infrastructure.databases.relational import get_relational_engine - - assert not os.path.exists(get_relational_engine().db_path), ( - "SQLite relational database is not empty" - ) - - # For FalkorDB, check if the graph database is empty - from cognee.infrastructure.databases.graph import get_graph_engine - - graph_engine = get_graph_engine() - graph_data = await graph_engine.get_graph_data() - nodes, edges = graph_data - assert len(nodes) == 0 and len(edges) == 0, "FalkorDB graph database is not empty" - - print("🎉 FalkorDB test completed successfully!") - print(" ✓ Data ingestion worked") - print(" ✓ Cognify processing worked") - print(" ✓ Search operations worked") - print(" ✓ Cleanup worked") - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main(), debug=True) diff --git a/distributed/pyproject.toml b/distributed/pyproject.toml index f61157236..e4832ea21 100644 --- a/distributed/pyproject.toml +++ b/distributed/pyproject.toml @@ -95,7 +95,6 @@ mistral = ["mistral-common>=1.5.2,<2"] anthropic = ["anthropic>=0.26.1,<0.27"] deepeval = ["deepeval>=2.0.1,<3"] posthog = ["posthog>=3.5.0,<4"] -falkordb = ["falkordb>=1.0.9,<2.0.0"] groq = ["groq>=0.8.0,<1.0.0"] chromadb = [ "chromadb>=0.3.0,<0.7", diff --git a/docker-compose.yml b/docker-compose.yml index 1c0a7dbd3..9f0e199a0 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -96,17 +96,6 @@ services: networks: - cognee-network - falkordb: - image: falkordb/falkordb:edge - container_name: falkordb - profiles: - - falkordb - ports: - - 6379:6379 - - 3001:3000 - networks: - - cognee-network - chromadb: image: chromadb/chroma:0.6.3 container_name: chromadb diff --git a/examples/database_examples/falkordb_example.py b/examples/database_examples/falkordb_example.py deleted file mode 100644 index 9e096fd81..000000000 --- a/examples/database_examples/falkordb_example.py +++ /dev/null @@ -1,87 +0,0 @@ -import os -import pathlib -import asyncio -import cognee -from cognee.modules.search.types import SearchType - - -async def main(): - """ - Example script demonstrating how to use Cognee with FalkorDB - - This example: - 1. Configures Cognee to use FalkorDB as graph database - 2. Sets up data directories - 3. Adds sample data to Cognee - 4. Processes (cognifies) the data - 5. Performs different types of searches - """ - # Configure FalkorDB as the graph database provider - cognee.config.set_graph_db_config( - { - "graph_database_url": "localhost", # FalkorDB URL (using Redis protocol) - "graph_database_port": 6379, - "graph_database_provider": "falkordb", - } - ) - - # Set up data directories for storing documents and system files - # You should adjust these paths to your needs - current_dir = pathlib.Path(__file__).parent - data_directory_path = str(current_dir / "data_storage") - cognee.config.data_root_directory(data_directory_path) - - cognee_directory_path = str(current_dir / "cognee_system") - cognee.config.system_root_directory(cognee_directory_path) - - # Clean any existing data (optional) - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - # Create a dataset - dataset_name = "falkordb_example" - - # Add sample text to the dataset - sample_text = """FalkorDB is a graph database that evolved from RedisGraph. - It is focused on providing high-performance graph operations. - FalkorDB uses sparse adjacency matrices to represent the graph data structure. - It supports the Cypher query language for querying graph data. - FalkorDB can be integrated with vector search capabilities for AI applications. - It provides a Redis module, allowing users to leverage Redis's features alongside graph capabilities.""" - - # Add the sample text to the dataset - await cognee.add([sample_text], dataset_name) - - # Process the added document to extract knowledge - await cognee.cognify([dataset_name]) - - # Now let's perform some searches - # 1. Search for insights related to "FalkorDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="FalkorDB") - print("\nInsights about FalkorDB:") - for result in insights_results: - print(f"- {result}") - - # 2. Search for text chunks related to "graph database" - chunks_results = await cognee.search( - query_type=SearchType.CHUNKS, query_text="graph database", datasets=[dataset_name] - ) - print("\nChunks about graph database:") - for result in chunks_results: - print(f"- {result}") - - # 3. Get graph completion related to databases - graph_completion_results = await cognee.search( - query_type=SearchType.GRAPH_COMPLETION, query_text="database" - ) - print("\nGraph completion for databases:") - for result in graph_completion_results: - print(f"- {result}") - - # Clean up (optional) - # await cognee.prune.prune_data() - # await cognee.prune.prune_system(metadata=True) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml index c29c84da9..2a1c9a777 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -99,7 +99,6 @@ mistral = ["mistral-common>=1.5.2,<2"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] -falkordb = ["falkordb>=1.0.9,<2.0.0"] groq = ["groq>=0.8.0,<1.0.0"] chromadb = [ "chromadb>=0.6,<0.7", From 321b648ef5820772140ff7de7aa0046e40f2963c Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Fri, 19 Sep 2025 13:34:11 +0200 Subject: [PATCH 003/139] fix: Returned the necessary port argument I accidentally deleted. --- cognee/infrastructure/databases/graph/get_graph_engine.py | 1 + 1 file changed, 1 insertion(+) diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index 594744983..38a6a2caa 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -32,6 +32,7 @@ def create_graph_engine( graph_database_name="", graph_database_username="", graph_database_password="", + graph_database_port="", ): """ Create a graph engine based on the specified provider type. From bd6efe8a211332238365a07f65687bee750eb1c9 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Mon, 22 Sep 2025 10:10:39 +0200 Subject: [PATCH 004/139] fix: Return some code I mistakenly deleted. --- cognee/infrastructure/databases/graph/get_graph_engine.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index 38a6a2caa..1861aa15c 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -44,13 +44,14 @@ def create_graph_engine( Parameters: ----------- - - graph_database_provider: The type of graph database provider to use (e.g., neo4j, - kuzu). - - graph_database_url: The URL for the graph database instance. Required for neo4j. + - graph_database_provider: The type of graph database provider to use (e.g., neo4j, falkor, kuzu). + - graph_database_url: The URL for the graph database instance. Required for neo4j and falkordb providers. - graph_database_username: The username for authentication with the graph database. Required for neo4j provider. - graph_database_password: The password for authentication with the graph database. Required for neo4j provider. + - graph_database_port: The port number for the graph database connection. Required + for the falkordb provider - graph_file_path: The filesystem path to the graph file. Required for the kuzu provider. From 415f9fd0be937eb122909afe8d1215c1568f39b4 Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Tue, 23 Sep 2025 13:16:44 +0530 Subject: [PATCH 005/139] Added Mistral support as LLM provider using litellm --- cognee/api/v1/add/add.py | 2 +- .../settings/routers/get_settings_router.py | 8 +- .../litellm_instructor/llm/get_llm_client.py | 17 + .../llm/mistral/__init__.py | 0 .../litellm_instructor/llm/mistral/adapter.py | 113 + cognee/modules/settings/get_settings.py | 23 + pyproject.toml | 3 +- uv.lock | 7444 +++++++++-------- 8 files changed, 3903 insertions(+), 3707 deletions(-) create mode 100644 cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py create mode 100644 cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index eeb867984..b10835573 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -133,7 +133,7 @@ async def add( - LLM_API_KEY: API key for your LLM provider (OpenAI, Anthropic, etc.) Optional: - - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama" + - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral" - LLM_MODEL: Model name (default: "gpt-5-mini") - DEFAULT_USER_EMAIL: Custom default user email - DEFAULT_USER_PASSWORD: Custom default user password diff --git a/cognee/api/v1/settings/routers/get_settings_router.py b/cognee/api/v1/settings/routers/get_settings_router.py index c85352746..c64ce2072 100644 --- a/cognee/api/v1/settings/routers/get_settings_router.py +++ b/cognee/api/v1/settings/routers/get_settings_router.py @@ -21,7 +21,13 @@ class SettingsDTO(OutDTO): class LLMConfigInputDTO(InDTO): - provider: Union[Literal["openai"], Literal["ollama"], Literal["anthropic"], Literal["gemini"]] + provider: Union[ + Literal["openai"], + Literal["ollama"], + Literal["anthropic"], + Literal["gemini"], + Literal["mistral"], + ] model: str api_key: str diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py index 049b27e04..cba3c5883 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py @@ -23,6 +23,7 @@ class LLMProvider(Enum): - ANTHROPIC: Represents the Anthropic provider. - CUSTOM: Represents a custom provider option. - GEMINI: Represents the Gemini provider. + - MISTRAL: Represents the Mistral AI provider. """ OPENAI = "openai" @@ -30,6 +31,7 @@ class LLMProvider(Enum): ANTHROPIC = "anthropic" CUSTOM = "custom" GEMINI = "gemini" + MISTRAL = "mistral" def get_llm_client(raise_api_key_error: bool = True): @@ -146,5 +148,20 @@ def get_llm_client(raise_api_key_error: bool = True): streaming=llm_config.llm_streaming, ) + elif provider == LLMProvider.MISTRAL: + if llm_config.llm_api_key is None: + raise LLMAPIKeyNotSetError() + + from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import ( + MistralAdapter, + ) + + return MistralAdapter( + api_key=llm_config.llm_api_key, + model=llm_config.llm_model, + max_completion_tokens=max_completion_tokens, + endpoint=llm_config.llm_endpoint, + ) + else: raise UnsupportedLLMProviderError(provider) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py new file mode 100644 index 000000000..a9cf68d6e --- /dev/null +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -0,0 +1,113 @@ +import litellm +import instructor +from pydantic import BaseModel +from typing import Type, Optional +from litellm import acompletion, JSONSchemaValidationError + +from cognee.shared.logging_utils import get_logger +from cognee.modules.observability.get_observe import get_observe +from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( + LLMInterface, +) +from cognee.infrastructure.llm.LLMGateway import LLMGateway +from cognee.infrastructure.llm.config import get_llm_config +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( + rate_limit_async, + sleep_and_retry_async, +) + +logger = get_logger() +observe = get_observe() + + +class MistralAdapter(LLMInterface): + """ + Adapter for Mistral AI API, for structured output generation and prompt display. + + Public methods: + - acreate_structured_output + - show_prompt + """ + + name = "Mistral" + model: str + api_key: str + max_completion_tokens: int + + def __init__(self, api_key: str, model: str, max_completion_tokens: int, endpoint: str = None): + from mistralai import Mistral + + self.model = model + self.max_completion_tokens = max_completion_tokens + + self.aclient = instructor.from_litellm( + litellm.acompletion, + mode=instructor.Mode.MISTRAL_TOOLS, + api_key=get_llm_config().llm_api_key, + ) + + @sleep_and_retry_async() + @rate_limit_async + async def acreate_structured_output( + self, text_input: str, system_prompt: str, response_model: Type[BaseModel] + ) -> BaseModel: + """ + Generate a response from the user query. + + Parameters: + ----------- + - text_input (str): The input text from the user to be processed. + - system_prompt (str): A prompt that sets the context for the query. + - response_model (Type[BaseModel]): The model to structure the response according to + its format. + + Returns: + -------- + - BaseModel: An instance of BaseModel containing the structured response. + """ + return await self.aclient.chat.completions.create( + model=self.model, + max_tokens=self.max_completion_tokens, + max_retries=5, + messages=[ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information + from the following input: {text_input}""", + }, + ], + response_model=response_model, + ) + + def show_prompt(self, text_input: str, system_prompt: str) -> str: + """ + Format and display the prompt for a user query. + + Parameters: + ----------- + - text_input (str): Input text from the user to be included in the prompt. + - system_prompt (str): The system prompt that will be shown alongside the user input. + + Returns: + -------- + - str: The formatted prompt string combining system prompt and user input. + """ + if not text_input: + text_input = "No user input provided." + if not system_prompt: + raise MissingSystemPromptPathError() + + system_prompt = LLMGateway.read_query_prompt(system_prompt) + + formatted_prompt = ( + f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" + if system_prompt + else None + ) + + return formatted_prompt diff --git a/cognee/modules/settings/get_settings.py b/cognee/modules/settings/get_settings.py index fa92c8043..071bcca36 100644 --- a/cognee/modules/settings/get_settings.py +++ b/cognee/modules/settings/get_settings.py @@ -15,6 +15,7 @@ class ModelName(Enum): ollama = "ollama" anthropic = "anthropic" gemini = "gemini" + mistral = "mistral" class LLMConfig(BaseModel): @@ -72,6 +73,10 @@ def get_settings() -> SettingsDict: "value": "gemini", "label": "Gemini", }, + { + "value": "mistral", + "label": "Mistral", + }, ] return SettingsDict.model_validate( @@ -134,6 +139,24 @@ def get_settings() -> SettingsDict: "label": "Gemini 2.0 Flash", }, ], + "mistral": [ + { + "value": "mistral-medium-2508", + "label": "Mistral Medium 3.1", + }, + { + "value": "magistral-medium-2509", + "label": "Magistral Medium 1.2", + }, + { + "value": "magistral-medium-2507", + "label": "Magistral Medium 1.1", + }, + { + "value": "mistral-large-2411", + "label": "Mistral Large 2.1", + }, + ], }, }, vector_db={ diff --git a/pyproject.toml b/pyproject.toml index d465f772e..6e80974a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,7 +65,8 @@ dependencies = [ "python-magic-bin<0.5 ; platform_system == 'Windows'", # Only needed for Windows "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", - "websockets>=15.0.1,<16.0.0" + 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a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py index a9cf68d6e..dbf0322b1 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -66,23 +66,39 @@ class MistralAdapter(LLMInterface): -------- - BaseModel: An instance of BaseModel containing the structured response. """ - return await self.aclient.chat.completions.create( - model=self.model, - max_tokens=self.max_completion_tokens, - max_retries=5, - messages=[ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": f"""Use the given format to extract information - from the following input: {text_input}""", - }, - ], - response_model=response_model, - ) + try: + messages = [ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information + from the following input: {text_input}""", + }, + ] + try: + response = await self.aclient.chat.completions.create( + model=self.model, + max_tokens=self.max_completion_tokens, + max_retries=5, + messages=messages, + response_model=response_model, + ) + if response.choices and response.choices[0].message.content: + content = response.choices[0].message.content + return response_model.model_validate_json(content) + else: + raise ValueError("Failed to get valid response after retries") + except litellm.exceptions.BadRequestError as e: + logger.error(f"Bad request error: {str(e)}") + raise ValueError(f"Invalid request: {str(e)}") + + except JSONSchemaValidationError as e: + logger.error(f"Schema validation failed: {str(e)}") + logger.debug(f"Raw response: {e.raw_response}") + raise ValueError(f"Response failed schema validation: {str(e)}") def show_prompt(self, text_input: str, system_prompt: str) -> str: """ diff --git a/pyproject.toml b/pyproject.toml index 6e80974a7..c2a6317f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,7 +66,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - "mistralai>=1.9.10", + ] [project.optional-dependencies] @@ -96,7 +96,7 @@ llama-index = ["llama-index-core>=0.12.11,<0.13"] gemini = ["google-generativeai>=0.8.4,<0.9"] huggingface = ["transformers>=4.46.3,<5"] ollama = ["transformers>=4.46.3,<5"] -mistral = ["mistral-common>=1.5.2,<2"] +mistral = ["mistral-common>=1.5.2,<2","mistralai>=1.9.10"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] From 52d38709691c6b4313ce30f36cfc3b237fa9a819 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 25 Sep 2025 22:09:18 +0100 Subject: [PATCH 007/139] fix: update cognee-mcp script alias and Dockerfile entrypoint --- cognee-mcp/entrypoint.sh | 18 +++++++++--------- cognee-mcp/pyproject.toml | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/cognee-mcp/entrypoint.sh b/cognee-mcp/entrypoint.sh index 53da83c11..e3ff849e0 100644 --- a/cognee-mcp/entrypoint.sh +++ b/cognee-mcp/entrypoint.sh @@ -48,27 +48,27 @@ if [ "$ENVIRONMENT" = "dev" ] || [ "$ENVIRONMENT" = "local" ]; then if [ "$DEBUG" = "true" ]; then echo "Waiting for the debugger to attach..." if [ "$TRANSPORT_MODE" = "sse" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration elif [ "$TRANSPORT_MODE" = "http" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration else - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee --transport stdio --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport stdio --no-migration fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration else - exec cognee --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration fi fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration else - exec cognee --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration fi fi diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index 8d9373bc9..e1a3a092c 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -36,4 +36,4 @@ dev = [ allow-direct-references = true [project.scripts] -cognee = "src:main" +cognee-mcp = "src:main" From aaa9a750d3ee8f1825f04764d74bc0b34ae6c31a Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Fri, 26 Sep 2025 15:13:41 +0100 Subject: [PATCH 008/139] refactor: keep "cognee" and "cognee-mcp", add deprecation warning for 'cognee' command --- cognee-mcp/pyproject.toml | 3 ++- cognee-mcp/src/__init__.py | 21 ++++++++++++++++++++- 2 files changed, 22 insertions(+), 2 deletions(-) diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index e1a3a092c..5c92deac8 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -36,4 +36,5 @@ dev = [ allow-direct-references = true [project.scripts] -cognee-mcp = "src:main" +cognee = "src:main" +cognee-mcp = "src:main_mcp" diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 1939efe9f..32a291cfb 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -1,8 +1,27 @@ from .server import main as server_main +import warnings def main(): - """Main entry point for the package.""" + """Deprecated main entry point for the package.""" import asyncio + + # Show deprecation warning + warnings.warn( + "The 'cognee' command for cognee-mcp is deprecated and will be removed in a future version. " + "Please use 'cognee-mcp' instead to avoid conflicts with the main cognee library.", + DeprecationWarning, + stacklevel=2 + ) + + print("⚠️ DEPRECATION WARNING: Use 'cognee-mcp' command instead of 'cognee'") + print(" This avoids conflicts with the main cognee library.") + print() asyncio.run(server_main()) + + +def main_mcp(): + """Clean main entry point for cognee-mcp command.""" + import asyncio + asyncio.run(server_main()) From d50455a1e6fd3395f5a11309fd63d9c24684ac0d Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Fri, 26 Sep 2025 15:14:47 +0100 Subject: [PATCH 009/139] ruff format --- cognee-mcp/src/__init__.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 32a291cfb..c71afdd1b 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -5,15 +5,15 @@ import warnings def main(): """Deprecated main entry point for the package.""" import asyncio - + # Show deprecation warning warnings.warn( "The 'cognee' command for cognee-mcp is deprecated and will be removed in a future version. " "Please use 'cognee-mcp' instead to avoid conflicts with the main cognee library.", DeprecationWarning, - stacklevel=2 + stacklevel=2, ) - + print("⚠️ DEPRECATION WARNING: Use 'cognee-mcp' command instead of 'cognee'") print(" This avoids conflicts with the main cognee library.") print() @@ -24,4 +24,5 @@ def main(): def main_mcp(): """Clean main entry point for cognee-mcp command.""" import asyncio + asyncio.run(server_main()) From de162cb491aadf9211f78602f632da831653c064 Mon Sep 17 00:00:00 2001 From: shehab-badawy Date: Thu, 11 Sep 2025 16:06:20 -0400 Subject: [PATCH 010/139] feat: Add preview step to delete command Signed-off-by: shehab-badawy --- cognee/cli/commands/delete_command.py | 55 +++++++++++++----- .../data/methods/get_deletion_counts.py | 57 +++++++++++++++++++ 2 files changed, 99 insertions(+), 13 deletions(-) create mode 100644 cognee/modules/data/methods/get_deletion_counts.py diff --git a/cognee/cli/commands/delete_command.py b/cognee/cli/commands/delete_command.py index 43e8b8e13..c9d13890e 100644 --- a/cognee/cli/commands/delete_command.py +++ b/cognee/cli/commands/delete_command.py @@ -6,6 +6,7 @@ from cognee.cli.reference import SupportsCliCommand from cognee.cli import DEFAULT_DOCS_URL import cognee.cli.echo as fmt from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.modules.data.methods.get_deletion_counts import get_deletion_counts class DeleteCommand(SupportsCliCommand): @@ -41,29 +42,56 @@ Be careful with deletion operations as they are irreversible. fmt.error("Please specify what to delete: --dataset-name, --user-id, or --all") return - # Build confirmation message - if args.all: - confirm_msg = "Delete ALL data from cognee?" - operation = "all data" - elif args.dataset_name: - confirm_msg = f"Delete dataset '{args.dataset_name}'?" - operation = f"dataset '{args.dataset_name}'" - elif args.user_id: - confirm_msg = f"Delete all data for user '{args.user_id}'?" - operation = f"data for user '{args.user_id}'" - - # Confirm deletion unless forced + # If --force is used, skip the preview and go straight to deletion if not args.force: + # --- START PREVIEW LOGIC --- + fmt.echo("Gathering data for preview...") + preview_data = asyncio.run( + get_deletion_counts( + dataset_name=args.dataset_name, + user_id=args.user_id, + all_data=args.all, + ) + ) + + if not preview_data or all(value == 0 for value in preview_data.values()): + fmt.success("No data found to delete.") + return + + fmt.echo("You are about to delete:") + if "datasets" in preview_data and preview_data["datasets"] > 0: + fmt.echo(f"- {preview_data['datasets']} datasets") + if "data_entries" in preview_data and preview_data["data_entries"] > 0: + fmt.echo(f"- {preview_data['data_entries']} data entries") + if "users" in preview_data and preview_data["users"] > 0: + fmt.echo( + f"- {preview_data['users']} {'users' if preview_data['users'] > 1 else 'user'}" + ) + fmt.echo("-" * 20) + fmt.warning("This operation is irreversible!") - if not fmt.confirm(confirm_msg): + if not fmt.confirm("Proceed?"): fmt.echo("Deletion cancelled.") return + # --- END PREVIEW LOGIC --- + + # Build operation message for success/failure logging + if args.all: + operation = "all data" + elif args.dataset_name: + operation = f"dataset '{args.dataset_name}'" + elif args.user_id: + operation = f"data for user '{args.user_id}'" + else: + operation = "data" fmt.echo(f"Deleting {operation}...") # Run the async delete function async def run_delete(): try: + # NOTE: The underlying cognee.delete() function is currently not working as expected. + # This is a separate bug that this preview feature helps to expose. if args.all: await cognee.delete(dataset_name=None, user_id=args.user_id) else: @@ -72,6 +100,7 @@ Be careful with deletion operations as they are irreversible. raise CliCommandInnerException(f"Failed to delete: {str(e)}") asyncio.run(run_delete()) + # This success message may be inaccurate due to the underlying bug, but we leave it for now. fmt.success(f"Successfully deleted {operation}") except Exception as e: diff --git a/cognee/modules/data/methods/get_deletion_counts.py b/cognee/modules/data/methods/get_deletion_counts.py new file mode 100644 index 000000000..cdd222645 --- /dev/null +++ b/cognee/modules/data/methods/get_deletion_counts.py @@ -0,0 +1,57 @@ +from sqlalchemy import select +from sqlalchemy.sql import func +from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.modules.data.models import Dataset, Data, DatasetData +from cognee.modules.users.models import User + + +async def get_deletion_counts( + dataset_name: str = None, user_id: str = None, all_data: bool = False +) -> dict: + """ + Calculates the number of items that will be deleted based on the provided arguments. + """ + relational_engine = get_relational_engine() + async with relational_engine.get_async_session() as session: + if dataset_name: + # Find the dataset by name + dataset_result = await session.execute( + select(Dataset).where(Dataset.name == dataset_name) + ) + dataset = dataset_result.scalar_one_or_none() + + if dataset is None: + return {"datasets": 0, "data_entries": 0} + + # Count data entries linked to this dataset + count_query = ( + select(func.count()) + .select_from(DatasetData) + .where(DatasetData.dataset_id == dataset.id) + ) + data_entry_count = (await session.execute(count_query)).scalar_one() + + return {"datasets": 1, "data_entries": data_entry_count} + + if all_data: + dataset_count = ( + await session.execute(select(func.count()).select_from(Dataset)) + ).scalar_one() + data_entry_count = ( + await session.execute(select(func.count()).select_from(Data)) + ).scalar_one() + user_count = ( + await session.execute(select(func.count()).select_from(User)) + ).scalar_one() + return { + "datasets": dataset_count, + "data_entries": data_entry_count, + "users": user_count, + } + + # Placeholder for user_id logic + if user_id: + # TODO: Implement counting logic for a specific user + return {"datasets": 0, "data_entries": 0, "users": 1} + + return {} From 18dcab3caccbd4e18c2b50bbef244ab2d3749853 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Mon, 29 Sep 2025 13:37:09 +0200 Subject: [PATCH 011/139] Update README with cognee features and deployment info Clarify the functionality and deployment of cognee. --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 25eb2d519..a1eebae73 100644 --- a/README.md +++ b/README.md @@ -76,6 +76,9 @@ Get started quickly with a Google Colab =1.8.9,<2.0.0"] monitoring = ["sentry-sdk[fastapi]>=2.9.0,<3", "langfuse>=2.32.0,<3"] +docling = ["docling>=2.54", "transformers>=4.55"] + [project.urls] Homepage = "https://www.cognee.ai" Repository = "https://github.com/topoteretes/cognee" diff --git a/uv.lock b/uv.lock index 94626c31e..fa2299683 100644 --- a/uv.lock +++ b/uv.lock @@ -1,21 +1,56 @@ version = 1 -revision = 2 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b/cognee/tasks/ingestion/save_data_item_to_storage.py index ae6bcc064..3fdb23e7e 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -27,11 +27,17 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str return await get_data_from_llama_index(data_item) - from docling_core.types import DoclingDocument + try: + from docling_core.types import DoclingDocument - if isinstance(data_item, DoclingDocument): - # Convert DoclingDocument to plain text and continue processing file - data_item = data_item.export_to_text() + if isinstance(data_item, DoclingDocument): + # Convert DoclingDocument to plain text and continue processing file + data_item = data_item.export_to_text() + except ImportError: + logger.debug( + "Docling import failed. Docling optional dependency not installed, so we don't expect Docling documents. " + "Continuing normal operation." + ) # data is a file object coming from upload. if hasattr(data_item, "file"): From 74bc7c942054c3203ad20639ef3c08c7d59aea5f Mon Sep 17 00:00:00 2001 From: Igor Ilic Date: Mon, 29 Sep 2025 21:22:21 +0200 Subject: [PATCH 014/139] refactor: set node_set to None for endpoint --- cognee/api/v1/add/routers/get_add_router.py | 6 +++++- cognee/api/v1/update/routers/get_update_router.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/cognee/api/v1/add/routers/get_add_router.py b/cognee/api/v1/add/routers/get_add_router.py index dfa7d275b..4d0063cc9 100644 --- a/cognee/api/v1/add/routers/get_add_router.py +++ b/cognee/api/v1/add/routers/get_add_router.py @@ -73,7 +73,11 @@ def get_add_router() -> APIRouter: try: add_run = await cognee_add( - data, datasetName, user=user, dataset_id=datasetId, node_set=node_set + data, + datasetName, + user=user, + dataset_id=datasetId, + node_set=node_set if node_set else None, ) if isinstance(add_run, PipelineRunErrored): diff --git a/cognee/api/v1/update/routers/get_update_router.py b/cognee/api/v1/update/routers/get_update_router.py index 144bdd9e6..4101e1e31 100644 --- a/cognee/api/v1/update/routers/get_update_router.py +++ b/cognee/api/v1/update/routers/get_update_router.py @@ -75,7 +75,7 @@ def get_update_router() -> APIRouter: data=data, dataset_id=dataset_id, user=user, - node_set=node_set, + node_set=node_set if node_set else None, ) # If any cognify run errored return JSONResponse with proper error status code From 0b5b0e5544e4eed446a2d8a78cdd3ee789d484fa Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Tue, 30 Sep 2025 11:40:42 +0200 Subject: [PATCH 015/139] fix: PR comment changes --- cognee/tasks/ingestion/save_data_item_to_storage.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 3fdb23e7e..aa2f37557 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -27,17 +27,10 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str return await get_data_from_llama_index(data_item) - try: + if "docling" in str(type(data_item)): from docling_core.types import DoclingDocument - if isinstance(data_item, DoclingDocument): - # Convert DoclingDocument to plain text and continue processing file data_item = data_item.export_to_text() - except ImportError: - logger.debug( - "Docling import failed. Docling optional dependency not installed, so we don't expect Docling documents. " - "Continuing normal operation." - ) # data is a file object coming from upload. if hasattr(data_item, "file"): From e74ee55137e3772b87ff60ba9d8d406abafa2ca0 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Tue, 30 Sep 2025 12:04:41 +0200 Subject: [PATCH 016/139] test: Add test to CI --- .github/workflows/examples_tests.yml | 26 +++++++++++++++++++++++ cognee/tests/test_add_docling_document.py | 3 --- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/.github/workflows/examples_tests.yml b/.github/workflows/examples_tests.yml index f4167a57a..e3de091eb 100644 --- a/.github/workflows/examples_tests.yml +++ b/.github/workflows/examples_tests.yml @@ -131,3 +131,29 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./examples/python/memify_coding_agent_example.py + + test_docling_add: + name: Run Add with Docling Test + runs-on: ubuntu-22.04 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: 'docling' + + - name: Run Docling Test + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./cognee/tests/test_add_docling_document.py diff --git a/cognee/tests/test_add_docling_document.py b/cognee/tests/test_add_docling_document.py index 172a3a800..2c82af66f 100644 --- a/cognee/tests/test_add_docling_document.py +++ b/cognee/tests/test_add_docling_document.py @@ -3,9 +3,6 @@ import cognee import os -# Provide your OpenAI LLM API KEY -os.environ["LLM_API_KEY"] = "" - async def main(): # Get file path to document to process From c8e6c1024bac9610fafa055c741b3d6cb944a46f Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Tue, 30 Sep 2025 12:05:43 +0200 Subject: [PATCH 017/139] chore: Fix formatting. --- cognee/tasks/ingestion/save_data_item_to_storage.py | 1 + 1 file changed, 1 insertion(+) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index aa2f37557..97538c84a 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -29,6 +29,7 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str if "docling" in str(type(data_item)): from docling_core.types import DoclingDocument + if isinstance(data_item, DoclingDocument): data_item = data_item.export_to_text() From 510926f56cb251de104c67e260bce172026e6ad1 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 30 Sep 2025 20:39:04 +0530 Subject: [PATCH 018/139] included scraping dependencies --- pyproject.toml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index b129524d7..ccae1db8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -73,7 +73,10 @@ api=[] distributed = [ "modal>=1.0.5,<2.0.0", ] - +scraping = [ + "tavily-python>=0.7.0", + "scrapy>=2.12.0" +] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] postgres = [ From 6348c9d8de04bd9e7f2a8db82bdfc313662d26de Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 30 Sep 2025 20:46:26 +0530 Subject: [PATCH 019/139] Created models.py --- cognee/tasks/web_scraper/models.py | 39 ++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 cognee/tasks/web_scraper/models.py diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py new file mode 100644 index 000000000..8d5f32f43 --- /dev/null +++ b/cognee/tasks/web_scraper/models.py @@ -0,0 +1,39 @@ +from cognee.infrastructure.engine.models import DataPoint +from typing import Optional, Dict, Any, List +from datetime import datetime + +class WebPage(DataPoint): + """Represents a scraped web page with metadata""" + url: str + title: Optional[str] + content: str + content_hash: str + scraped_at: datetime + last_modified: Optional[datetime] + status_code: int + content_type: str + page_size: int + extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used + metadata: dict = {"index_fields": ["url", "title", "scraped_at"]} + +class WebSite(DataPoint): + """Represents a website or domain being scraped""" + domain: str + base_url: str + robots_txt: Optional[str] + crawl_delay: float + last_crawled: datetime + page_count: int + scraping_config: Dict[str, Any] + metadata: dict = {"index_fields": ["domain", "base_url"]} + +class ScrapingJob(DataPoint): + """Represents a scraping job configuration""" + job_name: str + urls: List[str] + scraping_rules: Dict[str, Any] + schedule: Optional[str] # Cron-like schedule for recurring scrapes + status: str # "active", "paused", "completed", "failed" + last_run: Optional[datetime] + next_run: Optional[datetime] + metadata: dict = {"index_fields": ["job_name", "status"]} \ No newline at end of file From 45f00b022ff8905ef52f16948f53b74ced01f738 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Tue, 30 Sep 2025 17:22:43 +0200 Subject: [PATCH 020/139] test: Renamed s3 test. Commented out docling test. Fails until docling resolves their issue. --- .github/workflows/examples_tests.yml | 50 +++++++++++----------- .github/workflows/test_s3_file_storage.yml | 2 +- 2 files changed, 26 insertions(+), 26 deletions(-) diff --git a/.github/workflows/examples_tests.yml b/.github/workflows/examples_tests.yml index e3de091eb..cf96b718a 100644 --- a/.github/workflows/examples_tests.yml +++ b/.github/workflows/examples_tests.yml @@ -132,28 +132,28 @@ jobs: EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./examples/python/memify_coding_agent_example.py - test_docling_add: - name: Run Add with Docling Test - runs-on: ubuntu-22.04 - steps: - - name: Check out repository - uses: actions/checkout@v4 - - - name: Cognee Setup - uses: ./.github/actions/cognee_setup - with: - python-version: '3.11.x' - extra-dependencies: 'docling' - - - name: Run Docling Test - env: - OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} - LLM_MODEL: ${{ secrets.LLM_MODEL }} - LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} - LLM_API_KEY: ${{ secrets.LLM_API_KEY }} - LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} - EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} - EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} - EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} - EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} - run: uv run python ./cognee/tests/test_add_docling_document.py +# test_docling_add: +# name: Run Add with Docling Test +# runs-on: ubuntu-22.04 +# steps: +# - name: Check out repository +# uses: actions/checkout@v4 +# +# - name: Cognee Setup +# uses: ./.github/actions/cognee_setup +# with: +# python-version: '3.11.x' +# extra-dependencies: 'docling' +# +# - name: Run Docling Test +# env: +# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} +# LLM_MODEL: ${{ secrets.LLM_MODEL }} +# LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} +# LLM_API_KEY: ${{ secrets.LLM_API_KEY }} +# LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} +# EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} +# EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} +# EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} +# EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} +# run: uv run python ./cognee/tests/test_add_docling_document.py diff --git a/.github/workflows/test_s3_file_storage.yml b/.github/workflows/test_s3_file_storage.yml index a477d8933..1a9414626 100644 --- a/.github/workflows/test_s3_file_storage.yml +++ b/.github/workflows/test_s3_file_storage.yml @@ -7,7 +7,7 @@ permissions: contents: read jobs: - test-gemini: + test-s3-storage: name: Run S3 File Storage Test runs-on: ubuntu-22.04 steps: From 7ab000d891fa6ed6fbe6804050456ea55fad28ce Mon Sep 17 00:00:00 2001 From: Igor Ilic Date: Tue, 30 Sep 2025 18:12:22 +0200 Subject: [PATCH 021/139] refactor: Add test for updating of docs and visualization --- cognee/__init__.py | 1 + cognee/api/v1/update/update.py | 2 +- cognee/tests/test_library.py | 32 +++++++++++++++++++++++++++++++- 3 files changed, 33 insertions(+), 2 deletions(-) diff --git a/cognee/__init__.py b/cognee/__init__.py index 27dea1ad5..6e4d2a903 100644 --- a/cognee/__init__.py +++ b/cognee/__init__.py @@ -19,6 +19,7 @@ from .api.v1.add import add from .api.v1.delete import delete from .api.v1.cognify import cognify from .modules.memify import memify +from .api.v1.update import update from .api.v1.config.config import config from .api.v1.datasets.datasets import datasets from .api.v1.prune import prune diff --git a/cognee/api/v1/update/update.py b/cognee/api/v1/update/update.py index b4b1f5e5a..a421b3dc0 100644 --- a/cognee/api/v1/update/update.py +++ b/cognee/api/v1/update/update.py @@ -10,9 +10,9 @@ from cognee.api.v1.cognify import cognify async def update( data_id: UUID, data: Union[BinaryIO, list[BinaryIO], str, list[str]], + dataset_id: UUID, user: User = None, node_set: Optional[List[str]] = None, - dataset_id: Optional[UUID] = None, vector_db_config: dict = None, graph_db_config: dict = None, preferred_loaders: List[str] = None, diff --git a/cognee/tests/test_library.py b/cognee/tests/test_library.py index c5e6cc64b..33eb64f29 100755 --- a/cognee/tests/test_library.py +++ b/cognee/tests/test_library.py @@ -6,6 +6,7 @@ from cognee.modules.search.operations import get_history from cognee.modules.users.methods import get_default_user from cognee.shared.logging_utils import get_logger from cognee.modules.search.types import SearchType +from cognee import update logger = get_logger() @@ -42,7 +43,7 @@ async def main(): await cognee.add([text], dataset_name) - await cognee.cognify([dataset_name]) + cognify_run_info = await cognee.cognify([dataset_name]) from cognee.infrastructure.databases.vector import get_vector_engine @@ -77,6 +78,35 @@ async def main(): assert len(history) == 6, "Search history is not correct." + # Test updating of documents + # Get Pipeline Run object + pipeline_run_obj = list(cognify_run_info.values())[0] + for data_item in pipeline_run_obj.data_ingestion_info: + # Update all documents in dataset to only contain Mark and Cindy information + await update( + dataset_id=pipeline_run_obj.dataset_id, + data_id=data_item["data_id"], + data="Mark met with Cindy at a cafe.", + ) + + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="What information do you contain?" + ) + assert "Mark" in search_results[0], ( + "Failed to update document, no mention of Mark in search results" + ) + assert "Cindy" in search_results[0], ( + "Failed to update document, no mention of Cindy in search results" + ) + assert "Artificial intelligence" not in search_results[0], ( + "Failed to update document, Artificial intelligence still mentioned in search results" + ) + + # Test visualization + from cognee import visualize_graph + + await visualize_graph() + # Assert local data files are cleaned properly await cognee.prune.prune_data() data_root_directory = get_storage_config()["data_root_directory"] From 4e96e044050ddac10918c1cda0970ffc8ac2f5bd Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Wed, 1 Oct 2025 00:22:28 +0530 Subject: [PATCH 022/139] chore: update dependencies in pyproject.toml and uv.lock - Added "mistralai==1.9.10" to the dependencies in pyproject.toml. - Updated sdist entries in uv.lock to remove unnecessary upload-time fields for various packages. - Ensured consistency in package specifications across the project files. --- pyproject.toml | 2 +- uv.lock | 92 ++++++++++++++++++++++++++------------------------ 2 files changed, 48 insertions(+), 46 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5c4e7908c..9b16f11e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - + "mistralai==1.9.10", ] [project.optional-dependencies] diff --git a/uv.lock b/uv.lock index e380e5dba..f78f763ae 100644 --- 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++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 9226 insertions(+), 3 deletions(-) create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml index 9b16f11e5..8a53c86f1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - "mistralai==1.9.10", + "mistralai>=1.9.10", ] [project.optional-dependencies] @@ -86,7 +86,7 @@ llama-index = ["llama-index-core>=0.12.11,<0.13"] gemini = ["google-generativeai>=0.8.4,<0.9"] huggingface = ["transformers>=4.46.3,<5"] ollama = ["transformers>=4.46.3,<5"] -mistral = ["mistral-common>=1.5.2,<2","mistralai>=1.9.10"] +mistral = ["mistral-common>=1.5.2,<2"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] @@ -96,7 +96,7 @@ chromadb = [ "chromadb>=0.6,<0.7", "pypika==0.48.9", ] -docs = ["unstructured[csv, doc, docx, epub, md, odt, org, ppt, pptx, rst, rtf, tsv, 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Bring back deprecation warnings --- cognee-mcp/pyproject.toml | 3 ++- cognee-mcp/src/__init__.py | 22 +++++++++++++++++++++- cognee/api/v1/ui/ui.py | 2 +- 3 files changed, 24 insertions(+), 3 deletions(-) diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index e1a3a092c..bf9ecd51f 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -36,4 +36,5 @@ dev = [ allow-direct-references = true [project.scripts] -cognee-mcp = "src:main" +cognee = "src:main" +cognee-mcp = "src:main_mcp" \ No newline at end of file diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 1939efe9f..77d325085 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -1,8 +1,28 @@ from .server import main as server_main +import warnings +import sys def main(): - """Main entry point for the package.""" + """Deprecated main entry point for the package.""" + import asyncio + + warnings.warn( + "The 'cognee' command for cognee-mcp is deprecated and will be removed in a future version. " + "Please use 'cognee-mcp' instead to avoid conflicts with the main cognee library.", + DeprecationWarning, + stacklevel=2, + ) + + print("⚠️ DEPRECATION WARNING: Use 'cognee-mcp' command instead of 'cognee'", file=sys.stderr) + print(" This avoids conflicts with the main cognee library.", file=sys.stderr) + print(file=sys.stderr) + + asyncio.run(server_main()) + + +def main_mcp(): + """Clean main entry point for cognee-mcp command.""" import asyncio asyncio.run(server_main()) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index f22a1f00b..f4df0edde 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -516,7 +516,7 @@ def start_ui( env_file, "-e", "TRANSPORT_MODE=sse", - "cognee/cognee-mcp:daulet-dev", + "cognee/cognee-mcp:main", ], stdout=subprocess.PIPE, stderr=subprocess.PIPE, From 5b46f86be5c8a5c3065f3daa1bec2d5d52d56eb6 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Wed, 1 Oct 2025 17:59:53 +0200 Subject: [PATCH 026/139] test: Removed long text string about qunatum computers from tests. Used a file instead. --- cognee/tests/test_chromadb.py | 16 +++++--------- cognee/tests/test_deduplication.py | 8 +------ cognee/tests/test_kuzu.py | 17 ++++++-------- cognee/tests/test_lancedb.py | 16 +++++--------- cognee/tests/test_memgraph.py | 16 +++++--------- cognee/tests/test_neo4j.py | 16 +++++--------- cognee/tests/test_neptune_analytics_vector.py | 16 +++++--------- cognee/tests/test_permissions.py | 22 ++++++++----------- cognee/tests/test_pgvector.py | 18 ++++++--------- cognee/tests/test_remote_kuzu.py | 17 ++++++-------- cognee/tests/test_search_db.py | 14 +++++------- 11 files changed, 67 insertions(+), 109 deletions(-) diff --git a/cognee/tests/test_chromadb.py b/cognee/tests/test_chromadb.py index 01e1938d7..b663f674b 100644 --- a/cognee/tests/test_chromadb.py +++ b/cognee/tests/test_chromadb.py @@ -133,20 +133,16 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name_2) + await cognee.add([explanation_file_path_quantum], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) diff --git a/cognee/tests/test_deduplication.py b/cognee/tests/test_deduplication.py index bef813317..e799f36fc 100644 --- a/cognee/tests/test_deduplication.py +++ b/cognee/tests/test_deduplication.py @@ -49,13 +49,7 @@ async def test_deduplication(): await cognee.prune.prune_system(metadata=True) # Test deduplication of text input - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + text = os.path.join(pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt") await cognee.add([text], dataset_name) await cognee.add([text], dataset_name2) diff --git a/cognee/tests/test_kuzu.py b/cognee/tests/test_kuzu.py index 6afd4540a..349f58fc4 100644 --- a/cognee/tests/test_kuzu.py +++ b/cognee/tests/test_kuzu.py @@ -38,19 +38,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ - await cognee.add([text], dataset_name) + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) + + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/test_lancedb.py b/cognee/tests/test_lancedb.py index ea3b455cd..7a2bffe4d 100644 --- a/cognee/tests/test_lancedb.py +++ b/cognee/tests/test_lancedb.py @@ -131,20 +131,16 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name_2) + await cognee.add([explanation_file_path_quantum], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) diff --git a/cognee/tests/test_memgraph.py b/cognee/tests/test_memgraph.py index b21cf3735..e8ae6d6be 100644 --- a/cognee/tests/test_memgraph.py +++ b/cognee/tests/test_memgraph.py @@ -32,20 +32,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/test_neo4j.py b/cognee/tests/test_neo4j.py index 7f24e8418..6a7a7565c 100644 --- a/cognee/tests/test_neo4j.py +++ b/cognee/tests/test_neo4j.py @@ -32,20 +32,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/test_neptune_analytics_vector.py b/cognee/tests/test_neptune_analytics_vector.py index eececacdd..072c7d724 100644 --- a/cognee/tests/test_neptune_analytics_vector.py +++ b/cognee/tests/test_neptune_analytics_vector.py @@ -38,20 +38,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/test_permissions.py b/cognee/tests/test_permissions.py index 95f769263..3c8a4606a 100644 --- a/cognee/tests/test_permissions.py +++ b/cognee/tests/test_permissions.py @@ -34,25 +34,21 @@ async def main(): await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) # Add document for default user - await cognee.add([explanation_file_path], dataset_name="NLP") + await cognee.add([explanation_file_path_nlp], dataset_name="NLP") default_user = await get_default_user() - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) # Add document for test user test_user = await create_user("user@example.com", "example") - await cognee.add([text], dataset_name="QUANTUM", user=test_user) + await cognee.add([explanation_file_path_quantum], dataset_name="QUANTUM", user=test_user) nlp_cognify_result = await cognee.cognify(["NLP"], user=default_user) quantum_cognify_result = await cognee.cognify(["QUANTUM"], user=test_user) @@ -101,7 +97,7 @@ async def main(): add_error = False try: await cognee.add( - [explanation_file_path], + [explanation_file_path_nlp], dataset_name="QUANTUM", dataset_id=test_user_dataset_id, user=default_user, @@ -143,7 +139,7 @@ async def main(): # Add new data to test_users dataset from default_user await cognee.add( - [explanation_file_path], + [explanation_file_path_nlp], dataset_name="QUANTUM", dataset_id=test_user_dataset_id, user=default_user, @@ -216,7 +212,7 @@ async def main(): ) # Try deleting data from test_user dataset with default_user after getting delete permission - # Get the dataset data to find the ID of the remaining data item (explanation_file_path) + # Get the dataset data to find the ID of the remaining data item (explanation_file_path_nlp) test_user_dataset_data = await get_dataset_data(test_user_dataset_id) explanation_file_data_id = test_user_dataset_data[0].id diff --git a/cognee/tests/test_pgvector.py b/cognee/tests/test_pgvector.py index 059cf691a..cc78c536a 100644 --- a/cognee/tests/test_pgvector.py +++ b/cognee/tests/test_pgvector.py @@ -141,20 +141,16 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name_2) + await cognee.add([explanation_file_path_quantum], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) @@ -202,7 +198,7 @@ async def main(): history = await get_history(user.id) assert len(history) == 8, "Search history is not correct." - await test_local_file_deletion(text, explanation_file_path) + await test_local_file_deletion(explanation_file_path_quantum, explanation_file_path_nlp) await cognee.prune.prune_data() data_root_directory = get_storage_config()["data_root_directory"] diff --git a/cognee/tests/test_remote_kuzu.py b/cognee/tests/test_remote_kuzu.py index bbff48096..e0c3b223c 100644 --- a/cognee/tests/test_remote_kuzu.py +++ b/cognee/tests/test_remote_kuzu.py @@ -42,19 +42,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ - await cognee.add([text], dataset_name) + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) + + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/test_search_db.py b/cognee/tests/test_search_db.py index cb4636470..e24abd0f5 100644 --- a/cognee/tests/test_search_db.py +++ b/cognee/tests/test_search_db.py @@ -1,3 +1,5 @@ +import pathlib +import os import cognee from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge @@ -27,15 +29,11 @@ async def main(): text_1 = """Germany is located in europe right next to the Netherlands""" await cognee.add(text_1, dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) From 6f0756f312b4e46be72150b331104be837eb9a03 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Wed, 1 Oct 2025 18:10:57 +0200 Subject: [PATCH 027/139] test: Rollback deduplication test --- cognee/tests/test_deduplication.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/cognee/tests/test_deduplication.py b/cognee/tests/test_deduplication.py index e799f36fc..bef813317 100644 --- a/cognee/tests/test_deduplication.py +++ b/cognee/tests/test_deduplication.py @@ -49,7 +49,13 @@ async def test_deduplication(): await cognee.prune.prune_system(metadata=True) # Test deduplication of text input - text = os.path.join(pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt") + text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. + At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. + Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. + The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. + Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. + In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. + """ await cognee.add([text], dataset_name) await cognee.add([text], dataset_name2) From 70a2cc9d65c17ffafe10e6ae0717f572681adbb5 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 01:28:48 +0530 Subject: [PATCH 028/139] removed scrapy and added bs4 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ccae1db8f..eecbb9985 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,7 +75,7 @@ distributed = [ ] scraping = [ "tavily-python>=0.7.0", - "scrapy>=2.12.0" + "beautifulsoup4>=4.13.1" ] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] From 925bd38195c8a3489fc1b84350734c0f1dfe291b Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 01:32:00 +0530 Subject: [PATCH 029/139] Setup models.py and utils.py --- cognee/tasks/web_scraper/models.py | 9 +++++-- cognee/tasks/web_scraper/utils.py | 39 ++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+), 2 deletions(-) create mode 100644 cognee/tasks/web_scraper/utils.py diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py index 8d5f32f43..12ac91166 100644 --- a/cognee/tasks/web_scraper/models.py +++ b/cognee/tasks/web_scraper/models.py @@ -2,8 +2,10 @@ from cognee.infrastructure.engine.models import DataPoint from typing import Optional, Dict, Any, List from datetime import datetime + class WebPage(DataPoint): """Represents a scraped web page with metadata""" + url: str title: Optional[str] content: str @@ -16,8 +18,10 @@ class WebPage(DataPoint): extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used metadata: dict = {"index_fields": ["url", "title", "scraped_at"]} + class WebSite(DataPoint): """Represents a website or domain being scraped""" + domain: str base_url: str robots_txt: Optional[str] @@ -27,13 +31,14 @@ class WebSite(DataPoint): scraping_config: Dict[str, Any] metadata: dict = {"index_fields": ["domain", "base_url"]} + class ScrapingJob(DataPoint): """Represents a scraping job configuration""" + job_name: str urls: List[str] - scraping_rules: Dict[str, Any] schedule: Optional[str] # Cron-like schedule for recurring scrapes status: str # "active", "paused", "completed", "failed" last_run: Optional[datetime] next_run: Optional[datetime] - metadata: dict = {"index_fields": ["job_name", "status"]} \ No newline at end of file + metadata: dict = {"index_fields": ["job_name", "status"]} diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py new file mode 100644 index 000000000..bcf12ac32 --- /dev/null +++ b/cognee/tasks/web_scraper/utils.py @@ -0,0 +1,39 @@ +from tavily import AsyncTavilyClient +from bs4 import BeautifulSoup +import os +import requests +from typing import Dict, Any, List, Union + + +async def fetch_page_content(urls: Union[str, List[str]], extraction_rules: Dict[str, Any]) -> str: + if os.getenv("TAVILY_API_KEY") is not None: + return await fetch_with_tavily(urls) + else: + return await fetch_with_bs4(urls, extraction_rules) + + +async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: + client = AsyncTavilyClient() + results = await client.extract(urls, include_images=False) + result_dict = {} + for result in results["results"]: + result_dict[result["url"]] = result["raw_content"] + return result_dict + + +async def fetch_with_bs4(urls: Union[str,List[str]], extraction_rules: Dict) -> Dict[str]: + result_dict = {} + if isinstance(urls,str): + urls = [urls] + for url in urls: + response = requests.get(url, headers={"User-Agent": "Cognee-Scraper"}) + response.raise_for_status() + + soup = BeautifulSoup(response.text, "html.parser") + extracted_data = "" + + for field, selector in extraction_rules.items(): + element = soup.select_one(selector) + extracted_data += element.get_text(strip=True) if element else "" + + return result_dict From 60499c439cb5c0460bf597b405c24051451e9af2 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 01:54:56 +0530 Subject: [PATCH 030/139] Added logging --- cognee/tasks/web_scraper/utils.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index bcf12ac32..7c90a87a1 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -3,6 +3,23 @@ from bs4 import BeautifulSoup import os import requests from typing import Dict, Any, List, Union +from cognee.shared.logging_utils import get_logger + +logger = get_logger(__name__) + +try: + from tavily import AsyncTavilyClient +except ImportError: + logger.error( + "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" + ) + +try: + from bs4 import BeautifulSoup +except ImportError: + logger.error( + "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" + ) async def fetch_page_content(urls: Union[str, List[str]], extraction_rules: Dict[str, Any]) -> str: @@ -21,9 +38,9 @@ async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: return result_dict -async def fetch_with_bs4(urls: Union[str,List[str]], extraction_rules: Dict) -> Dict[str]: +async def fetch_with_bs4(urls: Union[str, List[str]], extraction_rules: Dict) -> Dict[str]: result_dict = {} - if isinstance(urls,str): + if isinstance(urls, str): urls = [urls] for url in urls: response = requests.get(url, headers={"User-Agent": "Cognee-Scraper"}) From c2839770356ff09e6621a3ce27e9cd2f068a78e9 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 02:01:46 +0530 Subject: [PATCH 031/139] switched httpx AsyncClient to fetch webpage --- cognee/tasks/web_scraper/utils.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 7c90a87a1..5ff105ec9 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -1,7 +1,7 @@ from tavily import AsyncTavilyClient from bs4 import BeautifulSoup import os -import requests +import httpx from typing import Dict, Any, List, Union from cognee.shared.logging_utils import get_logger @@ -42,15 +42,17 @@ async def fetch_with_bs4(urls: Union[str, List[str]], extraction_rules: Dict) -> result_dict = {} if isinstance(urls, str): urls = [urls] - for url in urls: - response = requests.get(url, headers={"User-Agent": "Cognee-Scraper"}) - response.raise_for_status() + async with httpx.AsyncClient(headers={"User-Agent": "Cognee-Scraper"}) as client: + for url in urls: + response = await client.get(url) + response.raise_for_status() - soup = BeautifulSoup(response.text, "html.parser") - extracted_data = "" + soup = BeautifulSoup(response.text, "html.parser") + extracted_data = "" + for field, selector in extraction_rules.items(): + element = soup.select_one(selector) + extracted_data += (element.get_text(strip=True) + "\n") if element else "" - for field, selector in extraction_rules.items(): - element = soup.select_one(selector) - extracted_data += element.get_text(strip=True) if element else "" + result_dict[url] = extracted_data.strip() return result_dict From 4979f43fc099433f56e828e8a8c3d5c604403aa4 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 02:21:33 +0530 Subject: [PATCH 032/139] Added playwright as a dependency --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index eecbb9985..e50c38f25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,7 +75,8 @@ distributed = [ ] scraping = [ "tavily-python>=0.7.0", - "beautifulsoup4>=4.13.1" + "beautifulsoup4>=4.13.1", + "playwright>=1.9.0", ] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] From 9c87a10848af92765285076abafd86aaa683cf7b Mon Sep 17 00:00:00 2001 From: shehab-badawy Date: Fri, 26 Sep 2025 22:27:32 -0400 Subject: [PATCH 033/139] feat: Add delete preview for --dataset-name and --all flags This commit introduces the preview functionality for the command. The preview displays a summary of what will be deleted before asking for user confirmation. The feature is fully functional for the following flags: - / : Correctly counts the number of data entries within the specified dataset. - : Correctly counts the total number of datasets, data entries, and users in the system. The logic for the flag is a work in progress. The current implementation uses a placeholder and needs a method to query a user directly by their ID to be completed. --- cognee/cli/commands/delete_command.py | 14 +--- .../data/methods/get_deletion_counts.py | 79 ++++++++++++++----- 2 files changed, 64 insertions(+), 29 deletions(-) diff --git a/cognee/cli/commands/delete_command.py b/cognee/cli/commands/delete_command.py index c9d13890e..0139dbe1a 100644 --- a/cognee/cli/commands/delete_command.py +++ b/cognee/cli/commands/delete_command.py @@ -54,21 +54,15 @@ Be careful with deletion operations as they are irreversible. ) ) - if not preview_data or all(value == 0 for value in preview_data.values()): + if not preview_data: fmt.success("No data found to delete.") return fmt.echo("You are about to delete:") - if "datasets" in preview_data and preview_data["datasets"] > 0: - fmt.echo(f"- {preview_data['datasets']} datasets") - if "data_entries" in preview_data and preview_data["data_entries"] > 0: - fmt.echo(f"- {preview_data['data_entries']} data entries") - if "users" in preview_data and preview_data["users"] > 0: - fmt.echo( - f"- {preview_data['users']} {'users' if preview_data['users'] > 1 else 'user'}" - ) + fmt.echo( + f"Datasets: {preview_data.datasets}\nEntries: {preview_data.entries}\nUsers: {preview_data.users}" + ) fmt.echo("-" * 20) - fmt.warning("This operation is irreversible!") if not fmt.confirm("Proceed?"): fmt.echo("Deletion cancelled.") diff --git a/cognee/modules/data/methods/get_deletion_counts.py b/cognee/modules/data/methods/get_deletion_counts.py index cdd222645..f985cf71d 100644 --- a/cognee/modules/data/methods/get_deletion_counts.py +++ b/cognee/modules/data/methods/get_deletion_counts.py @@ -1,16 +1,30 @@ +from uuid import UUID +from cognee.cli.exceptions import CliCommandException +from cognee.infrastructure.databases.exceptions.exceptions import EntityNotFoundError from sqlalchemy import select from sqlalchemy.sql import func from cognee.infrastructure.databases.relational import get_relational_engine from cognee.modules.data.models import Dataset, Data, DatasetData -from cognee.modules.users.models import User +from cognee.modules.users.models import User, DatasetDatabase +from cognee.modules.users.methods import get_user, get_default_user +from dataclasses import dataclass +import cognee.cli.echo as fmt + + +@dataclass +class DeletionCountsPreview: + datasets: int = 0 + data_entries: int = 0 + users: int = 0 async def get_deletion_counts( dataset_name: str = None, user_id: str = None, all_data: bool = False -) -> dict: +) -> DeletionCountsPreview: """ Calculates the number of items that will be deleted based on the provided arguments. """ + counts = DeletionCountsPreview() relational_engine = get_relational_engine() async with relational_engine.get_async_session() as session: if dataset_name: @@ -21,7 +35,10 @@ async def get_deletion_counts( dataset = dataset_result.scalar_one_or_none() if dataset is None: - return {"datasets": 0, "data_entries": 0} + fmt.error(f"No dataset with this name: {dataset_name}") + raise CliCommandException( + f"No Dataset exists with the name {dataset_name}", error_code=1 + ) # Count data entries linked to this dataset count_query = ( @@ -30,28 +47,52 @@ async def get_deletion_counts( .where(DatasetData.dataset_id == dataset.id) ) data_entry_count = (await session.execute(count_query)).scalar_one() + counts.users = 1 + counts.datasets = 1 + counts.entries = data_entry_count + return counts - return {"datasets": 1, "data_entries": data_entry_count} - - if all_data: - dataset_count = ( + elif all_data: + # Simplified logic: Get total counts directly from the tables. + counts.datasets = ( await session.execute(select(func.count()).select_from(Dataset)) ).scalar_one() - data_entry_count = ( + counts.entries = ( await session.execute(select(func.count()).select_from(Data)) ).scalar_one() - user_count = ( + counts.users = ( await session.execute(select(func.count()).select_from(User)) ).scalar_one() - return { - "datasets": dataset_count, - "data_entries": data_entry_count, - "users": user_count, - } + return counts # Placeholder for user_id logic - if user_id: - # TODO: Implement counting logic for a specific user - return {"datasets": 0, "data_entries": 0, "users": 1} - - return {} + elif user_id: + user = None + try: + user_uuid = UUID(user_id) + user = await get_user(user_uuid) + except (ValueError, EntityNotFoundError): + # Handles cases where user_id is not a valid UUID or user is not found + fmt.error(f"No user exists with ID {user_id}") + raise CliCommandException(f"No User exists with ID {user_id}", error_code=1) + user = await get_user(user_uuid) + if user: + counts.users = 1 + # Find all datasets owned by this user + datasets_query = select(Dataset).where(Dataset.owner_id == user.id) + user_datasets = (await session.execute(datasets_query)).scalars().all() + dataset_count = len(user_datasets) + counts.datasets = dataset_count + if dataset_count > 0: + dataset_ids = [d.id for d in user_datasets] + # Count all data entries across all of the user's datasets + data_count_query = ( + select(func.count()) + .select_from(DatasetData) + .where(DatasetData.dataset_id.in_(dataset_ids)) + ) + data_entry_count = (await session.execute(data_count_query)).scalar_one() + counts.entries = data_entry_count + else: + counts.entries = 0 + return counts From a744f8d43519891245a1aae91630e39aadbc00ee Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Thu, 2 Oct 2025 09:54:30 +0200 Subject: [PATCH 034/139] test: Rollback pgvector test. Was failing for some reason. --- cognee/tests/test_pgvector.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/cognee/tests/test_pgvector.py b/cognee/tests/test_pgvector.py index cc78c536a..f3c191da3 100644 --- a/cognee/tests/test_pgvector.py +++ b/cognee/tests/test_pgvector.py @@ -146,11 +146,15 @@ async def main(): ) await cognee.add([explanation_file_path_nlp], dataset_name_1) - explanation_file_path_quantum = os.path.join( - pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" - ) + text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. + At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. + Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. + The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. + Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. + In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. + """ - await cognee.add([explanation_file_path_quantum], dataset_name_2) + await cognee.add([text], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) @@ -198,7 +202,7 @@ async def main(): history = await get_history(user.id) assert len(history) == 8, "Search history is not correct." - await test_local_file_deletion(explanation_file_path_quantum, explanation_file_path_nlp) + await test_local_file_deletion(text, explanation_file_path_nlp) await cognee.prune.prune_data() data_root_directory = get_storage_config()["data_root_directory"] From a92f4bdf3fd13ba55a1fd811fb2fbe4da40917cf Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 2 Oct 2025 15:05:39 +0100 Subject: [PATCH 035/139] fix: update failing tests and refactor delete_preview implementation --- cognee/cli/commands/delete_command.py | 29 +++++++---- .../data/methods/get_deletion_counts.py | 48 ++++++++----------- .../cli_unit_tests/test_cli_commands.py | 17 +++++-- .../cli_unit_tests/test_cli_edge_cases.py | 9 +++- 4 files changed, 60 insertions(+), 43 deletions(-) diff --git a/cognee/cli/commands/delete_command.py b/cognee/cli/commands/delete_command.py index 0139dbe1a..46cf41367 100644 --- a/cognee/cli/commands/delete_command.py +++ b/cognee/cli/commands/delete_command.py @@ -46,13 +46,17 @@ Be careful with deletion operations as they are irreversible. if not args.force: # --- START PREVIEW LOGIC --- fmt.echo("Gathering data for preview...") - preview_data = asyncio.run( - get_deletion_counts( - dataset_name=args.dataset_name, - user_id=args.user_id, - all_data=args.all, + try: + preview_data = asyncio.run( + get_deletion_counts( + dataset_name=args.dataset_name, + user_id=args.user_id, + all_data=args.all, + ) ) - ) + except CliCommandException as e: + fmt.error(f"Error occured when fetching preview data: {str(e)}") + return if not preview_data: fmt.success("No data found to delete.") @@ -63,22 +67,27 @@ Be careful with deletion operations as they are irreversible. f"Datasets: {preview_data.datasets}\nEntries: {preview_data.entries}\nUsers: {preview_data.users}" ) fmt.echo("-" * 20) - fmt.warning("This operation is irreversible!") - if not fmt.confirm("Proceed?"): - fmt.echo("Deletion cancelled.") - return # --- END PREVIEW LOGIC --- # Build operation message for success/failure logging if args.all: + confirm_msg = "Delete ALL data from cognee?" operation = "all data" elif args.dataset_name: + confirm_msg = f"Delete dataset '{args.dataset_name}'?" operation = f"dataset '{args.dataset_name}'" elif args.user_id: + confirm_msg = f"Delete all data for user '{args.user_id}'?" operation = f"data for user '{args.user_id}'" else: operation = "data" + if not args.force: + fmt.warning("This operation is irreversible!") + if not fmt.confirm(confirm_msg): + fmt.echo("Deletion cancelled.") + return + fmt.echo(f"Deleting {operation}...") # Run the async delete function diff --git a/cognee/modules/data/methods/get_deletion_counts.py b/cognee/modules/data/methods/get_deletion_counts.py index f985cf71d..9237a8b85 100644 --- a/cognee/modules/data/methods/get_deletion_counts.py +++ b/cognee/modules/data/methods/get_deletion_counts.py @@ -5,10 +5,9 @@ from sqlalchemy import select from sqlalchemy.sql import func from cognee.infrastructure.databases.relational import get_relational_engine from cognee.modules.data.models import Dataset, Data, DatasetData -from cognee.modules.users.models import User, DatasetDatabase -from cognee.modules.users.methods import get_user, get_default_user +from cognee.modules.users.models import User +from cognee.modules.users.methods import get_user from dataclasses import dataclass -import cognee.cli.echo as fmt @dataclass @@ -35,7 +34,6 @@ async def get_deletion_counts( dataset = dataset_result.scalar_one_or_none() if dataset is None: - fmt.error(f"No dataset with this name: {dataset_name}") raise CliCommandException( f"No Dataset exists with the name {dataset_name}", error_code=1 ) @@ -72,27 +70,23 @@ async def get_deletion_counts( user_uuid = UUID(user_id) user = await get_user(user_uuid) except (ValueError, EntityNotFoundError): - # Handles cases where user_id is not a valid UUID or user is not found - fmt.error(f"No user exists with ID {user_id}") raise CliCommandException(f"No User exists with ID {user_id}", error_code=1) - user = await get_user(user_uuid) - if user: - counts.users = 1 - # Find all datasets owned by this user - datasets_query = select(Dataset).where(Dataset.owner_id == user.id) - user_datasets = (await session.execute(datasets_query)).scalars().all() - dataset_count = len(user_datasets) - counts.datasets = dataset_count - if dataset_count > 0: - dataset_ids = [d.id for d in user_datasets] - # Count all data entries across all of the user's datasets - data_count_query = ( - select(func.count()) - .select_from(DatasetData) - .where(DatasetData.dataset_id.in_(dataset_ids)) - ) - data_entry_count = (await session.execute(data_count_query)).scalar_one() - counts.entries = data_entry_count - else: - counts.entries = 0 - return counts + counts.users = 1 + # Find all datasets owned by this user + datasets_query = select(Dataset).where(Dataset.owner_id == user.id) + user_datasets = (await session.execute(datasets_query)).scalars().all() + dataset_count = len(user_datasets) + counts.datasets = dataset_count + if dataset_count > 0: + dataset_ids = [d.id for d in user_datasets] + # Count all data entries across all of the user's datasets + data_count_query = ( + select(func.count()) + .select_from(DatasetData) + .where(DatasetData.dataset_id.in_(dataset_ids)) + ) + data_entry_count = (await session.execute(data_count_query)).scalar_one() + counts.entries = data_entry_count + else: + counts.entries = 0 + return counts diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py index e5d6a0c64..7654a781a 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py @@ -12,7 +12,8 @@ from cognee.cli.commands.search_command import SearchCommand from cognee.cli.commands.cognify_command import CognifyCommand from cognee.cli.commands.delete_command import DeleteCommand from cognee.cli.commands.config_command import ConfigCommand -from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.cli.exceptions import CliCommandException +from cognee.modules.data.methods.get_deletion_counts import DeletionCountsPreview # Mock asyncio.run to properly handle coroutines @@ -282,13 +283,18 @@ class TestDeleteCommand: assert "all" in actions assert "force" in actions + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") @patch("cognee.cli.commands.delete_command.asyncio.run", side_effect=_mock_run) - def test_execute_delete_dataset_with_confirmation(self, mock_asyncio_run, mock_confirm): + def test_execute_delete_dataset_with_confirmation( + self, mock_asyncio_run, mock_confirm, mock_get_deletion_counts + ): """Test execute delete dataset with user confirmation""" # Mock the cognee module mock_cognee = MagicMock() mock_cognee.delete = AsyncMock() + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() with patch.dict(sys.modules, {"cognee": mock_cognee}): command = DeleteCommand() @@ -301,13 +307,16 @@ class TestDeleteCommand: command.execute(args) mock_confirm.assert_called_once_with(f"Delete dataset '{args.dataset_name}'?") - mock_asyncio_run.assert_called_once() + assert mock_asyncio_run.call_count == 2 assert asyncio.iscoroutine(mock_asyncio_run.call_args[0][0]) mock_cognee.delete.assert_awaited_once_with(dataset_name="test_dataset", user_id=None) + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") - def test_execute_delete_cancelled(self, mock_confirm): + def test_execute_delete_cancelled(self, mock_confirm, mock_get_deletion_counts): """Test execute when user cancels deletion""" + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() command = DeleteCommand() args = argparse.Namespace(dataset_name="test_dataset", user_id=None, all=False, force=False) diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py index d4e4b0ee5..de8681be9 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py @@ -13,6 +13,7 @@ from cognee.cli.commands.cognify_command import CognifyCommand from cognee.cli.commands.delete_command import DeleteCommand from cognee.cli.commands.config_command import ConfigCommand from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.modules.data.methods.get_deletion_counts import DeletionCountsPreview # Mock asyncio.run to properly handle coroutines @@ -396,13 +397,17 @@ class TestDeleteCommandEdgeCases: command.execute(args) mock_confirm.assert_called_once_with("Delete ALL data from cognee?") - mock_asyncio_run.assert_called_once() + assert mock_asyncio_run.call_count == 2 assert asyncio.iscoroutine(mock_asyncio_run.call_args[0][0]) mock_cognee.delete.assert_awaited_once_with(dataset_name=None, user_id="test_user") + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") - def test_delete_confirmation_keyboard_interrupt(self, mock_confirm): + def test_delete_confirmation_keyboard_interrupt(self, mock_confirm, mock_get_deletion_counts): """Test delete command when user interrupts confirmation""" + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() + command = DeleteCommand() args = argparse.Namespace(dataset_name="test_dataset", user_id=None, all=False, force=False) From edd119ef9729011b1d885d481f7fc83beda5cf5f Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Thu, 2 Oct 2025 22:04:50 +0530 Subject: [PATCH 036/139] first iteration of bs4_connector.py done --- cognee/tasks/web_scraper/bs4_connector.py | 270 ++++++++++++++++++++++ 1 file changed, 270 insertions(+) create mode 100644 cognee/tasks/web_scraper/bs4_connector.py diff --git a/cognee/tasks/web_scraper/bs4_connector.py b/cognee/tasks/web_scraper/bs4_connector.py new file mode 100644 index 000000000..20a587216 --- /dev/null +++ b/cognee/tasks/web_scraper/bs4_connector.py @@ -0,0 +1,270 @@ +import asyncio +import time +from typing import Union, List, Dict, Any, Optional +from bs4 import BeautifulSoup +import httpx +from cognee.shared.logging_utils import get_logger + +logger = get_logger(__name__) + +try: + from playwright.async_api import async_playwright +except ImportError: + logger.error("Failed to import playwright, make sure to install using pip install playwright>=1.9.0") + +try: + from bs4 import BeautifulSoup +except ImportError: + logger.error("Failed to import BeautifulSoup, make sure to install using pip install beautifulsoup4") + + + + + +class BeautifulSoupCrawler: + def __init__( + self, + *, + concurrency: int = 5, + delay_between_requests: float = 0.5, + timeout: float = 15.0, + max_retries: int = 2, + retry_delay_factor: float = 0.5, + headers: Optional[Dict[str, str]] = None, + ): + """ + concurrency: number of concurrent requests allowed + delay_between_requests: minimum seconds to wait between requests to the SAME domain + timeout: per-request timeout + max_retries: number of retries on network errors + retry_delay_factor: multiplier for exponential retry failure delay + headers: default headers for requests + use_httpx: require httpx for async HTTP. If not available, an informative error will be raised. + """ + self.concurrency = concurrency + self._sem = asyncio.Semaphore(concurrency) + self.delay_between_requests = delay_between_requests + self.timeout = timeout + self.max_retries = max_retries + self.retry_delay_factor = retry_delay_factor + self.headers = headers or {"User-agent": "Cognee-Scraper/1.0"} + self._last_request_time_per_domain: Dict[str, float] = {} + self._client = None + + # ---------- lifecycle helpers ---------- + async def _ensure_client(self): + if self._client is None: + self._client = httpx.AsyncClient(timeout=self.timeout, headers=self.headers) if httpx else None + + async def close(self): + if self._client: + await self._client.aclose() + self._client = None + + # ---------- rate limiting ---------- + def _domain_from_url(self, url: str) -> str: + # quick parse to domain + try: + from urllib.parse import urlparse + p = urlparse(url) + return p.netloc + except Exception: + return url + + async def _respect_rate_limit(self, url: str): + domain = self._domain_from_url(url) + last = self._last_request_time_per_domain.get(domain) + if last is None: + self._last_request_time_per_domain[domain] = time.time() + return + elapsed = time.time() - last + wait_for = self.delay_between_requests - elapsed + if wait_for > 0: + await asyncio.sleep(wait_for) + self._last_request_time_per_domain[domain] = time.time() + + # ----------- robots.txt handling could be added here ----------- + async def _is_url_allowed(self, url: str) -> bool: + robots_txt_url = f"{self._get_base_url(url)}/robots.txt" + robots_txt_content = await self._fetch_httpx(robots_txt_url) + robots_txt_content = robots_txt_content.lower() + user_agent_name = self.headers.get("User-agent") + pos = robots_txt_content.find(f"user-agent: {user_agent_name}") + if pos == -1: + pos = robots_txt_content.find(f"user-agent:*") + if pos == -1: + return True + + pos = robots_txt_content.find("disallow", pos) + # TODO: Research more about robots.txt format + + + + + # ---------- low-level fetchers ---------- + async def _fetch_httpx(self, url: str) -> str: + await self._ensure_client() + assert self._client is not None, "HTTP client not initialized" + attempt = 0 + while True: + try: + await self._respect_rate_limit(url) + resp = await self._client.get(url) + resp.raise_for_status() + return resp.text + except Exception as exc: + attempt += 1 + if attempt > self.max_retries: + raise + delay = self.retry_delay_factor * (2 ** (attempt - 1)) + await asyncio.sleep(delay) + + async def _render_with_playwright(self, url: str, js_wait: float = 1.0, timeout: Optional[float] = None) -> str: + if async_playwright is None: + raise RuntimeError("Playwright is not installed. Install with `pip install playwright` and run `playwright install`.") + # Basic Playwright rendering (Chromium). This is slower but renders JS. + attempt = 0 + while True: + try: + async with async_playwright() as p: + browser = await p.chromium.launch(headless=True) + context = await browser.new_context() + page = await context.new_page() + await page.goto(url, wait_until="networkidle", timeout=int((timeout or self.timeout) * 1000)) + # optional short wait to let in-page JS mutate DOM + if js_wait: + await asyncio.sleep(js_wait) + content = await page.content() + await browser.close() + return content + except Exception: + attempt += 1 + if attempt > self.max_retries: + raise + backoff = self.backoff_factor * (2 ** (attempt - 1)) + await asyncio.sleep(backoff) + + # ---------- extraction helpers ---------- + def _normalize_rule(self, rule) -> Dict[str, Any]: + if isinstance(rule, str): + return {"selector": rule, "attr": None, "all": False, "join_with": " "} + if isinstance(rule, dict): + return { + "selector": rule.get("selector"), + "attr": rule.get("attr"), + "all": bool(rule.get("all")), + "join_with": rule.get("join_with", " "), + "xpath": rule.get("xpath"), + } + raise ValueError("Invalid extraction rule") + + def _extract_with_bs4(self, html: str, rule: Dict[str, Any]) -> str: + soup = BeautifulSoup(html, "html.parser") + sel = rule.get("selector") + xpath = rule.get("xpath") + attr = rule.get("attr") + all_flag = rule.get("all", False) + join_with = rule.get("join_with", " ") + + if xpath: + # try lxml extraction via xpath if lxml is available + try: + from lxml import html as lxml_html + except Exception: + raise RuntimeError("XPath requested but lxml is not available. Install lxml or use CSS selectors.") + doc = lxml_html.fromstring(html) + nodes = doc.xpath(xpath) + texts = [] + for n in nodes: + if hasattr(n, "text_content"): + texts.append(n.text_content().strip()) + else: + texts.append(str(n).strip()) + return join_with.join(t for t in texts if t) + else: + if not sel: + return "" + if all_flag: + nodes = soup.select(sel) + pieces = [] + for el in nodes: + if attr: + val = el.get(attr) + if val: + pieces.append(val.strip()) + else: + text = el.get_text(strip=True) + if text: + pieces.append(text) + return join_with.join(pieces).strip() + else: + el = soup.select_one(sel) + if el is None: + return "" + if attr: + val = el.get(attr) + return (val or "").strip() + return el.get_text(strip=True) + + # ---------- public API (keeps the signature you asked for) ---------- + async def fetch_with_bs4( + self, + urls: Union[str, List[str]], + extraction_rules: Dict[str, Any], + *, + use_playwright: bool = False, + playwright_js_wait: float = 0.8, + join_all_matches: bool = False, # if True, for each rule use all matches (join them) + ) -> Dict[str, str]: + """ + Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). + Returns: dict[url] -> single concatenated string (trimmed) + Parameters: + - urls: str or list[str] + - extraction_rules: dict[field_name -> selector or rule-dict] + rule-dict keys: selector (CSS), xpath (optional), attr (optional), all(bool), join_with(str) + - use_playwright: if True, use Playwright to render JS (must be installed), otherwise normal fetch + - playwright_js_wait: seconds to wait after load for JS to mutate DOM + - join_all_matches: convenience: if True, treat each rule as all=True + """ + if isinstance(urls, str): + urls = [urls] + + # normalize rules + normalized_rules = {} + for field, rule in extraction_rules.items(): + r = self._normalize_rule(rule) + if join_all_matches: + r["all"] = True + normalized_rules[field] = r + + # concurrency control + gather tasks + async def _task(url: str): + async with self._sem: + # fetch (rendered or not) + if use_playwright: + html = await self._render_with_playwright(url, js_wait=playwright_js_wait, timeout=self.timeout) + else: + html = await self._fetch_httpx(url) + + # Extract and concatenate results into a single string + pieces = [] + for field, rule in normalized_rules.items(): + text = self._extract_with_bs4(html, rule) + if text: + pieces.append(text) + concatenated = " ".join(pieces).strip() + return url, concatenated + + tasks = [asyncio.create_task(_task(u)) for u in urls] + results = {} + for coro in asyncio.as_completed(tasks): + try: + url, text = await coro + except Exception as e: + # store empty string on failure (or raise depending on your policy) + results[url] = "" + # Optionally you could log the error; for now we'll attach empty string + continue + results[url] = text + return results From 38070c489bf0ed7c0cf74f7220ef3103e82be0ed Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 2 Oct 2025 17:39:10 +0100 Subject: [PATCH 037/139] fix `test_cli_edge_cases.py`, `test_delete_all_with_user_id` unit test --- .../tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py index de8681be9..ca27c0f67 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py @@ -379,13 +379,18 @@ class TestCognifyCommandEdgeCases: class TestDeleteCommandEdgeCases: """Test edge cases for DeleteCommand""" + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") @patch("cognee.cli.commands.delete_command.asyncio.run", side_effect=_mock_run) - def test_delete_all_with_user_id(self, mock_asyncio_run, mock_confirm): + def test_delete_all_with_user_id( + self, mock_asyncio_run, mock_confirm, mock_get_deletion_counts + ): """Test delete command with both --all and --user-id""" # Mock the cognee module mock_cognee = MagicMock() mock_cognee.delete = AsyncMock() + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() with patch.dict(sys.modules, {"cognee": mock_cognee}): command = DeleteCommand() From 1ab9d24cf078cbeb281267a0c662de07181d8f4c Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Fri, 3 Oct 2025 12:33:13 +0530 Subject: [PATCH 038/139] Changed bs4_connector.py to bs4_crawler.py --- cognee/tasks/web_scraper/bs4_crawler.py | 300 ++++++++++++++++++++++++ 1 file changed, 300 insertions(+) create mode 100644 cognee/tasks/web_scraper/bs4_crawler.py diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py new file mode 100644 index 000000000..df926bdf9 --- /dev/null +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -0,0 +1,300 @@ +import asyncio +import time +from typing import Union, List, Dict, Any, Optional +from urllib.parse import urlparse + +import httpx +from bs4 import BeautifulSoup +from cognee.shared.logging_utils import get_logger + +logger = get_logger(__name__) + +try: + from playwright.async_api import async_playwright +except ImportError: + logger.error( + "Failed to import playwright, make sure to install using pip install playwright>=1.9.0" + ) + async_playwright = None + +try: + from protego import Protego +except ImportError: + logger.error("Failed to import protego, make sure to install using pip install protego>=0.1") + Protego = None + + +class BeautifulSoupCrawler: + def __init__( + self, + *, + concurrency: int = 5, + crawl_delay: float = 0.5, + timeout: float = 15.0, + max_retries: int = 2, + retry_delay_factor: float = 0.5, + headers: Optional[Dict[str, str]] = None, + ): + """ + concurrency: number of concurrent requests allowed + crawl_delay: minimum seconds to wait between requests to the SAME domain + timeout: per-request timeout + max_retries: number of retries on network errors + retry_delay_factor: multiplier for exponential retry failure delay + headers: default headers for requests + """ + self.concurrency = concurrency + self._sem = asyncio.Semaphore(concurrency) + self.crawl_delay = crawl_delay + self.timeout = timeout + self.max_retries = max_retries + self.retry_delay_factor = retry_delay_factor + self.headers = headers or {"User-Agent": "Cognee-Scraper/1.0"} + self._last_request_time_per_domain: Dict[str, float] = {} + self._client: Optional[httpx.AsyncClient] = None + + # ---------- lifecycle helpers ---------- + async def _ensure_client(self): + if self._client is None: + self._client = httpx.AsyncClient(timeout=self.timeout, headers=self.headers) + + async def close(self): + if self._client: + await self._client.aclose() + self._client = None + + # ---------- rate limiting ---------- + def _domain_from_url(self, url: str) -> str: + try: + return urlparse(url).netloc + except Exception: + return url + + async def _respect_rate_limit(self, url: str, crawl_delay: Optional[float] = None): + domain = self._domain_from_url(url) + last = self._last_request_time_per_domain.get(domain) + delay = crawl_delay if crawl_delay is not None else self.crawl_delay + if last is None: + self._last_request_time_per_domain[domain] = time.time() + return + elapsed = time.time() - last + wait_for = delay - elapsed + if wait_for > 0: + await asyncio.sleep(wait_for) + self._last_request_time_per_domain[domain] = time.time() + + # ----------- robots.txt handling ----------- + async def _is_url_allowed(self, url: str) -> bool: + if Protego is None: + return True # fallback if Protego not installed + try: + parsed_url = urlparse(url) + robots_url = f"{parsed_url.scheme}://{parsed_url.netloc}/robots.txt" + content = await self._fetch_httpx(robots_url) + rp = Protego.parse(content) + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + return rp.can_fetch(agent, url) or rp.can_fetch("*", url) + except Exception: + return True # if no robots.txt, allow by default + + async def _get_crawl_delay(self, base_url: str) -> float: + if Protego is None: + return self.crawl_delay + try: + content = await self._fetch_httpx(base_url + "/robots.txt") + rp = Protego.parse(content) + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + delay = rp.crawl_delay(agent) or rp.crawl_delay("*") + return delay or self.crawl_delay + except Exception: + return self.crawl_delay + + # ---------- low-level fetchers ---------- + async def _fetch_httpx(self, url: str) -> str: + await self._ensure_client() + assert self._client is not None, "HTTP client not initialized" + attempt = 0 + while True: + try: + # get crawl delay from robots.txt if available + crawl_delay = await self._get_crawl_delay( + f"{urlparse(url).scheme}://{urlparse(url).netloc}" + ) + await self._respect_rate_limit(url, crawl_delay) + resp = await self._client.get(url) + resp.raise_for_status() + return resp.text + except Exception as exc: + attempt += 1 + if attempt > self.max_retries: + logger.error(f"Fetch failed for {url}: {exc}") + raise + delay = self.retry_delay_factor * (2 ** (attempt - 1)) + logger.warning(f"Retrying {url} after {delay:.2f}s (attempt {attempt})") + await asyncio.sleep(delay) + + async def _render_with_playwright( + self, url: str, js_wait: float = 1.0, timeout: Optional[float] = None + ) -> str: + if async_playwright is None: + raise RuntimeError( + "Playwright is not installed. Install with `pip install playwright` and run `playwright install`." + ) + attempt = 0 + while True: + try: + async with async_playwright() as p: + browser = await p.chromium.launch(headless=True) + try: + context = await browser.new_context() + page = await context.new_page() + await page.goto( + url, + wait_until="networkidle", + timeout=int((timeout or self.timeout) * 1000), + ) + if js_wait: + await asyncio.sleep(js_wait) + return await page.content() + finally: + await browser.close() + except Exception as exc: + attempt += 1 + if attempt > self.max_retries: + logger.error(f"Playwright fetch failed for {url}: {exc}") + raise + backoff = self.retry_delay_factor * (2 ** (attempt - 1)) + logger.warning( + f"Retrying playwright fetch {url} after {backoff:.2f}s (attempt {attempt})" + ) + await asyncio.sleep(backoff) + + # ---------- extraction helpers ---------- + def _normalize_rule(self, rule) -> Dict[str, Any]: + if isinstance(rule, str): + return {"selector": rule, "attr": None, "all": False, "join_with": " "} + if isinstance(rule, dict): + return { + "selector": rule.get("selector"), + "attr": rule.get("attr"), + "all": bool(rule.get("all")), + "join_with": rule.get("join_with", " "), + "xpath": rule.get("xpath"), + } + raise ValueError("Invalid extraction rule") + + def _extract_with_bs4(self, html: str, rule: Dict[str, Any]) -> str: + soup = BeautifulSoup(html, "html.parser") + sel = rule.get("selector") + xpath = rule.get("xpath") + attr = rule.get("attr") + all_flag = rule.get("all", False) + join_with = rule.get("join_with", " ") + + if xpath: + try: + from lxml import html as lxml_html + except Exception: + raise RuntimeError( + "XPath requested but lxml is not available. Install lxml or use CSS selectors." + ) + doc = lxml_html.fromstring(html) + nodes = doc.xpath(xpath) + texts = [] + for n in nodes: + if hasattr(n, "text_content"): + texts.append(n.text_content().strip()) + else: + texts.append(str(n).strip()) + return join_with.join(t for t in texts if t) + else: + if not sel: + return "" + if all_flag: + nodes = soup.select(sel) + pieces = [] + for el in nodes: + if attr: + val = el.get(attr) + if val: + pieces.append(val.strip()) + else: + text = el.get_text(strip=True) + if text: + pieces.append(text) + return join_with.join(pieces).strip() + else: + el = soup.select_one(sel) + if el is None: + return "" + if attr: + val = el.get(attr) + return (val or "").strip() + return el.get_text(strip=True) + + # ---------- public methods ---------- + async def fetch_with_bs4( + self, + urls: Union[str, List[str]], + extraction_rules: Dict[str, Any], + *, + use_playwright: bool = False, + playwright_js_wait: float = 0.8, + join_all_matches: bool = False, + structured: bool = False, # return structured output instead of concatenated string + ) -> Dict[str, Union[str, Dict[str, str]]]: + """ + Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). + Returns: dict[url] -> concatenated string OR structured dict depending on `structured`. + """ + if isinstance(urls, str): + urls = [urls] + + # normalize rules + normalized_rules = {} + for field, rule in extraction_rules.items(): + r = self._normalize_rule(rule) + if join_all_matches: + r["all"] = True + normalized_rules[field] = r + + async def _task(url: str): + async with self._sem: + allowed = await self._is_url_allowed(url) + if not allowed: + logger.warning(f"URL disallowed by robots.txt: {url}") + return url, "" if not structured else {} + + # fetch (rendered or not) + if use_playwright: + html = await self._render_with_playwright( + url, js_wait=playwright_js_wait, timeout=self.timeout + ) + else: + html = await self._fetch_httpx(url) + + if structured: + return url, { + field: self._extract_with_bs4(html, rule) + for field, rule in normalized_rules.items() + } + + pieces = [] + for field, rule in normalized_rules.items(): + text = self._extract_with_bs4(html, rule) + if text: + pieces.append(text) + concatenated = " ".join(pieces).strip() + return url, concatenated + + tasks = [asyncio.create_task(_task(u)) for u in urls] + results = {} + for coro in asyncio.as_completed(tasks): + try: + url, text = await coro + except Exception as e: + results[url] = {} if structured else "" + logger.error(f"Error processing {url}: {e}") + continue + results[url] = text + return results From 20fb77316c245117dd78e830adf269198184610e Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sat, 4 Oct 2025 15:01:13 +0530 Subject: [PATCH 039/139] Done with integration with add workflow when incremental_loading is set to False --- cognee/api/v1/add/add.py | 22 +- cognee/tasks/ingestion/ingest_data.py | 15 +- .../ingestion/save_data_item_to_storage.py | 45 ++- cognee/tasks/web_scraper/__init__.py | 8 + cognee/tasks/web_scraper/bs4_connector.py | 270 ------------------ cognee/tasks/web_scraper/bs4_crawler.py | 33 ++- cognee/tasks/web_scraper/config.py | 24 ++ cognee/tasks/web_scraper/utils.py | 101 +++++-- cognee/tasks/web_scraper/web_scraper_task.py | 63 ++++ 9 files changed, 269 insertions(+), 312 deletions(-) create mode 100644 cognee/tasks/web_scraper/__init__.py delete mode 100644 cognee/tasks/web_scraper/bs4_connector.py create mode 100644 cognee/tasks/web_scraper/config.py create mode 100644 cognee/tasks/web_scraper/web_scraper_task.py diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index eeb867984..5643ecc94 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -1,5 +1,5 @@ from uuid import UUID -from typing import Union, BinaryIO, List, Optional +from typing import Union, BinaryIO, List, Optional, Dict, Literal from cognee.modules.users.models import User from cognee.modules.pipelines import Task, run_pipeline @@ -11,6 +11,7 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( ) from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories +from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig async def add( @@ -23,12 +24,16 @@ async def add( dataset_id: Optional[UUID] = None, preferred_loaders: List[str] = None, incremental_loading: bool = True, + extraction_rules: Optional[Dict[str, str]] = None, + preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", + tavily_config: Optional[TavilyConfig] = None, + soup_crawler_config: Optional[SoupCrawlerConfig] = None, ): """ Add data to Cognee for knowledge graph processing. This is the first step in the Cognee workflow - it ingests raw data and prepares it - for processing. The function accepts various data formats including text, files, and + for processing. The function accepts various data formats including text, files, urls and binary streams, then stores them in a specified dataset for further processing. Prerequisites: @@ -143,7 +148,18 @@ async def add( """ tasks = [ Task(resolve_data_directories, include_subdirectories=True), - Task(ingest_data, dataset_name, user, node_set, dataset_id, preferred_loaders), + Task( + ingest_data, + dataset_name, + user, + node_set, + dataset_id, + preferred_loaders, + extraction_rules, + preferred_tool, + tavily_config, + soup_crawler_config, + ), ] await setup() diff --git a/cognee/tasks/ingestion/ingest_data.py b/cognee/tasks/ingestion/ingest_data.py index 3c20a2b13..a61f5d97b 100644 --- a/cognee/tasks/ingestion/ingest_data.py +++ b/cognee/tasks/ingestion/ingest_data.py @@ -1,7 +1,7 @@ import json import inspect from uuid import UUID -from typing import Union, BinaryIO, Any, List, Optional +from typing import Union, BinaryIO, Any, List, Optional, Dict, Literal import cognee.modules.ingestion as ingestion from cognee.infrastructure.databases.relational import get_relational_engine @@ -16,6 +16,7 @@ from cognee.modules.data.methods import ( get_dataset_data, load_or_create_datasets, ) +from cognee.tasks.web_scraper.config import SoupCrawlerConfig, TavilyConfig from .save_data_item_to_storage import save_data_item_to_storage from .data_item_to_text_file import data_item_to_text_file @@ -28,6 +29,10 @@ async def ingest_data( node_set: Optional[List[str]] = None, dataset_id: UUID = None, preferred_loaders: List[str] = None, + extraction_rules: Optional[Dict[str, str]] = None, + preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", + tavily_config: Optional[TavilyConfig] = None, + soup_crawler_config: Optional[SoupCrawlerConfig] = None, ): if not user: user = await get_default_user() @@ -78,7 +83,13 @@ async def ingest_data( for data_item in data: # Get file path of data item or create a file it doesn't exist - original_file_path = await save_data_item_to_storage(data_item) + original_file_path = await save_data_item_to_storage( + data_item, + extraction_rules=extraction_rules, + preferred_tool=preferred_tool, + tavily_config=tavily_config, + soup_crawler_config=soup_crawler_config, + ) # Transform file path to be OS usable actual_file_path = get_data_file_path(original_file_path) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 34f70e917..93df12732 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -1,12 +1,14 @@ import os from pathlib import Path from urllib.parse import urlparse -from typing import Union, BinaryIO, Any +from typing import Union, BinaryIO, Any, Dict from cognee.modules.ingestion.exceptions import IngestionError from cognee.modules.ingestion import save_data_to_file from cognee.shared.logging_utils import get_logger from pydantic_settings import BaseSettings, SettingsConfigDict +from cognee.tasks.web_scraper import check_valid_arguments_for_web_scraper +import asyncio logger = get_logger() @@ -17,10 +19,17 @@ class SaveDataSettings(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") +class HTMLContent(str): + def __new__(cls, value: str): + if not ("<" in value and ">" in value): + raise ValueError("Not valid HTML-like content") + return super().__new__(cls, value) + + settings = SaveDataSettings() -async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str: +async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], **kwargs) -> str: if "llama_index" in str(type(data_item)): # Dynamic import is used because the llama_index module is optional. from .transform_data import get_data_from_llama_index @@ -48,6 +57,38 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str # data is s3 file path if parsed_url.scheme == "s3": return data_item + elif parsed_url.scheme == "http" or parsed_url.scheme == "https": + # Validate URL by sending a HEAD request + try: + from cognee.tasks.web_scraper import fetch_page_content + + extraction_rules = kwargs.get("extraction_rules", None) + preferred_tool = kwargs.get("preferred_tool", "beautifulsoup") + tavily_config = kwargs.get("tavily_config", None) + soup_crawler_config = kwargs.get("soup_crawler_config", None) + check_valid_arguments_for_web_scraper( + extraction_rules=extraction_rules, + preferred_tool=preferred_tool, + tavily_config=tavily_config, + soup_crawler_config=soup_crawler_config, + ) + data = await fetch_page_content( + data_item, + extraction_rules=extraction_rules, + preferred_tool=preferred_tool, + tavily_config=tavily_config, + soup_crawler_config=soup_crawler_config, + ) + content = "" + for key, value in data.items(): + content += f"{key}:\n{value}\n\n" + else: + content = data[data_item] + return await save_data_to_file(content) + except Exception as e: + raise IngestionError( + message=f"Error ingesting webpage results of url {data_item}: {str(e)}" + ) # data is local file path elif parsed_url.scheme == "file": diff --git a/cognee/tasks/web_scraper/__init__.py b/cognee/tasks/web_scraper/__init__.py new file mode 100644 index 000000000..7560dee56 --- /dev/null +++ b/cognee/tasks/web_scraper/__init__.py @@ -0,0 +1,8 @@ +from .bs4_crawler import BeautifulSoupCrawler +from .utils import fetch_page_content, check_valid_arguments_for_web_scraper + +__all__ = [ + "BeautifulSoupCrawler", + "fetch_page_content", + "check_valid_arguments_for_web_scraper", +] diff --git a/cognee/tasks/web_scraper/bs4_connector.py b/cognee/tasks/web_scraper/bs4_connector.py deleted file mode 100644 index 20a587216..000000000 --- a/cognee/tasks/web_scraper/bs4_connector.py +++ /dev/null @@ -1,270 +0,0 @@ -import asyncio -import time -from typing import Union, List, Dict, Any, Optional -from bs4 import BeautifulSoup -import httpx -from cognee.shared.logging_utils import get_logger - -logger = get_logger(__name__) - -try: - from playwright.async_api import async_playwright -except ImportError: - logger.error("Failed to import playwright, make sure to install using pip install playwright>=1.9.0") - -try: - from bs4 import BeautifulSoup -except ImportError: - logger.error("Failed to import BeautifulSoup, make sure to install using pip install beautifulsoup4") - - - - - -class BeautifulSoupCrawler: - def __init__( - self, - *, - concurrency: int = 5, - delay_between_requests: float = 0.5, - timeout: float = 15.0, - max_retries: int = 2, - retry_delay_factor: float = 0.5, - headers: Optional[Dict[str, str]] = None, - ): - """ - concurrency: number of concurrent requests allowed - delay_between_requests: minimum seconds to wait between requests to the SAME domain - timeout: per-request timeout - max_retries: number of retries on network errors - retry_delay_factor: multiplier for exponential retry failure delay - headers: default headers for requests - use_httpx: require httpx for async HTTP. If not available, an informative error will be raised. - """ - self.concurrency = concurrency - self._sem = asyncio.Semaphore(concurrency) - self.delay_between_requests = delay_between_requests - self.timeout = timeout - self.max_retries = max_retries - self.retry_delay_factor = retry_delay_factor - self.headers = headers or {"User-agent": "Cognee-Scraper/1.0"} - self._last_request_time_per_domain: Dict[str, float] = {} - self._client = None - - # ---------- lifecycle helpers ---------- - async def _ensure_client(self): - if self._client is None: - self._client = httpx.AsyncClient(timeout=self.timeout, headers=self.headers) if httpx else None - - async def close(self): - if self._client: - await self._client.aclose() - self._client = None - - # ---------- rate limiting ---------- - def _domain_from_url(self, url: str) -> str: - # quick parse to domain - try: - from urllib.parse import urlparse - p = urlparse(url) - return p.netloc - except Exception: - return url - - async def _respect_rate_limit(self, url: str): - domain = self._domain_from_url(url) - last = self._last_request_time_per_domain.get(domain) - if last is None: - self._last_request_time_per_domain[domain] = time.time() - return - elapsed = time.time() - last - wait_for = self.delay_between_requests - elapsed - if wait_for > 0: - await asyncio.sleep(wait_for) - self._last_request_time_per_domain[domain] = time.time() - - # ----------- robots.txt handling could be added here ----------- - async def _is_url_allowed(self, url: str) -> bool: - robots_txt_url = f"{self._get_base_url(url)}/robots.txt" - robots_txt_content = await self._fetch_httpx(robots_txt_url) - robots_txt_content = robots_txt_content.lower() - user_agent_name = self.headers.get("User-agent") - pos = robots_txt_content.find(f"user-agent: {user_agent_name}") - if pos == -1: - pos = robots_txt_content.find(f"user-agent:*") - if pos == -1: - return True - - pos = robots_txt_content.find("disallow", pos) - # TODO: Research more about robots.txt format - - - - - # ---------- low-level fetchers ---------- - async def _fetch_httpx(self, url: str) -> str: - await self._ensure_client() - assert self._client is not None, "HTTP client not initialized" - attempt = 0 - while True: - try: - await self._respect_rate_limit(url) - resp = await self._client.get(url) - resp.raise_for_status() - return resp.text - except Exception as exc: - attempt += 1 - if attempt > self.max_retries: - raise - delay = self.retry_delay_factor * (2 ** (attempt - 1)) - await asyncio.sleep(delay) - - async def _render_with_playwright(self, url: str, js_wait: float = 1.0, timeout: Optional[float] = None) -> str: - if async_playwright is None: - raise RuntimeError("Playwright is not installed. Install with `pip install playwright` and run `playwright install`.") - # Basic Playwright rendering (Chromium). This is slower but renders JS. - attempt = 0 - while True: - try: - async with async_playwright() as p: - browser = await p.chromium.launch(headless=True) - context = await browser.new_context() - page = await context.new_page() - await page.goto(url, wait_until="networkidle", timeout=int((timeout or self.timeout) * 1000)) - # optional short wait to let in-page JS mutate DOM - if js_wait: - await asyncio.sleep(js_wait) - content = await page.content() - await browser.close() - return content - except Exception: - attempt += 1 - if attempt > self.max_retries: - raise - backoff = self.backoff_factor * (2 ** (attempt - 1)) - await asyncio.sleep(backoff) - - # ---------- extraction helpers ---------- - def _normalize_rule(self, rule) -> Dict[str, Any]: - if isinstance(rule, str): - return {"selector": rule, "attr": None, "all": False, "join_with": " "} - if isinstance(rule, dict): - return { - "selector": rule.get("selector"), - "attr": rule.get("attr"), - "all": bool(rule.get("all")), - "join_with": rule.get("join_with", " "), - "xpath": rule.get("xpath"), - } - raise ValueError("Invalid extraction rule") - - def _extract_with_bs4(self, html: str, rule: Dict[str, Any]) -> str: - soup = BeautifulSoup(html, "html.parser") - sel = rule.get("selector") - xpath = rule.get("xpath") - attr = rule.get("attr") - all_flag = rule.get("all", False) - join_with = rule.get("join_with", " ") - - if xpath: - # try lxml extraction via xpath if lxml is available - try: - from lxml import html as lxml_html - except Exception: - raise RuntimeError("XPath requested but lxml is not available. Install lxml or use CSS selectors.") - doc = lxml_html.fromstring(html) - nodes = doc.xpath(xpath) - texts = [] - for n in nodes: - if hasattr(n, "text_content"): - texts.append(n.text_content().strip()) - else: - texts.append(str(n).strip()) - return join_with.join(t for t in texts if t) - else: - if not sel: - return "" - if all_flag: - nodes = soup.select(sel) - pieces = [] - for el in nodes: - if attr: - val = el.get(attr) - if val: - pieces.append(val.strip()) - else: - text = el.get_text(strip=True) - if text: - pieces.append(text) - return join_with.join(pieces).strip() - else: - el = soup.select_one(sel) - if el is None: - return "" - if attr: - val = el.get(attr) - return (val or "").strip() - return el.get_text(strip=True) - - # ---------- public API (keeps the signature you asked for) ---------- - async def fetch_with_bs4( - self, - urls: Union[str, List[str]], - extraction_rules: Dict[str, Any], - *, - use_playwright: bool = False, - playwright_js_wait: float = 0.8, - join_all_matches: bool = False, # if True, for each rule use all matches (join them) - ) -> Dict[str, str]: - """ - Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). - Returns: dict[url] -> single concatenated string (trimmed) - Parameters: - - urls: str or list[str] - - extraction_rules: dict[field_name -> selector or rule-dict] - rule-dict keys: selector (CSS), xpath (optional), attr (optional), all(bool), join_with(str) - - use_playwright: if True, use Playwright to render JS (must be installed), otherwise normal fetch - - playwright_js_wait: seconds to wait after load for JS to mutate DOM - - join_all_matches: convenience: if True, treat each rule as all=True - """ - if isinstance(urls, str): - urls = [urls] - - # normalize rules - normalized_rules = {} - for field, rule in extraction_rules.items(): - r = self._normalize_rule(rule) - if join_all_matches: - r["all"] = True - normalized_rules[field] = r - - # concurrency control + gather tasks - async def _task(url: str): - async with self._sem: - # fetch (rendered or not) - if use_playwright: - html = await self._render_with_playwright(url, js_wait=playwright_js_wait, timeout=self.timeout) - else: - html = await self._fetch_httpx(url) - - # Extract and concatenate results into a single string - pieces = [] - for field, rule in normalized_rules.items(): - text = self._extract_with_bs4(html, rule) - if text: - pieces.append(text) - concatenated = " ".join(pieces).strip() - return url, concatenated - - tasks = [asyncio.create_task(_task(u)) for u in urls] - results = {} - for coro in asyncio.as_completed(tasks): - try: - url, text = await coro - except Exception as e: - # store empty string on failure (or raise depending on your policy) - results[url] = "" - # Optionally you could log the error; for now we'll attach empty string - continue - results[url] = text - return results diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index df926bdf9..514567317 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -91,6 +91,8 @@ class BeautifulSoupCrawler: parsed_url = urlparse(url) robots_url = f"{parsed_url.scheme}://{parsed_url.netloc}/robots.txt" content = await self._fetch_httpx(robots_url) + if content.strip() == "": + return True # no robots.txt means allowed rp = Protego.parse(content) agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") return rp.can_fetch(agent, url) or rp.can_fetch("*", url) @@ -114,23 +116,42 @@ class BeautifulSoupCrawler: await self._ensure_client() assert self._client is not None, "HTTP client not initialized" attempt = 0 + parsed = urlparse(url) + domain_root = f"{parsed.scheme}://{parsed.netloc}" + + # Handle robots.txt separately (no recursive crawl delay call) + is_robot = url.lower().endswith("/robots.txt") + while True: try: - # get crawl delay from robots.txt if available - crawl_delay = await self._get_crawl_delay( - f"{urlparse(url).scheme}://{urlparse(url).netloc}" - ) + # Only get crawl delay for non-robots.txt pages + crawl_delay = self.crawl_delay + if not is_robot: + try: + crawl_delay = await self._get_crawl_delay(domain_root) + except Exception as e: + logger.debug(f"Failed to fetch crawl delay for {domain_root}: {e}") + await self._respect_rate_limit(url, crawl_delay) resp = await self._client.get(url) resp.raise_for_status() return resp.text + except Exception as exc: + # Special case: if robots.txt failed, just return empty string + if is_robot: + logger.warning(f"Robots.txt not found or inaccessible at {url}: {exc}") + return "" + attempt += 1 if attempt > self.max_retries: - logger.error(f"Fetch failed for {url}: {exc}") + logger.error(f"Fetch failed for {url} after {attempt} attempts: {exc}") raise + delay = self.retry_delay_factor * (2 ** (attempt - 1)) - logger.warning(f"Retrying {url} after {delay:.2f}s (attempt {attempt})") + logger.warning( + f"Retrying {url} after {delay:.2f}s (attempt {attempt}) due to {exc}" + ) await asyncio.sleep(delay) async def _render_with_playwright( diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py new file mode 100644 index 000000000..505cef1df --- /dev/null +++ b/cognee/tasks/web_scraper/config.py @@ -0,0 +1,24 @@ +from pydantic import BaseModel, Field +from typing import Any, Dict, Optional +import os + + +class TavilyConfig(BaseModel): + api_key: str = os.getenv("TAVILY_API_KEY") + extract_depth: str = "basic" + format: str = "markdown" + timeout: int = Field(None, ge=1, le=60) + + +class SoupCrawlerConfig(BaseModel): + concurrency: int = (5,) + crawl_delay: float = (0.5,) + timeout: float = (15.0,) + max_retries: int = (2,) + retry_delay_factor: float = (0.5,) + headers: Optional[Dict[str, str]] = (None,) + extraction_rules: Dict[str, Any] + use_playwright: bool = False + playwright_js_wait: float = 0.8 + join_all_matches: bool = False + structured: bool = False diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 5ff105ec9..18d54d13a 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -1,9 +1,10 @@ from tavily import AsyncTavilyClient -from bs4 import BeautifulSoup +from .bs4_crawler import BeautifulSoupCrawler import os -import httpx -from typing import Dict, Any, List, Union +from .config import TavilyConfig, SoupCrawlerConfig +from typing import Dict, Any, List, Union, Optional, Literal from cognee.shared.logging_utils import get_logger +import asyncio logger = get_logger(__name__) @@ -22,37 +23,79 @@ except ImportError: ) -async def fetch_page_content(urls: Union[str, List[str]], extraction_rules: Dict[str, Any]) -> str: - if os.getenv("TAVILY_API_KEY") is not None: +async def fetch_page_content( + urls: Union[str, List[str]], + *, + preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", + extraction_rules: Optional[Dict[str, Any]] = None, + tavily_config: Optional[TavilyConfig] = None, + soup_crawler_config: Optional[SoupCrawlerConfig] = None, + use_playwright: Optional[bool] = False, + playwright_js_wait: Optional[float] = 0.8, + join_all_matches: Optional[bool] = False, + structured: Optional[bool] = False, +) -> Dict[str, Union[str, Dict[str, str]]]: + """ + Fetch page content using Tavily API if TAVILY_API_KEY is set, + otherwise fetch using BeautifulSoupCrawler directly. + + Parameters: + urls: single URL or list of URLs + extraction_rules: dict mapping field names -> CSS selector or rule + use_playwright: whether to render JS (BeautifulSoupCrawler) + playwright_js_wait: seconds to wait for JS to load + join_all_matches: join all matching elements per rule + structured: if True, returns structured dict instead of concatenated string (based on extraction_rules field names) + + Returns: + Dict mapping URL -> extracted string or structured dict + """ + if (os.getenv("TAVILY_API_KEY") or tavily_config.api_key) and preferred_tool == "tavily": return await fetch_with_tavily(urls) + else: - return await fetch_with_bs4(urls, extraction_rules) + crawler = BeautifulSoupCrawler() + extraction_rules = extraction_rules or soup_crawler_config.extraction_rules + if extraction_rules is None: + raise ValueError("extraction_rules must be provided when not using Tavily") + try: + results = await crawler.fetch_with_bs4( + urls, + extraction_rules, + use_playwright=use_playwright, + playwright_js_wait=playwright_js_wait, + join_all_matches=join_all_matches, + structured=structured, + ) + return results + except Exception as e: + logger.error(f"Error fetching page content: {str(e)}") async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: client = AsyncTavilyClient() - results = await client.extract(urls, include_images=False) - result_dict = {} - for result in results["results"]: - result_dict[result["url"]] = result["raw_content"] - return result_dict + results = await client.extract(urls) + for failed_result in results.get("failed_results", []): + logger.warning(f"Failed to fetch {failed_result}") + return_results = {} + for results in results.get("results", []): + return_results[results["url"]] = results["raw_content"] + return return_results -async def fetch_with_bs4(urls: Union[str, List[str]], extraction_rules: Dict) -> Dict[str]: - result_dict = {} - if isinstance(urls, str): - urls = [urls] - async with httpx.AsyncClient(headers={"User-Agent": "Cognee-Scraper"}) as client: - for url in urls: - response = await client.get(url) - response.raise_for_status() - - soup = BeautifulSoup(response.text, "html.parser") - extracted_data = "" - for field, selector in extraction_rules.items(): - element = soup.select_one(selector) - extracted_data += (element.get_text(strip=True) + "\n") if element else "" - - result_dict[url] = extracted_data.strip() - - return result_dict +def check_valid_arguments_for_web_scraper( + extraction_rules, preferred_tool, tavily_config, soup_crawler_config +): + if preferred_tool == "tavily": + if not (os.getenv("TAVILY_API_KEY") or (tavily_config and tavily_config.api_key)): + raise ValueError( + "TAVILY_API_KEY must be set in environment variables or tavily_config.api_key must be provided when preferred_tool is 'tavily'" + ) + else: + print(preferred_tool) + print(soup_crawler_config) + print(soup_crawler_config and soup_crawler_config.extraction_rules) + if not (extraction_rules or (soup_crawler_config and soup_crawler_config.extraction_rules)): + raise ValueError( + "extraction_rules must be provided when preferred_tool is 'beautifulsoup'" + ) diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py new file mode 100644 index 000000000..c06b54b8e --- /dev/null +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -0,0 +1,63 @@ +from cognee.tasks.storage.add_data_points import add_data_points +from cognee.tasks.storage.index_data_points import index_data_points +from cognee.tasks.storage.index_graph_edges import index_graph_edges +from cognee.infrastructure.databases.graph import get_graph_engine +from .models import WebPage, WebSite, ScrapingJob +from typing import Union, List, Dict +from urllib.parse import urlparse + + +async def web_scraper_task(url: Union[str, List[str]], **kwargs): + graph_engine = await get_graph_engine() + # Mapping between parsed_url object and urls + mappings = {} + web_scraping_job = ScrapingJob( + job_name="default_job", + urls=[url] if isinstance(url, str) else url, + scraping_rules={}, + schedule=None, + status="active", + last_run=None, + next_run=None, + ) + data_point_mappings: Dict[WebSite, List[WebPage]] = {} + if isinstance(url, List): + for single_url in url: + parsed_url = urlparse(single_url) + domain = parsed_url.netloc + base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" + if mappings.get(parsed_url): + mappings[parsed_url] = [single_url] + else: + mappings[parsed_url].append(single_url) + else: + if mappings.get(parsed_url): + mappings[parsed_url] = [single_url] + else: + mappings[parsed_url].append(single_url) + for parsed_url in mappings.keys(): + domain = parsed_url.netloc + base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" + web_site = WebSite( + domain=domain, + base_url=base_url, + robots_txt="", + crawl_delay=0, + last_crawled=None, + page_count=0, + scraping_config={}, + ) + for url in mappings[parsed_url]: + # Process each URL with the web scraping logic + web_page = WebPage( + url=url, + title="", + content="", + content_hash="", + scraped_at=None, + last_modified=None, + status_code=0, + content_type="", + page_size=0, + extraction_rules={}, + ) From fbef6675bcf8cd1ba1044296c877ba6795517a57 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sat, 4 Oct 2025 15:09:23 +0530 Subject: [PATCH 040/139] removed unused Dict import from typing --- .../loaders/external/webscraper_loader.py | 10 ++++++++++ cognee/tasks/ingestion/save_data_item_to_storage.py | 2 +- cognee/tests/tasks/web_scraping/web_scraping_test.py | 0 3 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 cognee/infrastructure/loaders/external/webscraper_loader.py create mode 100644 cognee/tests/tasks/web_scraping/web_scraping_test.py diff --git a/cognee/infrastructure/loaders/external/webscraper_loader.py b/cognee/infrastructure/loaders/external/webscraper_loader.py new file mode 100644 index 000000000..7ef56a948 --- /dev/null +++ b/cognee/infrastructure/loaders/external/webscraper_loader.py @@ -0,0 +1,10 @@ +from typing import List +from cognee.infrastructure.loaders.LoaderInterface import LoaderInterface +from cognee.shared.logging_utils import get_logger +from cognee.infrastructure.files.storage import get_file_storage, get_storage_config +from cognee.infrastructure.files.utils.get_file_metadata import get_file_metadata + +logger = get_logger(__name__) + +class WebScraperLoader(LoaderInterface): + pass \ No newline at end of file diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 93df12732..7da0bcddd 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -1,7 +1,7 @@ import os from pathlib import Path from urllib.parse import urlparse -from typing import Union, BinaryIO, Any, Dict +from typing import Union, BinaryIO, Any from cognee.modules.ingestion.exceptions import IngestionError from cognee.modules.ingestion import save_data_to_file diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py new file mode 100644 index 000000000..e69de29bb From da7ebc4574b40294f35e205d3cb9e1a735106f29 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sat, 4 Oct 2025 15:10:46 +0530 Subject: [PATCH 041/139] Removed asyncio import --- cognee/tasks/ingestion/save_data_item_to_storage.py | 1 - 1 file changed, 1 deletion(-) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 7da0bcddd..1bf53f57c 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -8,7 +8,6 @@ from cognee.modules.ingestion import save_data_to_file from cognee.shared.logging_utils import get_logger from pydantic_settings import BaseSettings, SettingsConfigDict from cognee.tasks.web_scraper import check_valid_arguments_for_web_scraper -import asyncio logger = get_logger() From ab6fc6540682fbde72fc8aa0ca91c62289c2a65f Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sat, 4 Oct 2025 19:40:37 +0530 Subject: [PATCH 042/139] Added global context for bs4crawler and tavily config --- cognee/context_global_variables.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cognee/context_global_variables.py b/cognee/context_global_variables.py index f6642e3dc..500846e27 100644 --- a/cognee/context_global_variables.py +++ b/cognee/context_global_variables.py @@ -12,6 +12,8 @@ from cognee.modules.users.methods import get_user # for different async tasks, threads and processes vector_db_config = ContextVar("vector_db_config", default=None) graph_db_config = ContextVar("graph_db_config", default=None) +soup_crawler_config = ContextVar("soup_crawler_config", default=None) +tavily_config = ContextVar("tavily_config", default=None) async def set_database_global_context_variables(dataset: Union[str, UUID], user_id: UUID): From 2cba31a086edbbdca0dce1bb87e663c9d4381ab2 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sat, 4 Oct 2025 21:26:25 +0530 Subject: [PATCH 043/139] Tested and Debugged scraping usage in cognee.add() pipeline --- cognee/api/v1/add/add.py | 21 +- .../loaders/external/webscraper_loader.py | 10 - cognee/tasks/ingestion/ingest_data.py | 12 +- .../ingestion/save_data_item_to_storage.py | 24 +- cognee/tasks/web_scraper/__init__.py | 3 +- cognee/tasks/web_scraper/utils.py | 65 ++--- .../tasks/web_scraping/web_scraping_test.py | 161 ++++++++++++ notebooks/data/graphrag | 1 - pyproject.toml | 2 + uv.lock | 237 ++++++++---------- 10 files changed, 320 insertions(+), 216 deletions(-) delete mode 100644 cognee/infrastructure/loaders/external/webscraper_loader.py delete mode 160000 notebooks/data/graphrag diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 5643ecc94..93102b160 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -1,4 +1,5 @@ from uuid import UUID +import os from typing import Union, BinaryIO, List, Optional, Dict, Literal from cognee.modules.users.models import User @@ -12,6 +13,11 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig +from cognee.context_global_variables import ( + tavily_config as tavily, + soup_crawler_config as soup_crawler, +) +from urllib.parse import urlparse async def add( @@ -25,7 +31,6 @@ async def add( preferred_loaders: List[str] = None, incremental_loading: bool = True, extraction_rules: Optional[Dict[str, str]] = None, - preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", tavily_config: Optional[TavilyConfig] = None, soup_crawler_config: Optional[SoupCrawlerConfig] = None, ): @@ -155,14 +160,20 @@ async def add( node_set, dataset_id, preferred_loaders, - extraction_rules, - preferred_tool, - tavily_config, - soup_crawler_config, ), ] await setup() + if not soup_crawler_config and extraction_rules: + soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) + + if not tavily_config and os.getenv("TAVILY_API_KEY"): + tavily_config = TavilyConfig() + + soup_crawler.set(soup_crawler_config) + tavily.set(tavily_config) + if urlparse(data).scheme in ["http", "https"]: + node_set = ["web_content"] if not node_set else node_set + ["web_content"] user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user) diff --git a/cognee/infrastructure/loaders/external/webscraper_loader.py b/cognee/infrastructure/loaders/external/webscraper_loader.py deleted file mode 100644 index 7ef56a948..000000000 --- a/cognee/infrastructure/loaders/external/webscraper_loader.py +++ /dev/null @@ -1,10 +0,0 @@ -from typing import List -from cognee.infrastructure.loaders.LoaderInterface import LoaderInterface -from cognee.shared.logging_utils import get_logger -from cognee.infrastructure.files.storage import get_file_storage, get_storage_config -from cognee.infrastructure.files.utils.get_file_metadata import get_file_metadata - -logger = get_logger(__name__) - -class WebScraperLoader(LoaderInterface): - pass \ No newline at end of file diff --git a/cognee/tasks/ingestion/ingest_data.py b/cognee/tasks/ingestion/ingest_data.py index a61f5d97b..b86bada19 100644 --- a/cognee/tasks/ingestion/ingest_data.py +++ b/cognee/tasks/ingestion/ingest_data.py @@ -29,10 +29,6 @@ async def ingest_data( node_set: Optional[List[str]] = None, dataset_id: UUID = None, preferred_loaders: List[str] = None, - extraction_rules: Optional[Dict[str, str]] = None, - preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", - tavily_config: Optional[TavilyConfig] = None, - soup_crawler_config: Optional[SoupCrawlerConfig] = None, ): if not user: user = await get_default_user() @@ -83,13 +79,7 @@ async def ingest_data( for data_item in data: # Get file path of data item or create a file it doesn't exist - original_file_path = await save_data_item_to_storage( - data_item, - extraction_rules=extraction_rules, - preferred_tool=preferred_tool, - tavily_config=tavily_config, - soup_crawler_config=soup_crawler_config, - ) + original_file_path = await save_data_item_to_storage(data_item) # Transform file path to be OS usable actual_file_path = get_data_file_path(original_file_path) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 1bf53f57c..d55b697c6 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -7,7 +7,7 @@ from cognee.modules.ingestion.exceptions import IngestionError from cognee.modules.ingestion import save_data_to_file from cognee.shared.logging_utils import get_logger from pydantic_settings import BaseSettings, SettingsConfigDict -from cognee.tasks.web_scraper import check_valid_arguments_for_web_scraper +from cognee.context_global_variables import tavily_config, soup_crawler_config logger = get_logger() @@ -61,28 +61,18 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], **kwar try: from cognee.tasks.web_scraper import fetch_page_content - extraction_rules = kwargs.get("extraction_rules", None) - preferred_tool = kwargs.get("preferred_tool", "beautifulsoup") - tavily_config = kwargs.get("tavily_config", None) - soup_crawler_config = kwargs.get("soup_crawler_config", None) - check_valid_arguments_for_web_scraper( - extraction_rules=extraction_rules, - preferred_tool=preferred_tool, - tavily_config=tavily_config, - soup_crawler_config=soup_crawler_config, - ) + tavily = tavily_config.get() + soup_crawler = soup_crawler_config.get() + data = await fetch_page_content( data_item, - extraction_rules=extraction_rules, - preferred_tool=preferred_tool, - tavily_config=tavily_config, - soup_crawler_config=soup_crawler_config, + preferred_tool="beautifulsoup" if soup_crawler else "tavily", + tavily_config=tavily, + soup_crawler_config=soup_crawler, ) content = "" for key, value in data.items(): content += f"{key}:\n{value}\n\n" - else: - content = data[data_item] return await save_data_to_file(content) except Exception as e: raise IngestionError( diff --git a/cognee/tasks/web_scraper/__init__.py b/cognee/tasks/web_scraper/__init__.py index 7560dee56..a37e17871 100644 --- a/cognee/tasks/web_scraper/__init__.py +++ b/cognee/tasks/web_scraper/__init__.py @@ -1,8 +1,7 @@ from .bs4_crawler import BeautifulSoupCrawler -from .utils import fetch_page_content, check_valid_arguments_for_web_scraper +from .utils import fetch_page_content __all__ = [ "BeautifulSoupCrawler", "fetch_page_content", - "check_valid_arguments_for_web_scraper", ] diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 18d54d13a..d6b57d615 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -8,32 +8,13 @@ import asyncio logger = get_logger(__name__) -try: - from tavily import AsyncTavilyClient -except ImportError: - logger.error( - "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" - ) - -try: - from bs4 import BeautifulSoup -except ImportError: - logger.error( - "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" - ) - async def fetch_page_content( urls: Union[str, List[str]], *, preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", - extraction_rules: Optional[Dict[str, Any]] = None, tavily_config: Optional[TavilyConfig] = None, soup_crawler_config: Optional[SoupCrawlerConfig] = None, - use_playwright: Optional[bool] = False, - playwright_js_wait: Optional[float] = 0.8, - join_all_matches: Optional[bool] = False, - structured: Optional[bool] = False, ) -> Dict[str, Union[str, Dict[str, str]]]: """ Fetch page content using Tavily API if TAVILY_API_KEY is set, @@ -50,22 +31,30 @@ async def fetch_page_content( Returns: Dict mapping URL -> extracted string or structured dict """ - if (os.getenv("TAVILY_API_KEY") or tavily_config.api_key) and preferred_tool == "tavily": + if preferred_tool == "tavily": + if tavily_config.api_key is None: + raise ValueError("TAVILY_API_KEY must be set in TavilyConfig to use Tavily") return await fetch_with_tavily(urls) - else: + elif preferred_tool == "beautifulsoup": + try: + from bs4 import BeautifulSoup + except ImportError: + logger.error( + "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" + ) crawler = BeautifulSoupCrawler() - extraction_rules = extraction_rules or soup_crawler_config.extraction_rules + extraction_rules = soup_crawler_config.extraction_rules if extraction_rules is None: raise ValueError("extraction_rules must be provided when not using Tavily") try: results = await crawler.fetch_with_bs4( urls, extraction_rules, - use_playwright=use_playwright, - playwright_js_wait=playwright_js_wait, - join_all_matches=join_all_matches, - structured=structured, + use_playwright=soup_crawler_config.use_playwright, + playwright_js_wait=soup_crawler_config.playwright_js_wait, + join_all_matches=soup_crawler_config.join_all_matches, + structured=soup_crawler_config.structured, ) return results except Exception as e: @@ -73,6 +62,12 @@ async def fetch_page_content( async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: + try: + from tavily import AsyncTavilyClient + except ImportError: + logger.error( + "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" + ) client = AsyncTavilyClient() results = await client.extract(urls) for failed_result in results.get("failed_results", []): @@ -81,21 +76,3 @@ async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: for results in results.get("results", []): return_results[results["url"]] = results["raw_content"] return return_results - - -def check_valid_arguments_for_web_scraper( - extraction_rules, preferred_tool, tavily_config, soup_crawler_config -): - if preferred_tool == "tavily": - if not (os.getenv("TAVILY_API_KEY") or (tavily_config and tavily_config.api_key)): - raise ValueError( - "TAVILY_API_KEY must be set in environment variables or tavily_config.api_key must be provided when preferred_tool is 'tavily'" - ) - else: - print(preferred_tool) - print(soup_crawler_config) - print(soup_crawler_config and soup_crawler_config.extraction_rules) - if not (extraction_rules or (soup_crawler_config and soup_crawler_config.extraction_rules)): - raise ValueError( - "extraction_rules must be provided when preferred_tool is 'beautifulsoup'" - ) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index e69de29bb..4f47bf7d2 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -0,0 +1,161 @@ +import asyncio +import cognee +from cognee.tasks.web_scraper.config import SoupCrawlerConfig + + +async def test_web_scraping_using_bs4(): + # 0. Prune only data (not full system prune) + await cognee.prune.prune_data() + + # 1. Setup test URL and extraction rules + url = "https://quotes.toscrape.com/" + rules = { + "quotes": {"selector": ".quote span.text", "all": True}, + "authors": {"selector": ".quote small", "all": True}, + } + + soup_config = SoupCrawlerConfig( + concurrency=5, + crawl_delay=0.5, + timeout=15.0, + max_retries=2, + retry_delay_factor=0.5, + extraction_rules=rules, + use_playwright=False, + structured=True, + ) + + # 2. Add / ingest the page + await cognee.add( + data=url, + soup_crawler_config=soup_config, + incremental_loading=False, + ) + + # 3. Cognify + await cognee.cognify() + + # 4. Search for a known quote + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0], ( + "Test failed! Albert Einstein not found in scraped data." + ) + print("Test passed! Found Albert Einstein in scraped data.") + print(results) + print("Web scraping test using bs4 completed.") + + +async def test_web_scraping_using_bs4_and_incremental_loading(): + # 0. Prune only data (not full system prune) + await cognee.prune.prune_data() + + # 1. Setup test URL and extraction rules + url = "https://books.toscrape.com/" + rules = {"titles": "article.product_pod h3 a", "prices": "article.product_pod p.price_color"} + + soup_config = SoupCrawlerConfig( + concurrency=1, + crawl_delay=0.1, + timeout=10.0, + max_retries=1, + retry_delay_factor=0.5, + extraction_rules=rules, + use_playwright=False, + structured=True, + ) + + # 2. Add / ingest the page + await cognee.add( + data=url, + soup_crawler_config=soup_config, + incremental_loading=True, + ) + + # 3. Cognify + await cognee.cognify() + + # 4. Search for a known book + results = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "51.77" in results[0], "Test failed! 'A Light in the Attic' not found in scraped data." + print("Test passed! Found 'A Light in the Attic' in scraped data.") + print(results) + print("Web scraping test using bs4 with incremental loading completed.") + + +async def test_web_scraping_using_tavily(): + # 0. Prune only data (not full system prune) + await cognee.prune.prune_data() + + # 1. Setup test URL and extraction rules + url = "https://quotes.toscrape.com/" + + # 2. Add / ingest the page + await cognee.add( + data=url, + incremental_loading=False, + ) + + # 3. Cognify + await cognee.cognify() + + # 4. Search for a known quote + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0], ( + "Test failed! Albert Einstein not found in scraped data." + ) + print("Test passed! Found Albert Einstein in scraped data.") + print(results) + print("Web scraping test using tavily completed.") + + +async def test_web_scraping_using_tavily_and_incremental_loading(): + # 0. Prune only data (not full system prune) + await cognee.prune.prune_data() + + # 1. Setup test URL and extraction rules + url = "https://quotes.toscrape.com/" + + # 2. Add / ingest the page + await cognee.add( + data=url, + incremental_loading=True, + ) + + # 3. Cognify + await cognee.cognify() + + # 4. Search for a known quote + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0], ( + "Test failed! Albert Einstein not found in scraped data." + ) + print("Test passed! Found Albert Einstein in scraped data.") + print(results) + print("Web scraping test using tavily with incremental loading completed.") + + +async def main(): + print("starting web scraping test using bs4 with incremental loading...") + await test_web_scraping_using_bs4_and_incremental_loading() + print("starting web scraping test using bs4 without incremental loading...") + await test_web_scraping_using_bs4() + print("starting web scraping test using tavily with incremental loading...") + await test_web_scraping_using_tavily_and_incremental_loading() + print("starting web scraping test using tavily without incremental loading...") + await test_web_scraping_using_tavily() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/notebooks/data/graphrag b/notebooks/data/graphrag deleted file mode 160000 index 130b84db9..000000000 --- a/notebooks/data/graphrag +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 130b84db9270734756d16918e5c86034777140fc diff --git a/pyproject.toml b/pyproject.toml index e50c38f25..d4d7939e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,6 +77,8 @@ scraping = [ "tavily-python>=0.7.0", "beautifulsoup4>=4.13.1", "playwright>=1.9.0", + "lxml>=4.9.3,<5.0.0", + "protego>=0.1", ] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] diff --git a/uv.lock b/uv.lock index 709b502c8..a06bebdd9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10, <=3.13" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", @@ -915,9 +915,6 @@ evals = [ { name = "gdown" }, { name = "plotly" }, ] -falkordb = [ - { name = "falkordb" }, -] gemini = [ { name = "google-generativeai" }, ] @@ -969,6 +966,13 @@ postgres-binary = [ posthog = [ { name = "posthog" }, ] +scraping = [ + { name = "beautifulsoup4" }, + { name = "lxml" }, + { name = "playwright" }, + { name = "protego" }, + { name = "tavily-python" }, +] [package.metadata] requires-dist = [ @@ -980,13 +984,13 @@ requires-dist = [ { name = "asyncpg", marker = "extra == 'postgres'", specifier = ">=0.30.0,<1.0.0" }, { name = "asyncpg", marker = "extra == 'postgres-binary'", specifier = ">=0.30.0,<1.0.0" }, { name = "baml-py", specifier = ">=0.201.0,<0.202.0" }, + { name = "beautifulsoup4", marker = "extra == 'scraping'", specifier = ">=4.13.1" }, { name = "chromadb", marker = "extra == 'chromadb'", specifier = ">=0.6,<0.7" }, { name = "coverage", marker = "extra == 'dev'", specifier = ">=7.3.2,<8" }, { name = "debugpy", marker = "extra == 'debug'", specifier = ">=1.8.9,<2.0.0" }, { name = "deepeval", marker = "extra == 'deepeval'", specifier = ">=3.0.1,<4" }, { name = "deptry", marker = "extra == 'dev'", specifier = ">=0.20.0,<0.21" }, { name = "dlt", extras = ["sqlalchemy"], specifier = ">=1.9.0,<2" }, - { name = "falkordb", marker = "extra == 'falkordb'", specifier = ">=1.0.9,<2.0.0" }, { name = "fastapi", specifier = ">=0.115.7,<1.0.0" }, { name = "fastapi-users", extras = ["sqlalchemy"], specifier = ">=14.0.1,<15.0.0" }, { name = "fastembed", marker = "python_full_version < '3.13' and extra == 'codegraph'", specifier = "<=0.6.0" }, @@ -1008,6 +1012,7 @@ requires-dist = [ { name = "limits", specifier = ">=4.4.1,<5" }, { name = "litellm", specifier = ">=1.71.0,<2.0.0" }, { name = "llama-index-core", marker = "extra == 'llama-index'", specifier = ">=0.12.11,<0.13" }, + { name = "lxml", marker = "extra == 'scraping'", specifier = ">=4.9.3,<5.0.0" }, { name = "matplotlib", specifier = ">=3.8.3,<4" }, { name = "mistral-common", marker = "extra == 'mistral'", specifier = ">=1.5.2,<2" }, { name = "mkdocs-material", marker = "extra == 'dev'", specifier = ">=9.5.42,<10" }, @@ -1027,9 +1032,11 @@ requires-dist = [ { name = "pandas", specifier = ">=2.2.2,<3.0.0" }, { name = "pgvector", marker = "extra == 'postgres'", specifier = ">=0.3.5,<0.4" }, { name = "pgvector", marker = "extra == 'postgres-binary'", specifier = ">=0.3.5,<0.4" }, + { name = "playwright", marker = "extra == 'scraping'", specifier = ">=1.9.0" }, { name = "plotly", marker = "extra == 'evals'", specifier = ">=6.0.0,<7" }, { name = "posthog", marker = "extra == 'posthog'", specifier = ">=3.5.0,<4" }, { name = "pre-commit", specifier = ">=4.0.1,<5" }, + { name = "protego", marker = "extra == 'scraping'", specifier = ">=0.1" }, { name = "psycopg2", marker = "extra == 'postgres'", specifier = ">=2.9.10,<3" }, { name = "psycopg2-binary", marker = "extra == 'postgres-binary'", specifier = ">=2.9.10,<3.0.0" }, { name = "pydantic", specifier = ">=2.10.5,<3.0.0" }, @@ -1055,6 +1062,7 @@ requires-dist = [ { name = "sentry-sdk", extras = ["fastapi"], specifier = ">=2.9.0,<3" }, { name = "sqlalchemy", specifier = ">=2.0.39,<3.0.0" }, { name = "structlog", specifier = ">=25.2.0,<26" }, + { name = "tavily-python", marker = "extra == 'scraping'", specifier = ">=0.7.0" }, { name = "tiktoken", specifier = 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14 deletions(-) diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index 514567317..19631a9fa 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -262,11 +262,10 @@ class BeautifulSoupCrawler: use_playwright: bool = False, playwright_js_wait: float = 0.8, join_all_matches: bool = False, - structured: bool = False, # return structured output instead of concatenated string - ) -> Dict[str, Union[str, Dict[str, str]]]: + ) -> Dict[str, str]: """ Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). - Returns: dict[url] -> concatenated string OR structured dict depending on `structured`. + Returns: dict[url] -> concatenated string of extracted content. """ if isinstance(urls, str): urls = [urls] @@ -284,7 +283,7 @@ class BeautifulSoupCrawler: allowed = await self._is_url_allowed(url) if not allowed: logger.warning(f"URL disallowed by robots.txt: {url}") - return url, "" if not structured else {} + return url, "" # fetch (rendered or not) if use_playwright: @@ -294,12 +293,6 @@ class BeautifulSoupCrawler: else: html = await self._fetch_httpx(url) - if structured: - return url, { - field: self._extract_with_bs4(html, rule) - for field, rule in normalized_rules.items() - } - pieces = [] for field, rule in normalized_rules.items(): text = self._extract_with_bs4(html, rule) @@ -314,8 +307,8 @@ class BeautifulSoupCrawler: try: url, text = await coro except Exception as e: - results[url] = {} if structured else "" + results[url] = "" logger.error(f"Error processing {url}: {e}") continue results[url] = text - return results + return results \ No newline at end of file diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index 505cef1df..4819800ab 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -21,4 +21,3 @@ class SoupCrawlerConfig(BaseModel): use_playwright: bool = False playwright_js_wait: float = 0.8 join_all_matches: bool = False - structured: bool = False diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index d6b57d615..c1b6ecb53 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -54,7 +54,6 @@ async def fetch_page_content( use_playwright=soup_crawler_config.use_playwright, playwright_js_wait=soup_crawler_config.playwright_js_wait, join_all_matches=soup_crawler_config.join_all_matches, - structured=soup_crawler_config.structured, ) return results except Exception as e: From 77ea7c4b1d7b1086d13b57a2b6f9fde3bb92a974 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sun, 5 Oct 2025 20:02:02 +0530 Subject: [PATCH 045/139] Added APScheduler --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index d4d7939e4..2432ab668 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -79,6 +79,7 @@ scraping = [ "playwright>=1.9.0", "lxml>=4.9.3,<5.0.0", "protego>=0.1", + "APScheduler>=3.10.0,<=3.11.0" ] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] From f148b1df89c9f55cc9f5caac4a57dda92415f949 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Sun, 5 Oct 2025 20:13:44 +0530 Subject: [PATCH 046/139] Added support for multiple base_url extraction --- cognee/api/v1/add/add.py | 4 +- cognee/tasks/web_scraper/bs4_crawler.py | 346 ++++++++++++++++-------- 2 files changed, 228 insertions(+), 122 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 93102b160..09cade767 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -1,6 +1,6 @@ from uuid import UUID import os -from typing import Union, BinaryIO, List, Optional, Dict, Literal +from typing import Union, BinaryIO, List, Optional, Dict, Any from cognee.modules.users.models import User from cognee.modules.pipelines import Task, run_pipeline @@ -30,7 +30,7 @@ async def add( dataset_id: Optional[UUID] = None, preferred_loaders: List[str] = None, incremental_loading: bool = True, - extraction_rules: Optional[Dict[str, str]] = None, + extraction_rules: Optional[Dict[str, Any]] = None, tavily_config: Optional[TavilyConfig] = None, soup_crawler_config: Optional[SoupCrawlerConfig] = None, ): diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index 19631a9fa..f9159b35f 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -2,6 +2,8 @@ import asyncio import time from typing import Union, List, Dict, Any, Optional from urllib.parse import urlparse +from dataclasses import dataclass, field +from functools import lru_cache import httpx from bs4 import BeautifulSoup @@ -24,6 +26,26 @@ except ImportError: Protego = None +@dataclass +class ExtractionRule: + """Normalized extraction rule""" + + selector: Optional[str] = None + xpath: Optional[str] = None + attr: Optional[str] = None + all: bool = False + join_with: str = " " + + +@dataclass +class RobotsTxtCache: + """Cache for robots.txt data""" + + protego: Any + crawl_delay: float + timestamp: float = field(default_factory=time.time) + + class BeautifulSoupCrawler: def __init__( self, @@ -34,6 +56,7 @@ class BeautifulSoupCrawler: max_retries: int = 2, retry_delay_factor: float = 0.5, headers: Optional[Dict[str, str]] = None, + robots_cache_ttl: float = 3600.0, # Cache robots.txt for 1 hour ): """ concurrency: number of concurrent requests allowed @@ -42,6 +65,7 @@ class BeautifulSoupCrawler: max_retries: number of retries on network errors retry_delay_factor: multiplier for exponential retry failure delay headers: default headers for requests + robots_cache_ttl: TTL for robots.txt cache in seconds """ self.concurrency = concurrency self._sem = asyncio.Semaphore(concurrency) @@ -50,8 +74,12 @@ class BeautifulSoupCrawler: self.max_retries = max_retries self.retry_delay_factor = retry_delay_factor self.headers = headers or {"User-Agent": "Cognee-Scraper/1.0"} + self.robots_cache_ttl = robots_cache_ttl + self._last_request_time_per_domain: Dict[str, float] = {} + self._robots_cache: Dict[str, RobotsTxtCache] = {} self._client: Optional[httpx.AsyncClient] = None + self._robots_lock = asyncio.Lock() # ---------- lifecycle helpers ---------- async def _ensure_client(self): @@ -63,20 +91,37 @@ class BeautifulSoupCrawler: await self._client.aclose() self._client = None + async def __aenter__(self): + await self._ensure_client() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + await self.close() + # ---------- rate limiting ---------- - def _domain_from_url(self, url: str) -> str: + @staticmethod + @lru_cache(maxsize=1024) + def _domain_from_url(url: str) -> str: try: return urlparse(url).netloc except Exception: return url + @staticmethod + @lru_cache(maxsize=1024) + def _get_domain_root(url: str) -> str: + parsed = urlparse(url) + return f"{parsed.scheme}://{parsed.netloc}" + async def _respect_rate_limit(self, url: str, crawl_delay: Optional[float] = None): domain = self._domain_from_url(url) last = self._last_request_time_per_domain.get(domain) delay = crawl_delay if crawl_delay is not None else self.crawl_delay + if last is None: self._last_request_time_per_domain[domain] = time.time() return + elapsed = time.time() - last wait_for = delay - elapsed if wait_for > 0: @@ -84,30 +129,75 @@ class BeautifulSoupCrawler: self._last_request_time_per_domain[domain] = time.time() # ----------- robots.txt handling ----------- + async def _get_robots_cache(self, domain_root: str) -> Optional[RobotsTxtCache]: + """Get cached robots.txt data if valid""" + if Protego is None: + return None + + cached = self._robots_cache.get(domain_root) + if cached and (time.time() - cached.timestamp) < self.robots_cache_ttl: + return cached + return None + + async def _fetch_and_cache_robots(self, domain_root: str) -> RobotsTxtCache: + """Fetch and cache robots.txt data""" + async with self._robots_lock: + # Check again after acquiring lock + cached = await self._get_robots_cache(domain_root) + if cached: + return cached + + robots_url = f"{domain_root}/robots.txt" + try: + await self._ensure_client() + await self._respect_rate_limit(robots_url, self.crawl_delay) + resp = await self._client.get(robots_url, timeout=5.0) + content = resp.text if resp.status_code == 200 else "" + except Exception as e: + logger.debug(f"Failed to fetch robots.txt from {domain_root}: {e}") + content = "" + + protego = Protego.parse(content) if content.strip() else None + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + + crawl_delay = self.crawl_delay + if protego: + delay = protego.crawl_delay(agent) or protego.crawl_delay("*") + crawl_delay = delay if delay else self.crawl_delay + + cache_entry = RobotsTxtCache(protego=protego, crawl_delay=crawl_delay) + self._robots_cache[domain_root] = cache_entry + return cache_entry + async def _is_url_allowed(self, url: str) -> bool: if Protego is None: - return True # fallback if Protego not installed - try: - parsed_url = urlparse(url) - robots_url = f"{parsed_url.scheme}://{parsed_url.netloc}/robots.txt" - content = await self._fetch_httpx(robots_url) - if content.strip() == "": - return True # no robots.txt means allowed - rp = Protego.parse(content) - agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") - return rp.can_fetch(agent, url) or rp.can_fetch("*", url) - except Exception: - return True # if no robots.txt, allow by default + return True - async def _get_crawl_delay(self, base_url: str) -> float: + try: + domain_root = self._get_domain_root(url) + cache = await self._get_robots_cache(domain_root) + if cache is None: + cache = await self._fetch_and_cache_robots(domain_root) + + if cache.protego is None: + return True + + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + return cache.protego.can_fetch(agent, url) or cache.protego.can_fetch("*", url) + except Exception as e: + logger.debug(f"Error checking robots.txt for {url}: {e}") + return True + + async def _get_crawl_delay(self, url: str) -> float: if Protego is None: return self.crawl_delay + try: - content = await self._fetch_httpx(base_url + "/robots.txt") - rp = Protego.parse(content) - agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") - delay = rp.crawl_delay(agent) or rp.crawl_delay("*") - return delay or self.crawl_delay + domain_root = self._get_domain_root(url) + cache = await self._get_robots_cache(domain_root) + if cache is None: + cache = await self._fetch_and_cache_robots(domain_root) + return cache.crawl_delay except Exception: return self.crawl_delay @@ -115,34 +205,17 @@ class BeautifulSoupCrawler: async def _fetch_httpx(self, url: str) -> str: await self._ensure_client() assert self._client is not None, "HTTP client not initialized" - attempt = 0 - parsed = urlparse(url) - domain_root = f"{parsed.scheme}://{parsed.netloc}" - # Handle robots.txt separately (no recursive crawl delay call) - is_robot = url.lower().endswith("/robots.txt") + attempt = 0 + crawl_delay = await self._get_crawl_delay(url) while True: try: - # Only get crawl delay for non-robots.txt pages - crawl_delay = self.crawl_delay - if not is_robot: - try: - crawl_delay = await self._get_crawl_delay(domain_root) - except Exception as e: - logger.debug(f"Failed to fetch crawl delay for {domain_root}: {e}") - await self._respect_rate_limit(url, crawl_delay) resp = await self._client.get(url) resp.raise_for_status() return resp.text - except Exception as exc: - # Special case: if robots.txt failed, just return empty string - if is_robot: - logger.warning(f"Robots.txt not found or inaccessible at {url}: {exc}") - return "" - attempt += 1 if attempt > self.max_retries: logger.error(f"Fetch failed for {url} after {attempt} attempts: {exc}") @@ -191,73 +264,72 @@ class BeautifulSoupCrawler: await asyncio.sleep(backoff) # ---------- extraction helpers ---------- - def _normalize_rule(self, rule) -> Dict[str, Any]: + @staticmethod + def _normalize_rule(rule: Union[str, Dict[str, Any]]) -> ExtractionRule: + """Normalize extraction rule to ExtractionRule dataclass""" if isinstance(rule, str): - return {"selector": rule, "attr": None, "all": False, "join_with": " "} + return ExtractionRule(selector=rule) if isinstance(rule, dict): - return { - "selector": rule.get("selector"), - "attr": rule.get("attr"), - "all": bool(rule.get("all")), - "join_with": rule.get("join_with", " "), - "xpath": rule.get("xpath"), - } - raise ValueError("Invalid extraction rule") + return ExtractionRule( + selector=rule.get("selector"), + xpath=rule.get("xpath"), + attr=rule.get("attr"), + all=bool(rule.get("all", False)), + join_with=rule.get("join_with", " "), + ) + raise ValueError(f"Invalid extraction rule: {rule}") - def _extract_with_bs4(self, html: str, rule: Dict[str, Any]) -> str: + def _extract_with_bs4(self, html: str, rule: ExtractionRule) -> str: + """Extract content using BeautifulSoup or lxml xpath""" soup = BeautifulSoup(html, "html.parser") - sel = rule.get("selector") - xpath = rule.get("xpath") - attr = rule.get("attr") - all_flag = rule.get("all", False) - join_with = rule.get("join_with", " ") - if xpath: + if rule.xpath: try: from lxml import html as lxml_html - except Exception: + except ImportError: raise RuntimeError( "XPath requested but lxml is not available. Install lxml or use CSS selectors." ) doc = lxml_html.fromstring(html) - nodes = doc.xpath(xpath) + nodes = doc.xpath(rule.xpath) texts = [] for n in nodes: if hasattr(n, "text_content"): texts.append(n.text_content().strip()) else: texts.append(str(n).strip()) - return join_with.join(t for t in texts if t) + return rule.join_with.join(t for t in texts if t) + + if not rule.selector: + return "" + + if rule.all: + nodes = soup.select(rule.selector) + pieces = [] + for el in nodes: + if rule.attr: + val = el.get(rule.attr) + if val: + pieces.append(val.strip()) + else: + text = el.get_text(strip=True) + if text: + pieces.append(text) + return rule.join_with.join(pieces).strip() else: - if not sel: + el = soup.select_one(rule.selector) + if el is None: return "" - if all_flag: - nodes = soup.select(sel) - pieces = [] - for el in nodes: - if attr: - val = el.get(attr) - if val: - pieces.append(val.strip()) - else: - text = el.get_text(strip=True) - if text: - pieces.append(text) - return join_with.join(pieces).strip() - else: - el = soup.select_one(sel) - if el is None: - return "" - if attr: - val = el.get(attr) - return (val or "").strip() - return el.get_text(strip=True) + if rule.attr: + val = el.get(rule.attr) + return (val or "").strip() + return el.get_text(strip=True) # ---------- public methods ---------- async def fetch_with_bs4( self, - urls: Union[str, List[str]], - extraction_rules: Dict[str, Any], + urls: Union[str, List[str], Dict[str, Dict[str, Any]]], + extraction_rules: Optional[Dict[str, Any]] = None, *, use_playwright: bool = False, playwright_js_wait: float = 0.8, @@ -265,50 +337,84 @@ class BeautifulSoupCrawler: ) -> Dict[str, str]: """ Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). - Returns: dict[url] -> concatenated string of extracted content. - """ - if isinstance(urls, str): - urls = [urls] - # normalize rules - normalized_rules = {} - for field, rule in extraction_rules.items(): - r = self._normalize_rule(rule) - if join_all_matches: - r["all"] = True - normalized_rules[field] = r + Args: + urls: Can be: + - A single URL string + - A list of URLs (uses extraction_rules for all) + - A dict mapping URL -> extraction_rules (URL-specific rules) + extraction_rules: Default rules when urls is a string or list + use_playwright: Whether to use Playwright for JS rendering + playwright_js_wait: Wait time after page load for JS + join_all_matches: Force all rules to extract all matching elements + + Returns: + dict[url] -> concatenated string of extracted content + """ + # Handle different input formats + url_rules_map: Dict[str, Dict[str, Any]] = {} + + if isinstance(urls, str): + if not extraction_rules: + raise ValueError("extraction_rules required when urls is a string") + url_rules_map[urls] = extraction_rules + elif isinstance(urls, list): + if not extraction_rules: + raise ValueError("extraction_rules required when urls is a list") + for url in urls: + url_rules_map[url] = extraction_rules + elif isinstance(urls, dict): + # URL-specific rules + url_rules_map = urls + else: + raise ValueError(f"Invalid urls type: {type(urls)}") + + # Normalize all rules + normalized_url_rules: Dict[str, List[ExtractionRule]] = {} + for url, rules in url_rules_map.items(): + normalized_rules = [] + for field, rule in rules.items(): + r = self._normalize_rule(rule) + if join_all_matches: + r.all = True + normalized_rules.append(r) + normalized_url_rules[url] = normalized_rules async def _task(url: str): async with self._sem: - allowed = await self._is_url_allowed(url) - if not allowed: - logger.warning(f"URL disallowed by robots.txt: {url}") + try: + allowed = await self._is_url_allowed(url) + if not allowed: + logger.warning(f"URL disallowed by robots.txt: {url}") + return url, "" + + # Fetch (rendered or not) + if use_playwright: + html = await self._render_with_playwright( + url, js_wait=playwright_js_wait, timeout=self.timeout + ) + else: + html = await self._fetch_httpx(url) + + # Extract content using URL-specific rules + pieces = [] + for rule in normalized_url_rules[url]: + text = self._extract_with_bs4(html, rule) + if text: + pieces.append(text) + + concatenated = " ".join(pieces).strip() + return url, concatenated + + except Exception as e: + logger.error(f"Error processing {url}: {e}") return url, "" - # fetch (rendered or not) - if use_playwright: - html = await self._render_with_playwright( - url, js_wait=playwright_js_wait, timeout=self.timeout - ) - else: - html = await self._fetch_httpx(url) - - pieces = [] - for field, rule in normalized_rules.items(): - text = self._extract_with_bs4(html, rule) - if text: - pieces.append(text) - concatenated = " ".join(pieces).strip() - return url, concatenated - - tasks = [asyncio.create_task(_task(u)) for u in urls] + tasks = [asyncio.create_task(_task(u)) for u in url_rules_map.keys()] results = {} + for coro in asyncio.as_completed(tasks): - try: - url, text = await coro - except Exception as e: - results[url] = "" - logger.error(f"Error processing {url}: {e}") - continue + url, text = await coro results[url] = text - return results \ No newline at end of file + + return results From f449fce0f11acd2ff6c71476ec2209e5015656be Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 02:27:20 +0530 Subject: [PATCH 047/139] Done with scraping_task successfully --- cognee/tasks/web_scraper/models.py | 17 +- cognee/tasks/web_scraper/web_scraper_task.py | 307 +++++++++++++++--- .../tasks/web_scraping/web_scraping_test.py | 3 +- 3 files changed, 264 insertions(+), 63 deletions(-) diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py index 12ac91166..538e11be8 100644 --- a/cognee/tasks/web_scraper/models.py +++ b/cognee/tasks/web_scraper/models.py @@ -1,4 +1,4 @@ -from cognee.infrastructure.engine.models import DataPoint +from cognee.infrastructure.engine import DataPoint from typing import Optional, Dict, Any, List from datetime import datetime @@ -7,7 +7,7 @@ class WebPage(DataPoint): """Represents a scraped web page with metadata""" url: str - title: Optional[str] + name: Optional[str] content: str content_hash: str scraped_at: datetime @@ -16,29 +16,32 @@ class WebPage(DataPoint): content_type: str page_size: int extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used - metadata: dict = {"index_fields": ["url", "title", "scraped_at"]} + description: str + metadata: dict = {"index_fields": ["url", "title", "scraped_at", "description"]} class WebSite(DataPoint): """Represents a website or domain being scraped""" - domain: str + name: str base_url: str robots_txt: Optional[str] crawl_delay: float last_crawled: datetime page_count: int scraping_config: Dict[str, Any] - metadata: dict = {"index_fields": ["domain", "base_url"]} + description: str + metadata: dict = {"index_fields": ["domain", "base_url","description"]} class ScrapingJob(DataPoint): """Represents a scraping job configuration""" - job_name: str + name: str urls: List[str] schedule: Optional[str] # Cron-like schedule for recurring scrapes status: str # "active", "paused", "completed", "failed" last_run: Optional[datetime] next_run: Optional[datetime] - metadata: dict = {"index_fields": ["job_name", "status"]} + description: str + metadata: dict = {"index_fields": ["job_name", "status","description"]} diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index c06b54b8e..e9f197045 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -1,63 +1,262 @@ -from cognee.tasks.storage.add_data_points import add_data_points +import os +import hashlib +from datetime import datetime +from typing import Union, List +from urllib.parse import urlparse +from uuid import uuid5, NAMESPACE_OID + +from apscheduler.schedulers.asyncio import AsyncIOScheduler +from apscheduler.triggers.cron import CronTrigger + +from cognee.infrastructure.databases.graph import get_graph_engine +from cognee.shared.logging_utils import get_logger from cognee.tasks.storage.index_data_points import index_data_points from cognee.tasks.storage.index_graph_edges import index_graph_edges -from cognee.infrastructure.databases.graph import get_graph_engine +from cognee.modules.engine.operations.setup import setup + from .models import WebPage, WebSite, ScrapingJob -from typing import Union, List, Dict -from urllib.parse import urlparse +from .config import SoupCrawlerConfig, TavilyConfig +from .utils import fetch_page_content + +logger = get_logger(__name__) -async def web_scraper_task(url: Union[str, List[str]], **kwargs): - graph_engine = await get_graph_engine() - # Mapping between parsed_url object and urls - mappings = {} - web_scraping_job = ScrapingJob( - job_name="default_job", - urls=[url] if isinstance(url, str) else url, - scraping_rules={}, - schedule=None, - status="active", - last_run=None, - next_run=None, +async def web_scraper_task( + url: Union[str, List[str]], + *, + schedule: str = None, + extraction_rules: dict = None, + tavily_api_key: str = os.getenv("TAVILY_API_KEY"), + soup_crawler_config: SoupCrawlerConfig = None, + tavily_config: TavilyConfig = None, + job_name: str = None, +): + """ + Scrapes one or more URLs and returns WebPage, WebSite, and ScrapingJob data points. + Unique IDs are assigned to each WebPage, WebSite, and ScrapingJob. + Includes a description field summarizing other fields for each data point. + """ + await setup() + graph_db = await get_graph_engine() + + if isinstance(url, str): + url = [url] + + soup_crawler_config, tavily_config, preferred_tool = check_arguments( + tavily_api_key, extraction_rules, tavily_config, soup_crawler_config ) - data_point_mappings: Dict[WebSite, List[WebPage]] = {} - if isinstance(url, List): - for single_url in url: - parsed_url = urlparse(single_url) - domain = parsed_url.netloc - base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" - if mappings.get(parsed_url): - mappings[parsed_url] = [single_url] - else: - mappings[parsed_url].append(single_url) - else: - if mappings.get(parsed_url): - mappings[parsed_url] = [single_url] - else: - mappings[parsed_url].append(single_url) - for parsed_url in mappings.keys(): + now = datetime.now() + job_name = job_name or f"scrape_{now.strftime('%Y%m%d_%H%M%S')}" + status = "active" + trigger = CronTrigger.from_crontab(schedule) if schedule else None + next_run = trigger.get_next_fire_time(None, now) if trigger else None + scraping_job_created = await graph_db.get_node(uuid5(NAMESPACE_OID, name=job_name)) + + # Create description for ScrapingJob + scraping_job_description = ( + f"Scraping job: {job_name}\n" + f"URLs: {', '.join(url)}\n" + f"Status: {status}\n" + f"Schedule: {schedule}\n" + f"Last run: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Next run: {next_run.strftime('%Y-%m-%d %H:%M:%S') if next_run else 'Not scheduled'}" + ) + + scraping_job = ScrapingJob( + id=uuid5(NAMESPACE_OID, name=job_name), + name=job_name, + urls=url, + status=status, + schedule=schedule, + last_run=now, + next_run=next_run, + description=scraping_job_description, + ) + + if scraping_job_created: + await graph_db.add_node(scraping_job) # Update existing scraping job + websites_dict = {} + webpages = [] + + # Fetch content + results = await fetch_page_content( + urls=url, + preferred_tool=preferred_tool, + tavily_config=tavily_config, + soup_crawler_config=soup_crawler_config, + ) + + for page_url, content in results.items(): + parsed_url = urlparse(page_url) domain = parsed_url.netloc base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" - web_site = WebSite( - domain=domain, - base_url=base_url, - robots_txt="", - crawl_delay=0, - last_crawled=None, - page_count=0, - scraping_config={}, - ) - for url in mappings[parsed_url]: - # Process each URL with the web scraping logic - web_page = WebPage( - url=url, - title="", - content="", - content_hash="", - scraped_at=None, - last_modified=None, - status_code=0, - content_type="", - page_size=0, - extraction_rules={}, + + # Create or update WebSite + if base_url not in websites_dict: + # Create description for WebSite + website_description = ( + f"Website: {domain}\n" + f"Base URL: {base_url}\n" + f"Last crawled: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Page count: 1\n" + f"Scraping tool: {preferred_tool}\n" + f"Robots.txt: {'Available' if websites_dict.get(base_url, {}).get('robots_txt') else 'Not set'}\n" + f"Crawl delay: 0.5 seconds" ) + + websites_dict[base_url] = WebSite( + id=uuid5(NAMESPACE_OID, name=domain), + name=domain, + base_url=base_url, + robots_txt=None, + crawl_delay=0.5, + last_crawled=now, + page_count=1, + scraping_config={ + "extraction_rules": extraction_rules or {}, + "tool": preferred_tool, + }, + description=website_description, + ) + if scraping_job_created: + await graph_db.add_node(websites_dict[base_url]) + else: + websites_dict[base_url].page_count += 1 + # Update description for existing WebSite + websites_dict[base_url].description = ( + f"Website: {domain}\n" + f"Base URL: {base_url}\n" + f"Last crawled: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Page count: {websites_dict[base_url].page_count}\n" + f"Scraping tool: {preferred_tool}\n" + f"Robots.txt: {'Available' if websites_dict[base_url].robots_txt else 'Not set'}\n" + f"Crawl delay: {websites_dict[base_url].crawl_delay} seconds" + ) + if scraping_job_created: + await graph_db.add_node(websites_dict[base_url]) + + # Create WebPage + content_str = content if isinstance(content, str) else str(content) + content_hash = hashlib.sha256(content_str.encode("utf-8")).hexdigest() + + # Create description for WebPage + webpage_description = ( + f"Webpage: {parsed_url.path.lstrip('/') or 'Home'}\n" + f"URL: {page_url}\n" + f"Scraped at: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Content hash: {content_hash[:16]}...\n" + f"Content type: text/html\n" + f"Page size: {len(content_str)} bytes\n" + f"Status code: 200" + ) + + webpage = WebPage( + id=uuid5(NAMESPACE_OID, name=parsed_url.path.lstrip("/")), + url=page_url, + name=get_path_after_base(base_url, page_url), + content=content_str, + content_hash=content_hash, + scraped_at=now, + last_modified=None, + status_code=200, + content_type="text/html", + page_size=len(content_str), + extraction_rules=extraction_rules or {}, + description=webpage_description, + ) + webpages.append(webpage) + + scraping_job.status = "completed" if webpages else "failed" + # Update ScrapingJob description with final status + scraping_job.description = ( + f"Scraping job: {job_name}\n" + f"URLs: {', '.join(url)}\n" + f"Status: {scraping_job.status}\n" + f"Last run: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Next run: {next_run.strftime('%Y-%m-%d %H:%M:%S') if next_run else 'Not scheduled'}" + ) + + websites = list(websites_dict.values()) + # Adding Nodes and Edges + node_mapping = {scraping_job.id: scraping_job} + edge_mapping = [] + + for website in websites: + node_mapping[website.id] = website + edge_mapping.append( + ( + scraping_job.id, + website.id, + "is_scraping", + { + "source_node_id": scraping_job.id, + "target_node_id": website.id, + "relationship_name": "is_scraping", + }, + ) + ) + + for webpage in webpages: + node_mapping[webpage.id] = webpage + parsed_url = urlparse(webpage.url) + base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" + edge_mapping.append( + ( + webpage.id, # Corrected: WebPage is the source, WebSite is the target + websites_dict[base_url].id, + "is_part_of", + { + "source_node_id": webpage.id, + "target_node_id": websites_dict[base_url].id, + "relationship_name": "is_part_of", + }, + ) + ) + + await graph_db.add_nodes(list(node_mapping.values())) + await graph_db.add_edges(edge_mapping) + await index_data_points(list(node_mapping.values())) + await index_graph_edges() + + return await graph_db.get_graph_data() + + +def check_arguments(tavily_api_key, extraction_rules, tavily_config, soup_crawler_config): + """ + Checking if the right argument are given, if not TypeError will be raised. + """ + preferred_tool = "beautifulsoup" + + if extraction_rules and not soup_crawler_config: + soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) + + if tavily_api_key: + if not tavily_config: + tavily_config = TavilyConfig(api_key=tavily_api_key) + else: + tavily_config.api_key = tavily_api_key + if not extraction_rules and not soup_crawler_config: + preferred_tool = "tavily" + + if not tavily_config and not soup_crawler_config: + raise TypeError("Make sure you pass arguments for web_scraper_task") + + return soup_crawler_config, tavily_config, preferred_tool + + +def get_path_after_base(base_url, url): + parsed_base = urlparse(base_url) + parsed_url = urlparse(url) + + # Ensure they have the same netloc (domain) + if parsed_base.netloc != parsed_url.netloc: + raise ValueError("Base URL and target URL are from different domains") + + # Return everything after base_url path + base_path = parsed_base.path.rstrip("/") + full_path = parsed_url.path + + if full_path.startswith(base_path): + return full_path[len(base_path) :].lstrip("/") + else: + return full_path.lstrip("/") diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 4f47bf7d2..15992d19b 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -4,9 +4,8 @@ from cognee.tasks.web_scraper.config import SoupCrawlerConfig async def test_web_scraping_using_bs4(): - # 0. Prune only data (not full system prune) await cognee.prune.prune_data() - + await cognee.prune.prune_system(metadata=True) # 1. Setup test URL and extraction rules url = "https://quotes.toscrape.com/" rules = { From e5633bc368b5da9f86b95fccf9558a625b63febb Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 03:44:24 +0530 Subject: [PATCH 048/139] corrected F402 error pointed out by ruff check --- cognee/tasks/web_scraper/bs4_crawler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index f9159b35f..57cfa46f8 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -373,7 +373,7 @@ class BeautifulSoupCrawler: normalized_url_rules: Dict[str, List[ExtractionRule]] = {} for url, rules in url_rules_map.items(): normalized_rules = [] - for field, rule in rules.items(): + for _, rule in rules.items(): r = self._normalize_rule(rule) if join_all_matches: r.all = True From 0f64f6804d406827f5bb6d90717238953479571c Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 03:45:09 +0530 Subject: [PATCH 049/139] Done adding cron job web scraping --- cognee/tasks/web_scraper/__init__.py | 4 ++ cognee/tasks/web_scraper/models.py | 4 +- cognee/tasks/web_scraper/web_scraper_task.py | 62 +++++++++++++++++++- 3 files changed, 66 insertions(+), 4 deletions(-) diff --git a/cognee/tasks/web_scraper/__init__.py b/cognee/tasks/web_scraper/__init__.py index a37e17871..8a142b515 100644 --- a/cognee/tasks/web_scraper/__init__.py +++ b/cognee/tasks/web_scraper/__init__.py @@ -1,7 +1,11 @@ from .bs4_crawler import BeautifulSoupCrawler from .utils import fetch_page_content +from .web_scraper_task import cron_web_scraper_task, web_scraper_task + __all__ = [ "BeautifulSoupCrawler", "fetch_page_content", + "cron_web_scraper_task", + "web_scraper_task", ] diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py index 538e11be8..18bb2e3ef 100644 --- a/cognee/tasks/web_scraper/models.py +++ b/cognee/tasks/web_scraper/models.py @@ -31,7 +31,7 @@ class WebSite(DataPoint): page_count: int scraping_config: Dict[str, Any] description: str - metadata: dict = {"index_fields": ["domain", "base_url","description"]} + metadata: dict = {"index_fields": ["domain", "base_url", "description"]} class ScrapingJob(DataPoint): @@ -44,4 +44,4 @@ class ScrapingJob(DataPoint): last_run: Optional[datetime] next_run: Optional[datetime] description: str - metadata: dict = {"index_fields": ["job_name", "status","description"]} + metadata: dict = {"index_fields": ["job_name", "status", "description"]} diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index e9f197045..9fb43c2cb 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -4,8 +4,7 @@ from datetime import datetime from typing import Union, List from urllib.parse import urlparse from uuid import uuid5, NAMESPACE_OID - -from apscheduler.schedulers.asyncio import AsyncIOScheduler +import asyncio from apscheduler.triggers.cron import CronTrigger from cognee.infrastructure.databases.graph import get_graph_engine @@ -18,9 +17,68 @@ from .models import WebPage, WebSite, ScrapingJob from .config import SoupCrawlerConfig, TavilyConfig from .utils import fetch_page_content +try: + from apscheduler.schedulers.background import BackgroundScheduler + + scheduler = BackgroundScheduler() +except ImportError: + raise ImportError("Please install apscheduler by pip install APScheduler >=3.10") + logger = get_logger(__name__) +async def cron_web_scraper_task( + url: Union[str, List[str]], + *, + schedule: str = None, + extraction_rules: dict = None, + tavily_api_key: str = os.getenv("TAVILY_API_KEY"), + soup_crawler_config: SoupCrawlerConfig = None, + tavily_config: TavilyConfig = None, + job_name: str = "scraping", +): + now = datetime.now() + job_name = job_name or f"scrape_{now.strftime('%Y%m%d_%H%M%S')}" + if schedule: + try: + trigger = CronTrigger.from_crontab(schedule) + except ValueError as e: + raise ValueError(f"Invalid cron string '{schedule}': {e}") + + scheduler.add_job( + lambda: asyncio.run( + web_scraper_task( + url=url, + schedule=schedule, + extraction_rules=extraction_rules, + tavily_api_key=tavily_api_key, + soup_crawler_config=soup_crawler_config, + tavily_config=tavily_config, + job_name=job_name, + ) + ), + trigger=trigger, + id=uuid5(NAMESPACE_OID, name=job_name), + name=f"WebScraper_{uuid5(NAMESPACE_OID, name=job_name)}", + replace_existing=True, + ) + + scheduler.start() + return + + # If no schedule, run immediately + print(f"[{datetime.now()}] Running web scraper task immediately...") + return await web_scraper_task( + url=url, + schedule=schedule, + extraction_rules=extraction_rules, + tavily_api_key=tavily_api_key, + soup_crawler_config=soup_crawler_config, + tavily_config=tavily_config, + job_name=job_name, + ) + + async def web_scraper_task( url: Union[str, List[str]], *, From 4d5146c8025ef968eea542b3b28d4c963c8e365b Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 04:00:15 +0530 Subject: [PATCH 050/139] Added Documentation --- cognee/tasks/web_scraper/__init__.py | 7 + cognee/tasks/web_scraper/bs4_crawler.py | 209 +++++++++++++++---- cognee/tasks/web_scraper/utils.py | 67 ++++-- cognee/tasks/web_scraper/web_scraper_task.py | 90 +++++++- 4 files changed, 305 insertions(+), 68 deletions(-) diff --git a/cognee/tasks/web_scraper/__init__.py b/cognee/tasks/web_scraper/__init__.py index 8a142b515..d8e580fad 100644 --- a/cognee/tasks/web_scraper/__init__.py +++ b/cognee/tasks/web_scraper/__init__.py @@ -1,3 +1,10 @@ +"""Web scraping module for cognee. + +This module provides tools for scraping web content, managing scraping jobs, and storing +data in a graph database. It includes classes and functions for crawling web pages using +BeautifulSoup or Tavily, defining data models, and handling scraping configurations. +""" + from .bs4_crawler import BeautifulSoupCrawler from .utils import fetch_page_content from .web_scraper_task import cron_web_scraper_task, web_scraper_task diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index 57cfa46f8..7ccf06a3d 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -1,10 +1,16 @@ +"""BeautifulSoup-based web crawler for extracting content from web pages. + +This module provides the BeautifulSoupCrawler class for fetching and extracting content +from web pages using BeautifulSoup or Playwright for JavaScript-rendered pages. It +supports robots.txt handling, rate limiting, and custom extraction rules. +""" + import asyncio import time from typing import Union, List, Dict, Any, Optional from urllib.parse import urlparse from dataclasses import dataclass, field from functools import lru_cache - import httpx from bs4 import BeautifulSoup from cognee.shared.logging_utils import get_logger @@ -28,7 +34,15 @@ except ImportError: @dataclass class ExtractionRule: - """Normalized extraction rule""" + """Normalized extraction rule for web content. + + Attributes: + selector: CSS selector for extraction (if any). + xpath: XPath expression for extraction (if any). + attr: HTML attribute to extract (if any). + all: If True, extract all matching elements; otherwise, extract first. + join_with: String to join multiple extracted elements. + """ selector: Optional[str] = None xpath: Optional[str] = None @@ -39,7 +53,13 @@ class ExtractionRule: @dataclass class RobotsTxtCache: - """Cache for robots.txt data""" + """Cache for robots.txt data. + + Attributes: + protego: Parsed robots.txt object (Protego instance). + crawl_delay: Delay between requests (in seconds). + timestamp: Time when the cache entry was created. + """ protego: Any crawl_delay: float @@ -47,6 +67,21 @@ class RobotsTxtCache: class BeautifulSoupCrawler: + """Crawler for fetching and extracting web content using BeautifulSoup. + + Supports asynchronous HTTP requests, Playwright for JavaScript rendering, robots.txt + compliance, and rate limiting. Extracts content using CSS selectors or XPath rules. + + Attributes: + concurrency: Number of concurrent requests allowed. + crawl_delay: Minimum seconds between requests to the same domain. + timeout: Per-request timeout in seconds. + max_retries: Number of retries for failed requests. + retry_delay_factor: Multiplier for exponential backoff on retries. + headers: HTTP headers for requests (e.g., User-Agent). + robots_cache_ttl: Time-to-live for robots.txt cache in seconds. + """ + def __init__( self, *, @@ -56,16 +91,18 @@ class BeautifulSoupCrawler: max_retries: int = 2, retry_delay_factor: float = 0.5, headers: Optional[Dict[str, str]] = None, - robots_cache_ttl: float = 3600.0, # Cache robots.txt for 1 hour + robots_cache_ttl: float = 3600.0, ): - """ - concurrency: number of concurrent requests allowed - crawl_delay: minimum seconds to wait between requests to the SAME domain - timeout: per-request timeout - max_retries: number of retries on network errors - retry_delay_factor: multiplier for exponential retry failure delay - headers: default headers for requests - robots_cache_ttl: TTL for robots.txt cache in seconds + """Initialize the BeautifulSoupCrawler. + + Args: + concurrency: Number of concurrent requests allowed. + crawl_delay: Minimum seconds between requests to the same domain. + timeout: Per-request timeout in seconds. + max_retries: Number of retries for failed requests. + retry_delay_factor: Multiplier for exponential backoff on retries. + headers: HTTP headers for requests (defaults to User-Agent: Cognee-Scraper/1.0). + robots_cache_ttl: Time-to-live for robots.txt cache in seconds. """ self.concurrency = concurrency self._sem = asyncio.Semaphore(concurrency) @@ -75,33 +112,42 @@ class BeautifulSoupCrawler: self.retry_delay_factor = retry_delay_factor self.headers = headers or {"User-Agent": "Cognee-Scraper/1.0"} self.robots_cache_ttl = robots_cache_ttl - self._last_request_time_per_domain: Dict[str, float] = {} self._robots_cache: Dict[str, RobotsTxtCache] = {} self._client: Optional[httpx.AsyncClient] = None self._robots_lock = asyncio.Lock() - # ---------- lifecycle helpers ---------- async def _ensure_client(self): + """Initialize the HTTP client if not already created.""" if self._client is None: self._client = httpx.AsyncClient(timeout=self.timeout, headers=self.headers) async def close(self): + """Close the HTTP client.""" if self._client: await self._client.aclose() self._client = None async def __aenter__(self): + """Enter the context manager, initializing the HTTP client.""" await self._ensure_client() return self async def __aexit__(self, exc_type, exc_val, exc_tb): + """Exit the context manager, closing the HTTP client.""" await self.close() - # ---------- rate limiting ---------- @staticmethod @lru_cache(maxsize=1024) def _domain_from_url(url: str) -> str: + """Extract the domain (netloc) from a URL. + + Args: + url: The URL to parse. + + Returns: + str: The domain (netloc) of the URL. + """ try: return urlparse(url).netloc except Exception: @@ -110,10 +156,24 @@ class BeautifulSoupCrawler: @staticmethod @lru_cache(maxsize=1024) def _get_domain_root(url: str) -> str: + """Get the root URL (scheme and netloc) from a URL. + + Args: + url: The URL to parse. + + Returns: + str: The root URL (e.g., "https://example.com"). + """ parsed = urlparse(url) return f"{parsed.scheme}://{parsed.netloc}" async def _respect_rate_limit(self, url: str, crawl_delay: Optional[float] = None): + """Enforce rate limiting for requests to the same domain. + + Args: + url: The URL to check. + crawl_delay: Custom crawl delay in seconds (if any). + """ domain = self._domain_from_url(url) last = self._last_request_time_per_domain.get(domain) delay = crawl_delay if crawl_delay is not None else self.crawl_delay @@ -128,9 +188,15 @@ class BeautifulSoupCrawler: await asyncio.sleep(wait_for) self._last_request_time_per_domain[domain] = time.time() - # ----------- robots.txt handling ----------- async def _get_robots_cache(self, domain_root: str) -> Optional[RobotsTxtCache]: - """Get cached robots.txt data if valid""" + """Get cached robots.txt data if valid. + + Args: + domain_root: The root URL (e.g., "https://example.com"). + + Returns: + Optional[RobotsTxtCache]: Cached robots.txt data or None if expired or not found. + """ if Protego is None: return None @@ -140,9 +206,18 @@ class BeautifulSoupCrawler: return None async def _fetch_and_cache_robots(self, domain_root: str) -> RobotsTxtCache: - """Fetch and cache robots.txt data""" + """Fetch and cache robots.txt data. + + Args: + domain_root: The root URL (e.g., "https://example.com"). + + Returns: + RobotsTxtCache: Cached robots.txt data with crawl delay. + + Raises: + Exception: If fetching robots.txt fails. + """ async with self._robots_lock: - # Check again after acquiring lock cached = await self._get_robots_cache(domain_root) if cached: return cached @@ -170,6 +245,14 @@ class BeautifulSoupCrawler: return cache_entry async def _is_url_allowed(self, url: str) -> bool: + """Check if a URL is allowed by robots.txt. + + Args: + url: The URL to check. + + Returns: + bool: True if the URL is allowed, False otherwise. + """ if Protego is None: return True @@ -189,6 +272,14 @@ class BeautifulSoupCrawler: return True async def _get_crawl_delay(self, url: str) -> float: + """Get the crawl delay for a URL from robots.txt. + + Args: + url: The URL to check. + + Returns: + float: Crawl delay in seconds. + """ if Protego is None: return self.crawl_delay @@ -201,8 +292,18 @@ class BeautifulSoupCrawler: except Exception: return self.crawl_delay - # ---------- low-level fetchers ---------- async def _fetch_httpx(self, url: str) -> str: + """Fetch a URL using HTTPX with retries. + + Args: + url: The URL to fetch. + + Returns: + str: The HTML content of the page. + + Raises: + Exception: If all retry attempts fail. + """ await self._ensure_client() assert self._client is not None, "HTTP client not initialized" @@ -230,6 +331,20 @@ class BeautifulSoupCrawler: async def _render_with_playwright( self, url: str, js_wait: float = 1.0, timeout: Optional[float] = None ) -> str: + """Fetch and render a URL using Playwright for JavaScript content. + + Args: + url: The URL to fetch. + js_wait: Seconds to wait for JavaScript to load. + timeout: Timeout for the request (in seconds, defaults to instance timeout). + + Returns: + str: The rendered HTML content. + + Raises: + RuntimeError: If Playwright is not installed. + Exception: If all retry attempts fail. + """ if async_playwright is None: raise RuntimeError( "Playwright is not installed. Install with `pip install playwright` and run `playwright install`." @@ -263,10 +378,19 @@ class BeautifulSoupCrawler: ) await asyncio.sleep(backoff) - # ---------- extraction helpers ---------- @staticmethod def _normalize_rule(rule: Union[str, Dict[str, Any]]) -> ExtractionRule: - """Normalize extraction rule to ExtractionRule dataclass""" + """Normalize an extraction rule to an ExtractionRule dataclass. + + Args: + rule: A string (CSS selector) or dict with extraction parameters. + + Returns: + ExtractionRule: Normalized extraction rule. + + Raises: + ValueError: If the rule is invalid. + """ if isinstance(rule, str): return ExtractionRule(selector=rule) if isinstance(rule, dict): @@ -280,7 +404,18 @@ class BeautifulSoupCrawler: raise ValueError(f"Invalid extraction rule: {rule}") def _extract_with_bs4(self, html: str, rule: ExtractionRule) -> str: - """Extract content using BeautifulSoup or lxml xpath""" + """Extract content from HTML using BeautifulSoup or lxml XPath. + + Args: + html: The HTML content to extract from. + rule: The extraction rule to apply. + + Returns: + str: The extracted content. + + Raises: + RuntimeError: If XPath is used but lxml is not installed. + """ soup = BeautifulSoup(html, "html.parser") if rule.xpath: @@ -325,7 +460,6 @@ class BeautifulSoupCrawler: return (val or "").strip() return el.get_text(strip=True) - # ---------- public methods ---------- async def fetch_with_bs4( self, urls: Union[str, List[str], Dict[str, Dict[str, Any]]], @@ -335,23 +469,22 @@ class BeautifulSoupCrawler: playwright_js_wait: float = 0.8, join_all_matches: bool = False, ) -> Dict[str, str]: - """ - Fetch one or more URLs and extract text using BeautifulSoup (or lxml xpath). + """Fetch and extract content from URLs using BeautifulSoup or Playwright. Args: - urls: Can be: - - A single URL string - - A list of URLs (uses extraction_rules for all) - - A dict mapping URL -> extraction_rules (URL-specific rules) - extraction_rules: Default rules when urls is a string or list - use_playwright: Whether to use Playwright for JS rendering - playwright_js_wait: Wait time after page load for JS - join_all_matches: Force all rules to extract all matching elements + urls: A single URL, list of URLs, or dict mapping URLs to extraction rules. + extraction_rules: Default extraction rules for string or list URLs. + use_playwright: If True, use Playwright for JavaScript rendering. + playwright_js_wait: Seconds to wait for JavaScript to load. + join_all_matches: If True, extract all matching elements for each rule. Returns: - dict[url] -> concatenated string of extracted content + Dict[str, str]: A dictionary mapping URLs to their extracted content. + + Raises: + ValueError: If extraction_rules are missing when required or if urls is invalid. + Exception: If fetching or extraction fails. """ - # Handle different input formats url_rules_map: Dict[str, Dict[str, Any]] = {} if isinstance(urls, str): @@ -364,12 +497,10 @@ class BeautifulSoupCrawler: for url in urls: url_rules_map[url] = extraction_rules elif isinstance(urls, dict): - # URL-specific rules url_rules_map = urls else: raise ValueError(f"Invalid urls type: {type(urls)}") - # Normalize all rules normalized_url_rules: Dict[str, List[ExtractionRule]] = {} for url, rules in url_rules_map.items(): normalized_rules = [] @@ -388,7 +519,6 @@ class BeautifulSoupCrawler: logger.warning(f"URL disallowed by robots.txt: {url}") return url, "" - # Fetch (rendered or not) if use_playwright: html = await self._render_with_playwright( url, js_wait=playwright_js_wait, timeout=self.timeout @@ -396,7 +526,6 @@ class BeautifulSoupCrawler: else: html = await self._fetch_httpx(url) - # Extract content using URL-specific rules pieces = [] for rule in normalized_url_rules[url]: text = self._extract_with_bs4(html, rule) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index c1b6ecb53..a14dbbd20 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -1,10 +1,13 @@ -from tavily import AsyncTavilyClient -from .bs4_crawler import BeautifulSoupCrawler -import os -from .config import TavilyConfig, SoupCrawlerConfig -from typing import Dict, Any, List, Union, Optional, Literal +"""Utilities for fetching web content using BeautifulSoup or Tavily. + +This module provides functions to fetch and extract content from web pages, supporting +both BeautifulSoup for custom extraction rules and Tavily for API-based scraping. +""" + +from typing import Dict, List, Union, Optional, Literal from cognee.shared.logging_utils import get_logger -import asyncio +from .bs4_crawler import BeautifulSoupCrawler +from .config import TavilyConfig, SoupCrawlerConfig logger = get_logger(__name__) @@ -16,20 +19,31 @@ async def fetch_page_content( tavily_config: Optional[TavilyConfig] = None, soup_crawler_config: Optional[SoupCrawlerConfig] = None, ) -> Dict[str, Union[str, Dict[str, str]]]: - """ - Fetch page content using Tavily API if TAVILY_API_KEY is set, - otherwise fetch using BeautifulSoupCrawler directly. + """Fetch content from one or more URLs using the specified tool. - Parameters: - urls: single URL or list of URLs - extraction_rules: dict mapping field names -> CSS selector or rule - use_playwright: whether to render JS (BeautifulSoupCrawler) - playwright_js_wait: seconds to wait for JS to load - join_all_matches: join all matching elements per rule - structured: if True, returns structured dict instead of concatenated string (based on extraction_rules field names) + This function retrieves web page content using either BeautifulSoup (with custom + extraction rules) or Tavily (API-based scraping). It handles single URLs or lists of + URLs and returns a dictionary mapping URLs to their extracted content. + + Args: + urls: A single URL (str) or a list of URLs (List[str]) to scrape. + preferred_tool: The scraping tool to use ("tavily" or "beautifulsoup"). + Defaults to "beautifulsoup". + tavily_config: Configuration for Tavily API, including API key. + Required if preferred_tool is "tavily". + soup_crawler_config: Configuration for BeautifulSoup crawler, including + extraction rules. Required if preferred_tool is "beautifulsoup" and + extraction_rules are needed. Returns: - Dict mapping URL -> extracted string or structured dict + Dict[str, Union[str, Dict[str, str]]]: A dictionary mapping each URL to its + extracted content (as a string for BeautifulSoup or a dict for Tavily). + + Raises: + ValueError: If Tavily API key is missing when using Tavily, or if + extraction_rules are not provided when using BeautifulSoup. + ImportError: If required dependencies (beautifulsoup4 or tavily-python) are not + installed. """ if preferred_tool == "tavily": if tavily_config.api_key is None: @@ -43,6 +57,7 @@ async def fetch_page_content( logger.error( "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" ) + raise crawler = BeautifulSoupCrawler() extraction_rules = soup_crawler_config.extraction_rules if extraction_rules is None: @@ -58,20 +73,34 @@ async def fetch_page_content( return results except Exception as e: logger.error(f"Error fetching page content: {str(e)}") + raise async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: + """Fetch content from URLs using the Tavily API. + + Args: + urls: A single URL (str) or a list of URLs (List[str]) to scrape. + + Returns: + Dict[str, str]: A dictionary mapping each URL to its raw content as a string. + + Raises: + ImportError: If tavily-python is not installed. + Exception: If the Tavily API request fails. + """ try: from tavily import AsyncTavilyClient except ImportError: logger.error( "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" ) + raise client = AsyncTavilyClient() results = await client.extract(urls) for failed_result in results.get("failed_results", []): logger.warning(f"Failed to fetch {failed_result}") return_results = {} - for results in results.get("results", []): - return_results[results["url"]] = results["raw_content"] + for result in results.get("results", []): + return_results[result["url"]] = result["raw_content"] return return_results diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index 9fb43c2cb..dc286500a 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -1,3 +1,10 @@ +"""Web scraping tasks for storing scraped data in a graph database. + +This module provides functions to scrape web content, create or update WebPage, WebSite, +and ScrapingJob data points, and store them in a Kuzu graph database. It supports +scheduled scraping tasks and ensures that node updates preserve existing graph edges. +""" + import os import hashlib from datetime import datetime @@ -22,7 +29,7 @@ try: scheduler = BackgroundScheduler() except ImportError: - raise ImportError("Please install apscheduler by pip install APScheduler >=3.10") + raise ImportError("Please install apscheduler by pip install APScheduler>=3.10") logger = get_logger(__name__) @@ -37,6 +44,28 @@ async def cron_web_scraper_task( tavily_config: TavilyConfig = None, job_name: str = "scraping", ): + """Schedule or run a web scraping task. + + This function schedules a recurring web scraping task using APScheduler or runs it + immediately if no schedule is provided. It delegates to web_scraper_task for actual + scraping and graph storage. + + Args: + url: A single URL or list of URLs to scrape. + schedule: A cron expression for scheduling (e.g., "0 0 * * *"). If None, runs immediately. + extraction_rules: Dictionary of extraction rules for BeautifulSoup (e.g., CSS selectors). + tavily_api_key: API key for Tavily. Defaults to TAVILY_API_KEY environment variable. + soup_crawler_config: Configuration for BeautifulSoup crawler. + tavily_config: Configuration for Tavily API. + job_name: Name of the scraping job. Defaults to "scraping". + + Returns: + Any: The result of web_scraper_task if run immediately, or None if scheduled. + + Raises: + ValueError: If the schedule is an invalid cron expression. + ImportError: If APScheduler is not installed. + """ now = datetime.now() job_name = job_name or f"scrape_{now.strftime('%Y%m%d_%H%M%S')}" if schedule: @@ -89,10 +118,29 @@ async def web_scraper_task( tavily_config: TavilyConfig = None, job_name: str = None, ): - """ - Scrapes one or more URLs and returns WebPage, WebSite, and ScrapingJob data points. - Unique IDs are assigned to each WebPage, WebSite, and ScrapingJob. - Includes a description field summarizing other fields for each data point. + """Scrape URLs and store data points in a Kuzu graph database. + + This function scrapes content from the provided URLs, creates or updates WebPage, + WebSite, and ScrapingJob data points, and stores them in a Kuzu graph database. + Each data point includes a description field summarizing its attributes. It creates + 'is_scraping' (ScrapingJob to WebSite) and 'is_part_of' (WebPage to WebSite) + relationships, preserving existing edges during node updates. + + Args: + url: A single URL or list of URLs to scrape. + schedule: A cron expression for scheduling (e.g., "0 0 * * *"). If None, runs once. + extraction_rules: Dictionary of extraction rules for BeautifulSoup (e.g., CSS selectors). + tavily_api_key: API key for Tavily. Defaults to TAVILY_API_KEY environment variable. + soup_crawler_config: Configuration for BeautifulSoup crawler. + tavily_config: Configuration for Tavily API. + job_name: Name of the scraping job. Defaults to a timestamp-based name. + + Returns: + Any: The graph data returned by the graph database. + + Raises: + TypeError: If neither tavily_config nor soup_crawler_config is provided. + Exception: If fetching content or database operations fail. """ await setup() graph_db = await get_graph_engine() @@ -260,7 +308,7 @@ async def web_scraper_task( base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" edge_mapping.append( ( - webpage.id, # Corrected: WebPage is the source, WebSite is the target + webpage.id, websites_dict[base_url].id, "is_part_of", { @@ -280,8 +328,20 @@ async def web_scraper_task( def check_arguments(tavily_api_key, extraction_rules, tavily_config, soup_crawler_config): - """ - Checking if the right argument are given, if not TypeError will be raised. + """Validate and configure arguments for web_scraper_task. + + Args: + tavily_api_key: API key for Tavily. + extraction_rules: Extraction rules for BeautifulSoup. + tavily_config: Configuration for Tavily API. + soup_crawler_config: Configuration for BeautifulSoup crawler. + + Returns: + Tuple[SoupCrawlerConfig, TavilyConfig, str]: Configured soup_crawler_config, + tavily_config, and preferred_tool ("tavily" or "beautifulsoup"). + + Raises: + TypeError: If neither tavily_config nor soup_crawler_config is provided. """ preferred_tool = "beautifulsoup" @@ -302,7 +362,19 @@ def check_arguments(tavily_api_key, extraction_rules, tavily_config, soup_crawle return soup_crawler_config, tavily_config, preferred_tool -def get_path_after_base(base_url, url): +def get_path_after_base(base_url: str, url: str) -> str: + """Extract the path after the base URL. + + Args: + base_url: The base URL (e.g., "https://example.com"). + url: The full URL to extract the path from. + + Returns: + str: The path after the base URL, with leading slashes removed. + + Raises: + ValueError: If the base URL and target URL are from different domains. + """ parsed_base = urlparse(base_url) parsed_url = urlparse(url) From 667bbd775e7afe7f93b9212a38c746f1642a800f Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 04:12:32 +0530 Subject: [PATCH 051/139] Added cron job and removed obvious comments --- .../tasks/web_scraping/web_scraping_test.py | 97 +++++++++++-------- 1 file changed, 56 insertions(+), 41 deletions(-) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 15992d19b..b817aea34 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -1,12 +1,13 @@ import asyncio import cognee from cognee.tasks.web_scraper.config import SoupCrawlerConfig +from cognee.tasks.web_scraper import cron_web_scraper_task async def test_web_scraping_using_bs4(): await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) - # 1. Setup test URL and extraction rules + url = "https://quotes.toscrape.com/" rules = { "quotes": {"selector": ".quote span.text", "all": True}, @@ -24,34 +25,26 @@ async def test_web_scraping_using_bs4(): structured=True, ) - # 2. Add / ingest the page await cognee.add( data=url, soup_crawler_config=soup_config, incremental_loading=False, ) - # 3. Cognify await cognee.cognify() - # 4. Search for a known quote results = await cognee.search( "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", query_type=cognee.SearchType.GRAPH_COMPLETION, ) - assert "Albert Einstein" in results[0], ( - "Test failed! Albert Einstein not found in scraped data." - ) + assert "Albert Einstein" in results[0] print("Test passed! Found Albert Einstein in scraped data.") - print(results) - print("Web scraping test using bs4 completed.") async def test_web_scraping_using_bs4_and_incremental_loading(): - # 0. Prune only data (not full system prune) await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) - # 1. Setup test URL and extraction rules url = "https://books.toscrape.com/" rules = {"titles": "article.product_pod h3 a", "prices": "article.product_pod p.price_color"} @@ -66,95 +59,117 @@ async def test_web_scraping_using_bs4_and_incremental_loading(): structured=True, ) - # 2. Add / ingest the page await cognee.add( data=url, soup_crawler_config=soup_config, incremental_loading=True, ) - # 3. Cognify await cognee.cognify() - # 4. Search for a known book results = await cognee.search( "What is the price of 'A Light in the Attic' book?", query_type=cognee.SearchType.GRAPH_COMPLETION, ) - assert "51.77" in results[0], "Test failed! 'A Light in the Attic' not found in scraped data." + assert "51.77" in results[0] print("Test passed! Found 'A Light in the Attic' in scraped data.") - print(results) - print("Web scraping test using bs4 with incremental loading completed.") async def test_web_scraping_using_tavily(): - # 0. Prune only data (not full system prune) await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) - # 1. Setup test URL and extraction rules url = "https://quotes.toscrape.com/" - # 2. Add / ingest the page await cognee.add( data=url, incremental_loading=False, ) - # 3. Cognify await cognee.cognify() - # 4. Search for a known quote results = await cognee.search( "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", query_type=cognee.SearchType.GRAPH_COMPLETION, ) - assert "Albert Einstein" in results[0], ( - "Test failed! Albert Einstein not found in scraped data." - ) + assert "Albert Einstein" in results[0] print("Test passed! Found Albert Einstein in scraped data.") - print(results) - print("Web scraping test using tavily completed.") async def test_web_scraping_using_tavily_and_incremental_loading(): - # 0. Prune only data (not full system prune) await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) - # 1. Setup test URL and extraction rules url = "https://quotes.toscrape.com/" - # 2. Add / ingest the page await cognee.add( data=url, incremental_loading=True, ) - # 3. Cognify await cognee.cognify() - # 4. Search for a known quote results = await cognee.search( "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", query_type=cognee.SearchType.GRAPH_COMPLETION, ) - assert "Albert Einstein" in results[0], ( - "Test failed! Albert Einstein not found in scraped data." - ) + assert "Albert Einstein" in results[0] print("Test passed! Found Albert Einstein in scraped data.") - print(results) - print("Web scraping test using tavily with incremental loading completed.") + + +# ---------- cron job tests ---------- +async def test_cron_web_scraper(): + urls = ["https://quotes.toscrape.com/", "https://books.toscrape.com/"] + extraction_rules = { + "quotes": {"selector": ".quote span.text", "all": True}, + "authors": {"selector": ".quote small", "all": True}, + "titles": "article.product_pod h3 a", + "prices": "article.product_pod p.price_color", + } + + # Run cron_web_scraper_task (schedule string is required) + await cron_web_scraper_task( + urls=urls, + schedule="*/5 * * * *", # every 5 minutes + extraction_rules=extraction_rules, + use_playwright=False, + ) + + # Wait until first run of cron job is done + await asyncio.sleep(120) + + # Validate that the scraped data is searchable + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0] + + results_books = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "51.77" in results_books[0] + + print("Cron job web_scraping test passed!") async def main(): - print("starting web scraping test using bs4 with incremental loading...") + print("Starting BS4 incremental loading test...") await test_web_scraping_using_bs4_and_incremental_loading() - print("starting web scraping test using bs4 without incremental loading...") + + print("Starting BS4 normal test...") await test_web_scraping_using_bs4() - print("starting web scraping test using tavily with incremental loading...") + + print("Starting Tavily incremental loading test...") await test_web_scraping_using_tavily_and_incremental_loading() - print("starting web scraping test using tavily without incremental loading...") + + print("Starting Tavily normal test...") await test_web_scraping_using_tavily() + print("Starting cron job test...") + await test_cron_web_scraper() + if __name__ == "__main__": asyncio.run(main()) From ae740eda96d6f7c0df106631c61d59b2f2e99793 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 04:23:10 +0530 Subject: [PATCH 052/139] Added related documentation --- cognee/api/v1/add/add.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 09cade767..c74ee69d8 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -78,6 +78,7 @@ async def add( - S3 path: "s3://my-bucket/documents/file.pdf" - List of mixed types: ["text content", "/path/file.pdf", "file://doc.txt", file_handle] - Binary file object: open("file.txt", "rb") + - url: A web link url (https or http) dataset_name: Name of the dataset to store data in. Defaults to "main_dataset". Create separate datasets to organize different knowledge domains. user: User object for authentication and permissions. Uses default user if None. @@ -88,6 +89,9 @@ async def add( vector_db_config: Optional configuration for vector database (for custom setups). graph_db_config: Optional configuration for graph database (for custom setups). dataset_id: Optional specific dataset UUID to use instead of dataset_name. + extraction_rules: Optional dictionary of rules (e.g., CSS selectors, XPath) for extracting specific content from web pages using BeautifulSoup + tavily_config: Optional configuration for Tavily API, including API key and extraction settings + soup_crawler_config: Optional configuration for BeautifulSoup crawler, specifying concurrency, crawl delay, and extraction rules. Returns: PipelineRunInfo: Information about the ingestion pipeline execution including: @@ -136,6 +140,21 @@ async def add( # Add a single file await cognee.add("/home/user/documents/analysis.pdf") + + # Add a single url and bs4 extract ingestion method + extraction_rules = { + "title": "h1", + "description": "p", + "more_info": "a[href*='more-info']" + } + await cognee.add("https://example.com",extraction_rules=extraction_rules) + + # Add a single url and tavily extract ingestion method + Make sure to TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable + await cognee.add("https://example.com") + + # Add multiple urls + await cognee.add(["https://example.com","https://books.toscrape.com"]) ``` Environment Variables: @@ -149,6 +168,7 @@ async def add( - DEFAULT_USER_PASSWORD: Custom default user password - VECTOR_DB_PROVIDER: "lancedb" (default), "chromadb", "pgvector" - GRAPH_DATABASE_PROVIDER: "kuzu" (default), "neo4j" + - TAVILY_API_KEY: YOUR_TAVILY_API_KEY """ tasks = [ From 1b5c099f8bdd6ddc1e3a62aa3ff9f71eed062396 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 17:15:25 +0530 Subject: [PATCH 053/139] CodeRabbit reviews solved --- cognee/api/v1/add/add.py | 19 +++-- .../ingestion/save_data_item_to_storage.py | 13 +++- cognee/tasks/web_scraper/config.py | 15 ++-- cognee/tasks/web_scraper/models.py | 7 +- cognee/tasks/web_scraper/utils.py | 16 +++-- cognee/tasks/web_scraper/web_scraper_task.py | 26 ++++--- .../tasks/web_scraping/web_scraping_test.py | 71 +++++++++++-------- uv.lock | 26 +++++++ 8 files changed, 125 insertions(+), 68 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index c74ee69d8..65ebb8748 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -140,7 +140,7 @@ async def add( # Add a single file await cognee.add("/home/user/documents/analysis.pdf") - + # Add a single url and bs4 extract ingestion method extraction_rules = { "title": "h1", @@ -148,11 +148,11 @@ async def add( "more_info": "a[href*='more-info']" } await cognee.add("https://example.com",extraction_rules=extraction_rules) - + # Add a single url and tavily extract ingestion method Make sure to TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable await cognee.add("https://example.com") - + # Add multiple urls await cognee.add(["https://example.com","https://books.toscrape.com"]) ``` @@ -186,13 +186,20 @@ async def add( await setup() if not soup_crawler_config and extraction_rules: soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) - if not tavily_config and os.getenv("TAVILY_API_KEY"): - tavily_config = TavilyConfig() + tavily_config = TavilyConfig(api_key=os.getenv("TAVILY_API_KEY")) soup_crawler.set(soup_crawler_config) tavily.set(tavily_config) - if urlparse(data).scheme in ["http", "https"]: + + http_schemes = {"http", "https"} + + def _is_http_url(item: Union[str, BinaryIO]) -> bool: + return isinstance(item, str) and urlparse(item).scheme in http_schemes + + if _is_http_url(data): + node_set = ["web_content"] if not node_set else node_set + ["web_content"] + elif isinstance(data, list) and any(_is_http_url(item) for item in data): node_set = ["web_content"] if not node_set else node_set + ["web_content"] user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user) diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index d55b697c6..c947d35c5 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -63,10 +63,19 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], **kwar tavily = tavily_config.get() soup_crawler = soup_crawler_config.get() + preferred_tool = "beautifulsoup" if soup_crawler else "tavily" + if preferred_tool == "tavily" and tavily is None: + raise IngestionError( + message="TavilyConfig must be set on the ingestion context when fetching HTTP URLs without a SoupCrawlerConfig." + ) + if preferred_tool == "beautifulsoup" and soup_crawler is None: + raise IngestionError( + message="SoupCrawlerConfig must be set on the ingestion context when using the BeautifulSoup scraper." + ) data = await fetch_page_content( data_item, - preferred_tool="beautifulsoup" if soup_crawler else "tavily", + preferred_tool=preferred_tool, tavily_config=tavily, soup_crawler_config=soup_crawler, ) @@ -74,6 +83,8 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], **kwar for key, value in data.items(): content += f"{key}:\n{value}\n\n" return await save_data_to_file(content) + except IngestionError: + raise except Exception as e: raise IngestionError( message=f"Error ingesting webpage results of url {data_item}: {str(e)}" diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index 4819800ab..e81db9b05 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -1,22 +1,21 @@ from pydantic import BaseModel, Field -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Literal import os class TavilyConfig(BaseModel): api_key: str = os.getenv("TAVILY_API_KEY") extract_depth: str = "basic" - format: str = "markdown" timeout: int = Field(None, ge=1, le=60) class SoupCrawlerConfig(BaseModel): - concurrency: int = (5,) - crawl_delay: float = (0.5,) - timeout: float = (15.0,) - max_retries: int = (2,) - retry_delay_factor: float = (0.5,) - headers: Optional[Dict[str, str]] = (None,) + concurrency: int = 5 + crawl_delay: float = 0.5 + timeout: float = 15.0 + max_retries: int = 2 + retry_delay_factor: float = 0.5 + headers: Optional[Dict[str, str]] = None extraction_rules: Dict[str, Any] use_playwright: bool = False playwright_js_wait: float = 0.8 diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py index 18bb2e3ef..297aebb4f 100644 --- a/cognee/tasks/web_scraper/models.py +++ b/cognee/tasks/web_scraper/models.py @@ -6,7 +6,6 @@ from datetime import datetime class WebPage(DataPoint): """Represents a scraped web page with metadata""" - url: str name: Optional[str] content: str content_hash: str @@ -17,7 +16,7 @@ class WebPage(DataPoint): page_size: int extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used description: str - metadata: dict = {"index_fields": ["url", "title", "scraped_at", "description"]} + metadata: dict = {"index_fields": ["name", "description"]} class WebSite(DataPoint): @@ -31,7 +30,7 @@ class WebSite(DataPoint): page_count: int scraping_config: Dict[str, Any] description: str - metadata: dict = {"index_fields": ["domain", "base_url", "description"]} + metadata: dict = {"index_fields": ["name", "description"]} class ScrapingJob(DataPoint): @@ -44,4 +43,4 @@ class ScrapingJob(DataPoint): last_run: Optional[datetime] next_run: Optional[datetime] description: str - metadata: dict = {"index_fields": ["job_name", "status", "description"]} + metadata: dict = {"index_fields": ["name", "description"]} diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index a14dbbd20..53346fb1c 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -46,9 +46,9 @@ async def fetch_page_content( installed. """ if preferred_tool == "tavily": - if tavily_config.api_key is None: + if not tavily_config or tavily_config.api_key is None: raise ValueError("TAVILY_API_KEY must be set in TavilyConfig to use Tavily") - return await fetch_with_tavily(urls) + return await fetch_with_tavily(urls, tavily_config) elif preferred_tool == "beautifulsoup": try: @@ -59,9 +59,9 @@ async def fetch_page_content( ) raise crawler = BeautifulSoupCrawler() - extraction_rules = soup_crawler_config.extraction_rules - if extraction_rules is None: + if not soup_crawler_config and soup_crawler_config.extraction_rules is None: raise ValueError("extraction_rules must be provided when not using Tavily") + extraction_rules = soup_crawler_config.extraction_rules try: results = await crawler.fetch_with_bs4( urls, @@ -76,7 +76,9 @@ async def fetch_page_content( raise -async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: +async def fetch_with_tavily( + urls: Union[str, List[str]], tavily_config: Optional[TavilyConfig] = None +) -> Dict[str, str]: """Fetch content from URLs using the Tavily API. Args: @@ -96,8 +98,8 @@ async def fetch_with_tavily(urls: Union[str, List[str]]) -> Dict[str, str]: "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" ) raise - client = AsyncTavilyClient() - results = await client.extract(urls) + client = AsyncTavilyClient(api_key=tavily_config.api_key if tavily_config else None) + results = await client.extract(urls, format="text") for failed_result in results.get("failed_results", []): logger.warning(f"Failed to fetch {failed_result}") return_results = {} diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index dc286500a..383580779 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -87,12 +87,12 @@ async def cron_web_scraper_task( ) ), trigger=trigger, - id=uuid5(NAMESPACE_OID, name=job_name), - name=f"WebScraper_{uuid5(NAMESPACE_OID, name=job_name)}", + id=job_name, + name=job_name or f"WebScraper_{uuid5(NAMESPACE_OID, name=job_name)}", replace_existing=True, ) - - scheduler.start() + if not scheduler.running: + scheduler.start() return # If no schedule, run immediately @@ -191,7 +191,6 @@ async def web_scraper_task( tavily_config=tavily_config, soup_crawler_config=soup_crawler_config, ) - for page_url, content in results.items(): parsed_url = urlparse(page_url) domain = parsed_url.netloc @@ -250,16 +249,15 @@ async def web_scraper_task( f"Webpage: {parsed_url.path.lstrip('/') or 'Home'}\n" f"URL: {page_url}\n" f"Scraped at: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" - f"Content hash: {content_hash[:16]}...\n" - f"Content type: text/html\n" + f"Content: {content_str}" + f"Content type: text\n" f"Page size: {len(content_str)} bytes\n" f"Status code: 200" ) - + page_extraction_rules = extraction_rules webpage = WebPage( - id=uuid5(NAMESPACE_OID, name=parsed_url.path.lstrip("/")), - url=page_url, - name=get_path_after_base(base_url, page_url), + id=uuid5(NAMESPACE_OID, name=page_url), + name=page_url, content=content_str, content_hash=content_hash, scraped_at=now, @@ -267,7 +265,7 @@ async def web_scraper_task( status_code=200, content_type="text/html", page_size=len(content_str), - extraction_rules=extraction_rules or {}, + extraction_rules=page_extraction_rules or {}, description=webpage_description, ) webpages.append(webpage) @@ -301,10 +299,10 @@ async def web_scraper_task( }, ) ) - + print(len(webpages)) for webpage in webpages: node_mapping[webpage.id] = webpage - parsed_url = urlparse(webpage.url) + parsed_url = urlparse(webpage.name) base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" edge_mapping.append( ( diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index b817aea34..3e1fa2f78 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -2,11 +2,17 @@ import asyncio import cognee from cognee.tasks.web_scraper.config import SoupCrawlerConfig from cognee.tasks.web_scraper import cron_web_scraper_task +from uuid import uuid5, NAMESPACE_OID +from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_engine +from dotenv import load_dotenv +from cognee.api.v1.visualize import visualize_graph + +load_dotenv() async def test_web_scraping_using_bs4(): await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) + await cognee.prune.prune_system() url = "https://quotes.toscrape.com/" rules = { @@ -119,6 +125,9 @@ async def test_web_scraping_using_tavily_and_incremental_loading(): # ---------- cron job tests ---------- async def test_cron_web_scraper(): + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + graph_db = await get_graph_engine() urls = ["https://quotes.toscrape.com/", "https://books.toscrape.com/"] extraction_rules = { "quotes": {"selector": ".quote span.text", "all": True}, @@ -127,45 +136,51 @@ async def test_cron_web_scraper(): "prices": "article.product_pod p.price_color", } - # Run cron_web_scraper_task (schedule string is required) + # Run cron_web_scraper_task await cron_web_scraper_task( - urls=urls, - schedule="*/5 * * * *", # every 5 minutes + url=urls, + schedule="*/3 * * * *", # every 3 minutes + job_name="cron_scraping_job", extraction_rules=extraction_rules, - use_playwright=False, ) - - # Wait until first run of cron job is done - await asyncio.sleep(120) - # Validate that the scraped data is searchable - results = await cognee.search( - "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", - query_type=cognee.SearchType.GRAPH_COMPLETION, - ) - assert "Albert Einstein" in results[0] + scraping_job_done = await graph_db.get_node(uuid5(NAMESPACE_OID, name="cron_scraping_job")) + while True: + if scraping_job_done: + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) - results_books = await cognee.search( - "What is the price of 'A Light in the Attic' book?", - query_type=cognee.SearchType.GRAPH_COMPLETION, - ) - assert "51.77" in results_books[0] + assert "Albert Einstein" in results[0] - print("Cron job web_scraping test passed!") + results_books = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + + assert "51.77" in results_books[0] + + print("Cron job web_scraping test passed!") + break + else: + scraping_job_done = await graph_db.get_node( + uuid5(NAMESPACE_OID, name="cron_scraping_job") + ) async def main(): - print("Starting BS4 incremental loading test...") - await test_web_scraping_using_bs4_and_incremental_loading() + # print("Starting BS4 incremental loading test...") + # await test_web_scraping_using_bs4_and_incremental_loading() - print("Starting BS4 normal test...") - await test_web_scraping_using_bs4() + # print("Starting BS4 normal test...") + # await test_web_scraping_using_bs4() - print("Starting Tavily incremental loading test...") - await test_web_scraping_using_tavily_and_incremental_loading() + # print("Starting Tavily incremental loading test...") + # await test_web_scraping_using_tavily_and_incremental_loading() - print("Starting Tavily normal test...") - await test_web_scraping_using_tavily() + # print("Starting Tavily normal test...") + # await test_web_scraping_using_tavily() print("Starting cron job test...") await test_cron_web_scraper() diff --git a/uv.lock b/uv.lock index a06bebdd9..dc005da37 100644 --- a/uv.lock +++ b/uv.lock @@ -244,6 +244,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, ] +[[package]] +name = "apscheduler" +version = "3.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "tzlocal" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/4e/00/6d6814ddc19be2df62c8c898c4df6b5b1914f3bd024b780028caa392d186/apscheduler-3.11.0.tar.gz", hash = "sha256:4c622d250b0955a65d5d0eb91c33e6d43fd879834bf541e0a18661ae60460133", size = 107347, upload-time = "2024-11-24T19:39:26.463Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d0/ae/9a053dd9229c0fde6b1f1f33f609ccff1ee79ddda364c756a924c6d8563b/APScheduler-3.11.0-py3-none-any.whl", hash = "sha256:fc134ca32e50f5eadcc4938e3a4545ab19131435e851abb40b34d63d5141c6da", size = 64004, upload-time = "2024-11-24T19:39:24.442Z" }, +] + [[package]] name = "argon2-cffi" version = "23.1.0" @@ -967,6 +979,7 @@ posthog = [ { name = "posthog" }, ] scraping = [ + { name = "apscheduler" }, { name = "beautifulsoup4" }, { name = "lxml" }, { name = "playwright" }, @@ -981,6 +994,7 @@ requires-dist = [ { name = "aiosqlite", specifier = ">=0.20.0,<1.0.0" }, { name = "alembic", specifier = ">=1.13.3,<2" }, { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.27" }, + { name = "apscheduler", marker = "extra == 'scraping'", specifier = ">=3.10.0,<=3.11.0" }, { name = "asyncpg", marker = "extra == 'postgres'", specifier = ">=0.30.0,<1.0.0" }, { name = "asyncpg", marker = "extra == 'postgres-binary'", specifier = ">=0.30.0,<1.0.0" }, { name = "baml-py", specifier = ">=0.201.0,<0.202.0" }, @@ -7693,6 +7707,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" }, ] +[[package]] +name = "tzlocal" +version = "5.3.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "tzdata", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/8b/2e/c14812d3d4d9cd1773c6be938f89e5735a1f11a9f184ac3639b93cef35d5/tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd", size = 30761, upload-time = "2025-03-05T21:17:41.549Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c2/14/e2a54fabd4f08cd7af1c07030603c3356b74da07f7cc056e600436edfa17/tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d", size = 18026, upload-time = "2025-03-05T21:17:39.857Z" }, +] + [[package]] name = "unstructured" version = "0.18.15" From 791e38b2c0b87095200e9cd4f6744d52fd37e46c Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:00:20 +0530 Subject: [PATCH 054/139] Solved more nitpick comments --- cognee/api/v1/add/add.py | 28 ++++++----- cognee/tasks/web_scraper/config.py | 4 +- cognee/tasks/web_scraper/utils.py | 11 ++++- .../tasks/web_scraping/web_scraping_test.py | 48 ++++++++----------- 4 files changed, 45 insertions(+), 46 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 65ebb8748..3942fcf5f 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -171,19 +171,7 @@ async def add( - TAVILY_API_KEY: YOUR_TAVILY_API_KEY """ - tasks = [ - Task(resolve_data_directories, include_subdirectories=True), - Task( - ingest_data, - dataset_name, - user, - node_set, - dataset_id, - preferred_loaders, - ), - ] - - await setup() + if not soup_crawler_config and extraction_rules: soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) if not tavily_config and os.getenv("TAVILY_API_KEY"): @@ -202,6 +190,20 @@ async def add( elif isinstance(data, list) and any(_is_http_url(item) for item in data): node_set = ["web_content"] if not node_set else node_set + ["web_content"] + tasks = [ + Task(resolve_data_directories, include_subdirectories=True), + Task( + ingest_data, + dataset_name, + user, + node_set, + dataset_id, + preferred_loaders, + ), + ] + + await setup() + user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user) await reset_dataset_pipeline_run_status( diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index e81db9b05..4b54c6470 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -1,12 +1,12 @@ from pydantic import BaseModel, Field -from typing import Any, Dict, Optional, Literal +from typing import Any, Dict, Optional import os class TavilyConfig(BaseModel): api_key: str = os.getenv("TAVILY_API_KEY") extract_depth: str = "basic" - timeout: int = Field(None, ge=1, le=60) + timeout: Optional[int] = Field(default=None, ge=1, le=60) class SoupCrawlerConfig(BaseModel): diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 53346fb1c..62a614e6e 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -58,8 +58,15 @@ async def fetch_page_content( "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" ) raise - crawler = BeautifulSoupCrawler() - if not soup_crawler_config and soup_crawler_config.extraction_rules is None: + crawler = BeautifulSoupCrawler( + concurrency=soup_crawler_config.concurrency, + crawl_delay=soup_crawler_config.crawl_delay, + timeout=soup_crawler_config.timeout, + max_retries=soup_crawler_config.max_retries, + retry_delay_factor=soup_crawler_config.retry_delay_factor, + headers=soup_crawler_config.headers, + ) + if not soup_crawler_config or soup_crawler_config.extraction_rules is None: raise ValueError("extraction_rules must be provided when not using Tavily") extraction_rules = soup_crawler_config.extraction_rules try: diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 3e1fa2f78..c598ef536 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -139,48 +139,38 @@ async def test_cron_web_scraper(): # Run cron_web_scraper_task await cron_web_scraper_task( url=urls, - schedule="*/3 * * * *", # every 3 minutes job_name="cron_scraping_job", extraction_rules=extraction_rules, ) + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) - scraping_job_done = await graph_db.get_node(uuid5(NAMESPACE_OID, name="cron_scraping_job")) - while True: - if scraping_job_done: - results = await cognee.search( - "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", - query_type=cognee.SearchType.GRAPH_COMPLETION, - ) + assert "Albert Einstein" in results[0] - assert "Albert Einstein" in results[0] + results_books = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) - results_books = await cognee.search( - "What is the price of 'A Light in the Attic' book?", - query_type=cognee.SearchType.GRAPH_COMPLETION, - ) + assert "51.77" in results_books[0] - assert "51.77" in results_books[0] - - print("Cron job web_scraping test passed!") - break - else: - scraping_job_done = await graph_db.get_node( - uuid5(NAMESPACE_OID, name="cron_scraping_job") - ) + print("Cron job web_scraping test passed!") async def main(): - # print("Starting BS4 incremental loading test...") - # await test_web_scraping_using_bs4_and_incremental_loading() + print("Starting BS4 incremental loading test...") + await test_web_scraping_using_bs4_and_incremental_loading() - # print("Starting BS4 normal test...") - # await test_web_scraping_using_bs4() + print("Starting BS4 normal test...") + await test_web_scraping_using_bs4() - # print("Starting Tavily incremental loading test...") - # await test_web_scraping_using_tavily_and_incremental_loading() + print("Starting Tavily incremental loading test...") + await test_web_scraping_using_tavily_and_incremental_loading() - # print("Starting Tavily normal test...") - # await test_web_scraping_using_tavily() + print("Starting Tavily normal test...") + await test_web_scraping_using_tavily() print("Starting cron job test...") await test_cron_web_scraper() From 3c9e5f830b5e93e37fdd9d98ce48dd7c85a4eacf Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:16:31 +0530 Subject: [PATCH 055/139] Solved more nitpick comments --- cognee/tasks/web_scraper/utils.py | 8 ++++---- cognee/tests/tasks/web_scraping/web_scraping_test.py | 1 - 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 62a614e6e..d5e1301bc 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -57,7 +57,10 @@ async def fetch_page_content( logger.error( "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" ) - raise + raise ImportError + if not soup_crawler_config or soup_crawler_config.extraction_rules is None: + raise ValueError("extraction_rules must be provided when not using Tavily") + extraction_rules = soup_crawler_config.extraction_rules crawler = BeautifulSoupCrawler( concurrency=soup_crawler_config.concurrency, crawl_delay=soup_crawler_config.crawl_delay, @@ -66,9 +69,6 @@ async def fetch_page_content( retry_delay_factor=soup_crawler_config.retry_delay_factor, headers=soup_crawler_config.headers, ) - if not soup_crawler_config or soup_crawler_config.extraction_rules is None: - raise ValueError("extraction_rules must be provided when not using Tavily") - extraction_rules = soup_crawler_config.extraction_rules try: results = await crawler.fetch_with_bs4( urls, diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index c598ef536..1c2ba09a8 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -28,7 +28,6 @@ async def test_web_scraping_using_bs4(): retry_delay_factor=0.5, extraction_rules=rules, use_playwright=False, - structured=True, ) await cognee.add( From 0a9b624010f72a083ee0db7b139694a1e66030d1 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:27:54 +0530 Subject: [PATCH 056/139] changed return type for fetch_page_content to Dict[str,str] --- cognee/tasks/web_scraper/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index d5e1301bc..28a19d69f 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -18,7 +18,7 @@ async def fetch_page_content( preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", tavily_config: Optional[TavilyConfig] = None, soup_crawler_config: Optional[SoupCrawlerConfig] = None, -) -> Dict[str, Union[str, Dict[str, str]]]: +) -> Dict[str, str]: """Fetch content from one or more URLs using the specified tool. This function retrieves web page content using either BeautifulSoup (with custom From 7fe1de770d0a44287a5e1adbdddd0d627b8a707f Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:29:58 +0530 Subject: [PATCH 057/139] Remove assignment to unused variable graph_db' --- cognee/tests/tasks/web_scraping/web_scraping_test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 1c2ba09a8..6ac89bbe6 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -126,7 +126,6 @@ async def test_web_scraping_using_tavily_and_incremental_loading(): async def test_cron_web_scraper(): await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) - graph_db = await get_graph_engine() urls = ["https://quotes.toscrape.com/", "https://books.toscrape.com/"] extraction_rules = { "quotes": {"selector": ".quote span.text", "all": True}, From d4ce340cb5e68061e63ff1ffffb07d371df2ff56 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:31:08 +0530 Subject: [PATCH 058/139] Removed unused imports --- cognee/tests/tasks/web_scraping/web_scraping_test.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 6ac89bbe6..c792c28cd 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -2,10 +2,7 @@ import asyncio import cognee from cognee.tasks.web_scraper.config import SoupCrawlerConfig from cognee.tasks.web_scraper import cron_web_scraper_task -from uuid import uuid5, NAMESPACE_OID -from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_engine from dotenv import load_dotenv -from cognee.api.v1.visualize import visualize_graph load_dotenv() From 1c0e0f0fe112b872203951f6d870d0dd2b735150 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 18:32:10 +0530 Subject: [PATCH 059/139] Solved more nitpick comments --- cognee/api/v1/add/add.py | 2 +- cognee/tasks/web_scraper/utils.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 3942fcf5f..c8720f33f 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -171,7 +171,7 @@ async def add( - TAVILY_API_KEY: YOUR_TAVILY_API_KEY """ - + if not soup_crawler_config and extraction_rules: soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) if not tavily_config and os.getenv("TAVILY_API_KEY"): diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 28a19d69f..12d2338ef 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -50,9 +50,9 @@ async def fetch_page_content( raise ValueError("TAVILY_API_KEY must be set in TavilyConfig to use Tavily") return await fetch_with_tavily(urls, tavily_config) - elif preferred_tool == "beautifulsoup": + if preferred_tool == "beautifulsoup": try: - from bs4 import BeautifulSoup + from bs4 import BeautifulSoup as _ # noqa: F401 except ImportError: logger.error( "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" From 263a8f4376f94dcb52081b9756289435be067f38 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Mon, 6 Oct 2025 14:18:39 +0100 Subject: [PATCH 060/139] fix: frontend process output streaming stuck due to incorrect output --- cognee/api/v1/ui/ui.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index f22a1f00b..438195990 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -627,7 +627,6 @@ def start_ui( env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - text=True, shell=True, ) else: @@ -637,7 +636,6 @@ def start_ui( env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - text=True, preexec_fn=os.setsid if hasattr(os, "setsid") else None, ) From 54f2580f2d5f0b37eadf1397eae148ee0daa8e70 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 19:02:11 +0530 Subject: [PATCH 061/139] Solved more nitpick comments --- cognee/api/v1/add/add.py | 2 +- cognee/tasks/web_scraper/utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index c8720f33f..93ddc0b49 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -150,7 +150,7 @@ async def add( await cognee.add("https://example.com",extraction_rules=extraction_rules) # Add a single url and tavily extract ingestion method - Make sure to TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable + Make sure to set TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable await cognee.add("https://example.com") # Add multiple urls diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 12d2338ef..f22a18aa5 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -36,7 +36,7 @@ async def fetch_page_content( extraction_rules are needed. Returns: - Dict[str, Union[str, Dict[str, str]]]: A dictionary mapping each URL to its + Dict[str, str]: A dictionary mapping each URL to its extracted content (as a string for BeautifulSoup or a dict for Tavily). Raises: From 1f36dd3d7194e0f4aca93ee54977aaf4e18c3ef8 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Mon, 6 Oct 2025 19:44:54 +0530 Subject: [PATCH 062/139] Solved nitpick comments --- cognee/tasks/web_scraper/config.py | 7 ++++--- cognee/tasks/web_scraper/utils.py | 12 ++++++++++-- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index 4b54c6470..e28f412e5 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -1,12 +1,13 @@ from pydantic import BaseModel, Field -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Literal import os class TavilyConfig(BaseModel): api_key: str = os.getenv("TAVILY_API_KEY") - extract_depth: str = "basic" - timeout: Optional[int] = Field(default=None, ge=1, le=60) + extract_depth: Literal["basic","advanced"] = "basic" + proxies: Optional[Dict[str,str]] = None + timeout: Optional[int] = Field(default=10, ge=1, le=60) class SoupCrawlerConfig(BaseModel): diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index f22a18aa5..1a300a37b 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -105,8 +105,16 @@ async def fetch_with_tavily( "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" ) raise - client = AsyncTavilyClient(api_key=tavily_config.api_key if tavily_config else None) - results = await client.extract(urls, format="text") + client = AsyncTavilyClient( + api_key=tavily_config.api_key if tavily_config else None, + proxies=tavily_config.proxies if tavily_config else None + ) + results = await client.extract( + urls, + format="text", + extract_depth=tavily_config.extract_depth if tavily_config else "basic", + timeout=tavily_config.timeout if tavily_config else 10 + ) for failed_result in results.get("failed_results", []): logger.warning(f"Failed to fetch {failed_result}") return_results = {} From 5dcd7e512fa354a1397b4b0b0a961283fd1598f8 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 01:09:41 +0530 Subject: [PATCH 063/139] Changes uv.lock --- uv.lock | 1167 ++++++++++++++++++++++++++++++++----------------------- 1 file changed, 690 insertions(+), 477 deletions(-) diff --git a/uv.lock b/uv.lock index dc005da37..95a485eef 100644 --- a/uv.lock +++ b/uv.lock @@ -2,17 +2,33 @@ version = 1 revision = 3 requires-python = ">=3.10, <=3.13" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version >= '3.13' and 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"python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", "python_full_version >= '3.13' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", + "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and 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"python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and 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platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", "python_full_version < '3.11' and sys_platform == 'emscripten'", ] dependencies = [ @@ -1198,14 +1220,26 @@ name = "contourpy" version = "1.3.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and 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(python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", + "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", "python_full_version >= '3.13' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", + "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')", "python_full_version == '3.12.*' and 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a/cognee/tasks/ingestion/ingest_data.py b/cognee/tasks/ingestion/ingest_data.py index b86bada19..3c20a2b13 100644 --- a/cognee/tasks/ingestion/ingest_data.py +++ b/cognee/tasks/ingestion/ingest_data.py @@ -1,7 +1,7 @@ import json import inspect from uuid import UUID -from typing import Union, BinaryIO, Any, List, Optional, Dict, Literal +from typing import Union, BinaryIO, Any, List, Optional import cognee.modules.ingestion as ingestion from cognee.infrastructure.databases.relational import get_relational_engine @@ -16,7 +16,6 @@ from cognee.modules.data.methods import ( get_dataset_data, load_or_create_datasets, ) -from cognee.tasks.web_scraper.config import SoupCrawlerConfig, TavilyConfig from .save_data_item_to_storage import save_data_item_to_storage from .data_item_to_text_file import data_item_to_text_file diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index c947d35c5..105f17c0d 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -28,7 +28,7 @@ class HTMLContent(str): settings = SaveDataSettings() -async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], **kwargs) -> str: +async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str: if "llama_index" in str(type(data_item)): # Dynamic import is used because the llama_index module is optional. from .transform_data import get_data_from_llama_index diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index 7ccf06a3d..f1efca3d6 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -137,7 +137,6 @@ class BeautifulSoupCrawler: """Exit the context manager, closing the HTTP client.""" await self.close() - @staticmethod @lru_cache(maxsize=1024) def _domain_from_url(url: str) -> str: """Extract the domain (netloc) from a URL. @@ -153,7 +152,6 @@ class BeautifulSoupCrawler: except Exception: return url - @staticmethod @lru_cache(maxsize=1024) def _get_domain_root(url: str) -> str: """Get the root URL (scheme and netloc) from a URL. @@ -378,7 +376,6 @@ class BeautifulSoupCrawler: ) await asyncio.sleep(backoff) - @staticmethod def _normalize_rule(rule: Union[str, Dict[str, Any]]) -> ExtractionRule: """Normalize an extraction rule to an ExtractionRule dataclass. diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index e28f412e5..2f7f6b3f6 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -20,4 +20,5 @@ class SoupCrawlerConfig(BaseModel): extraction_rules: Dict[str, Any] use_playwright: bool = False playwright_js_wait: float = 0.8 + robots_cache_ttl: float = 3600.0 join_all_matches: bool = False diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 1a300a37b..5af95a2fe 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -68,6 +68,7 @@ async def fetch_page_content( max_retries=soup_crawler_config.max_retries, retry_delay_factor=soup_crawler_config.retry_delay_factor, headers=soup_crawler_config.headers, + robots_cache_ttl=soup_crawler_config.robots_cache_ttl ) try: results = await crawler.fetch_with_bs4( From 3d53e8d6f1c45703931cf28203bdd19fb13a20e0 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 20:59:19 +0530 Subject: [PATCH 069/139] Removed print statement that I used for debugging --- cognee/tasks/web_scraper/web_scraper_task.py | 1 - 1 file changed, 1 deletion(-) diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index 383580779..969aef0d7 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -299,7 +299,6 @@ async def web_scraper_task( }, ) ) - print(len(webpages)) for webpage in webpages: node_mapping[webpage.id] = webpage parsed_url = urlparse(webpage.name) From 38cdacbcb68a6df3e91f2b5fdce3c48613404081 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 18:04:18 +0200 Subject: [PATCH 070/139] fix: Resolve issue with Gemini adapter (#1494) ## Description Resolve Gemini Adapter issues: 1. resolve embedding batch issue, 2. Resolve slowness because gemini tokenizer was sending word per word to Googles API to count tokens (using OpenAI's local tokenizer to count tokens for Gemini now) 3. Update deprecated library and move to instructor ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- .env.template | 9 +- .github/actions/cognee_setup/action.yml | 2 +- .github/workflows/test_llms.yml | 4 +- Dockerfile | 4 +- cognee-mcp/pyproject.toml | 1 + .../vector/embeddings/EmbeddingEngine.py | 9 + .../embeddings/FastembedEmbeddingEngine.py | 11 ++ .../embeddings/LiteLLMEmbeddingEngine.py | 21 ++- .../embeddings/OllamaEmbeddingEngine.py | 11 ++ .../databases/vector/embeddings/config.py | 8 + .../vector/embeddings/get_embedding_engine.py | 5 + .../litellm_instructor/llm/gemini/adapter.py | 178 +++++++++++------- .../llm/generic_llm_api/adapter.py | 7 +- .../litellm_instructor/llm/get_llm_client.py | 1 - .../litellm_instructor/llm/openai/adapter.py | 4 +- .../llm/tokenizer/Gemini/adapter.py | 15 +- cognee/tasks/storage/index_data_points.py | 2 +- cognee/tasks/storage/index_graph_edges.py | 4 +- .../databases/test_index_graph_edges.py | 3 +- poetry.lock | 149 ++------------- pyproject.toml | 1 - uv.lock | 103 +--------- 22 files changed, 222 insertions(+), 330 deletions(-) diff --git a/.env.template b/.env.template index b91f89540..b1e7057e2 100644 --- a/.env.template +++ b/.env.template @@ -30,6 +30,9 @@ EMBEDDING_DIMENSIONS=3072 EMBEDDING_MAX_TOKENS=8191 # If embedding key is not provided same key set for LLM_API_KEY will be used #EMBEDDING_API_KEY="your_api_key" +# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch, +# Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models +#EMBEDDING_BATCH_SIZE=2048 # If using BAML structured output these env variables will be used BAML_LLM_PROVIDER=openai @@ -52,18 +55,18 @@ BAML_LLM_API_VERSION="" ################################################################################ # Configure storage backend (local filesystem or S3) # STORAGE_BACKEND="local" # Default: uses local filesystem -# +# # -- To switch to S3 storage, uncomment and fill these: --------------------- # STORAGE_BACKEND="s3" # STORAGE_BUCKET_NAME="your-bucket-name" # AWS_REGION="us-east-1" # AWS_ACCESS_KEY_ID="your-access-key" # AWS_SECRET_ACCESS_KEY="your-secret-key" -# +# # -- S3 Root Directories (optional) ----------------------------------------- # DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data" # SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system" -# +# # -- Cache Directory (auto-configured for S3) ------------------------------- # When STORAGE_BACKEND=s3, cache automatically uses S3: s3://BUCKET/cognee/cache # To override the automatic S3 cache location, uncomment: diff --git a/.github/actions/cognee_setup/action.yml b/.github/actions/cognee_setup/action.yml index e46a42edb..1326f2d81 100644 --- a/.github/actions/cognee_setup/action.yml +++ b/.github/actions/cognee_setup/action.yml @@ -41,4 +41,4 @@ runs: EXTRA_ARGS="$EXTRA_ARGS --extra $extra" done fi - uv sync --extra api --extra docs --extra evals --extra gemini --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS + uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS diff --git a/.github/workflows/test_llms.yml b/.github/workflows/test_llms.yml index 5a0f947c9..6b0221309 100644 --- a/.github/workflows/test_llms.yml +++ b/.github/workflows/test_llms.yml @@ -27,7 +27,7 @@ jobs: env: LLM_PROVIDER: "gemini" LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }} - LLM_MODEL: "gemini/gemini-1.5-flash" + LLM_MODEL: "gemini/gemini-2.0-flash" EMBEDDING_PROVIDER: "gemini" EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }} EMBEDDING_MODEL: "gemini/text-embedding-004" @@ -83,4 +83,4 @@ jobs: EMBEDDING_MODEL: "openai/text-embedding-3-large" EMBEDDING_DIMENSIONS: "3072" EMBEDDING_MAX_TOKENS: "8191" - run: uv run python ./examples/python/simple_example.py \ No newline at end of file + run: uv run python ./examples/python/simple_example.py diff --git a/Dockerfile b/Dockerfile index be29f359a..b1b77513d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -31,7 +31,7 @@ COPY README.md pyproject.toml uv.lock entrypoint.sh ./ # Install the project's dependencies using the lockfile and settings RUN --mount=type=cache,target=/root/.cache/uv \ - uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable + uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable # Copy Alembic configuration COPY alembic.ini /app/alembic.ini @@ -42,7 +42,7 @@ COPY alembic/ /app/alembic COPY ./cognee /app/cognee COPY ./distributed /app/distributed RUN --mount=type=cache,target=/root/.cache/uv \ -uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable +uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable FROM python:3.12-slim-bookworm diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index e1a3a092c..e4fcab909 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -8,6 +8,7 @@ requires-python = ">=3.10" dependencies = [ # For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes. #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee", + # TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4 "cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.3.4", "fastmcp>=2.10.0,<3.0.0", "mcp>=1.12.0,<2.0.0", diff --git a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py index afa180c67..c410d99dc 100644 --- a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py @@ -34,3 +34,12 @@ class EmbeddingEngine(Protocol): - int: An integer representing the number of dimensions in the embedding vector. """ raise NotImplementedError() + + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + raise NotImplementedError() diff --git a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py index acb041e76..8f922d934 100644 --- a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py @@ -42,11 +42,13 @@ class FastembedEmbeddingEngine(EmbeddingEngine): model: Optional[str] = "openai/text-embedding-3-large", dimensions: Optional[int] = 3072, max_completion_tokens: int = 512, + batch_size: int = 100, ): self.model = model self.dimensions = dimensions self.max_completion_tokens = max_completion_tokens self.tokenizer = self.get_tokenizer() + self.batch_size = batch_size # self.retry_count = 0 self.embedding_model = TextEmbedding(model_name=model) @@ -101,6 +103,15 @@ class FastembedEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Instantiate and return the tokenizer used for preparing text for embedding. diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py index 0cb8286fc..d68941d25 100644 --- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py @@ -58,6 +58,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): endpoint: str = None, api_version: str = None, max_completion_tokens: int = 512, + batch_size: int = 100, ): self.api_key = api_key self.endpoint = endpoint @@ -68,6 +69,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): self.max_completion_tokens = max_completion_tokens self.tokenizer = self.get_tokenizer() self.retry_count = 0 + self.batch_size = batch_size enable_mocking = os.getenv("MOCK_EMBEDDING", "false") if isinstance(enable_mocking, bool): @@ -165,6 +167,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Load and return the appropriate tokenizer for the specified model based on the provider. @@ -183,9 +194,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): model=model, max_completion_tokens=self.max_completion_tokens ) elif "gemini" in self.provider.lower(): - tokenizer = GeminiTokenizer( - model=model, max_completion_tokens=self.max_completion_tokens + # Since Gemini tokenization needs to send an API request to get the token count we will use TikToken to + # count tokens as we calculate tokens word by word + tokenizer = TikTokenTokenizer( + model=None, max_completion_tokens=self.max_completion_tokens ) + # Note: Gemini Tokenizer expects an LLM model as input and not the embedding model + # tokenizer = GeminiTokenizer( + # llm_model=llm_model, max_completion_tokens=self.max_completion_tokens + # ) elif "mistral" in self.provider.lower(): tokenizer = MistralTokenizer( model=model, max_completion_tokens=self.max_completion_tokens diff --git a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py index e6e590597..e79ba3f6a 100644 --- a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py @@ -54,12 +54,14 @@ class OllamaEmbeddingEngine(EmbeddingEngine): max_completion_tokens: int = 512, endpoint: Optional[str] = "http://localhost:11434/api/embeddings", huggingface_tokenizer: str = "Salesforce/SFR-Embedding-Mistral", + batch_size: int = 100, ): self.model = model self.dimensions = dimensions self.max_completion_tokens = max_completion_tokens self.endpoint = endpoint self.huggingface_tokenizer_name = huggingface_tokenizer + self.batch_size = batch_size self.tokenizer = self.get_tokenizer() enable_mocking = os.getenv("MOCK_EMBEDDING", "false") @@ -122,6 +124,15 @@ class OllamaEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Load and return a HuggingFace tokenizer for the embedding engine. diff --git a/cognee/infrastructure/databases/vector/embeddings/config.py b/cognee/infrastructure/databases/vector/embeddings/config.py index 04a1f18f2..24f724151 100644 --- a/cognee/infrastructure/databases/vector/embeddings/config.py +++ b/cognee/infrastructure/databases/vector/embeddings/config.py @@ -19,9 +19,17 @@ class EmbeddingConfig(BaseSettings): embedding_api_key: Optional[str] = None embedding_api_version: Optional[str] = None embedding_max_completion_tokens: Optional[int] = 8191 + embedding_batch_size: Optional[int] = None huggingface_tokenizer: Optional[str] = None model_config = SettingsConfigDict(env_file=".env", extra="allow") + def model_post_init(self, __context) -> None: + # If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models + if not self.embedding_batch_size and self.embedding_provider.lower() == "openai": + self.embedding_batch_size = 2048 + elif not self.embedding_batch_size: + self.embedding_batch_size = 100 + def to_dict(self) -> dict: """ Serialize all embedding configuration settings to a dictionary. diff --git a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py index e7fcf4e94..de56c8971 100644 --- a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py +++ b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py @@ -31,6 +31,7 @@ def get_embedding_engine() -> EmbeddingEngine: config.embedding_endpoint, config.embedding_api_key, config.embedding_api_version, + config.embedding_batch_size, config.huggingface_tokenizer, llm_config.llm_api_key, llm_config.llm_provider, @@ -46,6 +47,7 @@ def create_embedding_engine( embedding_endpoint, embedding_api_key, embedding_api_version, + embedding_batch_size, huggingface_tokenizer, llm_api_key, llm_provider, @@ -84,6 +86,7 @@ def create_embedding_engine( model=embedding_model, dimensions=embedding_dimensions, max_completion_tokens=embedding_max_completion_tokens, + batch_size=embedding_batch_size, ) if embedding_provider == "ollama": @@ -95,6 +98,7 @@ def create_embedding_engine( max_completion_tokens=embedding_max_completion_tokens, endpoint=embedding_endpoint, huggingface_tokenizer=huggingface_tokenizer, + batch_size=embedding_batch_size, ) from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine @@ -108,4 +112,5 @@ def create_embedding_engine( model=embedding_model, dimensions=embedding_dimensions, max_completion_tokens=embedding_max_completion_tokens, + batch_size=embedding_batch_size, ) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py index 2c7b73eef..510d29ce8 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py @@ -1,115 +1,155 @@ -import litellm -from pydantic import BaseModel -from typing import Type, Optional -from litellm import acompletion, JSONSchemaValidationError +"""Adapter for Generic API LLM provider API""" -from cognee.shared.logging_utils import get_logger -from cognee.modules.observability.get_observe import get_observe -from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError +import litellm +import instructor +from typing import Type +from pydantic import BaseModel +from openai import ContentFilterFinishReasonError +from litellm.exceptions import ContentPolicyViolationError +from instructor.core import InstructorRetryException + +from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( rate_limit_async, sleep_and_retry_async, ) -logger = get_logger() -observe = get_observe() - class GeminiAdapter(LLMInterface): """ - Handles interactions with a language model API. + Adapter for Gemini API LLM provider. - Public methods include: - - acreate_structured_output - - show_prompt + This class initializes the API adapter with necessary credentials and configurations for + interacting with the gemini LLM models. It provides methods for creating structured outputs + based on user input and system prompts. + + Public methods: + - acreate_structured_output(text_input: str, system_prompt: str, response_model: + Type[BaseModel]) -> BaseModel """ - MAX_RETRIES = 5 + name: str + model: str + api_key: str def __init__( self, + endpoint, api_key: str, model: str, + api_version: str, max_completion_tokens: int, - endpoint: Optional[str] = None, - api_version: Optional[str] = None, - streaming: bool = False, - ) -> None: - self.api_key = api_key + fallback_model: str = None, + fallback_api_key: str = None, + fallback_endpoint: str = None, + ): self.model = model + self.api_key = api_key self.endpoint = endpoint self.api_version = api_version - self.streaming = streaming self.max_completion_tokens = max_completion_tokens - @observe(as_type="generation") + self.fallback_model = fallback_model + self.fallback_api_key = fallback_api_key + self.fallback_endpoint = fallback_endpoint + + self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) + @sleep_and_retry_async() @rate_limit_async async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: """ - Generate structured output from the language model based on the provided input and - system prompt. + Generate a response from a user query. - This method handles retries and raises a ValueError if the request fails or the response - does not conform to the expected schema, logging errors accordingly. + This asynchronous method sends a user query and a system prompt to a language model and + retrieves the generated response. It handles API communication and retries up to a + specified limit in case of request failures. Parameters: ----------- - - text_input (str): The user input text to generate a response for. - - system_prompt (str): The system's prompt or context to influence the language - model's generation. - - response_model (Type[BaseModel]): A model type indicating the expected format of - the response. + - text_input (str): The input text from the user to generate a response for. + - system_prompt (str): A prompt that provides context or instructions for the + response generation. + - response_model (Type[BaseModel]): A Pydantic model that defines the structure of + the expected response. Returns: -------- - - BaseModel: Returns the generated response as an instance of the specified response - model. + - BaseModel: An instance of the specified response model containing the structured + output from the language model. """ + try: - if response_model is str: - response_schema = {"type": "string"} - else: - response_schema = response_model + return await self.aclient.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": f"""{text_input}""", + }, + { + "role": "system", + "content": system_prompt, + }, + ], + api_key=self.api_key, + max_retries=5, + api_base=self.endpoint, + api_version=self.api_version, + response_model=response_model, + ) + except ( + ContentFilterFinishReasonError, + ContentPolicyViolationError, + InstructorRetryException, + ) as error: + if ( + isinstance(error, InstructorRetryException) + and "content management policy" not in str(error).lower() + ): + raise error - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": text_input}, - ] - - try: - response = await acompletion( - model=f"{self.model}", - messages=messages, - api_key=self.api_key, - max_completion_tokens=self.max_completion_tokens, - temperature=0.1, - response_format=response_schema, - timeout=100, - num_retries=self.MAX_RETRIES, + if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint): + raise ContentPolicyFilterError( + f"The provided input contains content that is not aligned with our content policy: {text_input}" ) - if response.choices and response.choices[0].message.content: - content = response.choices[0].message.content - if response_model is str: - return content - return response_model.model_validate_json(content) - - except litellm.exceptions.BadRequestError as e: - logger.error(f"Bad request error: {str(e)}") - raise ValueError(f"Invalid request: {str(e)}") - - raise ValueError("Failed to get valid response after retries") - - except JSONSchemaValidationError as e: - logger.error(f"Schema validation failed: {str(e)}") - logger.debug(f"Raw response: {e.raw_response}") - raise ValueError(f"Response failed schema validation: {str(e)}") + try: + return await self.aclient.chat.completions.create( + model=self.fallback_model, + messages=[ + { + "role": "user", + "content": f"""{text_input}""", + }, + { + "role": "system", + "content": system_prompt, + }, + ], + max_retries=5, + api_key=self.fallback_api_key, + api_base=self.fallback_endpoint, + response_model=response_model, + ) + except ( + ContentFilterFinishReasonError, + ContentPolicyViolationError, + InstructorRetryException, + ) as error: + if ( + isinstance(error, InstructorRetryException) + and "content management policy" not in str(error).lower() + ): + raise error + else: + raise ContentPolicyFilterError( + f"The provided input contains content that is not aligned with our content policy: {text_input}" + ) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py index 86adac25a..8c15a5804 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py @@ -6,7 +6,7 @@ from typing import Type from pydantic import BaseModel from openai import ContentFilterFinishReasonError from litellm.exceptions import ContentPolicyViolationError -from instructor.exceptions import InstructorRetryException +from instructor.core import InstructorRetryException from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( @@ -56,9 +56,7 @@ class GenericAPIAdapter(LLMInterface): self.fallback_api_key = fallback_api_key self.fallback_endpoint = fallback_endpoint - self.aclient = instructor.from_litellm( - litellm.acompletion, mode=instructor.Mode.JSON, api_key=api_key - ) + self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) @sleep_and_retry_async() @rate_limit_async @@ -102,6 +100,7 @@ class GenericAPIAdapter(LLMInterface): }, ], max_retries=5, + api_key=self.api_key, api_base=self.endpoint, response_model=response_model, ) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py index 049b27e04..0ae621428 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py @@ -143,7 +143,6 @@ def get_llm_client(raise_api_key_error: bool = True): max_completion_tokens=max_completion_tokens, endpoint=llm_config.llm_endpoint, api_version=llm_config.llm_api_version, - streaming=llm_config.llm_streaming, ) else: diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py index 7273de976..989b240ac 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py @@ -5,15 +5,13 @@ from typing import Type from pydantic import BaseModel from openai import ContentFilterFinishReasonError from litellm.exceptions import ContentPolicyViolationError -from instructor.exceptions import InstructorRetryException +from instructor.core import InstructorRetryException -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) from cognee.infrastructure.llm.exceptions import ( ContentPolicyFilterError, - MissingSystemPromptPathError, ) from cognee.infrastructure.files.utils.open_data_file import open_data_file from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( diff --git a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py index a57cff3f7..d595fba2d 100644 --- a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py @@ -3,6 +3,7 @@ from typing import List, Any from ..tokenizer_interface import TokenizerInterface +# NOTE: DEPRECATED as to count tokens you need to send an API request to Google it is too slow to use with Cognee class GeminiTokenizer(TokenizerInterface): """ Implements a tokenizer interface for the Gemini model, managing token extraction and @@ -16,10 +17,10 @@ class GeminiTokenizer(TokenizerInterface): def __init__( self, - model: str, + llm_model: str, max_completion_tokens: int = 3072, ): - self.model = model + self.llm_model = llm_model self.max_completion_tokens = max_completion_tokens # Get LLM API key from config @@ -28,12 +29,11 @@ class GeminiTokenizer(TokenizerInterface): get_llm_config, ) - config = get_embedding_config() llm_config = get_llm_config() - import google.generativeai as genai + from google import genai - genai.configure(api_key=config.embedding_api_key or llm_config.llm_api_key) + self.client = genai.Client(api_key=llm_config.llm_api_key) def extract_tokens(self, text: str) -> List[Any]: """ @@ -77,6 +77,7 @@ class GeminiTokenizer(TokenizerInterface): - int: The number of tokens in the given text. """ - import google.generativeai as genai - return len(genai.embed_content(model=f"models/{self.model}", content=text)) + tokens_response = self.client.models.count_tokens(model=self.llm_model, contents=text) + + return tokens_response.total_tokens diff --git a/cognee/tasks/storage/index_data_points.py b/cognee/tasks/storage/index_data_points.py index 9c363c04c..362412657 100644 --- a/cognee/tasks/storage/index_data_points.py +++ b/cognee/tasks/storage/index_data_points.py @@ -39,7 +39,7 @@ async def index_data_points(data_points: list[DataPoint]): field_name = index_name_and_field[first_occurence + 1 :] try: # In case the amount of indexable points is too large we need to send them in batches - batch_size = 100 + batch_size = vector_engine.embedding_engine.get_batch_size() for i in range(0, len(indexable_points), batch_size): batch = indexable_points[i : i + batch_size] await vector_engine.index_data_points(index_name, field_name, batch) diff --git a/cognee/tasks/storage/index_graph_edges.py b/cognee/tasks/storage/index_graph_edges.py index 2233ab99f..f4e32f8c8 100644 --- a/cognee/tasks/storage/index_graph_edges.py +++ b/cognee/tasks/storage/index_graph_edges.py @@ -9,7 +9,7 @@ from cognee.modules.graph.models.EdgeType import EdgeType logger = get_logger(level=ERROR) -async def index_graph_edges(batch_size: int = 1024): +async def index_graph_edges(): """ Indexes graph edges by creating and managing vector indexes for relationship types. @@ -72,6 +72,8 @@ async def index_graph_edges(batch_size: int = 1024): for index_name, indexable_points in index_points.items(): index_name, field_name = index_name.split(".") + # Get maximum batch size for embedding model + batch_size = vector_engine.embedding_engine.get_batch_size() # We save the data in batches of {batch_size} to not put a lot of pressure on the database for start in range(0, len(indexable_points), batch_size): batch = indexable_points[start : start + batch_size] diff --git a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py index 9cd96f5b9..48bbc53e3 100644 --- a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py +++ b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py @@ -1,5 +1,5 @@ import pytest -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, patch, MagicMock from cognee.tasks.storage.index_graph_edges import index_graph_edges @@ -16,6 +16,7 @@ async def test_index_graph_edges_success(): ], ) mock_vector_engine = AsyncMock() + mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100) # Patch the globals of the function so that when it does: # vector_engine = get_vector_engine() diff --git a/poetry.lock b/poetry.lock index f873ca5a0..85dc8c879 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. [[package]] name = "accelerate" @@ -878,7 +878,7 @@ description = "Extensible memoizing collections and decorators" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\"" files = [ {file = "cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6"}, {file = "cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32"}, @@ -2749,28 +2749,6 @@ files = [ {file = "giturlparse-0.12.0.tar.gz", hash = "sha256:c0fff7c21acc435491b1779566e038757a205c1ffdcb47e4f81ea52ad8c3859a"}, ] -[[package]] -name = "google-ai-generativelanguage" -version = "0.6.15" -description = "Google Ai Generativelanguage API client library" -optional = true -python-versions = ">=3.7" -groups = ["main"] -markers = "extra == \"gemini\"" -files = [ - {file = "google_ai_generativelanguage-0.6.15-py3-none-any.whl", hash = "sha256:5a03ef86377aa184ffef3662ca28f19eeee158733e45d7947982eb953c6ebb6c"}, - {file = "google_ai_generativelanguage-0.6.15.tar.gz", hash = "sha256:8f6d9dc4c12b065fe2d0289026171acea5183ebf2d0b11cefe12f3821e159ec3"}, -] - -[package.dependencies] -google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]} -google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev" -proto-plus = [ - {version = ">=1.22.3,<2.0.0dev"}, - {version = ">=1.25.0,<2.0.0dev", markers = "python_version >= \"3.13\""}, -] -protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0dev" - [[package]] name = "google-api-core" version = "2.25.1" @@ -2778,7 +2756,7 @@ description = "Google API client core library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\"" +markers = "extra == \"docs\"" files = [ {file = "google_api_core-2.25.1-py3-none-any.whl", hash = "sha256:8a2a56c1fef82987a524371f99f3bd0143702fecc670c72e600c1cda6bf8dbb7"}, {file = "google_api_core-2.25.1.tar.gz", hash = "sha256:d2aaa0b13c78c61cb3f4282c464c046e45fbd75755683c9c525e6e8f7ed0a5e8"}, @@ -2808,26 +2786,6 @@ grpc = ["grpcio (>=1.33.2,<2.0.0)", "grpcio (>=1.49.1,<2.0.0) ; python_version > grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"] grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"] -[[package]] -name = "google-api-python-client" -version = "2.183.0" -description = "Google API Client Library for Python" -optional = true -python-versions = ">=3.7" -groups = ["main"] -markers = "extra == \"gemini\"" -files = [ - {file = "google_api_python_client-2.183.0-py3-none-any.whl", hash = "sha256:2005b6e86c27be1db1a43f43e047a0f8e004159f3cceddecb08cf1624bddba31"}, - {file = "google_api_python_client-2.183.0.tar.gz", hash = "sha256:abae37e04fecf719388e5c02f707ed9cdf952f10b217c79a3e76c636762e3ea9"}, -] - -[package.dependencies] -google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0" -google-auth = ">=1.32.0,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" -google-auth-httplib2 = ">=0.2.0,<1.0.0" -httplib2 = ">=0.19.0,<1.0.0" -uritemplate = ">=3.0.1,<5" - [[package]] name = "google-auth" version = "2.41.0" @@ -2835,7 +2793,7 @@ description = "Google Authentication Library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\"" files = [ {file = "google_auth-2.41.0-py2.py3-none-any.whl", hash = "sha256:d8bed9b53ab63b7b0374656b8e1bef051f95bb14ecc0cf21ba49de7911d62e09"}, {file = "google_auth-2.41.0.tar.gz", hash = "sha256:c9d7b534ea4a5d9813c552846797fafb080312263cd4994d6622dd50992ae101"}, @@ -2856,23 +2814,6 @@ requests = ["requests (>=2.20.0,<3.0.0)"] testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"] urllib3 = ["packaging", "urllib3"] -[[package]] -name = "google-auth-httplib2" -version = "0.2.0" -description = "Google Authentication Library: httplib2 transport" -optional = true -python-versions = "*" -groups = ["main"] -markers = "extra == \"gemini\"" -files = [ - {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"}, - {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"}, -] - -[package.dependencies] -google-auth = "*" -httplib2 = ">=0.19.0" - [[package]] name = "google-cloud-vision" version = "3.10.2" @@ -2922,31 +2863,6 @@ websockets = ">=13.0.0,<15.1.0" aiohttp = ["aiohttp (<4.0.0)"] local-tokenizer = ["protobuf", "sentencepiece (>=0.2.0)"] -[[package]] -name = "google-generativeai" -version = "0.8.5" -description = "Google Generative AI High level API client library and tools." -optional = true -python-versions = ">=3.9" -groups = ["main"] -markers = "extra == \"gemini\"" -files = [ - {file = "google_generativeai-0.8.5-py3-none-any.whl", hash = "sha256:22b420817fb263f8ed520b33285f45976d5b21e904da32b80d4fd20c055123a2"}, -] - -[package.dependencies] -google-ai-generativelanguage = "0.6.15" -google-api-core = "*" -google-api-python-client = "*" -google-auth = ">=2.15.0" -protobuf = "*" -pydantic = "*" -tqdm = "*" -typing-extensions = "*" - -[package.extras] -dev = ["Pillow", "absl-py", "black", "ipython", "nose2", "pandas", "pytype", "pyyaml"] - [[package]] name = "googleapis-common-protos" version = "1.70.0" @@ -2954,7 +2870,7 @@ description = "Common protobufs used in Google APIs" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"}, {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"}, @@ -3104,7 +3020,7 @@ description = "HTTP/2-based RPC framework" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "grpcio-1.75.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:1712b5890b22547dd29f3215c5788d8fc759ce6dd0b85a6ba6e2731f2d04c088"}, {file = "grpcio-1.75.1-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8d04e101bba4b55cea9954e4aa71c24153ba6182481b487ff376da28d4ba46cf"}, @@ -3182,7 +3098,7 @@ description = "Status proto mapping for gRPC" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"docs\"" +markers = "extra == \"docs\"" files = [ {file = "grpcio_status-1.71.2-py3-none-any.whl", hash = "sha256:803c98cb6a8b7dc6dbb785b1111aed739f241ab5e9da0bba96888aa74704cfd3"}, {file = "grpcio_status-1.71.2.tar.gz", hash = "sha256:c7a97e176df71cdc2c179cd1847d7fc86cca5832ad12e9798d7fed6b7a1aab50"}, @@ -3374,22 +3290,6 @@ http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] trio = ["trio (>=0.22.0,<1.0)"] -[[package]] -name = "httplib2" -version = "0.31.0" -description = "A comprehensive HTTP client library." -optional = true -python-versions = ">=3.6" -groups = ["main"] -markers = "extra == \"gemini\"" -files = [ - 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{ name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" }, @@ -4907,9 +4829,9 @@ name = "nvidia-cusolver-cu12" version = "11.7.3.90" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform != 'emscripten'" }, - { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'emscripten'" }, - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" }, + { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, + { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" }, @@ -4920,7 +4842,7 @@ name = "nvidia-cusparse-cu12" version = "12.5.8.93" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" }, @@ -8280,7 +8202,7 @@ name = "triton" version = "3.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "setuptools", marker = "sys_platform != 'emscripten'" }, + { name = "setuptools", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/62/ee/0ee5f64a87eeda19bbad9bc54ae5ca5b98186ed00055281fd40fb4beb10e/triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7ff2785de9bc02f500e085420273bb5cc9c9bb767584a4aa28d6e360cec70128", size = 155430069, upload-time = "2025-07-30T19:58:21.715Z" }, @@ -8565,15 +8487,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140, upload-time = "2023-06-21T01:49:03.467Z" }, ] -[[package]] -name = "uritemplate" -version = "4.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/98/60/f174043244c5306c9988380d2cb10009f91563fc4b31293d27e17201af56/uritemplate-4.2.0.tar.gz", hash = "sha256:480c2ed180878955863323eea31b0ede668795de182617fef9c6ca09e6ec9d0e", size = 33267, upload-time = "2025-06-02T15:12:06.318Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a9/99/3ae339466c9183ea5b8ae87b34c0b897eda475d2aec2307cae60e5cd4f29/uritemplate-4.2.0-py3-none-any.whl", hash = "sha256:962201ba1c4edcab02e60f9a0d3821e82dfc5d2d6662a21abd533879bdb8a686", size = 11488, upload-time = "2025-06-02T15:12:03.405Z" }, -] - [[package]] name = "urllib3" version = "2.3.0" From fcd91a9709b749ccf34455265e445cc07eb5f08f Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 21:51:26 +0530 Subject: [PATCH 071/139] Added self as an argument to all previous methods that were static methods --- cognee/tasks/web_scraper/bs4_crawler.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py index f1efca3d6..568127311 100644 --- a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -138,7 +138,7 @@ class BeautifulSoupCrawler: await self.close() @lru_cache(maxsize=1024) - def _domain_from_url(url: str) -> str: + def _domain_from_url(self, url: str) -> str: """Extract the domain (netloc) from a URL. Args: @@ -153,7 +153,7 @@ class BeautifulSoupCrawler: return url @lru_cache(maxsize=1024) - def _get_domain_root(url: str) -> str: + def _get_domain_root(self, url: str) -> str: """Get the root URL (scheme and netloc) from a URL. Args: @@ -376,7 +376,7 @@ class BeautifulSoupCrawler: ) await asyncio.sleep(backoff) - def _normalize_rule(rule: Union[str, Dict[str, Any]]) -> ExtractionRule: + def _normalize_rule(self, rule: Union[str, Dict[str, Any]]) -> ExtractionRule: """Normalize an extraction rule to an ExtractionRule dataclass. Args: From fc660e402767ce0375084efc339587ed453faa12 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 22:15:06 +0530 Subject: [PATCH 072/139] Closed crawler instance in a finally block --- cognee/tasks/web_scraper/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 5af95a2fe..f8f4b7eab 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -82,6 +82,8 @@ async def fetch_page_content( except Exception as e: logger.error(f"Error fetching page content: {str(e)}") raise + finally: + crawler.close() async def fetch_with_tavily( From 0fd55a737f808222557d05eef8a2ae2bd08ed599 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 22:16:02 +0530 Subject: [PATCH 073/139] ruff formatted --- cognee/tasks/web_scraper/config.py | 4 ++-- cognee/tasks/web_scraper/utils.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index 2f7f6b3f6..30c24a7f0 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -5,8 +5,8 @@ import os class TavilyConfig(BaseModel): api_key: str = os.getenv("TAVILY_API_KEY") - extract_depth: Literal["basic","advanced"] = "basic" - proxies: Optional[Dict[str,str]] = None + extract_depth: Literal["basic", "advanced"] = "basic" + proxies: Optional[Dict[str, str]] = None timeout: Optional[int] = Field(default=10, ge=1, le=60) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index f8f4b7eab..46acc513e 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -68,7 +68,7 @@ async def fetch_page_content( max_retries=soup_crawler_config.max_retries, retry_delay_factor=soup_crawler_config.retry_delay_factor, headers=soup_crawler_config.headers, - robots_cache_ttl=soup_crawler_config.robots_cache_ttl + robots_cache_ttl=soup_crawler_config.robots_cache_ttl, ) try: results = await crawler.fetch_with_bs4( @@ -110,13 +110,13 @@ async def fetch_with_tavily( raise client = AsyncTavilyClient( api_key=tavily_config.api_key if tavily_config else None, - proxies=tavily_config.proxies if tavily_config else None + proxies=tavily_config.proxies if tavily_config else None, ) results = await client.extract( - urls, + urls, format="text", extract_depth=tavily_config.extract_depth if tavily_config else "basic", - timeout=tavily_config.timeout if tavily_config else 10 + timeout=tavily_config.timeout if tavily_config else 10, ) for failed_result in results.get("failed_results", []): logger.warning(f"Failed to fetch {failed_result}") From f59c278ae965d30b287d05bdf954e164283481ab Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 22:36:24 +0530 Subject: [PATCH 074/139] Added await --- cognee/tasks/web_scraper/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py index 46acc513e..6d094f423 100644 --- a/cognee/tasks/web_scraper/utils.py +++ b/cognee/tasks/web_scraper/utils.py @@ -83,7 +83,7 @@ async def fetch_page_content( logger.error(f"Error fetching page content: {str(e)}") raise finally: - crawler.close() + await crawler.close() async def fetch_with_tavily( From 49858c541654af66e9145d4d0a70f20812446638 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Tue, 7 Oct 2025 22:38:13 +0530 Subject: [PATCH 075/139] Made api_key field in TavilyConfig models to be Optional[str] type to allow None value --- cognee/tasks/web_scraper/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py index 30c24a7f0..2ee43ed32 100644 --- a/cognee/tasks/web_scraper/config.py +++ b/cognee/tasks/web_scraper/config.py @@ -4,7 +4,7 @@ import os class TavilyConfig(BaseModel): - api_key: str = os.getenv("TAVILY_API_KEY") + api_key: Optional[str] = os.getenv("TAVILY_API_KEY") extract_depth: Literal["basic", "advanced"] = "basic" proxies: Optional[Dict[str, str]] = None timeout: Optional[int] = Field(default=10, ge=1, le=60) From 7b5bba2b1835edd8f03f8441a980e2ccb794a870 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 19:14:46 +0200 Subject: [PATCH 076/139] refactor: Unify dataset resolution (#1488) ## Description Unified dataset resolution mechanisms across cognee ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [x] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- cognee/api/v1/add/add.py | 4 +- .../layers/resolve_authorized_user_dataset.py | 38 ++++++------------- .../resolve_authorized_user_datasets.py | 4 +- cognee/modules/search/methods/search.py | 8 +++- 4 files changed, 22 insertions(+), 32 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index eeb867984..1880735c2 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -148,7 +148,9 @@ async def add( await setup() - user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user) + user, authorized_dataset = await resolve_authorized_user_dataset( + dataset_name=dataset_name, dataset_id=dataset_id, user=user + ) await reset_dataset_pipeline_run_status( authorized_dataset.id, user, pipeline_names=["add_pipeline", "cognify_pipeline"] diff --git a/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py b/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py index 7e3d1c124..981c02112 100644 --- a/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py +++ b/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py @@ -1,47 +1,31 @@ from uuid import UUID +from typing import Optional -from cognee.api.v1.exceptions import DatasetNotFoundError from cognee.modules.users.models import User -from cognee.modules.users.methods import get_default_user -from cognee.modules.data.methods import ( - create_authorized_dataset, - get_authorized_dataset, - get_authorized_dataset_by_name, +from cognee.modules.pipelines.layers.resolve_authorized_user_datasets import ( + resolve_authorized_user_datasets, ) -async def resolve_authorized_user_dataset(dataset_id: UUID, dataset_name: str, user: User): +async def resolve_authorized_user_dataset( + dataset_name: str, dataset_id: Optional[UUID] = None, user: Optional[User] = None +): """ Function handles creation and dataset authorization if dataset already exist for Cognee. Verifies that provided user has necessary permission for provided Dataset. If Dataset does not exist creates the Dataset and gives permission for the user creating the dataset. Args: - dataset_id: Id of the dataset. dataset_name: Name of the dataset. + dataset_id: Id of the dataset. user: Cognee User request is being processed for, if None default user will be used. Returns: Tuple[User, Dataset]: A tuple containing the user and the authorized dataset. """ - if not user: - user = await get_default_user() - if dataset_id: - authorized_dataset = await get_authorized_dataset(user, dataset_id, "write") - elif dataset_name: - authorized_dataset = await get_authorized_dataset_by_name(dataset_name, user, "write") + user, authorized_datasets = await resolve_authorized_user_datasets( + datasets=dataset_id if dataset_id else dataset_name, user=user + ) - if not authorized_dataset: - authorized_dataset = await create_authorized_dataset( - dataset_name=dataset_name, user=user - ) - else: - raise ValueError("Either dataset_id or dataset_name must be provided.") - - if not authorized_dataset: - raise DatasetNotFoundError( - message=f"Dataset ({str(dataset_id) or dataset_name}) not found." - ) - - return user, authorized_dataset + return user, authorized_datasets[0] diff --git a/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py b/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py index f91064995..cda39d7bb 100644 --- a/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py +++ b/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py @@ -1,5 +1,5 @@ from uuid import UUID -from typing import Union, Tuple, List +from typing import Union, Tuple, List, Optional from cognee.modules.users.methods import get_default_user from cognee.modules.users.models import User @@ -13,7 +13,7 @@ from cognee.modules.data.methods import ( async def resolve_authorized_user_datasets( - datasets: Union[str, UUID, list[str], list[UUID]], user: User = None + datasets: Union[str, UUID, list[str], list[UUID]], user: Optional[User] = None ) -> Tuple[User, List[Dataset]]: """ Function handles creation and dataset authorization if datasets already exist for Cognee. diff --git a/cognee/modules/search/methods/search.py b/cognee/modules/search/methods/search.py index 8bcef815f..958af6444 100644 --- a/cognee/modules/search/methods/search.py +++ b/cognee/modules/search/methods/search.py @@ -19,7 +19,9 @@ from cognee.modules.search.types import ( from cognee.modules.search.operations import log_query, log_result from cognee.modules.users.models import User from cognee.modules.data.models import Dataset -from cognee.modules.users.permissions.methods import get_specific_user_permission_datasets +from cognee.modules.data.methods.get_authorized_existing_datasets import ( + get_authorized_existing_datasets, +) from .get_search_type_tools import get_search_type_tools from .no_access_control_search import no_access_control_search @@ -202,7 +204,9 @@ async def authorized_search( Not to be used outside of active access control mode. """ # Find datasets user has read access for (if datasets are provided only return them. Provided user has read access) - search_datasets = await get_specific_user_permission_datasets(user.id, "read", dataset_ids) + search_datasets = await get_authorized_existing_datasets( + datasets=dataset_ids, permission_type="read", user=user + ) if use_combined_context: search_responses = await search_in_datasets_context( From 3b8b8390570f1a16376b78e63cab9d5820902718 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 19:54:59 +0200 Subject: [PATCH 077/139] fix: Resolve pydantic issue regarding deprecation (#1508) ## Description Fix latest pydantic version issues ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- .../infrastructure/databases/graph/config.py | 18 +- .../databases/relational/config.py | 8 +- .../infrastructure/databases/vector/config.py | 14 +- uv.lock | 1572 ++++++++--------- 4 files changed, 756 insertions(+), 856 deletions(-) diff --git a/cognee/infrastructure/databases/graph/config.py b/cognee/infrastructure/databases/graph/config.py index d96de4520..b7907313c 100644 --- a/cognee/infrastructure/databases/graph/config.py +++ b/cognee/infrastructure/databases/graph/config.py @@ -50,26 +50,26 @@ class GraphConfig(BaseSettings): # Model validator updates graph_filename and path dynamically after class creation based on current database provider # If no specific graph_filename or path are provided @pydantic.model_validator(mode="after") - def fill_derived(cls, values): - provider = values.graph_database_provider.lower() + def fill_derived(self): + provider = self.graph_database_provider.lower() base_config = get_base_config() # Set default filename if no filename is provided - if not values.graph_filename: - values.graph_filename = f"cognee_graph_{provider}" + if not self.graph_filename: + self.graph_filename = f"cognee_graph_{provider}" # Handle graph file path - if values.graph_file_path: + if self.graph_file_path: # Check if absolute path is provided - values.graph_file_path = ensure_absolute_path( - os.path.join(values.graph_file_path, values.graph_filename) + self.graph_file_path = ensure_absolute_path( + os.path.join(self.graph_file_path, self.graph_filename) ) else: # Default path databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.graph_file_path = os.path.join(databases_directory_path, values.graph_filename) + self.graph_file_path = os.path.join(databases_directory_path, self.graph_filename) - return values + return self def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/databases/relational/config.py b/cognee/infrastructure/databases/relational/config.py index 4e3c26042..ff7c410a1 100644 --- a/cognee/infrastructure/databases/relational/config.py +++ b/cognee/infrastructure/databases/relational/config.py @@ -23,14 +23,14 @@ class RelationalConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") @pydantic.model_validator(mode="after") - def fill_derived(cls, values): + def fill_derived(self): # Set file path based on graph database provider if no file path is provided - if not values.db_path: + if not self.db_path: base_config = get_base_config() databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.db_path = databases_directory_path + self.db_path = databases_directory_path - return values + return self def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/databases/vector/config.py b/cognee/infrastructure/databases/vector/config.py index 23678cca4..b6d3ae644 100644 --- a/cognee/infrastructure/databases/vector/config.py +++ b/cognee/infrastructure/databases/vector/config.py @@ -30,21 +30,21 @@ class VectorConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") @pydantic.model_validator(mode="after") - def validate_paths(cls, values): + def validate_paths(self): base_config = get_base_config() # If vector_db_url is provided and is not a path skip checking if path is absolute (as it can also be a url) - if values.vector_db_url and Path(values.vector_db_url).exists(): + if self.vector_db_url and Path(self.vector_db_url).exists(): # Relative path to absolute - values.vector_db_url = ensure_absolute_path( - values.vector_db_url, + self.vector_db_url = ensure_absolute_path( + self.vector_db_url, ) - elif not values.vector_db_url: + elif not self.vector_db_url: # Default path databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.vector_db_url = os.path.join(databases_directory_path, "cognee.lancedb") + self.vector_db_url = os.path.join(databases_directory_path, "cognee.lancedb") - return values + return self def to_dict(self) -> dict: """ diff --git a/uv.lock b/uv.lock index a36416ce5..d9365a59d 100644 --- a/uv.lock +++ b/uv.lock @@ -53,7 +53,7 @@ wheels = [ [[package]] name = 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tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- .env.template | 4 ++-- cognee/infrastructure/llm/config.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.env.template b/.env.template index b1e7057e2..7fd3ba9e8 100644 --- a/.env.template +++ b/.env.template @@ -16,7 +16,7 @@ STRUCTURED_OUTPUT_FRAMEWORK="instructor" LLM_API_KEY="your_api_key" -LLM_MODEL="openai/gpt-4o-mini" +LLM_MODEL="openai/gpt-5-mini" LLM_PROVIDER="openai" LLM_ENDPOINT="" LLM_API_VERSION="" @@ -36,7 +36,7 @@ EMBEDDING_MAX_TOKENS=8191 # If using BAML structured output these env variables will be used BAML_LLM_PROVIDER=openai -BAML_LLM_MODEL="gpt-4o-mini" +BAML_LLM_MODEL="gpt-5-mini" BAML_LLM_ENDPOINT="" BAML_LLM_API_KEY="your_api_key" BAML_LLM_API_VERSION="" diff --git a/cognee/infrastructure/llm/config.py b/cognee/infrastructure/llm/config.py index 6658a6251..8fd196eaf 100644 --- a/cognee/infrastructure/llm/config.py +++ b/cognee/infrastructure/llm/config.py @@ -39,7 +39,7 @@ class LLMConfig(BaseSettings): structured_output_framework: str = "instructor" llm_provider: str = "openai" - llm_model: str = "openai/gpt-4o-mini" + llm_model: str = "openai/gpt-5-mini" llm_endpoint: str = "" llm_api_key: Optional[str] = None llm_api_version: Optional[str] = None @@ -48,7 +48,7 @@ class LLMConfig(BaseSettings): llm_max_completion_tokens: int = 16384 baml_llm_provider: str = "openai" - baml_llm_model: str = "gpt-4o-mini" + baml_llm_model: str = "gpt-5-mini" baml_llm_endpoint: str = "" baml_llm_api_key: Optional[str] = None baml_llm_temperature: float = 0.0 From dda44b0a4b0e12c8d6b172c83a1ed9387db50632 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 19:30:25 +0100 Subject: [PATCH 079/139] chore: change `cognee-mcp` deprecation message --- cognee-mcp/src/__init__.py | 35 ++++++++++++++++++++++++++++++++--- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 77d325085..933bcd73f 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -7,6 +7,36 @@ def main(): """Deprecated main entry point for the package.""" import asyncio + deprecation_notice = """ +DEPRECATION NOTICE +The CLI entry-point used to start the Cognee MCP service has been renamed from +"cognee" to "cognee-mcp". Calling the old entry-point will stop working in a +future release. + +WHAT YOU NEED TO DO: +Locate every place where you launch the MCP process and replace the final +argument cognee → cognee-mcp. + +For the example mcpServers block from Cursor shown below the change is: +{ + "mcpServers": { + "Cognee": { + "command": "uv", + "args": [ + "--directory", + "/path/to/cognee-mcp", + "run", + "cognee" // <-- CHANGE THIS to "cognee-mcp" + ] + } + } +} + +Continuing to use the old "cognee" entry-point will result in failures once it +is removed, so please update your configuration and any shell scripts as soon +as possible. +""" + warnings.warn( "The 'cognee' command for cognee-mcp is deprecated and will be removed in a future version. " "Please use 'cognee-mcp' instead to avoid conflicts with the main cognee library.", @@ -14,9 +44,8 @@ def main(): stacklevel=2, ) - print("⚠️ DEPRECATION WARNING: Use 'cognee-mcp' command instead of 'cognee'", file=sys.stderr) - print(" This avoids conflicts with the main cognee library.", file=sys.stderr) - print(file=sys.stderr) + print("⚠️ DEPRECATION WARNING", file=sys.stderr) + print(deprecation_notice, file=sys.stderr) asyncio.run(server_main()) From e7709f7bd079ca478df63737682a6d4dc8f6dcba Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 20:38:43 +0200 Subject: [PATCH 080/139] =?UTF-8?q?fix:=20Resolve=20issue=20with=20Kuzu=20?= =?UTF-8?q?graph=20database=20persistence=20on=20our=20local=20=E2=80=A6?= =?UTF-8?q?=20(#1490)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit …Docker ## Description Resolves issue with kuzu graph data persistence for Cognee backend docker ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Co-authored-by: Boris --- Dockerfile | 1 + .../databases/graph/kuzu/adapter.py | 28 +++++-- poetry.lock | 79 ++++++++++--------- pyproject.toml | 2 +- uv.lock | 50 ++++++------ 5 files changed, 90 insertions(+), 70 deletions(-) diff --git a/Dockerfile b/Dockerfile index b1b77513d..9b9a34d41 100644 --- a/Dockerfile +++ b/Dockerfile @@ -22,6 +22,7 @@ RUN apt-get update && apt-get install -y \ libpq-dev \ git \ curl \ + cmake \ clang \ build-essential \ && rm -rf /var/lib/apt/lists/* diff --git a/cognee/infrastructure/databases/graph/kuzu/adapter.py b/cognee/infrastructure/databases/graph/kuzu/adapter.py index 95f534788..7b772097f 100644 --- a/cognee/infrastructure/databases/graph/kuzu/adapter.py +++ b/cognee/infrastructure/databases/graph/kuzu/adapter.py @@ -48,6 +48,29 @@ class KuzuAdapter(GraphDBInterface): def _initialize_connection(self) -> None: """Initialize the Kuzu database connection and schema.""" + + def _install_json_extension(): + """ + Function handles installing of the json extension for the current Kuzu version. + This has to be done with an empty graph db before connecting to an existing database otherwise + missing json extension errors will be raised. + """ + try: + with tempfile.NamedTemporaryFile(mode="w", delete=True) as temp_file: + temp_graph_file = temp_file.name + tmp_db = Database( + temp_graph_file, + buffer_pool_size=2048 * 1024 * 1024, # 2048MB buffer pool + max_db_size=4096 * 1024 * 1024, + ) + tmp_db.init_database() + connection = Connection(tmp_db) + connection.execute("INSTALL JSON;") + except Exception as e: + logger.info(f"JSON extension already installed or not needed: {e}") + + _install_json_extension() + try: if "s3://" in self.db_path: with tempfile.NamedTemporaryFile(mode="w", delete=False) as temp_file: @@ -109,11 +132,6 @@ class KuzuAdapter(GraphDBInterface): self.db.init_database() self.connection = 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improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- .../vector/pgvector/PGVectorAdapter.py | 57 ++++++++++--------- 1 file changed, 29 insertions(+), 28 deletions(-) diff --git a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py index 5ddb656a3..1986fae48 100644 --- a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py +++ b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py @@ -125,41 +125,42 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): data_point_types = get_type_hints(DataPoint) vector_size = self.embedding_engine.get_vector_size() - async with self.VECTOR_DB_LOCK: - if not await self.has_collection(collection_name): + if not await self.has_collection(collection_name): + async with self.VECTOR_DB_LOCK: + if not await self.has_collection(collection_name): - class PGVectorDataPoint(Base): - """ - Represent a point in a vector data space with associated data and vector representation. + class PGVectorDataPoint(Base): + """ + Represent a point in a vector data space with associated data and vector representation. - This class inherits from Base and is associated with a database table defined by - __tablename__. It maintains the following public methods and instance variables: + This class inherits from Base and is associated with a database table defined by + __tablename__. It maintains the following public methods and instance variables: - - __init__(self, id, payload, vector): Initializes a new PGVectorDataPoint instance. + - __init__(self, id, payload, vector): Initializes a new PGVectorDataPoint instance. - Instance variables: - - id: Identifier for the data point, defined by data_point_types. - - payload: JSON data associated with the data point. - - vector: Vector representation of the data point, with size defined by vector_size. - """ + Instance variables: + - id: Identifier for the data point, defined by data_point_types. + - payload: JSON data associated with the data point. + - vector: Vector representation of the data point, with size defined by vector_size. + """ - __tablename__ = collection_name - __table_args__ = {"extend_existing": True} - # PGVector requires one column to be the primary key - id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True) - payload = Column(JSON) - vector = Column(self.Vector(vector_size)) + __tablename__ = collection_name + __table_args__ = {"extend_existing": True} + # PGVector requires one column to be the primary key + id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True) + payload = Column(JSON) + vector = Column(self.Vector(vector_size)) - def __init__(self, id, payload, vector): - self.id = id - self.payload = payload - self.vector = vector + def __init__(self, id, payload, vector): + self.id = id + self.payload = payload + self.vector = vector - async with self.engine.begin() as connection: - if len(Base.metadata.tables.keys()) > 0: - await connection.run_sync( - Base.metadata.create_all, tables=[PGVectorDataPoint.__table__] - ) + async with self.engine.begin() as connection: + if len(Base.metadata.tables.keys()) > 0: + await connection.run_sync( + Base.metadata.create_all, tables=[PGVectorDataPoint.__table__] + ) @retry( retry=retry_if_exception_type(DeadlockDetectedError), From 2b04a35309a9d2a9620d87a19ce833a77795cf31 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 21:16:43 +0200 Subject: [PATCH 082/139] chore: Update lock files (#1511) ## Description update lock files ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- poetry.lock | 43 ++----------------------------------------- uv.lock | 46 ++++++++-------------------------------------- 2 files changed, 10 insertions(+), 79 deletions(-) diff --git a/poetry.lock b/poetry.lock index 90b0a609a..551295733 100644 --- a/poetry.lock +++ b/poetry.lock @@ -510,7 +510,7 @@ description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "extra == \"falkordb\" and python_full_version < \"3.11.3\" or python_version == \"3.10\"" +markers = "python_version == \"3.10\"" files = [ {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"}, {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"}, @@ -2169,23 +2169,6 @@ files = [ [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""] -[[package]] -name = "falkordb" -version = "1.2.0" -description = "Python client for interacting with FalkorDB database" -optional = true -python-versions = "<4.0,>=3.8" -groups = ["main"] -markers = "extra == \"falkordb\"" -files = [ - {file = "falkordb-1.2.0-py3-none-any.whl", hash = "sha256:7572d9cc377735d22efc52fe6fe73c7a435422c827b6ea3ca223a850a77be12e"}, - {file = "falkordb-1.2.0.tar.gz", hash = "sha256:ce57365b86722d538e75aa5d438de67ecd8eb9478da612506d9812cd7f182d0b"}, -] - -[package.dependencies] -python-dateutil = ">=2.9.0,<3.0.0" -redis = ">=5.0.1,<6.0.0" - [[package]] name = "fastapi" version = "0.118.0" @@ -9646,27 +9629,6 @@ lxml = ["lxml (>=4.3,<6.0)"] networkx = ["networkx (>=2,<4)"] orjson = ["orjson (>=3.9.14,<4)"] -[[package]] -name = "redis" -version = "5.3.1" -description = "Python client for Redis database and key-value store" -optional = true -python-versions = ">=3.8" -groups = ["main"] -markers = "extra == \"falkordb\"" -files = [ - {file = "redis-5.3.1-py3-none-any.whl", hash = "sha256:dc1909bd24669cc31b5f67a039700b16ec30571096c5f1f0d9d2324bff31af97"}, - {file = "redis-5.3.1.tar.gz", hash = "sha256:ca49577a531ea64039b5a36db3d6cd1a0c7a60c34124d46924a45b956e8cf14c"}, -] - -[package.dependencies] -async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\""} -PyJWT = ">=2.9.0" - 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[ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index aacd07867..a6dd7d0f7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -140,7 +140,6 @@ Homepage = "https://www.cognee.ai" Repository = "https://github.com/topoteretes/cognee" [project.scripts] -cognee = "cognee.cli._cognee:main" cognee-cli = "cognee.cli._cognee:main" [build-system] From c81315d177136a2e5772b109ffbb48642a541a89 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Tue, 7 Oct 2025 22:07:11 +0200 Subject: [PATCH 084/139] chore: Update release version (#1514) ## Description ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a6dd7d0f7..3df57e1f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "cognee" -version = "0.3.4" +version = "0.3.5" description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning." authors = [ { name = "Vasilije Markovic" }, From ada375353402b6143d92b8be290c706f389c7580 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 21:18:00 +0100 Subject: [PATCH 085/139] fix: fix broken check_llm_provider healthcheck method --- cognee/api/health.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/cognee/api/health.py b/cognee/api/health.py index 1b1e45efa..14ebb8043 100644 --- a/cognee/api/health.py +++ b/cognee/api/health.py @@ -193,8 +193,17 @@ class HealthChecker: config = get_llm_config() - # Test actual API connection with minimal request - LLMGateway.show_prompt("test", "test.txt") + from pydantic import BaseModel + + class TestResponse(BaseModel): + status: str + + # Use a simple test to verify LLM connectivity + await LLMGateway.acreate_structured_output( + text_input="Respond with status: ok", + system_prompt="You are a test assistant. Respond with a simple status.", + response_model=TestResponse, + ) response_time = int((time.time() - start_time) * 1000) return ComponentHealth( From 405b65f85754f2a7631d3919ffe03c72e6227613 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 21:26:22 +0100 Subject: [PATCH 086/139] refactor: Simplify health check methods by using utility functions for LLM and embedding connections --- cognee/api/health.py | 21 ++++----------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/cognee/api/health.py b/cognee/api/health.py index 14ebb8043..0241a0712 100644 --- a/cognee/api/health.py +++ b/cognee/api/health.py @@ -189,21 +189,12 @@ class HealthChecker: start_time = time.time() try: from cognee.infrastructure.llm.config import get_llm_config - from cognee.infrastructure.llm import LLMGateway config = get_llm_config() - from pydantic import BaseModel + from cognee.infrastructure.llm.utils import test_llm_connection - class TestResponse(BaseModel): - status: str - - # Use a simple test to verify LLM connectivity - await LLMGateway.acreate_structured_output( - text_input="Respond with status: ok", - system_prompt="You are a test assistant. Respond with a simple status.", - response_model=TestResponse, - ) + await test_llm_connection() response_time = int((time.time() - start_time) * 1000) return ComponentHealth( @@ -226,13 +217,9 @@ class HealthChecker: """Check embedding service health (non-critical).""" start_time = time.time() try: - from cognee.infrastructure.databases.vector.embeddings.get_embedding_engine import ( - get_embedding_engine, - ) + from cognee.infrastructure.llm.utils import test_embedding_connection - # Test actual embedding generation with minimal text - engine = get_embedding_engine() - await engine.embed_text(["test"]) + await test_embedding_connection() response_time = int((time.time() - start_time) * 1000) return ComponentHealth( From 0c59bcdda2d761c785b54d100f58088b46e7424d Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 21:49:02 +0100 Subject: [PATCH 087/139] Rerun notebooks with latest cognee - 0.3.5 --- notebooks/cognee_demo.ipynb | 1204 ++++++++---------------- notebooks/cognee_multimedia_demo.ipynb | 234 +++-- notebooks/cognee_simple_demo.ipynb | 684 +++----------- notebooks/ontology_demo.ipynb | 941 +++++++++++++++++- 4 files changed, 1582 insertions(+), 1481 deletions(-) diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb index 51eeab560..2a6568a51 100644 --- a/notebooks/cognee_demo.ipynb +++ b/notebooks/cognee_demo.ipynb @@ -68,30 +68,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8aba58c24daec519", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T11:38:36.328621Z", - "start_time": "2025-06-30T11:38:36.068313Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2.4-local\n" - ] - } - ], - "source": [ - "import cognee\n", - "print(cognee.__version__)" - ] - }, { "cell_type": "markdown", "id": "074f0ea8-c659-4736-be26-be4b0e5ac665", @@ -110,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "df16431d0f48b006", "metadata": { "ExecuteTime": { @@ -419,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "9f1a1dbd", "metadata": { "ExecuteTime": { @@ -433,9 +409,32 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:01:17.533854\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:36:22.969905\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-15-58.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:18.755266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:36:23.577073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:36:23.577696\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "\n", + "\u001b[2m2025-10-07T20:36:28.324602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. 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"text": [ + "0.3.5-local\n" ] } ], @@ -445,7 +444,8 @@ "import cognee\n", "\n", "await cognee.prune.prune_data()\n", - "await cognee.prune.prune_system(metadata=True)" + "await cognee.prune.prune_system(metadata=True)\n", + "print(cognee.__version__)" ] }, { @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "904df61ba484a8e5", "metadata": { "ExecuteTime": { @@ -466,7 +466,117 @@ "start_time": "2025-06-30T11:39:32.388973Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User 17d4a6b1-da2b-46b8-a607-06969eda0881 has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:33:26.264681\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: 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[\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:33:26.393955\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:33:26.394440\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:33:26.394743\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('a9cc087f-1fe6-54d9-8626-06fb6bee185e')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('2352a9c1-c822-59e1-a38a-a62fa0009143')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('8175b7ba-1eeb-512e-9c9e-9ac7859cc505')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('fb1b835a-d9ae-5599-ba17-3dcfb767f297')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('9ce0cfb9-a3f7-5b95-b7e2-c8824363fa8a')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('e64a82b1-6070-53a4-b631-cca67bce7d14')}])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import cognee\n", "\n", @@ -561,881 +671,389 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:01:47.379592\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.364927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.554990\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.365793\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.737751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.366370\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.869950\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.367252\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:48.007947\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:01:48 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:37.367570\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:37.367948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:37.368650\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.369534\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.282550\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.369944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.320418\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.370804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.371064\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.371413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science, stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics, university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322397\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.373605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.373898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.374241\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323719\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.392711\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.404807\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist at innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.407830\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324780\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist at datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.410319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.412354\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325515\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.414016\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325718\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.420301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325964\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.427979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.432593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326477\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'keras' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.439575\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326728\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.445716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.327255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hadoop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.456232\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.327861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spark' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.370377\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tableau' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.420182\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'matplotlib' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.455440\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328590\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'natural language processing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.456243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328763\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.456685\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329041\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457029\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457363\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329454\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457690\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.330158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.330367\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved prediction accuracy by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:50.397043\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:14 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.351674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.358793\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.360301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.361050\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:51.361631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:16.127289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:16 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:51.362581\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:51.363138\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:51.363844\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:51.364552\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:24.159354\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.365273\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.f.a. in graphic design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.365922\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.366327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.366630\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:27 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.367065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:53.924808\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:55.543740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.025361\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.440785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.170836\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.446845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.306326\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.447421\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.446538\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.447928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.579414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.448478\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.712543\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.449018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.841720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.449488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.971284\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.450019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.100235\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.450742\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.233945\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.451246\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programming language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.390158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.451757\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.533576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:29 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:56.452305\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.452770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.453216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.453631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.132445\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical analysis software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.140870\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454425\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.141437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spss' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.141811\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.455234\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cloud platform' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in data science, carnegie mellon university (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.455609\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'aws' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in computer science, university of michigan (2011)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'azure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456536\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.143079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.143669\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.457388\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.144377\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.501611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.146600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist, alpha analytics (2017 – present)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.505241\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.148160\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist, techinsights (2013 – 2017)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.505755\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.148944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506086\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job_title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.149419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506420\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.149899\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506687\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2017' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506949\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.151386\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.152026\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507994\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programming_language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.152702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'java' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508283\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153915\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'xgboost' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509066\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep_learning_framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.154201\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaborn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.154668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plotly' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155091\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mysql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509855\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'academic_qualification' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155395\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mongodb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.510156\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'masters or ph.d. in data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.510369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical modeling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.510617\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.909307\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.912471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.912916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:53 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.913239\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.913593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:53.183427\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:04:53 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:56.913979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.914278\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.914655\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.914927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:05.322305\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915658\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.916228\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.916564\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:57.029740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.034344\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:08.880095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.034871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.011845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.035333\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.139270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.035762\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.287708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.485773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036543\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educationalinstitution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.625262\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.763675\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037182\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fieldofstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.933624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.078157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.222889\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.038189\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales manager' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.380248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.038472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales representative' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.536508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:05:10 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:57.038860\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:57.039102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales strategy and planning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:57.039364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team leadership and development' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.039673\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'crm software' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.019994\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040029\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'negotiation and relationship building' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.026138\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027020\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027427\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041191\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041458\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041729\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028180\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.519924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028485\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.623680\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.029151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624457\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.029443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.030216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624985\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.031108\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.625245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.031843\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.625479\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.032486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.292226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.300823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.827732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.040051\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.036096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.648684\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.036641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649221\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.037043\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649550\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038085\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038562\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spss' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038903\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'aws' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650723\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'azure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650926\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039529\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:01.349738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist (quantumtech)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.022925\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'junior data scientist (datacore solutions)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.680974\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.040135\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.976034\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.040332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved model efficiency by 20%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.976591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.976959\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977514\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.978068\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:27.001049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:27 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:34:05.183360\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:34:05.183819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:34:05.184132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:05.184426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:34.782530\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.184708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.185092\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.185378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.323602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:06.166424\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:06.166969\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.167301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:37.896055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168035\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.023334\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168356\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.163692\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168621\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.306820\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.437716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.850334\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.566678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851074\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.701049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.837828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.971017\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.852348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:39.106825\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.852612\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:39.269402\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:06:39.416821\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:39 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.076839\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.089869\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.090647\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'contact' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091481\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david.thompson@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 456-7890' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092896\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093203\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093480\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior graphic designer (creativeworks agency)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094411\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'graphic designer (visual innovations)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096076\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe photoshop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096539\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe illustrator' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096817\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe indesign' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097309\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'html' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'css' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097934\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'specialty' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.098206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'branding and identity' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.098423\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'typography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:42.898490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:43 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:50.774170\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.710744\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.844417\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.974409\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.120036\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.258343\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.389231\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.519095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.648012\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.777905\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.903188\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:58.041288\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:58.183908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:58 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.506882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.514484\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'email' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515918\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica.miller@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.516371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phone' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.516680\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 567-8901' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517023\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.a. in business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517660\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518556\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518816\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519156\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales manager' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales representative' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'managed a sales team of 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521489\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '20% increase in annual revenue' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521725\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'expanded customer base by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'award' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522325\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'top salesperson award (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales strategy and planning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523495\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team leadership and development' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'negotiation and relationship building' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'salesforce' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'zoho' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:29 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:31.465804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:31 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:40.613929\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:46.891337\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:48.115206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:52.397824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.325055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.712006\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.866332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:56.078882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:56.461802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:58.451988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:58.834324\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:59.456885\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:59.835685\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:59 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:10:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:10:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.004741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.013274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.013770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014545\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist (machine learning)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.015535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.015856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analytics team' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016573\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016923\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.017917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.018571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.018975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.019720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.020396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021063\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021983\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'master or phd' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023130\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'problem-solving' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'attention to detail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mentoring' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cross-functional collaboration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024195\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'large, complex datasets' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'responsibility' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025003\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive models integration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025196\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stay updated with ml advancements' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analyze datasets and extract insights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'develop and implement ml algorithms' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:10:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:51.648268\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:10:52 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:11:04 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:11:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:05.002175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:11 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:12 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.526730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.672123\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.807143\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.938931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.131661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.287410\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.455194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:07.852868\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] } ], @@ -1455,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "9dd29caf28c272d1", "metadata": { "ExecuteTime": { @@ -1469,17 +1087,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:14:03.046467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-32-51.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:34:17.282583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:14:03.841002\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:03.841488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:05.798338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:05.799781\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:17.283063\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] } ], @@ -1514,7 +1124,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "e5e7dfc8", "metadata": { "ExecuteTime": { @@ -1523,28 +1133,20 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709534883499146}\n", - "{'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'payload': {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica.miller@example.com'}, 'score': 0.7205207943916321}\n", - "{'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'payload': {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'david.thompson@example.com'}, 'score': 0.775915801525116}\n", - "{'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'payload': {'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'salesforce'}, 'score': 1.3353958129882812}\n", - "{'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'payload': {'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'created_at': 1756300271229, 'updated_at': 1756300271229, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495608568191528}\n", - "{'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'payload': {'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'b.s. in computer science, university of michigan (2011)'}, 'score': 1.3630532026290894}\n", - "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n", - "{'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'payload': {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '(555) 456-7890'}, 'score': 1.3842216730117798}\n", - "{'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'payload': {'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in data science, carnegie mellon university (2013)'}, 'score': 1.396019697189331}\n", - "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042032957077026}\n" + "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709534883499146}\n", + "{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495469093322754}\n", + "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n", + "{'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'payload': {'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist'}, 'score': 1.3935251235961914}\n", + "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042136669158936}\n", + "{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4521194696426392}\n", + "{'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'payload': {'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sales manager'}, 'score': 1.45354425907135}\n", + "{'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'payload': {'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years experience'}, 'score': 1.456857442855835}\n", + "{'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'payload': {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'created_at': 1759869236359, 'updated_at': 1759869236359, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'dr. emily carter'}, 'score': 1.4710627794265747}\n", + "{'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'payload': {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in statistics'}, 'score': 1.4715948104858398}\n" ] } ], @@ -1599,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "id": "21a3e9a6", "metadata": { "ExecuteTime": { @@ -1613,15 +1215,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:15:00.678844\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:36.551256\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:00.683634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.197175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.048173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.197911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.048720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.198373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.049052\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:37.198826\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" ] }, { @@ -1631,17 +1233,17 @@ "\n", "\\Extracted summaries are:\n", "\n", - "{'id': 'd63c0626-f304-5b51-81be-b7a42cdb7d88', 'created_at': 1756299804334, 'updated_at': 1756299804334, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with 8+ years’ experience in machine learning and predictive analytics, specializing in NLP, deep learning, and production model deployment.'}\n", + "{'id': '4c47f1ef-54a5-54c9-bc4f-f23c45477451', 'created_at': 1759869237535, 'updated_at': 1759869237535, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer with over 8 years in visual design and branding, skilled in Adobe Creative Suite, focused on crafting engaging visuals.'}\n", "\n", - "{'id': '9c02f5ca-e629-5930-b178-1c6bee059689', 'created_at': 1756300070938, 'updated_at': 1756300070938, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Graphic designer with 8+ years’ experience specializing in branding and visual design; skilled in Adobe Creative Suite and basic web design.'}\n", + "{'id': 'f99dc79b-af29-5f70-9fcb-5bb62790994a', 'created_at': 1759869241394, 'updated_at': 1759869241394, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist specializing in Machine Learning at TechNova Solutions.'}\n", "\n", - "{'id': '54db4268-2156-5ae2-ba54-8c6c0d9bd3ed', 'created_at': 1756299905496, 'updated_at': 1756299905496, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Data Scientist specializing in machine learning and statistical modeling, skilled at working with large datasets and turning data into actionable business outcomes.'}\n", + "{'id': 'df7d916e-6056-5302-ad95-b5c46d5dac42', 'created_at': 1759869245597, 'updated_at': 1759869245597, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist proficient in machine learning and statistical analysis, experienced in managing extensive datasets and converting data into strategic business insights.'}\n", "\n", - "{'id': '799d1f2a-4c78-517f-b09b-ede659507d4d', 'created_at': 1756300180835, 'updated_at': 1756300180835, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Seasoned sales leader with a strong record of driving revenue growth and building high-performing teams.'}\n", + "{'id': '7144d310-9f4e-5b13-acaa-1f53bdd77cc0', 'created_at': 1759869243709, 'updated_at': 1759869243709, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Sales Manager with a proven history of driving revenue growth and cultivating effective teams. Strong leadership and communication abilities.'}\n", "\n", - "{'id': 'a3534071-8c78-5f37-99ea-0f14ea971422', 'created_at': 1756300269688, 'updated_at': 1756300269688, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'TechNova Solutions is hiring a Senior Data Scientist (Machine Learning) in San Francisco to design, train, and operationalize ML models, analyze large datasets, and guide junior team members.'}\n", + "{'id': '352ab868-e930-5009-a855-14b83894520d', 'created_at': 1759869243053, 'updated_at': 1759869243053, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of experience in machine learning, focused on utilizing data for business improvements and product optimization.'}\n", "\n", - "{'id': '594514cb-0399-5389-a1d9-4d6873606c70', 'created_at': 1756299994959, 'updated_at': 1756299994959, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years’ experience in machine learning and statistical modeling, focused on financial forecasting and predictive analytics. Proven track record of boosting model efficiency by 20%; skilled in Python, R, PyTorch, scikit-learn, SAS, SPSS, and cloud platforms (AWS, Azure). Holds an M.S. in Statistics and a B.S. in Applied Mathematics.'}\n", + "{'id': '9b808a69-a954-5e68-af72-045c3e7f652a', 'created_at': 1759869235560, 'updated_at': 1759869235560, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Senior Data Scientist with 8+ years in machine learning and predictive analytics, proficient in creating advanced algorithms and deploying scalable models in production.'}\n", "\n" ] } @@ -1668,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "id": "c7a8abff", "metadata": { "ExecuteTime": { @@ -1682,15 +1284,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:15:07.822953\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:44.865101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting chunk retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:07.823567\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting chunk retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.083571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 chunks from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.131266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 chunks from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.084214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 chunk payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.132088\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 chunk payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.084687\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.132522\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:45.085107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n" ] }, { @@ -1701,17 +1303,17 @@ "\n", "Extracted chunks are:\n", "\n", - "{'id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'created_at': 1756299995290, 'updated_at': 1756299995290, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", + "{'id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'created_at': 1759869243385, 'updated_at': 1759869243385, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", "\n", - "{'id': '075c882b-ae90-50c0-bc60-71c8da1d898d', 'created_at': 1756300181483, 'updated_at': 1756300181483, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", + "{'id': '51c1e778-f0fe-5cfa-bb0b-8c7a8a28aae5', 'created_at': 1759869243982, 'updated_at': 1759869243982, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", "\n", - "{'id': '0af63ca5-47a0-50f3-b4d8-3463a1d0a749', 'created_at': 1756300071327, 'updated_at': 1756300071327, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", + "{'id': 'e7c43949-a4d6-5cdc-90e0-a7046a528a95', 'created_at': 1759869237781, 'updated_at': 1759869237781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", "\n", - "{'id': '15190e7c-0ec2-5d99-ae68-5d76cb6bb414', 'created_at': 1756299805159, 'updated_at': 1756299805159, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", + "{'id': 'beb4b9bc-703c-5e20-8512-d4468c849277', 'created_at': 1759869235792, 'updated_at': 1759869235792, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", "\n", - "{'id': 'fde5c9b9-ff07-5f7a-ba70-afc421666787', 'created_at': 1756299905819, 'updated_at': 1756299905819, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", + "{'id': '2d052615-b28f-511c-ac15-1bdb7a155ad7', 'created_at': 1759869245851, 'updated_at': 1759869245851, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", "\n", - "{'id': '15790448-7267-5a84-9dd7-be4997889842', 'created_at': 1756300270349, 'updated_at': 1756300270349, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", + "{'id': '1d3c1a95-e2ec-562a-a326-2eadfa556bf4', 'created_at': 1759869241747, 'updated_at': 1759869241747, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", "\n" ] } @@ -1733,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "id": "706a3954", "metadata": {}, "outputs": [ @@ -1745,51 +1347,43 @@ "\n", "Extracted sentences are:\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'ontology_valid': False}, {'id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'name': 'python', 'type': 'Entity', 'created_at': 1756300245018, 'updated_at': 1756300245018, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used for data science and machine learning.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'ontology_valid': False}, {'id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'name': 'python', 'type': 'Entity', 'created_at': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'ontology_valid': False}, {'id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'name': 'r', 'type': 'Entity', 'created_at': 1756300245019, 'updated_at': 1756300245019, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language for statistical analysis.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'ontology_valid': False}, {'id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'name': 'r', 'type': 'Entity', 'created_at': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'contains', 'source_node_id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'target_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'updated_at': '2025-08-27 13:06:34'}, {'id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'name': '', 'type': 'DocumentChunk', 'created_at': 1756299910531, 'updated_at': 1756299910531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['text']}, 'belongs_to_set': None, 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n', 'chunk_size': 329, 'chunk_index': 0, 'cut_type': 'sentence_cut'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'ontology_valid': False}, {'id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'name': 'pytorch', 'type': 'Entity', 'created_at': 1759869236453, 'updated_at': 1759869236453, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Machine learning framework used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'aab60399-8979-5c71-bf42-0c3d41593871', 'ontology_valid': False}, {'id': 'aab60399-8979-5c71-bf42-0c3d41593871', 'name': 'm.s. in statistics', 'type': 'Entity', 'created_at': 1756299984028, 'updated_at': 1756299984028, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'institution: University of Washington\\ncompletion_date: Date_2014'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'contains', 'source_node_id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'target_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'updated_at': '2025-10-07 20:34:03'}, {'id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'name': '', 'type': 'DocumentChunk', 'created_at': 1759869217411, 'updated_at': 1759869217411, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['text']}, 'belongs_to_set': None, 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n', 'chunk_size': 329, 'chunk_index': 0, 'cut_type': 'sentence_cut'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4b4d74c0-999c-55dc-a8dd-eea5be5a90f1', 'ontology_valid': False}, {'id': '4b4d74c0-999c-55dc-a8dd-eea5be5a90f1', 'name': 'b.s. in applied mathematics', 'type': 'Entity', 'created_at': 1756299984029, 'updated_at': 1756299984029, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'institution: University of Texas at Austin\\ncompletion_date: Date_2012'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'obtained', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'ontology_valid': False}, {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'name': 'm.s. in statistics', 'type': 'Entity', 'created_at': 1759869236448, 'updated_at': 1759869236448, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Master's degree in Statistics from University of Washington.\"})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'ontology_valid': False}, {'id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'name': 'pytorch', 'type': 'Entity', 'created_at': 1756300245021, 'updated_at': 1756300245021, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Deep learning framework.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'obtained', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '5abf79a8-2cdc-56a9-b78a-fd8150e9e891', 'ontology_valid': False}, {'id': '5abf79a8-2cdc-56a9-b78a-fd8150e9e891', 'name': 'b.s. in applied mathematics', 'type': 'Entity', 'created_at': 1759869236449, 'updated_at': 1759869236449, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Bachelor's degree in Applied Mathematics from University of Texas at Austin.\"})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c689d93a-230a-566a-a4e6-8e461e56586c', 'ontology_valid': False}, {'id': 'c689d93a-230a-566a-a4e6-8e461e56586c', 'name': 'scikit-learn', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'works_at', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '0d980f2a-09dd-581e-acc3-cc2d87c1bab4', 'ontology_valid': False}, {'id': '0d980f2a-09dd-581e-acc3-cc2d87c1bab4', 'name': 'quantumtech', 'type': 'Entity', 'created_at': 1759869236450, 'updated_at': 1759869236450, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Company where Sarah Nguyen works as a Data Scientist.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'ontology_valid': False}, {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'name': 'sas', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'worked_at', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '95ac0551-38fc-5187-a422-533aeb7e8db0', 'ontology_valid': False}, {'id': '95ac0551-38fc-5187-a422-533aeb7e8db0', 'name': 'datacore solutions', 'type': 'Entity', 'created_at': 1759869236451, 'updated_at': 1759869236451, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Company where Sarah Nguyen worked as a Junior Data Scientist.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'ontology_valid': False}, {'id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'name': 'spss', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'ontology_valid': False}, {'id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'name': 'scikit-learn', 'type': 'Entity', 'created_at': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Machine learning framework used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'ontology_valid': False}, {'id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'name': 'aws', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'ontology_valid': False}, {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'name': 'sas', 'type': 'Entity', 'created_at': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'ontology_valid': False}, {'id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'name': 'azure', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'ontology_valid': False}, {'id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'name': 'spss', 'type': 'Entity', 'created_at': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'ontology_valid': False}, {'id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'name': 'data scientist (quantumtech)', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2016\\nend_date: Date_Present\\nresponsibilities: Designed and implemented machine learning algorithms for financial forecasting; Improved model efficiency by 20% through algorithm optimization'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'ontology_valid': False}, {'id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'name': 'aws', 'type': 'Entity', 'created_at': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'ontology_valid': False}, {'id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'name': 'junior data scientist (datacore solutions)', 'type': 'Entity', 'created_at': 1756299984040, 'updated_at': 1756299984040, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2014\\nend_date: Date_2016\\nresponsibilities: Assisted in developing predictive models for supply chain optimization; Conducted data cleaning and preprocessing on large datasets'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'ontology_valid': False}, {'id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'name': 'azure', 'type': 'Entity', 'created_at': 1759869236456, 'updated_at': 1759869236456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n", "\n", - "({'id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'name': 'email', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email'}, {'relationship_name': 'is_a', 'source_node_id': 'b6365021-70ae-53ea-83e7-885714b56092', 'target_node_id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'updated_at': '2025-08-27 13:09:40'}, {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'name': 'jessica.miller@example.com', 'type': 'Entity', 'created_at': 1756300165516, 'updated_at': 1756300165516, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Primary contact email'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:33:55'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1759869228456, 'updated_at': 1759869228456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Senior Data Scientist with expertise in machine learning and predictive analytics.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:03:24'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1756299793321, 'updated_at': 1756299793321, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: emily.carter@example.com; phone: (555) 123-4567; profession: Senior Data Scientist; years_experience: 8; summary: Senior Data Scientist with over 8 years of experience in machine learning and predictive analytics'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:33:57'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1759869231360, 'updated_at': 1759869231360, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Creative Graphic Designer with over 8 years of experience in visual design and branding.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:05:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1756299890141, 'updated_at': 1756299890141, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: michael.rodriguez@example.com, phone: (555) 234-5678, summary: Data Scientist with background in machine learning and statistical modeling'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1759869236913, 'updated_at': 1759869236913, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist with a strong background in machine learning and statistical modeling.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1756300059090, 'updated_at': 1756300059090, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'summary: Creative Graphic Designer with over 8 years of experience in visual design and branding'})\n", - "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:09:40'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", - "\n", - "({'id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'name': 'team', 'type': 'EntityType', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'team'}, {'relationship_name': 'is_a', 'source_node_id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'target_node_id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'updated_at': '2025-08-27 13:11:09'}, {'id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'name': 'analytics team', 'type': 'Entity', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Dynamic analytics team at TechNova Solutions.'})\n", - "\n", - "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'name': 'david.thompson@example.com', 'type': 'Entity', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: david.thompson@example.com'})\n", - "\n", - "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'name': '(555) 456-7890', 'type': 'Entity', 'created_at': 1756300059092, 'updated_at': 1756300059092, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'phone: (555) 456-7890'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1759869237035, 'updated_at': 1759869237035, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", "\n" ] } @@ -1806,7 +1400,19 @@ "execution_count": null, "id": "d42b3245", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# Only exit in interactive mode, not during GitHub Actions\n", "import os\n", @@ -1845,7 +1451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/cognee_multimedia_demo.ipynb b/notebooks/cognee_multimedia_demo.ipynb index 72342250b..608e11956 100644 --- a/notebooks/cognee_multimedia_demo.ipynb +++ b/notebooks/cognee_multimedia_demo.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:44.613431Z", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:46.739157Z", @@ -97,6 +97,38 @@ "# os.environ[\"DB_PASSWORD\"]=\"cognee\"" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:37:13.488510\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-23.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:37:14.172414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:37:14.172932\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + } + ], + "source": [ + "import cognee\n", + "print(cognee.__version__)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -114,31 +146,24 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T14:33:41.256195\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-27_14-00-27.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:37:20.743332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:42.133224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:20.776490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:42.133667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:23.387773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:43.785214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:44.215920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[1mDeleting cache... \u001b[0m\n", "\n", - "\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\n", - "\u001b[92m15:33:44 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n" + "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "User 37ea34fa-cae7-4bea-8cb3-1ba234688771 has registered.\n" + "User 03f552c1-331f-40b2-a99b-b3b05aa93e0d has registered.\n" ] }, { @@ -148,164 +173,131 @@ "\n", "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.440270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.691142\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.690756\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.691670\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.996600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.692087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.287352\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.693388\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.287759\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.693668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.694024\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.708303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:52 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\u001b[2m2025-10-07T20:37:24.708776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.455447\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.599686\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709654\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.806593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.075106\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.420233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.209912\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.420796\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.355890\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:53 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:28.421255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m15:33:57 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\u001b[2m2025-10-07T20:37:28.423491\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.423881\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.407561\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.424259\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.560808\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.434168\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.713507\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453069\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.897060\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453489\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.938027\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.093101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.255165\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.428623\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.588682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:58 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:28.462413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.466745\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.706892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.470294\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.707703\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.476006\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708083\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.030103\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708440\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.065148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.065868\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.709129\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.066315\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.709475\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'how many programmers does it take to change a light bulb? none, thats a hardware problem.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.066713\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:24.553989\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:32.067064\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.067410\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:32.883579\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.202761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.680233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.203355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.825933\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.203785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.975352\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.126720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204544\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.275404\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204964\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'humor' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.424984\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:34.265785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.576258\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.003525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.754472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.952187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.912219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.970171\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.053036\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.024476\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.220157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.025311\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.388094\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:34:37 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:38.025564\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.025803\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.010321\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.012394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.012794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026663\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013111\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.680393\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.680986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013598\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'joke' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681647\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.014215\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer joke' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:02.040520\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:35:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:38.682229\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:11.589828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.446614\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.622281\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.820192\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.004173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.518803\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.756519\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.978364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:37:38.682567\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "{UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('17b5c469-a8ce-5347-bea5-ab3dba767d13')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('5b1e3c7e-d837-5704-a3b3-53abdda3a84f')}])}" + "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}" ] }, "execution_count": 4, @@ -349,23 +341,29 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T14:36:09.273837\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.668682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "\u001b[2m2025-08-27T14:36:09.275355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.933137\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 2 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.691101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 2 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.933995\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 2 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.691827\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 2 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.934301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.692207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 2 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:37:42.934604\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 2 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'id': 'facab42e-12fc-557e-aaf4-09c02ae1cd4f', 'created_at': 1756305273061, 'updated_at': 1756305273061, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers won't change a light bulb — it's considered a hardware issue.\"}\n", - "{'id': '958f2bc9-060b-5500-b14a-19b300cc99aa', 'created_at': 1756305311791, 'updated_at': 1756305311791, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'One-line programmer joke: changing a light bulb is labeled a hardware issue.'}\n" + "{'id': '766ac5d6-1a81-530e-a934-61e2bf505d9b', 'created_at': 1759869455990, 'updated_at': 1759869455990, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'A humorous take on programmers and light bulbs.'}\n", + "{'id': '2862798a-0dfc-5994-a3ca-9f4329f42f06', 'created_at': 1759869455989, 'updated_at': 1759869455989, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers won't change a light bulb.\"}\n" ] } ], @@ -429,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/cognee_simple_demo.ipynb b/notebooks/cognee_simple_demo.ipynb index 6ca42ada5..161e03d4b 100644 --- a/notebooks/cognee_simple_demo.ipynb +++ b/notebooks/cognee_simple_demo.ipynb @@ -79,580 +79,246 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:34:14.905059\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-44-42.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:38:23.321871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-35.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:15.858104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:23.924664\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:15.859638\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. 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See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:22.954992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.824653\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.160900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.825175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - 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"\u001b[2m2025-08-27T13:34:23.536911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.836754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.685341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.836303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847599\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.058213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.084248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.967450\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.232775\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.980303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.378181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.998286\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.555749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.998936\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.803351\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:30.999638\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:31.006879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:31.119544\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.668159\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669164\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669470\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669777\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.670086\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.670369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.670667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit-hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.974856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.670972\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.976689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.976989\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671450\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977311\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671650\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671857\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977842\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672094\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978119\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672398\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978445\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tunnel or well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672607\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dodo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heap of sticks and dry leaves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672847\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lory' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'long low hall' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eaglet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979218\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673399\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duck' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'three legged glass table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'historical figure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979726\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tiny golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'william the conqueror' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979974\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674120\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mary ann' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small passage' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674384\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lovely garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jar labeled orange marmalade' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caterpillar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980758\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cupboard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675153\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle labeled drink me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675376\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small glass box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cake labeled eat me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675837\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mushroom' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981672\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676060\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'father william' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves and fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676333\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'character' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982174\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bookchapter' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676573\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pigeon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter i down the rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676801\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'baby' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982656\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter ii the pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cheshire cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983007\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hatter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duck' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dodo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677694\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'queen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lory' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677922\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eaglet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magpie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'may' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984380\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canary' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984798\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'young crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985030\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animalclass' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cats' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679587\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dogs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the rose tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'event' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'w. rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the mad tea party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986089\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680632\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986292\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681429\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986527\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle (drink me)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gryphon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'thimble' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soldiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986962\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'comfits' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682200\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_1' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987297\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'event' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682379\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_2' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caucus-race' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682699\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_3' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grew large event' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tortoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988057\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683560\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizards' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988461\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magic bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'food' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988654\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size-changing cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684025\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tarts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988869\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pebbles' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mushroom' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684671\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caterpillar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684888\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'subject' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989498\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hookah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685135\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989699\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill (lizard)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'arithmetic' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685715\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mystery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685998\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990390\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mary ann' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686239\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'court' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990541\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'place' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686484\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990742\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'window' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686886\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 14' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687125\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991246\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chimney' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 16' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991431\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cucumber-frame' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'place' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991636\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wood' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688321\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'puppy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688615\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'father william' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992222\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689141\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'child-life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992402\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size change' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689345\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple_joys' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992893\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pigeon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689662\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple_sorrows' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993054\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'serpent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:55.033467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trees' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:03.406344\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993435\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.113087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fish-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.113738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frog-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114015\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994053\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114579\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994264\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114971\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'baby' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.115220\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.994797\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cheshire cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.994960\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'kitchen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995114\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cauldron' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995283\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995444\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frying-pan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'invitation letter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996298\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'queen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hatter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'game' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996993\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a mad tea-party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998205\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998409\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the hatters watch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998581\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'house of the march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998980\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'five' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'two' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999302\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seven' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'group' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soldiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.000078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'courtiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.000273\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'royal children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.001132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.002873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.003453\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.004815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'treacle-well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'elsie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lacie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tillie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'riddle: why is a raven like a writing-desk?' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'song: twinkle twinkle little bat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tea-time' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.007096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.007449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'executioner' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.008948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gryphon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009604\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet game' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet ground' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hedgehog' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010238\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flamingo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010795\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flower-pot' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'players' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011425\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old turtle (tortoise)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.012966\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill the lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.014629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015124\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015383\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'snail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'porpoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'owl' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.016462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'panther' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea-shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'court of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.018187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille (dance)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.019379\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.020269\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beautiful soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.021436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting song' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'school (sea-school)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeling and writhing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022812\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ambition' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'distraction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'uglification' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'derision' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mystery (ancient and modern)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024376\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'drawling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stretching' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fainting in coils' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laughing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025502\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grief' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025949\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'conger eel (drawling-master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab (classics master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026692\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tarts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pencil' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.027897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'slate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.028631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchesss cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.028848\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizard (bill)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.029560\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.030437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.030900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031211\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trial of the knave' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'document' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031563\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'parchment scroll' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031771\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trumpet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'teacup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032145\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bread-and-butter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032520\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'inkstand' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032767\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canvas bag' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.033132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.034834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alices dream' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.036198\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.037172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fourteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.037973\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fifteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sixteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038606\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'she' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039022\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dull reality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039400\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039578\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grass' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039913\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeds' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040074\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rattling teacups' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040285\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tinkling sheep-bells' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040468\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shepherd boy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040662\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other queer noises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040836\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'busy farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.041105\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cattle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.041323\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grown woman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple and loving heart' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042329\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other little children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'strange tale' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dream of wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple sorrows' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple joys' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043331\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'child-life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043505\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'happy summer days' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:02.674965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:16.723969\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.505751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.656760\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.853916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.999416\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.182065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.379190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.534779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:07.115479\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "{UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('692741cd-46e5-5988-85e9-f3901d104b7e')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('899de74a-1bef-5afd-a478-1ea944503514')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('6a0c7501-47bc-5632-b79c-3343d8a0b2a2')}])}" + "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('3ad0b58b-2b39-5bf8-97de-4db67bd2555c')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}" ] }, "execution_count": 3, @@ -662,6 +328,7 @@ ], "source": [ "import cognee\n", + "print(cognee.__version__)\n", "await cognee.add(file_path)\n", "await cognee.cognify()" ] @@ -685,20 +352,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:36:30.994342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 239 nodes, 745 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.164471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:36:31.598044\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.04s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[92m14:36:31 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:07.474073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "['Acknowledged.']" + "['1. Alice \\n2. White Rabbit \\n3. March Hare \\n4. Hatter \\n5. Cheshire Cat \\n6. Queen of Hearts \\n7. Knave of Hearts \\n8. Dormouse']" ] }, "execution_count": 4, @@ -712,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "883ce50d2d9dc584", "metadata": {}, "outputs": [ @@ -720,44 +382,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:02 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - 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"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m" + "\n", + "\u001b[2m2025-10-07T20:39:36.551739\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:39:36.896038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "[\"Alice ended up in Wonderland by following a curious White Rabbit that she encountered while sitting on a riverbank. The Rabbit muttered about being late and carried a watch, piquing Alice's curiosity. She followed it down a rabbit hole, which led her to a fantastical world.\"]" + "['Alice ended up in Wonderland by following a hurried White Rabbit down a rabbit-hole after feeling bored and drowsy.']" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "677e1bc52aa078b6", "metadata": {}, "outputs": [ @@ -776,44 +413,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m" + "\n", + "\u001b[2m2025-10-07T20:39:43.171619\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:39:43.468210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "['Alice is portrayed as a curious and adventurous girl who explores Wonderland. She often questions her identity and the world around her, showing a blend of innocence and boldness. Alice displays a willingness to engage with bizarre situations and characters, while also being contemplative about her experiences.']" + "[\"Alice is described as a curious girl who exhibits a desire for adventure and exploration. She is imaginative, pondering various whimsical questions and thoughts as she navigates the oddities of Wonderland. Her personality shows signs of being thoughtful and reflective, often giving herself advice, though she doesn't always follow it. Despite her adventures and the surreal situations she encounters, she maintains a sense of bravery and a degree of confidence in her interactions with the fantastical characters she meets.\"]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -832,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "6effdae590b795d3", "metadata": {}, "outputs": [ @@ -841,9 +453,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:36:40.283583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:50.413314\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:36:40.284941\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:50.413846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { @@ -861,7 +473,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -932,7 +544,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/ontology_demo.ipynb b/notebooks/ontology_demo.ipynb index ef4a046b8..17d7feb28 100644 --- a/notebooks/ontology_demo.ipynb +++ b/notebooks/ontology_demo.ipynb @@ -36,36 +36,61 @@ }, { "cell_type": "code", + "execution_count": null, "id": "8cf7ba29f9a150af", "metadata": {}, + "outputs": [], "source": [ "# Install required package\n", "# !pip install cognee" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 1, "id": "abb86851", "metadata": {}, + "outputs": [], "source": [ "import os\n", "\n", "# Set up OpenAI API key (required for Cognee's LLM functionality)\n", "if \"LLM_API_KEY\" not in os.environ:\n", " os.environ[\"LLM_API_KEY\"] = \"your-api-key-here\" # Replace with your API key" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 2, "id": "d825d126b3a0ec26", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:15.192965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-25-04.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:15.894155\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:15.894641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + } + ], "source": [ "# Import required libraries\n", "import cognee\n", + "print(cognee.__version__)\n", "from cognee.shared.logging_utils import get_logger\n", "\n", "cognee.config.set_llm_model(\"gpt-4o-mini\")\n", @@ -73,9 +98,7 @@ "from cognee.api.v1.search import SearchType\n", "\n", "logger = get_logger()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -92,8 +115,10 @@ }, { "cell_type": "code", + "execution_count": 3, "id": "4d0e4a58e4207a7d", "metadata": {}, + "outputs": [], "source": [ "async def run_pipeline(config=None):\n", " # Clean existing data\n", @@ -124,9 +149,7 @@ " )\n", " answers.append(search_results)\n", " return answers" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -140,8 +163,433 @@ }, { "cell_type": "code", + "execution_count": 4, "id": "1363772d2b48f5c0", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:24.236015\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology loaded successfully from file: ../examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.236931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLookup built: 4 classes, 50 individuals\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.348030\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.387417\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Results WITH ontology ---\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:26.930731\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n", + "\n", + "\u001b[1mDeleting cache... \u001b[0m\n", + "\n", + "\u001b[1m✓ Cache deleted successfully! 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Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.628591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFalling back to PyPDF loader for /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.629517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReading PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.pypdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.747011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m 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[\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: No languages specified, defaulting to English.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:36.849367\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.863627\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.726911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m 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[\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.759801\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760398\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760701\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760968\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#Hypertension match was found for found for 'hypertension' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.763146\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#HighCholesterol match was found for found for 'cholesterol' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.763622\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#AtrialFibrillation match was found for found for 'atrial fibrillation' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.764459\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#HeartFailure match was found for found for 'heart failure' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.765517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CoronaryArteryDisease match was found for found for 'coronary heart disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.765914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'filtered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766546\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'boiled coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766895\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767142\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767784\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769313\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769628\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769899\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.770226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.770777\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'smoking history' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771177\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771432\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research method' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771696\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771952\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cohort study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.772202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.772565\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#ModerateCoffeeConsumption match was found for found for 'moderate coffee consumption' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:58.393172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.885405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.886605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.887338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.888151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.888781\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.889249\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.889792\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.890369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.890717\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.891286\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.891705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.892208\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.892732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.893359\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.893834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.894316\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.894743\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'methodology' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cox regression models' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895772\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.896129\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrients 2021, 13, 1241' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.896884\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dietary habit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897435\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease (cvd)' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.899072\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#Cancer match was found for found for 'cancer' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.900268\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.900642\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901370\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901726\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research finding' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902070\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee death association' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902739\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption types' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903068\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903728\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904441\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trigonelline' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905223\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'melanoidins' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health benefit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption mortality reduction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.907285\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.907833\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adult population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.908178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lifestyle' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.908658\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#MediterraneanDiet match was found for found for 'mediterranean lifestyle' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909236\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909603\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909852\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910155\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutritional study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study participants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910775\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ethics committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911403\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data contribution' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:03.240933\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:04.242533\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.771183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.771815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772143\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772389\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772610\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772909\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.773236\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:11.284104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.335459\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336033\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336293\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336526\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336901\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.337200\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.337497\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.351757\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.656347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:17.595983\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:18.045113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:19.425864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:19.711301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:21.796373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:22.366335\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q: What are common risk factors for Type 2 Diabetes?\n", + "A: ['Common risk factors for Type 2 Diabetes include:\\n1. Obesity\\n2. Hypertension (high blood pressure)\\n3. High cholesterol\\n4. Smoking\\n5. Cardiovascular disease\\n6. Heart failure']\n", + "\n", + "Q: What preventive measures reduce the risk of Hypertension?\n", + "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. Low sodium diet\\n2. Moderate coffee consumption\\n3. Regular exercise\\n4. Maintaining a healthy lifestyle']\n", + "\n", + "Q: What symptoms indicate possible Cardiovascular Disease?\n", + "A: ['Symptoms indicating possible cardiovascular disease include:\\n1. Chest pain\\n2. Shortness of breath\\n3. Fatigue']\n", + "\n", + "Q: What diseases are associated with Obesity?\n", + "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, hypertension, high cholesterol, and high blood pressure.']\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:48.134301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + } + ], "source": [ "from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver\n", "from cognee.modules.ontology.ontology_config import Config\n", @@ -168,14 +616,411 @@ "answers_with = await query_pipeline(questions)\n", "for q, a in zip(questions, answers_with):\n", " print(f\"Q: {q}\\nA: {a}\\n\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 5, "id": "3aa18f4cdd5ceff6", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:23.957345\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Results WITHOUT ontology ---\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:25.980678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mDeleting cache... \u001b[0m\n", + "\n", + "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.100988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.101412\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.101746\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.102449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.102713\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.103039\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.115879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mProcessing PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User 215612a9-f107-44d8-9263-d680872182c9 has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf ...\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.605043\u001b[0m [\u001b[33m\u001b[1mwarning \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. 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"\u001b[2m2025-10-07T20:41:27.349508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:27.387202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:27.398908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.152674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.153506\u001b[0m [\u001b[32m\u001b[1minfo 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[\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.155919\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.156428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.156854\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spain' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157532\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.158267\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'journal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.158702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrients' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159393\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-03-17' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-07' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160058\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160442\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160809\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161404\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162033\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'license' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162473\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cc by' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162946\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.163295\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.163722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cvd mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165120\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165657\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical measure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165990\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffees hazard ratio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166280\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166547\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'oxidative stress' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166889\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'measurement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167111\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167340\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167630\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167945\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrient' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169838\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170109\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spanish population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170432\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171027\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'self-reported data' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171340\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171649\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172150\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ethical committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172534\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcomes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172825\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee preparation method' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical power' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173499\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'informed consent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sample size' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.669259\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.669912\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.670464\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.670996\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.671558\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.671838\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672203\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672619\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672998\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673566\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673909\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674199\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee preparation methods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674587\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675105\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study period 2000-2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676116\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption effects' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676642\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pilot study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.677362\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.677669\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemicalcompound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678326\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678644\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medicalcondition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679278\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679582\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679851\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'smoking' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680130\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680382\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'researchstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680968\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health studies' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681265\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'researchmethod' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681565\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medicalprocedure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681841\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiac surgery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.725265\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:53.910668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:57.080341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.770067\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.803614\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804032\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804279\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804531\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.805061\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.805294\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.709736\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.710292\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.710708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.711324\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.711677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.712008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.712225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.725796\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:03.005337\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:05.945308\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:07.476846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:11.047320\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:11.394591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:14.634449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:14.913245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q: What are common risk factors for Type 2 Diabetes?\n", + "A: ['Common risk factors for Type 2 Diabetes include obesity (as indicated by a body mass index of 25 kg/m² or more), sedentary lifestyle, poor dietary habits, smoking habits, and the presence of chronic conditions such as hypertension and high blood cholesterol. Additionally, waist circumference measurement can indicate risk levels, with increased risk seen in men with a waist over 102 cm and women over 88 cm.']\n", + "\n", + "Q: What preventive measures reduce the risk of Hypertension?\n", + "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. **Moderate Coffee Consumption**: Regular moderate coffee consumption (1-4 cups per day) is associated with a decreased risk of developing hypertension.\\n2. **Dietary Antioxidants**: Coffee contains antioxidants, such as chlorogenic acid, which may inhibit inflammation and support cardiovascular health.\\n3. **Healthy Preparation Methods**: Choosing filtered coffee over boiled coffee can reduce cholesterol levels and promote better heart health.\\n4. **Lifestyle Factors**: Maintaining overall cardiovascular health through lifestyle choices can also contribute to lowering the risk of hypertension.']\n", + "\n", + "Q: What symptoms indicate possible Cardiovascular Disease?\n", + "A: ['Possible symptoms indicating cardiovascular disease include:\\n1. Chest pain or discomfort\\n2. Shortness of breath\\n3. Fatigue or weakness\\n4. Irregular heartbeat\\n5. Swelling in the legs, ankles, or feet \\n6. Lightheadedness or dizziness \\n7. Nausea or cold sweat \\n\\nIt is important to consult a healthcare professional for proper diagnosis and treatment.']\n", + "\n", + "Q: What diseases are associated with Obesity?\n", + "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, and hypertension. Obesity is a significant risk factor for these health conditions, often leading to increased mortality and other health complications.']\n", + "\n" + ] + } + ], "source": [ "# Run without ontology\n", "print(\"\\n--- Results WITHOUT ontology ---\\n\")\n", @@ -183,9 +1028,7 @@ "answers_without = await query_pipeline(questions)\n", "for q, a in zip(questions, answers_without):\n", " print(f\"Q: {q}\\nA: {a}\\n\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -199,8 +1042,40 @@ }, { "cell_type": "code", + "execution_count": 6, "id": "36ee2a360f47a054", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:42:22.959024\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:22.959720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'/Users/daulet/graph_visualization.html'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import webbrowser\n", "import os\n", @@ -210,9 +1085,7 @@ "html_file = os.path.join(home_dir, \"graph_visualization.html\")\n", "display(html_file)\n", "webbrowser.open(f\"file://{html_file}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -250,8 +1123,22 @@ }, { "cell_type": "code", + "execution_count": null, "id": "8d2a0fe555a7bc0f", "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# Only exit in interactive mode, not during GitHub Actions\n", "import os\n", @@ -262,17 +1149,15 @@ " os._exit(0)\n", "else:\n", " print(\"Skipping kernel exit - running in GitHub Actions\")" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "code", - "source": "", + "execution_count": null, "id": "adb6601890237b6a", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { @@ -291,7 +1176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, From ee079604f41613c26c18c4a900e70f85fe70336c Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 22:03:16 +0100 Subject: [PATCH 088/139] chore: keep exception handling changes to instructor adapters --- .github/workflows/test_s3_file_storage.yml | 4 ++++ .../litellm_instructor/llm/generic_llm_api/adapter.py | 4 ++-- .../litellm_instructor/llm/openai/adapter.py | 6 +++--- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test_s3_file_storage.yml b/.github/workflows/test_s3_file_storage.yml index a477d8933..ce50aa4f5 100644 --- a/.github/workflows/test_s3_file_storage.yml +++ b/.github/workflows/test_s3_file_storage.yml @@ -6,6 +6,10 @@ on: permissions: contents: read +env: + RUNTIME__LOG_LEVEL: ERROR + ENV: 'test' + jobs: test-gemini: name: Run S3 File Storage Test diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py index 8c15a5804..917599d4d 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py @@ -118,7 +118,7 @@ class GenericAPIAdapter(LLMInterface): if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint): raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error try: return await self.aclient.chat.completions.create( @@ -151,4 +151,4 @@ class GenericAPIAdapter(LLMInterface): else: raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py index 989b240ac..527f64d75 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py @@ -146,11 +146,11 @@ class OpenAIAdapter(LLMInterface): ContentFilterFinishReasonError, ContentPolicyViolationError, InstructorRetryException, - ): + ) as e: if not (self.fallback_model and self.fallback_api_key): raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from e try: return await self.aclient.chat.completions.create( @@ -183,7 +183,7 @@ class OpenAIAdapter(LLMInterface): else: raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error @observe @sleep_and_retry_sync() From 978549106c81204eaaa99e44e0a0d8dc168c3512 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 22:04:26 +0100 Subject: [PATCH 089/139] chore: use dev env in test_s3 CI --- .github/workflows/test_s3_file_storage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test_s3_file_storage.yml b/.github/workflows/test_s3_file_storage.yml index ce50aa4f5..04d140513 100644 --- a/.github/workflows/test_s3_file_storage.yml +++ b/.github/workflows/test_s3_file_storage.yml @@ -8,7 +8,7 @@ permissions: env: RUNTIME__LOG_LEVEL: ERROR - ENV: 'test' + ENV: 'dev' jobs: test-gemini: From ea33854d4906e92da8600ff6575ef5ea4f7787ff Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 14:06:13 +0530 Subject: [PATCH 090/139] Removed print statement logging and used cognee inbuilt logger and updated doctrings. --- cognee/tasks/web_scraper/web_scraper_task.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index 969aef0d7..570cf80c3 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -96,7 +96,7 @@ async def cron_web_scraper_task( return # If no schedule, run immediately - print(f"[{datetime.now()}] Running web scraper task immediately...") + logger.info(f"[{datetime.now()}] Running web scraper task immediately...") return await web_scraper_task( url=url, schedule=schedule, @@ -118,10 +118,10 @@ async def web_scraper_task( tavily_config: TavilyConfig = None, job_name: str = None, ): - """Scrape URLs and store data points in a Kuzu graph database. + """Scrape URLs and store data points in a Graph database. This function scrapes content from the provided URLs, creates or updates WebPage, - WebSite, and ScrapingJob data points, and stores them in a Kuzu graph database. + WebSite, and ScrapingJob data points, and stores them in a Graph database. Each data point includes a description field summarizing its attributes. It creates 'is_scraping' (ScrapingJob to WebSite) and 'is_part_of' (WebPage to WebSite) relationships, preserving existing edges during node updates. From 8d27da659a9301f7c577dddae4bbadc095283ef3 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 14:07:01 +0530 Subject: [PATCH 091/139] removed dotenv imports --- cognee/tests/tasks/web_scraping/web_scraping_test.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index c792c28cd..7ba1b9956 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -2,9 +2,6 @@ import asyncio import cognee from cognee.tasks.web_scraper.config import SoupCrawlerConfig from cognee.tasks.web_scraper import cron_web_scraper_task -from dotenv import load_dotenv - -load_dotenv() async def test_web_scraping_using_bs4(): From af71cba07fc177c4869f1f2a349f9f1d28de7494 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 14:10:29 +0530 Subject: [PATCH 092/139] Trying to resolve uv.lock --- uv.lock | 604 +++++++++++++++++++++++++++++++++----------------------- 1 file changed, 356 insertions(+), 248 deletions(-) diff --git a/uv.lock b/uv.lock index 0a2058c24..574e20ef3 100644 --- a/uv.lock +++ b/uv.lock @@ -2,23 +2,39 @@ version = 1 revision = 3 requires-python = ">=3.10, <=3.13" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", - "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'", - "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'", - 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.utils import fetch_page_content try: - from apscheduler.schedulers.background import BackgroundScheduler + from apscheduler.triggers.cron import CronTrigger + from apscheduler.schedulers.asyncio import AsyncIOScheduler - scheduler = BackgroundScheduler() + scheduler = AsyncIOScheduler() except ImportError: raise ImportError("Please install apscheduler by pip install APScheduler>=3.10") @@ -75,17 +74,16 @@ async def cron_web_scraper_task( raise ValueError(f"Invalid cron string '{schedule}': {e}") scheduler.add_job( - lambda: asyncio.run( - web_scraper_task( - url=url, - schedule=schedule, - extraction_rules=extraction_rules, - tavily_api_key=tavily_api_key, - soup_crawler_config=soup_crawler_config, - tavily_config=tavily_config, - job_name=job_name, - ) - ), + web_scraper_task, + kwargs={ + "url": url, + "schedule": schedule, + "extraction_rules": extraction_rules, + "tavily_api_key": tavily_api_key, + "soup_crawler_config": soup_crawler_config, + "tavily_config": tavily_config, + "job_name": job_name, + }, trigger=trigger, id=job_name, name=job_name or f"WebScraper_{uuid5(NAMESPACE_OID, name=job_name)}", @@ -249,7 +247,7 @@ async def web_scraper_task( f"Webpage: {parsed_url.path.lstrip('/') or 'Home'}\n" f"URL: {page_url}\n" f"Scraped at: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" - f"Content: {content_str}" + f"Content: {content_str}\n" f"Content type: text\n" f"Page size: {len(content_str)} bytes\n" f"Status code: 200" From 599ef4a141bc85745ebca0bf304a9ee90a770f79 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 16:10:35 +0530 Subject: [PATCH 094/139] solved nitpick comments --- cognee/tasks/web_scraper/web_scraper_task.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index a77e732cb..52b73b5b5 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -25,14 +25,22 @@ from .utils import fetch_page_content try: from apscheduler.triggers.cron import CronTrigger from apscheduler.schedulers.asyncio import AsyncIOScheduler - - scheduler = AsyncIOScheduler() except ImportError: raise ImportError("Please install apscheduler by pip install APScheduler>=3.10") logger = get_logger(__name__) +_scheduler = None + + +def get_scheduler(): + global _scheduler + if _scheduler is None: + _scheduler = AsyncIOScheduler() + return _scheduler + + async def cron_web_scraper_task( url: Union[str, List[str]], *, @@ -73,6 +81,7 @@ async def cron_web_scraper_task( except ValueError as e: raise ValueError(f"Invalid cron string '{schedule}': {e}") + scheduler = get_scheduler() scheduler.add_job( web_scraper_task, kwargs={ From 6acc44de1629e8e6836ecd161f7587e559558afe Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 20:14:35 +0100 Subject: [PATCH 095/139] Improve exception handling to provide clearer error context --- cognee/cli/commands/add_command.py | 6 +++--- cognee/cli/commands/cognify_command.py | 6 +++--- cognee/cli/commands/config_command.py | 16 +++++++++------- cognee/cli/commands/delete_command.py | 6 +++--- cognee/cli/commands/search_command.py | 6 +++--- .../graph/neptune_driver/neptune_utils.py | 2 +- .../relational/sqlalchemy/SqlAlchemyAdapter.py | 2 +- .../embeddings/FastembedEmbeddingEngine.py | 4 +++- .../vector/embeddings/LiteLLMEmbeddingEngine.py | 2 +- .../users/methods/get_authenticated_user.py | 4 +++- .../modules/users/roles/methods/create_role.py | 4 ++-- .../users/tenants/methods/create_tenant.py | 4 ++-- 12 files changed, 34 insertions(+), 28 deletions(-) diff --git a/cognee/cli/commands/add_command.py b/cognee/cli/commands/add_command.py index 4260b52bc..d3fea7f80 100644 --- a/cognee/cli/commands/add_command.py +++ b/cognee/cli/commands/add_command.py @@ -70,11 +70,11 @@ After adding data, use `cognee cognify` to process it into knowledge graphs. await cognee.add(data=data_to_add, dataset_name=args.dataset_name) fmt.success(f"Successfully added data to dataset '{args.dataset_name}'") except Exception as e: - raise CliCommandInnerException(f"Failed to add data: {str(e)}") + raise CliCommandInnerException(f"Failed to add data: {str(e)}") from e asyncio.run(run_add()) except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error adding data: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error adding data: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/cognify_command.py b/cognee/cli/commands/cognify_command.py index 894bfcd37..16eaf0454 100644 --- a/cognee/cli/commands/cognify_command.py +++ b/cognee/cli/commands/cognify_command.py @@ -107,7 +107,7 @@ After successful cognify processing, use `cognee search` to query the knowledge ) return result except Exception as e: - raise CliCommandInnerException(f"Failed to cognify: {str(e)}") + raise CliCommandInnerException(f"Failed to cognify: {str(e)}") from e result = asyncio.run(run_cognify()) @@ -124,5 +124,5 @@ After successful cognify processing, use `cognee search` to query the knowledge except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error during cognification: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error during cognification: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/config_command.py b/cognee/cli/commands/config_command.py index 25333e77a..752db6403 100644 --- a/cognee/cli/commands/config_command.py +++ b/cognee/cli/commands/config_command.py @@ -79,8 +79,10 @@ Configuration changes will affect how cognee processes and stores data. except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error managing configuration: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException( + f"Error managing configuration: {str(e)}", error_code=1 + ) from e def _handle_get(self, args: argparse.Namespace) -> None: try: @@ -122,7 +124,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.note("Configuration viewing not fully implemented yet") except Exception as e: - raise CliCommandInnerException(f"Failed to get configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to get configuration: {str(e)}") from e def _handle_set(self, args: argparse.Namespace) -> None: try: @@ -141,7 +143,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.error(f"Failed to set configuration key '{args.key}'") except Exception as e: - raise CliCommandInnerException(f"Failed to set configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to set configuration: {str(e)}") from e def _handle_unset(self, args: argparse.Namespace) -> None: try: @@ -189,7 +191,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.note("Use 'cognee config list' to see all available configuration options") except Exception as e: - raise CliCommandInnerException(f"Failed to unset configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to unset configuration: {str(e)}") from e def _handle_list(self, args: argparse.Namespace) -> None: try: @@ -209,7 +211,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.echo(" cognee config reset - Reset all to defaults") except Exception as e: - raise CliCommandInnerException(f"Failed to list configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to list configuration: {str(e)}") from e def _handle_reset(self, args: argparse.Namespace) -> None: try: @@ -222,4 +224,4 @@ Configuration changes will affect how cognee processes and stores data. fmt.echo("This would reset all settings to their default values") except Exception as e: - raise CliCommandInnerException(f"Failed to reset configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to reset configuration: {str(e)}") from e diff --git a/cognee/cli/commands/delete_command.py b/cognee/cli/commands/delete_command.py index 46cf41367..8400d3b0f 100644 --- a/cognee/cli/commands/delete_command.py +++ b/cognee/cli/commands/delete_command.py @@ -100,7 +100,7 @@ Be careful with deletion operations as they are irreversible. else: await cognee.delete(dataset_name=args.dataset_name, user_id=args.user_id) except Exception as e: - raise CliCommandInnerException(f"Failed to delete: {str(e)}") + raise CliCommandInnerException(f"Failed to delete: {str(e)}") from e asyncio.run(run_delete()) # This success message may be inaccurate due to the underlying bug, but we leave it for now. @@ -108,5 +108,5 @@ Be careful with deletion operations as they are irreversible. except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error deleting data: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error deleting data: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/search_command.py b/cognee/cli/commands/search_command.py index 3540a833c..efd18591b 100644 --- a/cognee/cli/commands/search_command.py +++ b/cognee/cli/commands/search_command.py @@ -108,7 +108,7 @@ Search Types & Use Cases: ) return results except Exception as e: - raise CliCommandInnerException(f"Failed to search: {str(e)}") + raise CliCommandInnerException(f"Failed to search: {str(e)}") from e results = asyncio.run(run_search()) @@ -145,5 +145,5 @@ Search Types & Use Cases: except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error searching: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error searching: {str(e)}", error_code=1) from e diff --git a/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py b/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py index 0f71bd4e9..e3b4c0a6a 100644 --- a/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py +++ b/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py @@ -53,7 +53,7 @@ def parse_neptune_url(url: str) -> Tuple[str, str]: return graph_id, region except Exception as e: - raise ValueError(f"Failed to parse Neptune Analytics URL '{url}': {str(e)}") + raise ValueError(f"Failed to parse Neptune Analytics URL '{url}': {str(e)}") from e def validate_graph_id(graph_id: str) -> bool: diff --git a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py index 88d2abc7e..380ce9917 100644 --- a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py +++ b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py @@ -283,7 +283,7 @@ class SQLAlchemyAdapter: try: data_entity = (await session.scalars(select(Data).where(Data.id == data_id))).one() except (ValueError, NoResultFound) as e: - raise EntityNotFoundError(message=f"Entity not found: {str(e)}") + raise EntityNotFoundError(message=f"Entity not found: {str(e)}") from e # Check if other data objects point to the same raw data location raw_data_location_entities = ( diff --git a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py index 8f922d934..e34ab5d9d 100644 --- a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py @@ -90,7 +90,9 @@ class FastembedEmbeddingEngine(EmbeddingEngine): except Exception as error: logger.error(f"Embedding error in FastembedEmbeddingEngine: {str(error)}") - raise EmbeddingException(f"Failed to index data points using model {self.model}") + raise EmbeddingException( + f"Failed to index data points using model {self.model}" + ) from error def get_vector_size(self) -> int: """ diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py index d68941d25..ccef7bfcd 100644 --- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py @@ -150,7 +150,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): litellm.exceptions.NotFoundError, ) as e: logger.error(f"Embedding error with model {self.model}: {str(e)}") - raise EmbeddingException(f"Failed to index data points using model {self.model}") + raise EmbeddingException(f"Failed to index data points using model {self.model}") from e except Exception as error: logger.error("Error embedding text: %s", str(error)) diff --git a/cognee/modules/users/methods/get_authenticated_user.py b/cognee/modules/users/methods/get_authenticated_user.py index a2dd2330e..d78215892 100644 --- a/cognee/modules/users/methods/get_authenticated_user.py +++ b/cognee/modules/users/methods/get_authenticated_user.py @@ -37,6 +37,8 @@ async def get_authenticated_user( except Exception as e: # Convert any get_default_user failure into a proper HTTP 500 error logger.error(f"Failed to create default user: {str(e)}") - raise HTTPException(status_code=500, detail=f"Failed to create default user: {str(e)}") + raise HTTPException( + status_code=500, detail=f"Failed to create default user: {str(e)}" + ) from e return user diff --git a/cognee/modules/users/roles/methods/create_role.py b/cognee/modules/users/roles/methods/create_role.py index a57f000fe..aa7eda91d 100644 --- a/cognee/modules/users/roles/methods/create_role.py +++ b/cognee/modules/users/roles/methods/create_role.py @@ -40,8 +40,8 @@ async def create_role( # Add association directly to the association table role = Role(name=role_name, tenant_id=tenant.id) session.add(role) - except IntegrityError: - raise EntityAlreadyExistsError(message="Role already exists for tenant.") + except IntegrityError as e: + raise EntityAlreadyExistsError(message="Role already exists for tenant.") from e await session.commit() await session.refresh(role) diff --git a/cognee/modules/users/tenants/methods/create_tenant.py b/cognee/modules/users/tenants/methods/create_tenant.py index 09dfc8855..bfd23e08f 100644 --- a/cognee/modules/users/tenants/methods/create_tenant.py +++ b/cognee/modules/users/tenants/methods/create_tenant.py @@ -35,5 +35,5 @@ async def create_tenant(tenant_name: str, user_id: UUID) -> UUID: await session.merge(user) await session.commit() return tenant.id - except IntegrityError: - raise EntityAlreadyExistsError(message="Tenant already exists.") + except IntegrityError as e: + raise EntityAlreadyExistsError(message="Tenant already exists.") from e From 63a1463073227429e729f33df82ef23427b38fdc Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 16:03:12 +0100 Subject: [PATCH 096/139] Deprecate `SearchType.INSIGHTS`, replace all references to default search type - `SearchType.GRAPH_COMPLETION` --- cognee-mcp/src/server.py | 2 +- cognee/api/v1/cognify/cognify.py | 2 +- .../modules/retrieval/insights_retriever.py | 133 ---------- .../search/methods/get_search_type_tools.py | 5 - cognee/modules/search/types/SearchType.py | 1 - cognee/tests/test_chromadb.py | 2 +- cognee/tests/test_kuzu.py | 2 +- cognee/tests/test_lancedb.py | 2 +- cognee/tests/test_library.py | 2 +- cognee/tests/test_memgraph.py | 2 +- cognee/tests/test_neo4j.py | 2 +- cognee/tests/test_neptune_analytics_vector.py | 2 +- cognee/tests/test_pgvector.py | 2 +- cognee/tests/test_remote_kuzu.py | 2 +- cognee/tests/test_s3_file_storage.py | 2 +- .../retrieval/insights_retriever_test.py | 251 ------------------ evals/src/qa/qa_benchmark_cognee.py | 2 +- .../database_examples/chromadb_example.py | 4 +- examples/database_examples/kuzu_example.py | 4 +- examples/database_examples/neo4j_example.py | 4 +- .../neptune_analytics_example.py | 2 +- .../database_examples/pgvector_example.py | 4 +- examples/python/simple_example.py | 4 +- notebooks/cognee_demo.ipynb | 2 +- notebooks/neptune-analytics-example.ipynb | 2 +- 25 files changed, 31 insertions(+), 411 deletions(-) delete mode 100644 cognee/modules/retrieval/insights_retriever.py delete mode 100644 cognee/tests/unit/modules/retrieval/insights_retriever_test.py diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index cc6eac09e..1eceae07b 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -255,7 +255,7 @@ async def cognify( # 2. Get entity relationships and connections relationships = await cognee.search( "connections between concepts", - query_type=SearchType.INSIGHTS + query_type=SearchType.GRAPH_COMPLETION ) # 3. Find relevant document chunks diff --git a/cognee/api/v1/cognify/cognify.py b/cognee/api/v1/cognify/cognify.py index 1292d243a..c3045f00a 100644 --- a/cognee/api/v1/cognify/cognify.py +++ b/cognee/api/v1/cognify/cognify.py @@ -148,7 +148,7 @@ async def cognify( # 2. Get entity relationships and connections relationships = await cognee.search( "connections between concepts", - query_type=SearchType.INSIGHTS + query_type=SearchType.GRAPH_COMPLETION ) # 3. Find relevant document chunks diff --git a/cognee/modules/retrieval/insights_retriever.py b/cognee/modules/retrieval/insights_retriever.py deleted file mode 100644 index 10aa288e6..000000000 --- a/cognee/modules/retrieval/insights_retriever.py +++ /dev/null @@ -1,133 +0,0 @@ -import asyncio -from typing import Any, Optional - -from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge, Node -from cognee.modules.retrieval.base_graph_retriever import BaseGraphRetriever -from cognee.shared.logging_utils import get_logger -from cognee.infrastructure.databases.graph import get_graph_engine -from cognee.infrastructure.databases.vector import get_vector_engine -from cognee.modules.retrieval.exceptions.exceptions import NoDataError -from cognee.infrastructure.databases.vector.exceptions.exceptions import CollectionNotFoundError - -logger = get_logger("InsightsRetriever") - - -class InsightsRetriever(BaseGraphRetriever): - """ - Retriever for handling graph connection-based insights. - - Public methods include: - - get_context - - get_completion - - Instance variables include: - - exploration_levels - - top_k - """ - - def __init__(self, exploration_levels: int = 1, top_k: Optional[int] = 5): - """Initialize retriever with exploration levels and search parameters.""" - self.exploration_levels = exploration_levels - self.top_k = top_k - - async def get_context(self, query: str) -> list: - """ - Find neighbours of a given node in the graph. - - If the provided query does not correspond to an existing node, - search for similar entities and retrieve their connections. - Reraises NoDataError if there is no data found in the system. - - Parameters: - ----------- - - - query (str): A string identifier for the node whose neighbours are to be - retrieved. - - Returns: - -------- - - - list: A list of unique connections found for the queried node. - """ - if query is None: - return [] - - node_id = query - graph_engine = await get_graph_engine() - exact_node = await graph_engine.extract_node(node_id) - - if exact_node is not None and "id" in exact_node: - node_connections = await graph_engine.get_connections(str(exact_node["id"])) - else: - vector_engine = get_vector_engine() - - try: - results = await asyncio.gather( - vector_engine.search("Entity_name", query_text=query, limit=self.top_k), - vector_engine.search("EntityType_name", query_text=query, limit=self.top_k), - ) - except CollectionNotFoundError as error: - logger.error("Entity collections not found") - raise NoDataError("No data found in the system, please add data first.") from error - - results = [*results[0], *results[1]] - relevant_results = [result for result in results if result.score < 0.5][: self.top_k] - - if len(relevant_results) == 0: - return [] - - node_connections_results = await asyncio.gather( - *[graph_engine.get_connections(result.id) for result in relevant_results] - ) - - node_connections = [] - for neighbours in node_connections_results: - node_connections.extend(neighbours) - - unique_node_connections_map = {} - unique_node_connections = [] - - for node_connection in node_connections: - if "id" not in node_connection[0] or "id" not in node_connection[2]: - continue - - unique_id = f"{node_connection[0]['id']} {node_connection[1]['relationship_name']} {node_connection[2]['id']}" - if unique_id not in unique_node_connections_map: - unique_node_connections_map[unique_id] = True - unique_node_connections.append(node_connection) - - return unique_node_connections - # return [ - # Edge( - # node1=Node(node_id=connection[0]["id"], attributes=connection[0]), - # node2=Node(node_id=connection[2]["id"], attributes=connection[2]), - # attributes={ - # **connection[1], - # "relationship_type": connection[1]["relationship_name"], - # }, - # ) - # for connection in unique_node_connections - # ] - - async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """ - Returns the graph connections context. - - If a context is not provided, it fetches the context using the query provided. - - Parameters: - ----------- - - - query (str): A string identifier used to fetch the context. - - context (Optional[Any]): An optional context to use for the completion; if None, - it fetches the context based on the query. (default None) - - Returns: - -------- - - - Any: The context used for the completion, which is either provided or fetched - based on the query. - """ - if context is None: - context = await self.get_context(query) - return context diff --git a/cognee/modules/search/methods/get_search_type_tools.py b/cognee/modules/search/methods/get_search_type_tools.py index 9cf67785e..72e2db89a 100644 --- a/cognee/modules/search/methods/get_search_type_tools.py +++ b/cognee/modules/search/methods/get_search_type_tools.py @@ -9,7 +9,6 @@ from cognee.modules.search.exceptions import UnsupportedSearchTypeError # Retrievers from cognee.modules.retrieval.user_qa_feedback import UserQAFeedback from cognee.modules.retrieval.chunks_retriever import ChunksRetriever -from cognee.modules.retrieval.insights_retriever import InsightsRetriever from cognee.modules.retrieval.summaries_retriever import SummariesRetriever from cognee.modules.retrieval.completion_retriever import CompletionRetriever from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever @@ -44,10 +43,6 @@ async def get_search_type_tools( SummariesRetriever(top_k=top_k).get_completion, SummariesRetriever(top_k=top_k).get_context, ], - SearchType.INSIGHTS: [ - InsightsRetriever(top_k=top_k).get_completion, - InsightsRetriever(top_k=top_k).get_context, - ], SearchType.CHUNKS: [ ChunksRetriever(top_k=top_k).get_completion, ChunksRetriever(top_k=top_k).get_context, diff --git a/cognee/modules/search/types/SearchType.py b/cognee/modules/search/types/SearchType.py index 418aec0b5..4536c0e01 100644 --- a/cognee/modules/search/types/SearchType.py +++ b/cognee/modules/search/types/SearchType.py @@ -3,7 +3,6 @@ from enum import Enum class SearchType(Enum): SUMMARIES = "SUMMARIES" - INSIGHTS = "INSIGHTS" CHUNKS = "CHUNKS" RAG_COMPLETION = "RAG_COMPLETION" GRAPH_COMPLETION = "GRAPH_COMPLETION" diff --git a/cognee/tests/test_chromadb.py b/cognee/tests/test_chromadb.py index 01e1938d7..f7d82a042 100644 --- a/cognee/tests/test_chromadb.py +++ b/cognee/tests/test_chromadb.py @@ -159,7 +159,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_kuzu.py b/cognee/tests/test_kuzu.py index 6afd4540a..76a79f2b2 100644 --- a/cognee/tests/test_kuzu.py +++ b/cognee/tests/test_kuzu.py @@ -61,7 +61,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_lancedb.py b/cognee/tests/test_lancedb.py index ea3b455cd..1e667dcde 100644 --- a/cognee/tests/test_lancedb.py +++ b/cognee/tests/test_lancedb.py @@ -157,7 +157,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_library.py b/cognee/tests/test_library.py index c5e6cc64b..fa621ff12 100755 --- a/cognee/tests/test_library.py +++ b/cognee/tests/test_library.py @@ -51,7 +51,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_memgraph.py b/cognee/tests/test_memgraph.py index b21cf3735..9e2e94b36 100644 --- a/cognee/tests/test_memgraph.py +++ b/cognee/tests/test_memgraph.py @@ -56,7 +56,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_neo4j.py b/cognee/tests/test_neo4j.py index 7f24e8418..90427baf2 100644 --- a/cognee/tests/test_neo4j.py +++ b/cognee/tests/test_neo4j.py @@ -56,7 +56,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_neptune_analytics_vector.py b/cognee/tests/test_neptune_analytics_vector.py index eececacdd..be868537f 100644 --- a/cognee/tests/test_neptune_analytics_vector.py +++ b/cognee/tests/test_neptune_analytics_vector.py @@ -60,7 +60,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_pgvector.py b/cognee/tests/test_pgvector.py index 059cf691a..11ed9bf2e 100644 --- a/cognee/tests/test_pgvector.py +++ b/cognee/tests/test_pgvector.py @@ -167,7 +167,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_remote_kuzu.py b/cognee/tests/test_remote_kuzu.py index bbff48096..2c3b054a6 100644 --- a/cognee/tests/test_remote_kuzu.py +++ b/cognee/tests/test_remote_kuzu.py @@ -65,7 +65,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_s3_file_storage.py b/cognee/tests/test_s3_file_storage.py index 3a20b232c..c7fc62cf2 100755 --- a/cognee/tests/test_s3_file_storage.py +++ b/cognee/tests/test_s3_file_storage.py @@ -47,7 +47,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/unit/modules/retrieval/insights_retriever_test.py b/cognee/tests/unit/modules/retrieval/insights_retriever_test.py deleted file mode 100644 index 21dbc98dd..000000000 --- a/cognee/tests/unit/modules/retrieval/insights_retriever_test.py +++ /dev/null @@ -1,251 +0,0 @@ -import os -import pytest -import pathlib - -import cognee -from cognee.low_level import setup -from cognee.tasks.storage import add_data_points -from cognee.modules.engine.models import Entity, EntityType -from cognee.infrastructure.databases.graph import get_graph_engine -from cognee.infrastructure.databases.vector import get_vector_engine -from cognee.modules.retrieval.exceptions.exceptions import NoDataError -from cognee.modules.retrieval.insights_retriever import InsightsRetriever - - -class TestInsightsRetriever: - @pytest.mark.asyncio - async def test_insights_context_simple(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_simple" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_simple" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - await setup() - - entityTypePerson = EntityType( - name="Person", - description="An individual", - ) - - person1 = Entity( - name="Steve Rodger", - is_a=entityTypePerson, - description="An American actor, comedian, and filmmaker", - ) - - person2 = Entity( - name="Mike Broski", - is_a=entityTypePerson, - description="Financial advisor and philanthropist", - ) - - person3 = Entity( - name="Christina Mayer", - is_a=entityTypePerson, - description="Maker of next generation of iconic American music videos", - ) - - entityTypeCompany = EntityType( - name="Company", - description="An organization that operates on an annual basis", - ) - - company1 = Entity( - name="Apple", - is_a=entityTypeCompany, - description="An American multinational technology company headquartered in Cupertino, California", - ) - - company2 = Entity( - name="Google", - is_a=entityTypeCompany, - description="An American multinational technology company that specializes in Internet-related services and products", - ) - - company3 = Entity( - name="Facebook", - is_a=entityTypeCompany, - description="An American social media, messaging, and online platform", - ) - - entities = [person1, person2, person3, company1, company2, company3] - - await add_data_points(entities) - - retriever = InsightsRetriever() - - context = await retriever.get_context("Mike") - - assert context[0][0]["name"] == "Mike Broski", "Failed to get Mike Broski" - - @pytest.mark.asyncio - async def test_insights_context_complex(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_complex" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_complex" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - await setup() - - entityTypePerson = EntityType( - name="Person", - description="An individual", - ) - - person1 = Entity( - name="Steve Rodger", - is_a=entityTypePerson, - description="An American actor, comedian, and filmmaker", - ) - - person2 = Entity( - name="Mike Broski", - is_a=entityTypePerson, - description="Financial advisor and philanthropist", - ) - - person3 = Entity( - name="Christina Mayer", - is_a=entityTypePerson, - description="Maker of next generation of iconic American music videos", - ) - - person4 = Entity( - name="Jason Statham", - is_a=entityTypePerson, - description="An American actor", - ) - - person5 = Entity( - name="Mike Tyson", - is_a=entityTypePerson, - description="A former professional boxer from the United States", - ) - - entityTypeCompany = EntityType( - name="Company", - description="An organization that operates on an annual basis", - ) - - company1 = Entity( - name="Apple", - is_a=entityTypeCompany, - description="An American multinational technology company headquartered in Cupertino, California", - ) - - company2 = Entity( - name="Google", - is_a=entityTypeCompany, - description="An American multinational technology company that specializes in Internet-related services and products", - ) - - company3 = Entity( - name="Facebook", - is_a=entityTypeCompany, - description="An American social media, messaging, and online platform", - ) - - entities = [person1, person2, person3, company1, company2, company3] - - await add_data_points(entities) - - graph_engine = await get_graph_engine() - - await graph_engine.add_edges( - [ - ( - (str)(person1.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person1.id, - target_node_id=company1.id, - ), - ), - ( - (str)(person2.id), - (str)(company2.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person2.id, - target_node_id=company2.id, - ), - ), - ( - (str)(person3.id), - (str)(company3.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person3.id, - target_node_id=company3.id, - ), - ), - ( - (str)(person4.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person4.id, - target_node_id=company1.id, - ), - ), - ( - (str)(person5.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person5.id, - target_node_id=company1.id, - ), - ), - ] - ) - - retriever = InsightsRetriever(top_k=20) - - context = await retriever.get_context("Christina") - - assert context[0][0]["name"] == "Christina Mayer", "Failed to get Christina Mayer" - - @pytest.mark.asyncio - async def test_insights_context_on_empty_graph(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_on_empty_graph" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_on_empty_graph" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - retriever = InsightsRetriever() - - with pytest.raises(NoDataError): - await retriever.get_context("Christina Mayer") - - vector_engine = get_vector_engine() - await vector_engine.create_collection("Entity_name", payload_schema=Entity) - await vector_engine.create_collection("EntityType_name", payload_schema=EntityType) - - context = await retriever.get_context("Christina Mayer") - assert context == [], "Returned context should be empty on an empty graph" diff --git a/evals/src/qa/qa_benchmark_cognee.py b/evals/src/qa/qa_benchmark_cognee.py index 2a09f250d..e4b2faf85 100644 --- a/evals/src/qa/qa_benchmark_cognee.py +++ b/evals/src/qa/qa_benchmark_cognee.py @@ -34,7 +34,7 @@ class CogneeConfig(QABenchmarkConfig): system_prompt_path: str = "answer_simple_question_benchmark2.txt" # Search parameters (fallback if not using eval framework) - search_type: SearchType = SearchType.INSIGHTS + search_type: SearchType = SearchType.GRAPH_COMPLETION # Clean slate on initialization clean_start: bool = True diff --git a/examples/database_examples/chromadb_example.py b/examples/database_examples/chromadb_example.py index defa7f78d..71005fb4f 100644 --- a/examples/database_examples/chromadb_example.py +++ b/examples/database_examples/chromadb_example.py @@ -57,7 +57,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "ChromaDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="ChromaDB") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="ChromaDB" + ) print("\nInsights about ChromaDB:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/kuzu_example.py b/examples/database_examples/kuzu_example.py index a31404cbc..ab24a0e07 100644 --- a/examples/database_examples/kuzu_example.py +++ b/examples/database_examples/kuzu_example.py @@ -55,7 +55,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "KuzuDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="KuzuDB") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="KuzuDB" + ) print("\nInsights about KuzuDB:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/neo4j_example.py b/examples/database_examples/neo4j_example.py index 45985610f..b5b6bfbbc 100644 --- a/examples/database_examples/neo4j_example.py +++ b/examples/database_examples/neo4j_example.py @@ -64,7 +64,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "Neo4j" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Neo4j") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="Neo4j" + ) print("\nInsights about Neo4j:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/neptune_analytics_example.py b/examples/database_examples/neptune_analytics_example.py index 5d36e2803..d98d1768c 100644 --- a/examples/database_examples/neptune_analytics_example.py +++ b/examples/database_examples/neptune_analytics_example.py @@ -79,7 +79,7 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "Neptune Analytics" insights_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text="Neptune Analytics" + query_type=SearchType.GRAPH_COMPLETION, query_text="Neptune Analytics" ) print("\n========Insights about Neptune Analytics========:") for result in insights_results: diff --git a/examples/database_examples/pgvector_example.py b/examples/database_examples/pgvector_example.py index 19dbb44e9..026cbfd08 100644 --- a/examples/database_examples/pgvector_example.py +++ b/examples/database_examples/pgvector_example.py @@ -69,7 +69,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "PGVector" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="PGVector") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="PGVector" + ) print("\nInsights about PGVector:") for result in insights_results: print(f"- {result}") diff --git a/examples/python/simple_example.py b/examples/python/simple_example.py index 41be06f25..c13e48f85 100644 --- a/examples/python/simple_example.py +++ b/examples/python/simple_example.py @@ -50,7 +50,9 @@ async def main(): query_text = "Tell me about NLP" print(f"Searching cognee for insights with query: '{query_text}'") # Query cognee for insights on the added text - search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=query_text) + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text=query_text + ) print("Search results:") # Display results diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb index 51eeab560..8983ccb09 100644 --- a/notebooks/cognee_demo.ipynb +++ b/notebooks/cognee_demo.ipynb @@ -1795,7 +1795,7 @@ } ], "source": [ - "search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=node_name)\n", + "search_results = await cognee.search(query_type=SearchType.GRAPH_COMPLETION, query_text=node_name)\n", "print(\"\\n\\nExtracted sentences are:\\n\")\n", "for result in search_results:\n", " print(f\"{result}\\n\")" diff --git a/notebooks/neptune-analytics-example.ipynb b/notebooks/neptune-analytics-example.ipynb index 197918db5..e80ea4dcb 100644 --- a/notebooks/neptune-analytics-example.ipynb +++ b/notebooks/neptune-analytics-example.ipynb @@ -295,7 +295,7 @@ "cell_type": "code", "source": [ "# Search graph insights\n", - "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.INSIGHTS)\n", + "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n", "print(\"\\nInsights about Neptune Analytics:\")\n", "for result in insights_results:\n", " src_node = result[0].get(\"name\", result[0][\"type\"])\n", From 97f90c95ed6371631e598bdb76682b2cd6c2cace Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Tue, 7 Oct 2025 16:09:11 +0100 Subject: [PATCH 097/139] Remove all references to `SearchType.INSIGHTS` across the codebase, meaningfully replacing it with `SearchType.GRAPH_COMPLETION` where applicable. --- .../src/modules/chat/hooks/useChat.ts | 17 ----------------- cognee-mcp/README.md | 2 +- cognee-mcp/src/server.py | 11 ----------- cognee/api/v1/responses/default_tools.py | 1 - cognee/api/v1/responses/dispatch_function.py | 2 +- .../api/v1/responses/routers/default_tools.py | 1 - cognee/api/v1/search/search.py | 9 --------- cognee/cli/commands/search_command.py | 4 ---- cognee/cli/config.py | 1 - .../llm/prompts/search_type_selector_prompt.txt | 5 ----- .../utils/description_to_codepart_search.py | 2 +- .../cli_tests/cli_unit_tests/test_cli_utils.py | 1 - 12 files changed, 3 insertions(+), 53 deletions(-) diff --git a/cognee-frontend/src/modules/chat/hooks/useChat.ts b/cognee-frontend/src/modules/chat/hooks/useChat.ts index 613f9134f..ed5bc4d79 100644 --- a/cognee-frontend/src/modules/chat/hooks/useChat.ts +++ b/cognee-frontend/src/modules/chat/hooks/useChat.ts @@ -89,15 +89,6 @@ export default function useChat(dataset: Dataset) { } -interface Node { - name: string; -} - -interface Relationship { - relationship_name: string; -} - -type InsightMessage = [Node, Relationship, Node]; // eslint-disable-next-line @typescript-eslint/no-explicit-any function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: string): string { @@ -106,14 +97,6 @@ function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: strin } switch (searchType) { - case "INSIGHTS": - return systemMessage.map((message: InsightMessage) => { - const [node1, relationship, node2] = message; - if (node1.name && node2.name) { - return `${node1.name} ${relationship.relationship_name} ${node2.name}.`; - } - return ""; - }).join("\n"); case "SUMMARIES": return systemMessage.map((message: { text: string }) => message.text).join("\n"); case "CHUNKS": diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index 8d973725b..aa6442993 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -266,7 +266,7 @@ The MCP server exposes its functionality through tools. Call them from any MCP c - **codify**: Analyse a code repository, build a code graph, stores it in memory -- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, INSIGHTS +- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS - **list_data**: List all datasets and their data items with IDs for deletion operations diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index 1eceae07b..e7c82c99b 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -478,11 +478,6 @@ async def search(search_query: str, search_type: str) -> list: Best for: Direct document retrieval, specific fact-finding. Returns: LLM responses based on relevant text chunks. - **INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - Returns: Formatted relationship data and entity connections. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. @@ -524,7 +519,6 @@ async def search(search_query: str, search_type: str) -> list: - "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data - "CODE": Returns code-related knowledge in JSON format - "CHUNKS": Returns raw text chunks from the knowledge graph - - "INSIGHTS": Returns relationships between nodes in readable format - "SUMMARIES": Returns pre-generated hierarchical summaries - "CYPHER": Direct graph database queries - "FEELING_LUCKY": Automatically selects best search type @@ -537,7 +531,6 @@ async def search(search_query: str, search_type: str) -> list: A list containing a single TextContent object with the search results. The format of the result depends on the search_type: - **GRAPH_COMPLETION/RAG_COMPLETION**: Conversational AI response strings - - **INSIGHTS**: Formatted relationship descriptions and entity connections - **CHUNKS**: Relevant text passages with source metadata - **SUMMARIES**: Hierarchical summaries from general to specific - **CODE**: Structured code information with context @@ -547,7 +540,6 @@ async def search(search_query: str, search_type: str) -> list: Performance & Optimization: - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal) - - **INSIGHTS**: Fast, returns structured relationships without LLM processing - **CHUNKS**: Fastest, pure vector similarity search without LLM - **SUMMARIES**: Fast, returns pre-computed summaries - **CODE**: Medium speed, specialized for code understanding @@ -586,9 +578,6 @@ async def search(search_query: str, search_type: str) -> list: return str(search_results[0]) elif search_type.upper() == "CHUNKS": return str(search_results) - elif search_type.upper() == "INSIGHTS": - results = retrieved_edges_to_string(search_results) - return results else: return str(search_results) diff --git a/cognee/api/v1/responses/default_tools.py b/cognee/api/v1/responses/default_tools.py index 295d132f1..f3d643df2 100644 --- a/cognee/api/v1/responses/default_tools.py +++ b/cognee/api/v1/responses/default_tools.py @@ -14,7 +14,6 @@ DEFAULT_TOOLS = [ "type": "string", "description": "Type of search to perform", "enum": [ - "INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE", diff --git a/cognee/api/v1/responses/dispatch_function.py b/cognee/api/v1/responses/dispatch_function.py index 85388b564..aea37c350 100644 --- a/cognee/api/v1/responses/dispatch_function.py +++ b/cognee/api/v1/responses/dispatch_function.py @@ -59,7 +59,7 @@ async def handle_search(arguments: Dict[str, Any], user) -> list: valid_search_types = ( search_tool["parameters"]["properties"]["search_type"]["enum"] if search_tool - else ["INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"] + else ["CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"] ) if search_type_str not in valid_search_types: diff --git a/cognee/api/v1/responses/routers/default_tools.py b/cognee/api/v1/responses/routers/default_tools.py index e43620a4b..4194e3376 100644 --- a/cognee/api/v1/responses/routers/default_tools.py +++ b/cognee/api/v1/responses/routers/default_tools.py @@ -14,7 +14,6 @@ DEFAULT_TOOLS = [ "type": "string", "description": "Type of search to perform", "enum": [ - "INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE", diff --git a/cognee/api/v1/search/search.py b/cognee/api/v1/search/search.py index 7209c6036..0a9e76e96 100644 --- a/cognee/api/v1/search/search.py +++ b/cognee/api/v1/search/search.py @@ -52,11 +52,6 @@ async def search( Best for: Direct document retrieval, specific fact-finding. Returns: LLM responses based on relevant text chunks. - **INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - Returns: Formatted relationship data and entity connections. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. @@ -124,9 +119,6 @@ async def search( **GRAPH_COMPLETION/RAG_COMPLETION**: [List of conversational AI response strings] - **INSIGHTS**: - [List of formatted relationship descriptions and entity connections] - **CHUNKS**: [List of relevant text passages with source metadata] @@ -146,7 +138,6 @@ async def search( Performance & Optimization: - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal) - - **INSIGHTS**: Fast, returns structured relationships without LLM processing - **CHUNKS**: Fastest, pure vector similarity search without LLM - **SUMMARIES**: Fast, returns pre-computed summaries - **CODE**: Medium speed, specialized for code understanding diff --git a/cognee/cli/commands/search_command.py b/cognee/cli/commands/search_command.py index 3540a833c..4c53cc260 100644 --- a/cognee/cli/commands/search_command.py +++ b/cognee/cli/commands/search_command.py @@ -31,10 +31,6 @@ Search Types & Use Cases: Traditional RAG using document chunks without graph structure. Best for: Direct document retrieval, specific fact-finding. -**INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. diff --git a/cognee/cli/config.py b/cognee/cli/config.py index 31e8693c7..d016608c1 100644 --- a/cognee/cli/config.py +++ b/cognee/cli/config.py @@ -19,7 +19,6 @@ COMMAND_DESCRIPTIONS = { SEARCH_TYPE_CHOICES = [ "GRAPH_COMPLETION", "RAG_COMPLETION", - "INSIGHTS", "CHUNKS", "SUMMARIES", "CODE", diff --git a/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt b/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt index 7ed2e72fc..1a00bce7e 100644 --- a/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt +++ b/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt @@ -10,8 +10,6 @@ Here are the available `SearchType` tools and their specific functions: - Summarizing large amounts of information - Quick understanding of complex subjects -* **`INSIGHTS`**: The `INSIGHTS` search type discovers connections and relationships between entities in the knowledge graph. - **Best for:** - Discovering how entities are connected @@ -95,9 +93,6 @@ Here are the available `SearchType` tools and their specific functions: Query: "Summarize the key findings from these research papers" Response: `SUMMARIES` -Query: "What is the relationship between the methodologies used in these papers?" -Response: `INSIGHTS` - Query: "When was Einstein born?" Response: `CHUNKS` diff --git a/cognee/modules/retrieval/utils/description_to_codepart_search.py b/cognee/modules/retrieval/utils/description_to_codepart_search.py index a61feb574..649edb6ed 100644 --- a/cognee/modules/retrieval/utils/description_to_codepart_search.py +++ b/cognee/modules/retrieval/utils/description_to_codepart_search.py @@ -62,7 +62,7 @@ async def code_description_to_code_part( try: if include_docs: - search_results = await search(query_text=query, query_type="INSIGHTS") + search_results = await search(query_text=query, query_type="GRAPH_COMPLETION") concatenated_descriptions = " ".join( obj["description"] diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py index 00ee5f442..05de0c70b 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py @@ -53,7 +53,6 @@ class TestCliConfig: expected_types = [ "GRAPH_COMPLETION", "RAG_COMPLETION", - "INSIGHTS", "CHUNKS", "SUMMARIES", "CODE", From 66022751e959b293472d0793e011bada0edf521c Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 16:51:03 +0530 Subject: [PATCH 098/139] Addressed code rabbit comment on shortening content --- cognee/tasks/web_scraper/models.py | 2 +- cognee/tasks/web_scraper/web_scraper_task.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py index 297aebb4f..aed2db0ed 100644 --- a/cognee/tasks/web_scraper/models.py +++ b/cognee/tasks/web_scraper/models.py @@ -16,7 +16,7 @@ class WebPage(DataPoint): page_size: int extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used description: str - metadata: dict = {"index_fields": ["name", "description"]} + metadata: dict = {"index_fields": ["name", "description", "content"]} class WebSite(DataPoint): diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py index 52b73b5b5..52154c6ef 100644 --- a/cognee/tasks/web_scraper/web_scraper_task.py +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -250,13 +250,13 @@ async def web_scraper_task( # Create WebPage content_str = content if isinstance(content, str) else str(content) content_hash = hashlib.sha256(content_str.encode("utf-8")).hexdigest() - + content_preview = content_str[:500] + ("..." if len(content_str) > 500 else "") # Create description for WebPage webpage_description = ( f"Webpage: {parsed_url.path.lstrip('/') or 'Home'}\n" f"URL: {page_url}\n" f"Scraped at: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" - f"Content: {content_str}\n" + f"Content: {content_preview}\n" f"Content type: text\n" f"Page size: {len(content_str)} bytes\n" f"Status code: 200" From a9d410e7dcdb34a87859dd21bd74301f526133f5 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Wed, 8 Oct 2025 16:56:35 +0530 Subject: [PATCH 099/139] resolving uv.lock conflict --- uv.lock | 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cognee/api/v1/ui/ui.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index 108806ba2..7711cb6ac 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -505,6 +505,8 @@ def start_ui( cwd = os.getcwd() env_file = os.path.join(cwd, ".env") try: + image = "cognee/cognee-mcp:main" + subprocess.run(["docker", "pull", image], check=True) mcp_process = subprocess.Popen( [ "docker", From 806298d508b8bc22a1665f828020284ca177ab2f Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Wed, 8 Oct 2025 18:36:08 +0100 Subject: [PATCH 101/139] create cognee_client for handling api calls --- cognee-mcp/pyproject.toml | 1 + cognee-mcp/src/cognee_client.py | 339 ++++++++++++++++++++++++++++++++ cognee-mcp/uv.lock | 30 +-- 3 files changed, 356 insertions(+), 14 deletions(-) create mode 100644 cognee-mcp/src/cognee_client.py diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index bc0ebeac5..c5a21a1a7 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -13,6 +13,7 @@ dependencies = [ "fastmcp>=2.10.0,<3.0.0", "mcp>=1.12.0,<2.0.0", "uv>=0.6.3,<1.0.0", + "httpx>=0.27.0,<1.0.0", ] authors = [ diff --git a/cognee-mcp/src/cognee_client.py b/cognee-mcp/src/cognee_client.py new file mode 100644 index 000000000..e22b8c060 --- /dev/null +++ b/cognee-mcp/src/cognee_client.py @@ -0,0 +1,339 @@ +""" +Cognee Client abstraction that supports both direct function calls and HTTP API calls. + +This module provides a unified interface for interacting with Cognee, supporting: +- Direct mode: Directly imports and calls cognee functions (default behavior) +- API mode: Makes HTTP requests to a running Cognee FastAPI server +""" + +import sys +from typing import Optional, Any, List, Dict +from uuid import UUID +from contextlib import redirect_stdout +import httpx +from cognee.shared.logging_utils import get_logger + +logger = get_logger() + + +class CogneeClient: + """ + Unified client for interacting with Cognee via direct calls or HTTP API. + + Parameters + ---------- + api_url : str, optional + Base URL of the Cognee API server (e.g., "http://localhost:8000"). + If None, uses direct cognee function calls. + api_token : str, optional + Authentication token for API requests. Required if api_url is provided. + """ + + def __init__(self, api_url: Optional[str] = None, api_token: Optional[str] = None): + self.api_url = api_url.rstrip("/") if api_url else None + self.api_token = api_token + self.use_api = api_url is not None + + if self.use_api: + logger.info(f"Cognee client initialized in API mode: {self.api_url}") + self.client = httpx.AsyncClient(timeout=300.0) # 5 minute timeout for long operations + else: + logger.info("Cognee client initialized in direct mode") + # Import cognee only if we're using direct mode + import cognee as _cognee + + self.cognee = _cognee + + def _get_headers(self) -> Dict[str, str]: + """Get headers for API requests.""" + headers = {"Content-Type": "application/json"} + if self.api_token: + headers["Authorization"] = f"Bearer {self.api_token}" + return headers + + async def add( + self, data: Any, dataset_name: str = "main_dataset", node_set: Optional[List[str]] = None + ) -> Dict[str, Any]: + """ + Add data to Cognee for processing. + + Parameters + ---------- + data : Any + Data to add (text, file path, etc.) + dataset_name : str + Name of the dataset to add data to + node_set : List[str], optional + List of node identifiers for graph organization + + Returns + ------- + Dict[str, Any] + Result of the add operation + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/add" + + # For API mode, we need to handle file uploads differently + # For now, we'll assume data is text content + files = {"data": ("data.txt", str(data), "text/plain")} + form_data = { + "datasetName": dataset_name, + } + if node_set: + form_data["node_set"] = node_set + + response = await self.client.post( + endpoint, + files=files, + data=form_data, + headers={"Authorization": f"Bearer {self.api_token}"} if self.api_token else {}, + ) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + await self.cognee.add(data, dataset_name=dataset_name, node_set=node_set) + return {"status": "success", "message": "Data added successfully"} + + async def cognify( + self, + datasets: Optional[List[str]] = None, + custom_prompt: Optional[str] = None, + graph_model: Any = None, + ) -> Dict[str, Any]: + """ + Transform data into a knowledge graph. + + Parameters + ---------- + datasets : List[str], optional + List of dataset names to process + custom_prompt : str, optional + Custom prompt for entity extraction + graph_model : Any, optional + Custom graph model (only used in direct mode) + + Returns + ------- + Dict[str, Any] + Result of the cognify operation + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/cognify" + payload = { + "datasets": datasets or ["main_dataset"], + "run_in_background": False, + } + if custom_prompt: + payload["custom_prompt"] = custom_prompt + + response = await self.client.post(endpoint, json=payload, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + kwargs = {} + if custom_prompt: + kwargs["custom_prompt"] = custom_prompt + if graph_model: + kwargs["graph_model"] = graph_model + + await self.cognee.cognify(**kwargs) + return {"status": "success", "message": "Cognify completed successfully"} + + async def search( + self, + query_text: str, + query_type: str, + datasets: Optional[List[str]] = None, + system_prompt: Optional[str] = None, + top_k: int = 10, + ) -> Any: + """ + Search the knowledge graph. + + Parameters + ---------- + query_text : str + The search query + query_type : str + Type of search (e.g., "GRAPH_COMPLETION", "INSIGHTS", etc.) + datasets : List[str], optional + List of datasets to search + system_prompt : str, optional + System prompt for completion searches + top_k : int + Maximum number of results + + Returns + ------- + Any + Search results + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/search" + payload = {"query": query_text, "search_type": query_type.upper(), "top_k": top_k} + if datasets: + payload["datasets"] = datasets + if system_prompt: + payload["system_prompt"] = system_prompt + + response = await self.client.post(endpoint, json=payload, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.search.types import SearchType + + with redirect_stdout(sys.stderr): + results = await self.cognee.search( + query_type=SearchType[query_type.upper()], query_text=query_text + ) + return results + + async def delete(self, data_id: UUID, dataset_id: UUID, mode: str = "soft") -> Dict[str, Any]: + """ + Delete data from a dataset. + + Parameters + ---------- + data_id : UUID + ID of the data to delete + dataset_id : UUID + ID of the dataset containing the data + mode : str + Deletion mode ("soft" or "hard") + + Returns + ------- + Dict[str, Any] + Result of the deletion + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/delete" + params = {"data_id": str(data_id), "dataset_id": str(dataset_id), "mode": mode} + + response = await self.client.delete( + endpoint, params=params, headers=self._get_headers() + ) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.users.methods import get_default_user + + with redirect_stdout(sys.stderr): + user = await get_default_user() + result = await self.cognee.delete( + data_id=data_id, dataset_id=dataset_id, mode=mode, user=user + ) + return result + + async def prune_data(self) -> Dict[str, Any]: + """ + Prune all data from the knowledge graph. + + Returns + ------- + Dict[str, Any] + Result of the prune operation + """ + if self.use_api: + # Note: The API doesn't expose a prune endpoint, so we'll need to handle this + # For now, raise an error + raise NotImplementedError("Prune operation is not available via API") + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + await self.cognee.prune.prune_data() + return {"status": "success", "message": "Data pruned successfully"} + + async def prune_system(self, metadata: bool = True) -> Dict[str, Any]: + """ + Prune system data from the knowledge graph. + + Parameters + ---------- + metadata : bool + Whether to prune metadata + + Returns + ------- + Dict[str, Any] + Result of the prune operation + """ + if self.use_api: + # Note: The API doesn't expose a prune endpoint + raise NotImplementedError("Prune system operation is not available via API") + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + await self.cognee.prune.prune_system(metadata=metadata) + return {"status": "success", "message": "System pruned successfully"} + + async def get_pipeline_status(self, dataset_ids: List[UUID], pipeline_name: str) -> str: + """ + Get the status of a pipeline run. + + Parameters + ---------- + dataset_ids : List[UUID] + List of dataset IDs + pipeline_name : str + Name of the pipeline + + Returns + ------- + str + Status information + """ + if self.use_api: + # Note: This would need a custom endpoint on the API side + raise NotImplementedError("Pipeline status is not available via API") + else: + # Direct mode: Call cognee directly + from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status + + with redirect_stdout(sys.stderr): + status = await get_pipeline_status(dataset_ids, pipeline_name) + return str(status) + + async def list_datasets(self) -> List[Dict[str, Any]]: + """ + List all datasets. + + Returns + ------- + List[Dict[str, Any]] + List of datasets + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/datasets" + response = await self.client.get(endpoint, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.users.methods import get_default_user + from cognee.modules.data.methods import get_datasets + + with redirect_stdout(sys.stderr): + user = await get_default_user() + datasets = await get_datasets(user.id) + return [ + {"id": str(d.id), "name": d.name, "created_at": str(d.created_at)} + for d in datasets + ] + + async def close(self): + """Close the HTTP client if in API mode.""" + if self.use_api and hasattr(self, "client"): + await self.client.aclose() diff --git a/cognee-mcp/uv.lock b/cognee-mcp/uv.lock index e31741613..dbbc6542c 100644 --- a/cognee-mcp/uv.lock +++ b/cognee-mcp/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'", @@ -737,6 +737,7 @@ source = { editable = "." } dependencies = [ { name = "cognee", extra = ["codegraph", "docs", "gemini", "huggingface", "neo4j", "postgres"] }, { name = "fastmcp" }, + { name = "httpx" }, { name = "mcp" }, { name = "uv" }, ] @@ -750,6 +751,7 @@ dev = [ requires-dist = [ { name = "cognee", extras = ["postgres", "codegraph", "gemini", "huggingface", "docs", "neo4j"], specifier = "==0.3.4" }, { name = "fastmcp", specifier = ">=2.10.0,<3.0.0" }, + { name = "httpx", specifier = ">=0.27.0,<1.0.0" }, { name = "mcp", specifier = ">=1.12.0,<2.0.0" }, { name = "uv", specifier = ">=0.6.3,<1.0.0" }, ] @@ -1026,7 +1028,7 @@ version = "3.24.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "attrs" }, - { name = "docstring-parser", marker = "python_full_version < '4.0'" }, + { name = "docstring-parser", marker = "python_full_version < '4'" }, { name = "rich" }, { name = "rich-rst" }, { name = "typing-extensions", marker = "python_full_version < '3.11'" }, @@ -1309,17 +1311,17 @@ name = "fastembed" version = "0.6.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub" }, - { name = "loguru" }, - { name = "mmh3" }, + { name = "huggingface-hub", marker = "python_full_version < '3.13'" }, + { name = "loguru", marker = "python_full_version < '3.13'" }, + { name = "mmh3", marker = "python_full_version < '3.13'" }, { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, - { name = "onnxruntime" }, - { name = "pillow" }, - { name = "py-rust-stemmers" }, - { name = "requests" }, - { name = "tokenizers" }, - { name = "tqdm" }, + { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.13'" }, + { name = "onnxruntime", marker = "python_full_version < '3.13'" }, + { name = "pillow", marker = "python_full_version < '3.13'" }, + { name = "py-rust-stemmers", marker = "python_full_version < '3.13'" }, + { name = "requests", marker = "python_full_version < '3.13'" }, + { name = "tokenizers", marker = "python_full_version < '3.13'" }, + { name = "tqdm", marker = "python_full_version < '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/c6/f4/036a656c605f63dc25f11284f60f69900a54a19c513e1ae60d21d6977e75/fastembed-0.6.0.tar.gz", hash = "sha256:5c9ead25f23449535b07243bbe1f370b820dcc77ec2931e61674e3fe7ff24733", size = 50731, upload-time = "2025-02-26T13:50:33.031Z" } wheels = [ @@ -2526,8 +2528,8 @@ name = "loguru" version = "0.7.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, - { name = "win32-setctime", marker = "sys_platform == 'win32'" }, + { name = "colorama", marker = "python_full_version < '3.13' and sys_platform == 'win32'" }, + { name = "win32-setctime", marker = "python_full_version < '3.13' and sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/3a/05/a1dae3dffd1116099471c643b8924f5aa6524411dc6c63fdae648c4f1aca/loguru-0.7.3.tar.gz", hash = "sha256:19480589e77d47b8d85b2c827ad95d49bf31b0dcde16593892eb51dd18706eb6", size = 63559, upload-time = "2024-12-06T11:20:56.608Z" } wheels = [ From 3e23c96595b9c70ae28ebbd9c3ac8322c3fe892f Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Wed, 8 Oct 2025 18:36:44 +0100 Subject: [PATCH 102/139] route server tools calls through cognee_client --- cognee-mcp/src/server.py | 263 +++++++++++++++++++++++++++------------ 1 file changed, 185 insertions(+), 78 deletions(-) diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index cc6eac09e..c390d9ac6 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -2,28 +2,25 @@ import json import os import sys import argparse -import cognee import asyncio import subprocess from pathlib import Path +from typing import Optional from cognee.shared.logging_utils import get_logger, setup_logging, get_log_file_location import importlib.util from contextlib import redirect_stdout import mcp.types as types from mcp.server import FastMCP -from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status -from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id -from cognee.modules.users.methods import get_default_user -from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline -from cognee.modules.search.types import SearchType -from cognee.shared.data_models import KnowledgeGraph from cognee.modules.storage.utils import JSONEncoder from starlette.responses import JSONResponse from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware import uvicorn +# Import the new CogneeClient abstraction +from cognee_client import CogneeClient + try: from cognee.tasks.codingagents.coding_rule_associations import ( @@ -41,6 +38,9 @@ mcp = FastMCP("Cognee") logger = get_logger() +# Global CogneeClient instance (will be initialized in main()) +cognee_client: Optional[CogneeClient] = None + async def run_sse_with_cors(): """Custom SSE transport with CORS middleware.""" @@ -141,11 +141,20 @@ async def cognee_add_developer_rules( with redirect_stdout(sys.stderr): logger.info(f"Starting cognify for: {file_path}") try: - await cognee.add(file_path, node_set=["developer_rules"]) - model = KnowledgeGraph + await cognee_client.add(file_path, node_set=["developer_rules"]) + + model = None if graph_model_file and graph_model_name: - model = load_class(graph_model_file, graph_model_name) - await cognee.cognify(graph_model=model) + if cognee_client.use_api: + logger.warning( + "Custom graph models are not supported in API mode, ignoring." + ) + else: + from cognee.shared.data_models import KnowledgeGraph + + model = load_class(graph_model_file, graph_model_name) + + await cognee_client.cognify(graph_model=model) logger.info(f"Cognify finished for: {file_path}") except Exception as e: logger.error(f"Cognify failed for {file_path}: {str(e)}") @@ -293,15 +302,20 @@ async def cognify( # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): logger.info("Cognify process starting.") - if graph_model_file and graph_model_name: - graph_model = load_class(graph_model_file, graph_model_name) - else: - graph_model = KnowledgeGraph - await cognee.add(data) + graph_model = None + if graph_model_file and graph_model_name: + if cognee_client.use_api: + logger.warning("Custom graph models are not supported in API mode, ignoring.") + else: + from cognee.shared.data_models import KnowledgeGraph + + graph_model = load_class(graph_model_file, graph_model_name) + + await cognee_client.add(data) try: - await cognee.cognify(graph_model=graph_model, custom_prompt=custom_prompt) + await cognee_client.cognify(custom_prompt=custom_prompt, graph_model=graph_model) logger.info("Cognify process finished.") except Exception as e: logger.error("Cognify process failed.") @@ -354,16 +368,19 @@ async def save_interaction(data: str) -> list: with redirect_stdout(sys.stderr): logger.info("Save interaction process starting.") - await cognee.add(data, node_set=["user_agent_interaction"]) + await cognee_client.add(data, node_set=["user_agent_interaction"]) try: - await cognee.cognify() + await cognee_client.cognify() logger.info("Save interaction process finished.") - logger.info("Generating associated rules from interaction data.") - await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules") - - logger.info("Associated rules generated from interaction data.") + # Rule associations only work in direct mode + if not cognee_client.use_api: + logger.info("Generating associated rules from interaction data.") + await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules") + logger.info("Associated rules generated from interaction data.") + else: + logger.warning("Rule associations are not available in API mode, skipping.") except Exception as e: logger.error("Save interaction process failed.") @@ -420,11 +437,18 @@ async def codify(repo_path: str) -> list: - All stdout is redirected to stderr to maintain MCP communication integrity """ + if cognee_client.use_api: + error_msg = "❌ Codify operation is not available in API mode. Please use direct mode for code graph pipeline." + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + async def codify_task(repo_path: str): # NOTE: MCP uses stdout to communicate, we must redirect all output # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): logger.info("Codify process starting.") + from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline + results = [] async for result in run_code_graph_pipeline(repo_path, False): results.append(result) @@ -574,23 +598,40 @@ async def search(search_query: str, search_type: str) -> list: # NOTE: MCP uses stdout to communicate, we must redirect all output # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): - search_results = await cognee.search( - query_type=SearchType[search_type.upper()], query_text=search_query + search_results = await cognee_client.search( + query_text=search_query, query_type=search_type ) - if search_type.upper() == "CODE": - return json.dumps(search_results, cls=JSONEncoder) - elif ( - search_type.upper() == "GRAPH_COMPLETION" or search_type.upper() == "RAG_COMPLETION" - ): - return str(search_results[0]) - elif search_type.upper() == "CHUNKS": - return str(search_results) - elif search_type.upper() == "INSIGHTS": - results = retrieved_edges_to_string(search_results) - return results + # Handle different result formats based on API vs direct mode + if cognee_client.use_api: + # API mode returns JSON-serialized results + if isinstance(search_results, str): + return search_results + elif isinstance(search_results, list): + if ( + search_type.upper() in ["GRAPH_COMPLETION", "RAG_COMPLETION"] + and len(search_results) > 0 + ): + return str(search_results[0]) + return str(search_results) + else: + return json.dumps(search_results, cls=JSONEncoder) else: - return str(search_results) + # Direct mode processing + if search_type.upper() == "CODE": + return json.dumps(search_results, cls=JSONEncoder) + elif ( + search_type.upper() == "GRAPH_COMPLETION" + or search_type.upper() == "RAG_COMPLETION" + ): + return str(search_results[0]) + elif search_type.upper() == "CHUNKS": + return str(search_results) + elif search_type.upper() == "INSIGHTS": + results = retrieved_edges_to_string(search_results) + return results + else: + return str(search_results) search_results = await search_task(search_query, search_type) return [types.TextContent(type="text", text=search_results)] @@ -623,6 +664,10 @@ async def get_developer_rules() -> list: async def fetch_rules_from_cognee() -> str: """Collect all developer rules from Cognee""" with redirect_stdout(sys.stderr): + if cognee_client.use_api: + logger.warning("Developer rules retrieval is not available in API mode") + return "Developer rules retrieval is not available in API mode" + developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules") return developer_rules @@ -662,16 +707,24 @@ async def list_data(dataset_id: str = None) -> list: with redirect_stdout(sys.stderr): try: - user = await get_default_user() output_lines = [] if dataset_id: - # List data for specific dataset + # Detailed data listing for specific dataset is only available in direct mode + if cognee_client.use_api: + return [ + types.TextContent( + type="text", + text="❌ Detailed data listing for specific datasets is not available in API mode.\nPlease use the API directly or use direct mode.", + ) + ] + + from cognee.modules.users.methods import get_default_user + from cognee.modules.data.methods import get_dataset, get_dataset_data + logger.info(f"Listing data for dataset: {dataset_id}") dataset_uuid = UUID(dataset_id) - - # Get the dataset information - from cognee.modules.data.methods import get_dataset, get_dataset_data + user = await get_default_user() dataset = await get_dataset(user.id, dataset_uuid) @@ -700,11 +753,9 @@ async def list_data(dataset_id: str = None) -> list: output_lines.append(" (No data items in this dataset)") else: - # List all datasets + # List all datasets - works in both modes logger.info("Listing all datasets") - from cognee.modules.data.methods import get_datasets - - datasets = await get_datasets(user.id) + datasets = await cognee_client.list_datasets() if not datasets: return [ @@ -719,20 +770,21 @@ async def list_data(dataset_id: str = None) -> list: output_lines.append("") for i, dataset in enumerate(datasets, 1): - # Get data count for each dataset - from cognee.modules.data.methods import get_dataset_data - - data_items = await get_dataset_data(dataset.id) - - output_lines.append(f"{i}. 📁 {dataset.name}") - output_lines.append(f" Dataset ID: {dataset.id}") - output_lines.append(f" Created: {dataset.created_at}") - output_lines.append(f" Data items: {len(data_items)}") + # In API mode, dataset is a dict; in direct mode, it's formatted as dict + if isinstance(dataset, dict): + output_lines.append(f"{i}. 📁 {dataset.get('name', 'Unnamed')}") + output_lines.append(f" Dataset ID: {dataset.get('id')}") + output_lines.append(f" Created: {dataset.get('created_at', 'N/A')}") + else: + output_lines.append(f"{i}. 📁 {dataset.name}") + output_lines.append(f" Dataset ID: {dataset.id}") + output_lines.append(f" Created: {dataset.created_at}") output_lines.append("") - output_lines.append("💡 To see data items in a specific dataset, use:") - output_lines.append(' list_data(dataset_id="your-dataset-id-here")') - output_lines.append("") + if not cognee_client.use_api: + output_lines.append("💡 To see data items in a specific dataset, use:") + output_lines.append(' list_data(dataset_id="your-dataset-id-here")') + output_lines.append("") output_lines.append("🗑️ To delete specific data, use:") output_lines.append(' delete(data_id="data-id", dataset_id="dataset-id")') @@ -801,12 +853,9 @@ async def delete(data_id: str, dataset_id: str, mode: str = "soft") -> list: data_uuid = UUID(data_id) dataset_uuid = UUID(dataset_id) - # Get default user for the operation - user = await get_default_user() - - # Call the cognee delete function - result = await cognee.delete( - data_id=data_uuid, dataset_id=dataset_uuid, mode=mode, user=user + # Call the cognee delete function via client + result = await cognee_client.delete( + data_id=data_uuid, dataset_id=dataset_uuid, mode=mode ) logger.info(f"Delete operation completed successfully: {result}") @@ -853,11 +902,21 @@ async def prune(): ----- - This operation cannot be undone. All memory data will be permanently deleted. - The function prunes both data content (using prune_data) and system metadata (using prune_system) + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - return [types.TextContent(type="text", text="Pruned")] + try: + await cognee_client.prune_data() + await cognee_client.prune_system(metadata=True) + return [types.TextContent(type="text", text="Pruned")] + except NotImplementedError: + error_msg = "❌ Prune operation is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Prune operation failed: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] @mcp.tool() @@ -880,13 +939,26 @@ async def cognify_status(): - The function retrieves pipeline status specifically for the "cognify_pipeline" on the "main_dataset" - Status information includes job progress, execution time, and completion status - The status is returned in string format for easy reading + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - user = await get_default_user() - status = await get_pipeline_status( - [await get_unique_dataset_id("main_dataset", user)], "cognify_pipeline" - ) - return [types.TextContent(type="text", text=str(status))] + try: + from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id + from cognee.modules.users.methods import get_default_user + + user = await get_default_user() + status = await cognee_client.get_pipeline_status( + [await get_unique_dataset_id("main_dataset", user)], "cognify_pipeline" + ) + return [types.TextContent(type="text", text=str(status))] + except NotImplementedError: + error_msg = "❌ Pipeline status is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Failed to get cognify status: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] @mcp.tool() @@ -909,13 +981,26 @@ async def codify_status(): - The function retrieves pipeline status specifically for the "cognify_code_pipeline" on the "codebase" dataset - Status information includes job progress, execution time, and completion status - The status is returned in string format for easy reading + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - user = await get_default_user() - status = await get_pipeline_status( - [await get_unique_dataset_id("codebase", user)], "cognify_code_pipeline" - ) - return [types.TextContent(type="text", text=str(status))] + try: + from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id + from cognee.modules.users.methods import get_default_user + + user = await get_default_user() + status = await cognee_client.get_pipeline_status( + [await get_unique_dataset_id("codebase", user)], "cognify_code_pipeline" + ) + return [types.TextContent(type="text", text=str(status))] + except NotImplementedError: + error_msg = "❌ Pipeline status is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Failed to get codify status: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] def node_to_string(node): @@ -949,6 +1034,8 @@ def load_class(model_file, model_name): async def main(): + global cognee_client + parser = argparse.ArgumentParser() parser.add_argument( @@ -992,12 +1079,30 @@ async def main(): help="Argument stops database migration from being attempted", ) + # Cognee API connection options + parser.add_argument( + "--api-url", + default=None, + help="Base URL of a running Cognee FastAPI server (e.g., http://localhost:8000). " + "If provided, the MCP server will connect to the API instead of using cognee directly.", + ) + + parser.add_argument( + "--api-token", + default=None, + help="Authentication token for the Cognee API. Required if --api-url is provided.", + ) + args = parser.parse_args() + # Initialize the global CogneeClient + cognee_client = CogneeClient(api_url=args.api_url, api_token=args.api_token) + mcp.settings.host = args.host mcp.settings.port = args.port - if not args.no_migration: + # Skip migrations when in API mode (the API server handles its own database) + if not args.no_migration and not args.api_url: # Run Alembic migrations from the main cognee directory where alembic.ini is located logger.info("Running database migrations...") migration_result = subprocess.run( @@ -1020,6 +1125,8 @@ async def main(): sys.exit(1) logger.info("Database migrations done.") + elif args.api_url: + logger.info("Skipping database migrations (using API mode)") logger.info(f"Starting MCP server with transport: {args.transport}") if args.transport == "stdio": From 1970e857da09852f431dcb07e67ac068c8df39fd Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Thu, 9 Oct 2025 09:53:27 +0200 Subject: [PATCH 103/139] chore: fixes based on PR comments --- .github/workflows/examples_tests.yml | 50 +- poetry.lock | 1611 +++++++++++++++++++++++--- 2 files changed, 1468 insertions(+), 193 deletions(-) diff --git a/.github/workflows/examples_tests.yml b/.github/workflows/examples_tests.yml index cf96b718a..4c714b984 100644 --- a/.github/workflows/examples_tests.yml +++ b/.github/workflows/examples_tests.yml @@ -132,28 +132,28 @@ jobs: EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./examples/python/memify_coding_agent_example.py -# test_docling_add: -# name: Run Add with Docling Test -# runs-on: ubuntu-22.04 -# steps: -# - name: Check out repository -# uses: actions/checkout@v4 -# -# - name: Cognee Setup -# uses: ./.github/actions/cognee_setup -# with: -# python-version: '3.11.x' -# extra-dependencies: 'docling' -# -# - name: Run Docling Test -# env: -# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} -# LLM_MODEL: ${{ secrets.LLM_MODEL }} -# LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} -# LLM_API_KEY: ${{ secrets.LLM_API_KEY }} -# LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} -# EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} -# EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} -# EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} -# EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} -# run: uv run python ./cognee/tests/test_add_docling_document.py + test_docling_add: + name: Run Add with Docling Test + runs-on: macos-15 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: 'docling' + + - name: Run Docling Test + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./cognee/tests/test_add_docling_document.py diff --git a/poetry.lock b/poetry.lock index c0394cc7a..7c422dcdc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,38 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. + +[[package]] +name = "accelerate" +version = "1.10.1" +description = "Accelerate" +optional = true +python-versions = ">=3.9.0" +groups = ["main"] +markers = "extra == \"docling\"" +files = [ + {file = "accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11"}, + {file = "accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8"}, +] + +[package.dependencies] +huggingface_hub = ">=0.21.0" +numpy = ">=1.17,<3.0.0" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +safetensors = ">=0.4.3" +torch = ">=2.0.0" + +[package.extras] +deepspeed = ["deepspeed"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.11.2,<0.12.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,<0.12.0)"] +rich = ["rich"] +sagemaker = ["sagemaker"] +test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +test-fp8 = ["torchao"] +test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"] +test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "trackio", "wandb"] +testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] [[package]] name = "aiobotocore" @@ -722,7 +756,7 @@ description = "Screen-scraping library" optional = true python-versions = ">=3.7.0" groups = ["main"] -markers = "extra == \"notebook\" or extra == \"dev\" or extra == \"docs\" or extra == \"evals\"" +markers = "extra == \"notebook\" or extra == \"dev\" or extra == \"docs\" or extra == \"evals\" or extra == \"docling\"" files = [ {file = "beautifulsoup4-4.13.5-py3-none-any.whl", hash = "sha256:642085eaa22233aceadff9c69651bc51e8bf3f874fb6d7104ece2beb24b47c4a"}, {file = "beautifulsoup4-4.13.5.tar.gz", hash = "sha256:5e70131382930e7c3de33450a2f54a63d5e4b19386eab43a5b34d594268f3695"}, @@ -1157,7 +1191,7 @@ description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" groups = ["main"] -markers = "(platform_system == \"Windows\" or sys_platform == \"win32\" or extra == \"llama-index\" or extra == \"dev\" or os_name == \"nt\") and (platform_system == \"Windows\" or sys_platform == \"win32\" or extra == \"llama-index\" or extra == \"dev\" or extra == \"chromadb\")" +markers = "(platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"dev\" or extra == \"chromadb\" or sys_platform == \"win32\") and (platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"dev\" or os_name == \"nt\" or sys_platform == \"win32\")" files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, @@ -1816,7 +1850,7 @@ description = "serialize all of Python" optional = true python-versions = ">=3.8" groups = ["main"] -markers = "extra == \"dev\"" +markers = "extra == \"dev\" or extra == \"docling\"" files = [ {file = "dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049"}, {file = "dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0"}, @@ -1977,6 +2011,165 @@ idna = ["idna (>=3.10)"] trio = ["trio (>=0.30)"] wmi = ["wmi (>=1.5.1) ; platform_system == \"Windows\""] +[[package]] +name = "docling" +version = "2.55.1" +description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications." +optional = true +python-versions = "<4.0,>=3.9" +groups = ["main"] +markers = "extra == \"docling\"" +files = [ + {file = "docling-2.55.1-py3-none-any.whl", hash = "sha256:895aba282c6cca9ca1f6b9ff57c2002e4f581f722c608aa671d68382d4d61e07"}, + {file = "docling-2.55.1.tar.gz", hash = "sha256:e60a5612b2b993efd8a0b5464aff1b9868e3cab5c2e239c863709e6b780f3c57"}, +] + +[package.dependencies] +accelerate = ">=1.0.0,<2" +beautifulsoup4 = ">=4.12.3,<5.0.0" +certifi = ">=2024.7.4" +docling-core = {version = ">=2.48.2,<3.0.0", extras = ["chunking"]} +docling-ibm-models = ">=3.9.1,<4" +docling-parse = ">=4.4.0,<5.0.0" +easyocr = ">=1.7,<2.0" +filetype = ">=1.2.0,<2.0.0" +huggingface_hub = ">=0.23,<1" +lxml = ">=4.0.0,<6.0.0" +marko = ">=2.1.2,<3.0.0" +openpyxl = ">=3.1.5,<4.0.0" +pandas = ">=2.1.4,<3.0.0" +pillow = ">=10.0.0,<12.0.0" +pluggy = ">=1.0.0,<2.0.0" +polyfactory = ">=2.22.2" +pydantic = ">=2.0.0,<3.0.0" +pydantic-settings = ">=2.3.0,<3.0.0" +pylatexenc = ">=2.10,<3.0" +pypdfium2 = ">=4.30.0,<4.30.1 || >4.30.1,<5.0.0" +python-docx = ">=1.1.2,<2.0.0" +python-pptx = 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["matplotlib", "pandas", "tabulate"] + [[package]] name = "tweepy" version = "4.16.0" @@ -10939,7 +12213,7 @@ description = "Provider of IANA time zone data" optional = true python-versions = ">=2" groups = ["main"] -markers = "extra == \"docs\" or extra == \"evals\" or extra == \"dlt\"" +markers = "extra == \"docs\" or extra == \"evals\" or extra == \"docling\" or extra == \"dlt\"" files = [ {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"}, {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"}, @@ -11130,7 +12404,7 @@ description = "Fast implementation of asyncio event loop on top of libuv" optional = true python-versions = ">=3.8.0" groups = ["main"] -markers = "platform_python_implementation != \"PyPy\" and extra == \"chromadb\" and sys_platform != \"win32\" and sys_platform != \"cygwin\"" +markers = "platform_python_implementation != \"PyPy\" 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"mkdocs-minify-plugin", "mkdocstrings", "mypy", "notebook", "pre-commit", "pylint", "pytest", "pytest-asyncio", "pytest-cov", "ruff", "tweepy"] distributed = ["modal"] dlt = ["dlt"] +docling = ["docling", "transformers"] docs = ["unstructured"] evals = ["gdown", "matplotlib", "pandas", "plotly", "scikit-learn"] falkordb = ["falkordb"] @@ -11963,4 +13238,4 @@ posthog = ["posthog"] [metadata] lock-version = "2.1" python-versions = ">=3.10,<=3.13" -content-hash = "49d2d5207803b6b56e0bbf2be871941f4fc4535d0bc823815e0a7cbe61e12574" +content-hash = "88d5ff181b2c044cb4c981830be4b73c58e3ec46b2930f9fc7cdfe92d4909cdd" From 01632988fe4811af29e929a68d0c68c4717d94a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andrej=20Mili=C4=87evi=C4=87?= <85933103+siillee@users.noreply.github.com> Date: Thu, 9 Oct 2025 12:39:18 +0200 Subject: [PATCH 104/139] test: replace neo4j usages in cicd with reusable local instances (#1507) ## Description Changed from remote to local Neo4j instance in tests because CI was failing due to multiple tests using the remote instance in parallel. ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [x] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [x] **I have tested my changes thoroughly before submitting this PR** - [x] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Co-authored-by: Andrej Milicevic Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com> --- .github/workflows/db_examples_tests.yml | 10 ++-- .github/workflows/graph_db_tests.yml | 10 ++-- .../relational_db_migration_tests.yml | 10 ++-- .github/workflows/search_db_tests.yml | 47 ++++++------------- .github/workflows/temporal_graph_tests.yml | 24 +++------- .github/workflows/weighted_edges_tests.yml | 7 +++ 6 files changed, 49 insertions(+), 59 deletions(-) diff --git a/.github/workflows/db_examples_tests.yml b/.github/workflows/db_examples_tests.yml index a1a81b4d4..51ac9a82a 100644 --- a/.github/workflows/db_examples_tests.yml +++ b/.github/workflows/db_examples_tests.yml @@ -54,6 +54,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Run Neo4j Example env: ENV: dev @@ -66,9 +70,9 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_USERNAME: "neo4j" - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: | uv run python examples/database_examples/neo4j_example.py diff --git a/.github/workflows/graph_db_tests.yml b/.github/workflows/graph_db_tests.yml index e7e5a11b8..b07f6232f 100644 --- a/.github/workflows/graph_db_tests.yml +++ b/.github/workflows/graph_db_tests.yml @@ -71,6 +71,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Run default Neo4j env: ENV: 'dev' @@ -83,9 +87,9 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} - GRAPH_DATABASE_USERNAME: "neo4j" + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_neo4j.py - name: Run Weighted Edges Tests with Neo4j diff --git a/.github/workflows/relational_db_migration_tests.yml b/.github/workflows/relational_db_migration_tests.yml index 26fd7e150..6a37e308e 100644 --- a/.github/workflows/relational_db_migration_tests.yml +++ b/.github/workflows/relational_db_migration_tests.yml @@ -186,6 +186,10 @@ jobs: python-version: '3.11.x' extra-dependencies: "postgres" + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Install specific db dependency run: echo "Dependencies already installed in setup" @@ -206,9 +210,9 @@ jobs: env: ENV: 'dev' GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} - GRAPH_DATABASE_USERNAME: "neo4j" + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} LLM_PROVIDER: openai LLM_MODEL: ${{ secrets.LLM_MODEL }} diff --git a/.github/workflows/search_db_tests.yml b/.github/workflows/search_db_tests.yml index 0b749526d..e3e46dd97 100644 --- a/.github/workflows/search_db_tests.yml +++ b/.github/workflows/search_db_tests.yml @@ -51,20 +51,6 @@ jobs: name: Search test for Neo4j/LanceDB/Sqlite runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }} - services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 steps: - name: Check out @@ -77,6 +63,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -94,9 +84,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'lancedb' DB_PROVIDER: 'sqlite' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_search_db.py run-kuzu-pgvector-postgres-search-tests: @@ -158,19 +148,6 @@ jobs: runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/pgvector/postgres') }} services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 postgres: image: pgvector/pgvector:pg17 env: @@ -196,6 +173,10 @@ jobs: python-version: ${{ inputs.python-version }} extra-dependencies: "postgres" + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -213,9 +194,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'pgvector' DB_PROVIDER: 'postgres' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} DB_NAME: cognee_db DB_HOST: 127.0.0.1 DB_PORT: 5432 diff --git a/.github/workflows/temporal_graph_tests.yml b/.github/workflows/temporal_graph_tests.yml index 4156dffff..66f7d60ee 100644 --- a/.github/workflows/temporal_graph_tests.yml +++ b/.github/workflows/temporal_graph_tests.yml @@ -51,20 +51,6 @@ jobs: name: Temporal Graph test Neo4j (lancedb + sqlite) runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }} - services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 steps: - name: Check out @@ -77,6 +63,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -94,9 +84,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'lancedb' DB_PROVIDER: 'sqlite' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_temporal_graph.py run_temporal_graph_kuzu_postgres_pgvector: diff --git a/.github/workflows/weighted_edges_tests.yml b/.github/workflows/weighted_edges_tests.yml index df5e048f2..874ef6ea4 100644 --- a/.github/workflows/weighted_edges_tests.yml +++ b/.github/workflows/weighted_edges_tests.yml @@ -86,12 +86,19 @@ jobs: with: python-version: '3.11' + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" - name: Run Weighted Edges Tests env: GRAPH_DATABASE_PROVIDER: ${{ matrix.graph_db_provider }} + GRAPH_DATABASE_URL: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-url || '' }} + GRAPH_DATABASE_USERNAME: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-username || '' }} + GRAPH_DATABASE_PASSWORD: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-password || '' }} run: | uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v --tb=short From faeca138d9615deadae1b61bbce5145cd0d91d81 Mon Sep 17 00:00:00 2001 From: hajdul88 <52442977+hajdul88@users.noreply.github.com> Date: Thu, 9 Oct 2025 14:06:25 +0200 Subject: [PATCH 105/139] fix: fixes distributed pipeline (#1454) ## Description This PR fixes distributed pipeline + updates core changes in distr logic. ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) - [x] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [x] Code refactoring - [x] Performance improvement - [ ] Other (please specify): ## Changes Made Fixes distributed pipeline: -Changed spawning logic + adds incremental loading to run_tasks_diistributed -Adds batching to consumer nodes -Fixes consumer stopping criteria by adding stop signal + handling -Changed edge embedding solution to avoid huge network load in a case of a multicontainer environment ## Testing Tested it by running 1GB on modal + manually ## Screenshots/Videos (if applicable) None ## Pre-submission Checklist - [x] **I have tested my changes thoroughly before submitting this PR** - [x] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## Related Issues None ## Additional Notes None ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Co-authored-by: Boris Co-authored-by: Boris Arzentar --- .github/workflows/distributed_test.yml | 73 + .github/workflows/test_suites.yml | 8 +- .../codingagents/coding_rule_associations.py | 3 +- .../databases/graph/neo4j_driver/adapter.py | 3 +- .../graph/neo4j_driver/deadlock_retry.py | 2 +- .../modules/data/methods/get_dataset_data.py | 5 +- cognee/modules/engine/models/TableRow.py | 1 - .../modules/pipelines/operations/run_tasks.py | 183 +- .../operations/run_tasks_data_item.py | 261 + .../operations/run_tasks_distributed.py | 152 +- .../retrieval/graph_completion_retriever.py | 2 +- cognee/modules/retrieval/user_qa_feedback.py | 5 +- .../codingagents/coding_rule_associations.py | 3 +- cognee/tasks/graph/extract_graph_from_data.py | 2 + cognee/tasks/storage/add_data_points.py | 13 +- cognee/tasks/storage/index_graph_edges.py | 21 +- distributed/Dockerfile | 3 - distributed/entrypoint.py | 30 +- distributed/poetry.lock | 12238 ---------------- distributed/pyproject.toml | 185 - distributed/signal.py | 5 + .../workers/data_point_saving_worker.py | 98 +- distributed/workers/graph_saving_worker.py | 118 +- 23 files changed, 654 insertions(+), 12760 deletions(-) create mode 100644 .github/workflows/distributed_test.yml create mode 100644 cognee/modules/pipelines/operations/run_tasks_data_item.py delete mode 100644 distributed/poetry.lock delete mode 100644 distributed/pyproject.toml create mode 100644 distributed/signal.py diff --git a/.github/workflows/distributed_test.yml b/.github/workflows/distributed_test.yml new file mode 100644 index 000000000..57bbb7459 --- /dev/null +++ b/.github/workflows/distributed_test.yml @@ -0,0 +1,73 @@ +name: Distributed Cognee test with modal +permissions: + contents: read +on: + workflow_call: + inputs: + python-version: + required: false + type: string + default: '3.11.x' + secrets: + LLM_MODEL: + required: true + LLM_ENDPOINT: + required: true + LLM_API_KEY: + required: true + LLM_API_VERSION: + required: true + EMBEDDING_MODEL: + required: true + EMBEDDING_ENDPOINT: + required: true + EMBEDDING_API_KEY: + required: true + EMBEDDING_API_VERSION: + required: true + OPENAI_API_KEY: + required: true + +jobs: + run-server-start-test: + name: Distributed Cognee test (Modal) + runs-on: ubuntu-22.04 + steps: + - name: Check out + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: "distributed postgres" + + - name: Run Distributed Cognee (Modal) + env: + ENV: 'dev' + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} + MODAL_SECRET_NAME: ${{ secrets.MODAL_SECRET_NAME }} + GRAPH_DATABASE_PROVIDER: "neo4j" + GRAPH_DATABASE_URL: ${{ secrets.AZURE_NEO4j_URL }} + GRAPH_DATABASE_USERNAME: ${{ secrets.AZURE_NEO4J_USERNAME }} + GRAPH_DATABASE_PASSWORD: ${{ secrets.AZURE_NEO4J_PW }} + DB_PROVIDER: "postgres" + DB_NAME: ${{ secrets.AZURE_POSTGRES_DB_NAME }} + DB_HOST: ${{ secrets.AZURE_POSTGRES_HOST }} + DB_PORT: ${{ secrets.AZURE_POSTGRES_PORT }} + DB_USERNAME: ${{ secrets.AZURE_POSTGRES_USERNAME }} + DB_PASSWORD: ${{ secrets.AZURE_POSTGRES_PW }} + VECTOR_DB_PROVIDER: "pgvector" + COGNEE_DISTRIBUTED: "true" + run: uv run modal run ./distributed/entrypoint.py diff --git a/.github/workflows/test_suites.yml b/.github/workflows/test_suites.yml index ff18f2962..2f1bdebf0 100644 --- a/.github/workflows/test_suites.yml +++ b/.github/workflows/test_suites.yml @@ -27,6 +27,12 @@ jobs: uses: ./.github/workflows/e2e_tests.yml secrets: inherit + distributed-tests: + name: Distributed Cognee Test + needs: [ basic-tests, e2e-tests, graph-db-tests ] + uses: ./.github/workflows/distributed_test.yml + secrets: inherit + cli-tests: name: CLI Tests uses: ./.github/workflows/cli_tests.yml @@ -104,7 +110,7 @@ jobs: db-examples-tests: name: DB Examples Tests - needs: [vector-db-tests, graph-db-tests, relational-db-migration-tests] + needs: [vector-db-tests, graph-db-tests, relational-db-migration-tests, distributed-tests] uses: ./.github/workflows/db_examples_tests.yml secrets: inherit diff --git a/cognee-mcp/src/codingagents/coding_rule_associations.py b/cognee-mcp/src/codingagents/coding_rule_associations.py index a34c9233b..c860cb9f1 100644 --- a/cognee-mcp/src/codingagents/coding_rule_associations.py +++ b/cognee-mcp/src/codingagents/coding_rule_associations.py @@ -117,5 +117,4 @@ async def add_rule_associations(data: str, rules_nodeset_name: str): if len(edges_to_save) > 0: await graph_engine.add_edges(edges_to_save) - - await index_graph_edges() + await index_graph_edges(edges_to_save) diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py index 03b16eb33..520295ed2 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py @@ -68,6 +68,7 @@ class Neo4jAdapter(GraphDBInterface): auth=auth, max_connection_lifetime=120, notifications_min_severity="OFF", + keep_alive=True, ) async def initialize(self) -> None: @@ -205,7 +206,7 @@ class Neo4jAdapter(GraphDBInterface): { "node_id": str(node.id), "label": type(node).__name__, - "properties": self.serialize_properties(node.model_dump()), + "properties": self.serialize_properties(dict(node)), } for node in nodes ] diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py b/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py index 35d37383f..ae9e1219b 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py @@ -8,7 +8,7 @@ from cognee.infrastructure.utils.calculate_backoff import calculate_backoff logger = get_logger("deadlock_retry") -def deadlock_retry(max_retries=5): +def deadlock_retry(max_retries=10): """ Decorator that automatically retries an asynchronous function when rate limit errors occur. diff --git a/cognee/modules/data/methods/get_dataset_data.py b/cognee/modules/data/methods/get_dataset_data.py index afe61ddc5..5d06e9067 100644 --- a/cognee/modules/data/methods/get_dataset_data.py +++ b/cognee/modules/data/methods/get_dataset_data.py @@ -9,7 +9,10 @@ async def get_dataset_data(dataset_id: UUID) -> list[Data]: async with db_engine.get_async_session() as session: result = await session.execute( - select(Data).join(Data.datasets).filter((Dataset.id == dataset_id)) + select(Data) + .join(Data.datasets) + .filter((Dataset.id == dataset_id)) + .order_by(Data.data_size.desc()) ) data = list(result.scalars().all()) diff --git a/cognee/modules/engine/models/TableRow.py b/cognee/modules/engine/models/TableRow.py index dd07612fb..ad82d68e3 100644 --- a/cognee/modules/engine/models/TableRow.py +++ b/cognee/modules/engine/models/TableRow.py @@ -5,7 +5,6 @@ from typing import Optional class TableRow(DataPoint): name: str - is_a: Optional[TableType] = None description: str properties: str diff --git a/cognee/modules/pipelines/operations/run_tasks.py b/cognee/modules/pipelines/operations/run_tasks.py index 62d4972ad..4a0c77309 100644 --- a/cognee/modules/pipelines/operations/run_tasks.py +++ b/cognee/modules/pipelines/operations/run_tasks.py @@ -4,35 +4,28 @@ import asyncio from uuid import UUID from typing import Any, List from functools import wraps -from sqlalchemy import select -import cognee.modules.ingestion as ingestion from cognee.infrastructure.databases.graph import get_graph_engine from cognee.infrastructure.databases.relational import get_relational_engine from cognee.modules.pipelines.operations.run_tasks_distributed import run_tasks_distributed from cognee.modules.users.models import User -from cognee.modules.data.models import Data -from cognee.infrastructure.files.utils.open_data_file import open_data_file from cognee.shared.logging_utils import get_logger from cognee.modules.users.methods import get_default_user from cognee.modules.pipelines.utils import generate_pipeline_id from cognee.modules.pipelines.exceptions import PipelineRunFailedError -from cognee.tasks.ingestion import save_data_item_to_storage, resolve_data_directories +from cognee.tasks.ingestion import resolve_data_directories from cognee.modules.pipelines.models.PipelineRunInfo import ( PipelineRunCompleted, PipelineRunErrored, PipelineRunStarted, - PipelineRunYield, - PipelineRunAlreadyCompleted, ) -from cognee.modules.pipelines.models.DataItemStatus import DataItemStatus - from cognee.modules.pipelines.operations import ( log_pipeline_run_start, log_pipeline_run_complete, log_pipeline_run_error, ) from .run_tasks_with_telemetry import run_tasks_with_telemetry +from .run_tasks_data_item import run_tasks_data_item from ..tasks.task import Task @@ -68,176 +61,6 @@ async def run_tasks( context: dict = None, incremental_loading: bool = False, ): - async def _run_tasks_data_item_incremental( - data_item, - dataset, - tasks, - pipeline_name, - pipeline_id, - pipeline_run_id, - context, - user, - ): - db_engine = get_relational_engine() - # If incremental_loading of data is set to True don't process documents already processed by pipeline - # If data is being added to Cognee for the first time calculate the id of the data - if not isinstance(data_item, Data): - file_path = await save_data_item_to_storage(data_item) - # Ingest data and add metadata - async with open_data_file(file_path) as file: - classified_data = ingestion.classify(file) - # data_id is the hash of file contents + owner id to avoid duplicate data - data_id = ingestion.identify(classified_data, user) - else: - # If data was already processed by Cognee get data id - data_id = data_item.id - - # Check pipeline status, if Data already processed for pipeline before skip current processing - async with db_engine.get_async_session() as session: - data_point = ( - await session.execute(select(Data).filter(Data.id == data_id)) - ).scalar_one_or_none() - if data_point: - if ( - data_point.pipeline_status.get(pipeline_name, {}).get(str(dataset.id)) - == DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED - ): - yield { - "run_info": PipelineRunAlreadyCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - return - - try: - # Process data based on data_item and list of tasks - async for result in run_tasks_with_telemetry( - tasks=tasks, - data=[data_item], - user=user, - pipeline_name=pipeline_id, - context=context, - ): - yield PipelineRunYield( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - payload=result, - ) - - # Update pipeline status for Data element - async with db_engine.get_async_session() as session: - data_point = ( - await session.execute(select(Data).filter(Data.id == data_id)) - ).scalar_one_or_none() - data_point.pipeline_status[pipeline_name] = { - str(dataset.id): DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED - } - await session.merge(data_point) - await session.commit() - - yield { - "run_info": PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - - except Exception as error: - # Temporarily swallow error and try to process rest of documents first, then re-raise error at end of data ingestion pipeline - logger.error( - f"Exception caught while processing data: {error}.\n Data processing failed for data item: {data_item}." - ) - yield { - "run_info": PipelineRunErrored( - pipeline_run_id=pipeline_run_id, - payload=repr(error), - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - - if os.getenv("RAISE_INCREMENTAL_LOADING_ERRORS", "true").lower() == "true": - raise error - - async def _run_tasks_data_item_regular( - data_item, - dataset, - tasks, - pipeline_id, - pipeline_run_id, - context, - user, - ): - # Process data based on data_item and list of tasks - async for result in run_tasks_with_telemetry( - tasks=tasks, - data=[data_item], - user=user, - pipeline_name=pipeline_id, - context=context, - ): - yield PipelineRunYield( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - payload=result, - ) - - yield { - "run_info": PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ) - } - - async def _run_tasks_data_item( - data_item, - dataset, - tasks, - pipeline_name, - pipeline_id, - pipeline_run_id, - context, - user, - incremental_loading, - ): - # Go through async generator and return data item processing result. Result can be PipelineRunAlreadyCompleted when data item is skipped, - # PipelineRunCompleted when processing was successful and PipelineRunErrored if there were issues - result = None - if incremental_loading: - async for result in _run_tasks_data_item_incremental( - data_item=data_item, - dataset=dataset, - tasks=tasks, - pipeline_name=pipeline_name, - pipeline_id=pipeline_id, - pipeline_run_id=pipeline_run_id, - context=context, - user=user, - ): - pass - else: - async for result in _run_tasks_data_item_regular( - data_item=data_item, - dataset=dataset, - tasks=tasks, - pipeline_id=pipeline_id, - pipeline_run_id=pipeline_run_id, - context=context, - user=user, - ): - pass - - return result - if not user: user = await get_default_user() @@ -269,7 +92,7 @@ async def run_tasks( # Create async tasks per data item that will run the pipeline for the data item data_item_tasks = [ asyncio.create_task( - _run_tasks_data_item( + run_tasks_data_item( data_item, dataset, tasks, diff --git a/cognee/modules/pipelines/operations/run_tasks_data_item.py b/cognee/modules/pipelines/operations/run_tasks_data_item.py new file mode 100644 index 000000000..94fc631a8 --- /dev/null +++ b/cognee/modules/pipelines/operations/run_tasks_data_item.py @@ -0,0 +1,261 @@ +""" +Data item processing functions for pipeline operations. + +This module contains reusable functions for processing individual data items +within pipeline operations, supporting both incremental and regular processing modes. +""" + +import os +from typing import Any, Dict, AsyncGenerator, Optional +from sqlalchemy import select + +import cognee.modules.ingestion as ingestion +from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.infrastructure.files.utils.open_data_file import open_data_file +from cognee.shared.logging_utils import get_logger +from cognee.modules.users.models import User +from cognee.modules.data.models import Data, Dataset +from cognee.tasks.ingestion import save_data_item_to_storage +from cognee.modules.pipelines.models.PipelineRunInfo import ( + PipelineRunCompleted, + PipelineRunErrored, + PipelineRunYield, + PipelineRunAlreadyCompleted, +) +from cognee.modules.pipelines.models.DataItemStatus import DataItemStatus +from cognee.modules.pipelines.operations.run_tasks_with_telemetry import run_tasks_with_telemetry +from ..tasks.task import Task + +logger = get_logger("run_tasks_data_item") + + +async def run_tasks_data_item_incremental( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, +) -> AsyncGenerator[Dict[str, Any], None]: + """ + Process a single data item with incremental loading support. + + This function handles incremental processing by checking if the data item + has already been processed for the given pipeline and dataset. If it has, + it skips processing and returns a completion status. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_name: Name of the pipeline + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + + Yields: + Dict containing run_info and data_id for each processing step + """ + db_engine = get_relational_engine() + + # If incremental_loading of data is set to True don't process documents already processed by pipeline + # If data is being added to Cognee for the first time calculate the id of the data + if not isinstance(data_item, Data): + file_path = await save_data_item_to_storage(data_item) + # Ingest data and add metadata + async with open_data_file(file_path) as file: + classified_data = ingestion.classify(file) + # data_id is the hash of file contents + owner id to avoid duplicate data + data_id = ingestion.identify(classified_data, user) + else: + # If data was already processed by Cognee get data id + data_id = data_item.id + + # Check pipeline status, if Data already processed for pipeline before skip current processing + async with db_engine.get_async_session() as session: + data_point = ( + await session.execute(select(Data).filter(Data.id == data_id)) + ).scalar_one_or_none() + if data_point: + if ( + data_point.pipeline_status.get(pipeline_name, {}).get(str(dataset.id)) + == DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED + ): + yield { + "run_info": PipelineRunAlreadyCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + return + + try: + # Process data based on data_item and list of tasks + async for result in run_tasks_with_telemetry( + tasks=tasks, + data=[data_item], + user=user, + pipeline_name=pipeline_id, + context=context, + ): + yield PipelineRunYield( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + payload=result, + ) + + # Update pipeline status for Data element + async with db_engine.get_async_session() as session: + data_point = ( + await session.execute(select(Data).filter(Data.id == data_id)) + ).scalar_one_or_none() + data_point.pipeline_status[pipeline_name] = { + str(dataset.id): DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED + } + await session.merge(data_point) + await session.commit() + + yield { + "run_info": PipelineRunCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + + except Exception as error: + # Temporarily swallow error and try to process rest of documents first, then re-raise error at end of data ingestion pipeline + logger.error( + f"Exception caught while processing data: {error}.\n Data processing failed for data item: {data_item}." + ) + yield { + "run_info": PipelineRunErrored( + pipeline_run_id=pipeline_run_id, + payload=repr(error), + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + + if os.getenv("RAISE_INCREMENTAL_LOADING_ERRORS", "true").lower() == "true": + raise error + + +async def run_tasks_data_item_regular( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, +) -> AsyncGenerator[Dict[str, Any], None]: + """ + Process a single data item in regular (non-incremental) mode. + + This function processes a data item without checking for previous processing + status, executing all tasks on the data item. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + + Yields: + Dict containing run_info for each processing step + """ + # Process data based on data_item and list of tasks + async for result in run_tasks_with_telemetry( + tasks=tasks, + data=[data_item], + user=user, + pipeline_name=pipeline_id, + context=context, + ): + yield PipelineRunYield( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + payload=result, + ) + + yield { + "run_info": PipelineRunCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ) + } + + +async def run_tasks_data_item( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, + incremental_loading: bool, +) -> Optional[Dict[str, Any]]: + """ + Process a single data item, choosing between incremental and regular processing. + + This is the main entry point for data item processing that delegates to either + incremental or regular processing based on the incremental_loading flag. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_name: Name of the pipeline + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + incremental_loading: Whether to use incremental processing + + Returns: + Dict containing the final processing result, or None if processing was skipped + """ + # Go through async generator and return data item processing result. Result can be PipelineRunAlreadyCompleted when data item is skipped, + # PipelineRunCompleted when processing was successful and PipelineRunErrored if there were issues + result = None + if incremental_loading: + async for result in run_tasks_data_item_incremental( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_name=pipeline_name, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + ): + pass + else: + async for result in run_tasks_data_item_regular( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + ): + pass + + return result diff --git a/cognee/modules/pipelines/operations/run_tasks_distributed.py b/cognee/modules/pipelines/operations/run_tasks_distributed.py index d7dc2a403..95cdb0266 100644 --- a/cognee/modules/pipelines/operations/run_tasks_distributed.py +++ b/cognee/modules/pipelines/operations/run_tasks_distributed.py @@ -3,49 +3,96 @@ try: except ModuleNotFoundError: modal = None +from typing import Any, List, Optional +from uuid import UUID + +from cognee.modules.pipelines.tasks.task import Task from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.pipelines.models import ( PipelineRunStarted, - PipelineRunYield, PipelineRunCompleted, + PipelineRunErrored, ) -from cognee.modules.pipelines.operations import log_pipeline_run_start, log_pipeline_run_complete -from cognee.modules.pipelines.utils.generate_pipeline_id import generate_pipeline_id +from cognee.modules.pipelines.operations import ( + log_pipeline_run_start, + log_pipeline_run_complete, + log_pipeline_run_error, +) +from cognee.modules.pipelines.utils import generate_pipeline_id from cognee.modules.users.methods import get_default_user from cognee.shared.logging_utils import get_logger - -from .run_tasks_with_telemetry import run_tasks_with_telemetry - +from cognee.modules.users.models import User +from cognee.modules.pipelines.exceptions import PipelineRunFailedError +from cognee.tasks.ingestion import resolve_data_directories +from .run_tasks_data_item import run_tasks_data_item logger = get_logger("run_tasks_distributed()") - if modal: + import os from distributed.app import app from distributed.modal_image import image + secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + @app.function( retries=3, image=image, timeout=86400, max_containers=50, - secrets=[modal.Secret.from_name("distributed_cognee")], + secrets=[modal.Secret.from_name(secret_name)], ) - async def run_tasks_on_modal(tasks, data_item, user, pipeline_name, context): - pipeline_run = run_tasks_with_telemetry(tasks, data_item, user, pipeline_name, context) + async def run_tasks_on_modal( + data_item, + dataset_id: UUID, + tasks: List[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[dict], + user: User, + incremental_loading: bool, + ): + """ + Wrapper that runs the run_tasks_data_item function. + This is the function/code that runs on modal executor and produces the graph/vector db objects + """ + from cognee.infrastructure.databases.relational import get_relational_engine - run_info = None + async with get_relational_engine().get_async_session() as session: + from cognee.modules.data.models import Dataset - async for pipeline_run_info in pipeline_run: - run_info = pipeline_run_info + dataset = await session.get(Dataset, dataset_id) - return run_info + result = await run_tasks_data_item( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_name=pipeline_name, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + incremental_loading=incremental_loading, + ) + + return result -async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, context): +async def run_tasks_distributed( + tasks: List[Task], + dataset_id: UUID, + data: List[Any] = None, + user: User = None, + pipeline_name: str = "unknown_pipeline", + context: dict = None, + incremental_loading: bool = False, +): if not user: user = await get_default_user() + # Get dataset object db_engine = get_relational_engine() async with db_engine.get_async_session() as session: from cognee.modules.data.models import Dataset @@ -53,9 +100,7 @@ async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, co dataset = await session.get(Dataset, dataset_id) pipeline_id = generate_pipeline_id(user.id, dataset.id, pipeline_name) - pipeline_run = await log_pipeline_run_start(pipeline_id, pipeline_name, dataset_id, data) - pipeline_run_id = pipeline_run.pipeline_run_id yield PipelineRunStarted( @@ -65,30 +110,67 @@ async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, co payload=data, ) - data_count = len(data) if isinstance(data, list) else 1 + try: + if not isinstance(data, list): + data = [data] - arguments = [ - [tasks] * data_count, - [[data_item] for data_item in data[:data_count]] if data_count > 1 else [data], - [user] * data_count, - [pipeline_name] * data_count, - [context] * data_count, - ] + data = await resolve_data_directories(data) - async for result in run_tasks_on_modal.map.aio(*arguments): - logger.info(f"Received result: {result}") + number_of_data_items = len(data) if isinstance(data, list) else 1 - yield PipelineRunYield( + data_item_tasks = [ + data, + [dataset.id] * number_of_data_items, + [tasks] * number_of_data_items, + [pipeline_name] * number_of_data_items, + [pipeline_id] * number_of_data_items, + [pipeline_run_id] * number_of_data_items, + [context] * number_of_data_items, + [user] * number_of_data_items, + [incremental_loading] * number_of_data_items, + ] + + results = [] + async for result in run_tasks_on_modal.map.aio(*data_item_tasks): + if not result: + continue + results.append(result) + + # Remove skipped results + results = [r for r in results if r] + + # If any data item failed, raise PipelineRunFailedError + errored = [ + r + for r in results + if r and r.get("run_info") and isinstance(r["run_info"], PipelineRunErrored) + ] + if errored: + raise PipelineRunFailedError("Pipeline run failed. Data item could not be processed.") + + await log_pipeline_run_complete( + pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data + ) + + yield PipelineRunCompleted( pipeline_run_id=pipeline_run_id, dataset_id=dataset.id, dataset_name=dataset.name, - payload=result, + data_ingestion_info=results, ) - await log_pipeline_run_complete(pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data) + except Exception as error: + await log_pipeline_run_error( + pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data, error + ) - yield PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ) + yield PipelineRunErrored( + pipeline_run_id=pipeline_run_id, + payload=repr(error), + dataset_id=dataset.id, + dataset_name=dataset.name, + data_ingestion_info=locals().get("results"), + ) + + if not isinstance(error, PipelineRunFailedError): + raise diff --git a/cognee/modules/retrieval/graph_completion_retriever.py b/cognee/modules/retrieval/graph_completion_retriever.py index 1772c6300..80815a42a 100644 --- a/cognee/modules/retrieval/graph_completion_retriever.py +++ b/cognee/modules/retrieval/graph_completion_retriever.py @@ -194,7 +194,7 @@ class GraphCompletionRetriever(BaseGraphRetriever): belongs_to_set=interactions_node_set, ) - await add_data_points(data_points=[cognee_user_interaction], update_edge_collection=False) + await add_data_points(data_points=[cognee_user_interaction]) relationships = [] relationship_name = "used_graph_element_to_answer" diff --git a/cognee/modules/retrieval/user_qa_feedback.py b/cognee/modules/retrieval/user_qa_feedback.py index 21e3d8e59..6f6c4ed8e 100644 --- a/cognee/modules/retrieval/user_qa_feedback.py +++ b/cognee/modules/retrieval/user_qa_feedback.py @@ -8,7 +8,7 @@ from cognee.shared.logging_utils import get_logger from cognee.modules.retrieval.base_feedback import BaseFeedback from cognee.modules.retrieval.utils.models import CogneeUserFeedback from cognee.modules.retrieval.utils.models import UserFeedbackEvaluation -from cognee.tasks.storage import add_data_points +from cognee.tasks.storage import add_data_points, index_graph_edges logger = get_logger("CompletionRetriever") @@ -47,7 +47,7 @@ class UserQAFeedback(BaseFeedback): belongs_to_set=feedbacks_node_set, ) - await add_data_points(data_points=[cognee_user_feedback], update_edge_collection=False) + await add_data_points(data_points=[cognee_user_feedback]) relationships = [] relationship_name = "gives_feedback_to" @@ -76,6 +76,7 @@ class UserQAFeedback(BaseFeedback): if len(relationships) > 0: graph_engine = await get_graph_engine() await graph_engine.add_edges(relationships) + await index_graph_edges(relationships) await graph_engine.apply_feedback_weight( node_ids=to_node_ids, weight=feedback_sentiment.score ) diff --git a/cognee/tasks/codingagents/coding_rule_associations.py b/cognee/tasks/codingagents/coding_rule_associations.py index 191204df3..d8d29ab91 100644 --- a/cognee/tasks/codingagents/coding_rule_associations.py +++ b/cognee/tasks/codingagents/coding_rule_associations.py @@ -124,5 +124,4 @@ async def add_rule_associations( if len(edges_to_save) > 0: await graph_engine.add_edges(edges_to_save) - - await index_graph_edges() + await index_graph_edges(edges_to_save) diff --git a/cognee/tasks/graph/extract_graph_from_data.py b/cognee/tasks/graph/extract_graph_from_data.py index ea26eaae0..49b51af2d 100644 --- a/cognee/tasks/graph/extract_graph_from_data.py +++ b/cognee/tasks/graph/extract_graph_from_data.py @@ -4,6 +4,7 @@ from pydantic import BaseModel from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.ontology.ontology_env_config import get_ontology_env_config +from cognee.tasks.storage import index_graph_edges from cognee.tasks.storage.add_data_points import add_data_points from cognee.modules.ontology.ontology_config import Config from cognee.modules.ontology.get_default_ontology_resolver import ( @@ -88,6 +89,7 @@ async def integrate_chunk_graphs( if len(graph_edges) > 0: await graph_engine.add_edges(graph_edges) + await index_graph_edges(graph_edges) return data_chunks diff --git a/cognee/tasks/storage/add_data_points.py b/cognee/tasks/storage/add_data_points.py index 1c7a4c2bc..ad1693e82 100644 --- a/cognee/tasks/storage/add_data_points.py +++ b/cognee/tasks/storage/add_data_points.py @@ -10,9 +10,7 @@ from cognee.tasks.storage.exceptions import ( ) -async def add_data_points( - data_points: List[DataPoint], update_edge_collection: bool = True -) -> List[DataPoint]: +async def add_data_points(data_points: List[DataPoint]) -> List[DataPoint]: """ Add a batch of data points to the graph database by extracting nodes and edges, deduplicating them, and indexing them for retrieval. @@ -25,9 +23,6 @@ async def add_data_points( Args: data_points (List[DataPoint]): A list of data points to process and insert into the graph. - update_edge_collection (bool, optional): - Whether to update the edge index after adding edges. - Defaults to True. Returns: List[DataPoint]: @@ -73,12 +68,10 @@ async def add_data_points( graph_engine = await get_graph_engine() + await graph_engine.add_nodes(nodes) await index_data_points(nodes) - await graph_engine.add_nodes(nodes) await graph_engine.add_edges(edges) - - if update_edge_collection: - await index_graph_edges() + await index_graph_edges(edges) return data_points diff --git a/cognee/tasks/storage/index_graph_edges.py b/cognee/tasks/storage/index_graph_edges.py index f4e32f8c8..b7bf7a2b9 100644 --- a/cognee/tasks/storage/index_graph_edges.py +++ b/cognee/tasks/storage/index_graph_edges.py @@ -1,15 +1,18 @@ from cognee.modules.engine.utils.generate_edge_id import generate_edge_id -from cognee.shared.logging_utils import get_logger, ERROR +from cognee.shared.logging_utils import get_logger from collections import Counter - +from typing import Optional, Dict, Any, List, Tuple, Union from cognee.infrastructure.databases.vector import get_vector_engine from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.graph.models.EdgeType import EdgeType +from cognee.infrastructure.databases.graph.graph_db_interface import EdgeData -logger = get_logger(level=ERROR) +logger = get_logger() -async def index_graph_edges(): +async def index_graph_edges( + edges_data: Union[List[EdgeData], List[Tuple[str, str, str, Optional[Dict[str, Any]]]]] = None, +): """ Indexes graph edges by creating and managing vector indexes for relationship types. @@ -35,13 +38,17 @@ async def index_graph_edges(): index_points = {} vector_engine = get_vector_engine() - graph_engine = await get_graph_engine() + + if edges_data is None: + graph_engine = await get_graph_engine() + _, edges_data = await graph_engine.get_graph_data() + logger.warning( + "Your graph edge embedding is deprecated, please pass edges to the index_graph_edges directly." + ) except Exception as e: logger.error("Failed to initialize engines: %s", e) raise RuntimeError("Initialization error") from e - _, edges_data = await graph_engine.get_graph_data() - edge_types = Counter( item.get("relationship_name") for edge in edges_data diff --git a/distributed/Dockerfile b/distributed/Dockerfile index 6ac818d45..99c92cfea 100644 --- a/distributed/Dockerfile +++ b/distributed/Dockerfile @@ -29,6 +29,3 @@ RUN poetry install --extras neo4j --extras postgres --extras aws --extras distri COPY cognee/ /app/cognee COPY distributed/ /app/distributed -RUN chmod +x /app/distributed/entrypoint.sh - -ENTRYPOINT ["/app/distributed/entrypoint.sh"] diff --git a/distributed/entrypoint.py b/distributed/entrypoint.py index 977c92c95..0f1f83d81 100644 --- a/distributed/entrypoint.py +++ b/distributed/entrypoint.py @@ -10,6 +10,7 @@ from distributed.app import app from distributed.queues import add_nodes_and_edges_queue, add_data_points_queue from distributed.workers.graph_saving_worker import graph_saving_worker from distributed.workers.data_point_saving_worker import data_point_saving_worker +from distributed.signal import QueueSignal logger = get_logger() @@ -23,13 +24,14 @@ async def main(): await add_nodes_and_edges_queue.clear.aio() await add_data_points_queue.clear.aio() - number_of_graph_saving_workers = 1 # Total number of graph_saving_worker to spawn - number_of_data_point_saving_workers = 5 # Total number of graph_saving_worker to spawn + number_of_graph_saving_workers = 1 # Total number of graph_saving_worker to spawn (MAX 1) + number_of_data_point_saving_workers = ( + 10 # Total number of graph_saving_worker to spawn (MAX 10) + ) - results = [] consumer_futures = [] - # await prune.prune_data() # We don't want to delete files on s3 + await prune.prune_data() # This prunes the data from the file storage # Delete DBs and saved files from metastore await prune.prune_system(metadata=True) @@ -45,16 +47,28 @@ async def main(): worker_future = data_point_saving_worker.spawn() consumer_futures.append(worker_future) + """ Example: Setting and adding S3 path as input s3_bucket_path = os.getenv("S3_BUCKET_PATH") s3_data_path = "s3://" + s3_bucket_path await cognee.add(s3_data_path, dataset_name="s3-files") + """ + await cognee.add( + [ + "Audi is a German car manufacturer", + "The Netherlands is next to Germany", + "Berlin is the capital of Germany", + "The Rhine is a major European river", + "BMW produces luxury vehicles", + ], + dataset_name="s3-files", + ) await cognee.cognify(datasets=["s3-files"]) - # Push empty tuple into the queue to signal the end of data. - await add_nodes_and_edges_queue.put.aio(()) - await add_data_points_queue.put.aio(()) + # Put Processing end signal into the queues to stop the consumers + await add_nodes_and_edges_queue.put.aio(QueueSignal.STOP) + await add_data_points_queue.put.aio(QueueSignal.STOP) for consumer_future in consumer_futures: try: @@ -64,8 +78,6 @@ async def main(): except Exception as e: logger.error(e) - print(results) - if __name__ == "__main__": asyncio.run(main()) diff --git a/distributed/poetry.lock b/distributed/poetry.lock deleted file mode 100644 index 4eb4d6808..000000000 --- a/distributed/poetry.lock +++ /dev/null @@ -1,12238 +0,0 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. - -[[package]] -name = "aiobotocore" -version = "2.23.2" -description = "Async client for aws services using botocore and aiohttp" -optional = false -python-versions = ">=3.9" -groups = ["main"] -files = [ - {file = "aiobotocore-2.23.2-py3-none-any.whl", hash = "sha256:5ca24feb49be73bd6cd92e82e95aefb0647c07bb85ca57000a0361b9554503d8"}, - 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-[metadata] -lock-version = "2.1" -python-versions = ">=3.10,<=3.13" -content-hash = "d15e6b5d065016613be0b8c015cccf85e7f63891353c97636d136d14e5c8f62e" diff --git a/distributed/pyproject.toml b/distributed/pyproject.toml deleted file mode 100644 index e4832ea21..000000000 --- a/distributed/pyproject.toml +++ /dev/null @@ -1,185 +0,0 @@ -[project] -name = "cognee" -version = "0.2.2.dev0" -description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning." -authors = [ - { name = "Vasilije Markovic" }, - { name = "Boris Arzentar" }, -] -requires-python = ">=3.10,<=3.13" -readme = "README.md" -license = "Apache-2.0" -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: Apache Software License", - "Topic :: Software Development :: Libraries", - "Operating System :: MacOS :: MacOS X", - "Operating System :: POSIX :: Linux", - "Operating System :: Microsoft :: Windows", -] -dependencies = [ - "openai>=1.80.1,<2", - "python-dotenv>=1.0.1,<2.0.0", - "pydantic>=2.11.7,<3.0.0", - "pydantic-settings>=2.10.1,<3", - "typing_extensions>=4.12.2,<5.0.0", - "nltk>=3.9.1,<4.0.0", - "numpy>=1.26.4, <=4.0.0", - "pandas>=2.2.2,<3.0.0", - # Note: New s3fs and boto3 versions don't work well together - # Always use comaptible fixed versions of these two dependencies - "s3fs[boto3]==2025.3.2", - "sqlalchemy>=2.0.39,<3.0.0", - "aiosqlite>=0.20.0,<1.0.0", - "tiktoken>=0.8.0,<1.0.0", - "litellm>=1.57.4, <1.71.0", - "instructor>=1.9.1,<2.0.0", - "langfuse>=2.32.0,<3", - "filetype>=1.2.0,<2.0.0", - "aiohttp>=3.11.14,<4.0.0", - "aiofiles>=23.2.1,<24.0.0", - "rdflib>=7.1.4,<7.2.0", - "pypdf>=4.1.0,<7.0.0", - "jinja2>=3.1.3,<4", - "matplotlib>=3.8.3,<4", - "networkx>=3.4.2,<4", - "lancedb>=0.24.0,<1.0.0", - "alembic>=1.13.3,<2", - "pre-commit>=4.0.1,<5", - "scikit-learn>=1.6.1,<2", - "limits>=4.4.1,<5", - "fastapi>=0.115.7,<1.0.0", - "python-multipart>=0.0.20,<1.0.0", - "fastapi-users[sqlalchemy]>=14.0.1,<15.0.0", - 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-[project.urls] -Homepage = "https://www.cognee.ai" -Repository = "https://github.com/topoteretes/cognee" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build] -exclude = [ - "/bin", - "/dist", - "/.data", - "/.github", - "/alembic", - "/deployment", - "/cognee-mcp", - "/cognee-frontend", - "/examples", - "/helm", - "/licenses", - "/logs", - "/notebooks", - "/profiling", - "/tests", - "/tools", -] - -[tool.hatch.build.targets.wheel] -packages = ["cognee", "distributed"] - -[tool.ruff] -line-length = 100 -exclude = [ - "migrations/", # Ignore migrations directory - "notebooks/", # Ignore notebook files - "build/", # Ignore build directory - "cognee/pipelines.py", - "cognee/modules/users/models/Group.py", - "cognee/modules/users/models/ACL.py", - "cognee/modules/pipelines/models/Task.py", - "cognee/modules/data/models/Dataset.py" -] - -[tool.ruff.lint] -ignore = ["F401"] diff --git a/distributed/signal.py b/distributed/signal.py new file mode 100644 index 000000000..d4e94d151 --- /dev/null +++ b/distributed/signal.py @@ -0,0 +1,5 @@ +from enum import Enum + + +class QueueSignal(str, Enum): + STOP = "STOP" diff --git a/distributed/workers/data_point_saving_worker.py b/distributed/workers/data_point_saving_worker.py index 178b63041..fe842b5e1 100644 --- a/distributed/workers/data_point_saving_worker.py +++ b/distributed/workers/data_point_saving_worker.py @@ -1,16 +1,17 @@ +import os import modal import asyncio from sqlalchemy.exc import OperationalError, DBAPIError from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential from distributed.app import app +from distributed.signal import QueueSignal from distributed.modal_image import image from distributed.queues import add_data_points_queue from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.vector import get_vector_engine - logger = get_logger("data_point_saving_worker") @@ -39,55 +40,84 @@ def is_deadlock_error(error): return False +secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + + @app.function( retries=3, image=image, timeout=86400, - max_containers=5, - secrets=[modal.Secret.from_name("distributed_cognee")], + max_containers=10, + secrets=[modal.Secret.from_name(secret_name)], ) async def data_point_saving_worker(): print("Started processing of data points; starting vector engine queue.") vector_engine = get_vector_engine() + # Defines how many data packets do we glue together from the modal queue before embedding call and ingestion + BATCH_SIZE = 25 + stop_seen = False while True: + if stop_seen: + print("Finished processing all data points; stopping vector engine queue consumer.") + return True + if await add_data_points_queue.len.aio() != 0: try: - add_data_points_request = await add_data_points_queue.get.aio(block=False) + print("Remaining elements in queue:") + print(await add_data_points_queue.len.aio()) + + # collect batched requests + batched_points = {} + for _ in range(min(BATCH_SIZE, await add_data_points_queue.len.aio())): + add_data_points_request = await add_data_points_queue.get.aio(block=False) + + if not add_data_points_request: + continue + + if add_data_points_request == QueueSignal.STOP: + await add_data_points_queue.put.aio(QueueSignal.STOP) + stop_seen = True + break + + if len(add_data_points_request) == 2: + collection_name, data_points = add_data_points_request + if collection_name not in batched_points: + batched_points[collection_name] = [] + batched_points[collection_name].extend(data_points) + else: + print("NoneType or invalid request detected.") + + if batched_points: + for collection_name, data_points in batched_points.items(): + print( + f"Adding {len(data_points)} data points to '{collection_name}' collection." + ) + + @retry( + retry=retry_if_exception_type(VectorDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def add_data_points(): + try: + await vector_engine.create_data_points( + collection_name, data_points, distributed=False + ) + except DBAPIError as error: + if is_deadlock_error(error): + raise VectorDatabaseDeadlockError() + except OperationalError as error: + if is_deadlock_error(error): + raise VectorDatabaseDeadlockError() + + await add_data_points() + print(f"Finished adding data points to '{collection_name}'.") + except modal.exception.DeserializationError as error: logger.error(f"Deserialization error: {str(error)}") continue - if len(add_data_points_request) == 0: - print("Finished processing all data points; stopping vector engine queue.") - return True - - if len(add_data_points_request) == 2: - (collection_name, data_points) = add_data_points_request - - print(f"Adding {len(data_points)} data points to '{collection_name}' collection.") - - @retry( - retry=retry_if_exception_type(VectorDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def add_data_points(): - try: - await vector_engine.create_data_points( - collection_name, data_points, distributed=False - ) - except DBAPIError as error: - if is_deadlock_error(error): - raise VectorDatabaseDeadlockError() - except OperationalError as error: - if is_deadlock_error(error): - raise VectorDatabaseDeadlockError() - - await add_data_points() - - print("Finished adding data points.") - else: print("No jobs, go to sleep.") await asyncio.sleep(5) diff --git a/distributed/workers/graph_saving_worker.py b/distributed/workers/graph_saving_worker.py index a0202eea5..67c034225 100644 --- a/distributed/workers/graph_saving_worker.py +++ b/distributed/workers/graph_saving_worker.py @@ -1,8 +1,10 @@ +import os import modal import asyncio from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential from distributed.app import app +from distributed.signal import QueueSignal from distributed.modal_image import image from distributed.queues import add_nodes_and_edges_queue @@ -10,7 +12,6 @@ from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.graph import get_graph_engine from cognee.infrastructure.databases.graph.config import get_graph_config - logger = get_logger("graph_saving_worker") @@ -37,68 +38,91 @@ def is_deadlock_error(error): return False +secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + + @app.function( retries=3, image=image, timeout=86400, - max_containers=5, - secrets=[modal.Secret.from_name("distributed_cognee")], + max_containers=1, + secrets=[modal.Secret.from_name(secret_name)], ) async def graph_saving_worker(): print("Started processing of nodes and edges; starting graph engine queue.") graph_engine = await get_graph_engine() + # Defines how many data packets do we glue together from the queue before ingesting them into the graph database + BATCH_SIZE = 25 + stop_seen = False while True: + if stop_seen: + print("Finished processing all data points; stopping graph engine queue consumer.") + return True + if await add_nodes_and_edges_queue.len.aio() != 0: try: - nodes_and_edges = await add_nodes_and_edges_queue.get.aio(block=False) + print("Remaining elements in queue:") + print(await add_nodes_and_edges_queue.len.aio()) + + all_nodes, all_edges = [], [] + for _ in range(min(BATCH_SIZE, await add_nodes_and_edges_queue.len.aio())): + nodes_and_edges = await add_nodes_and_edges_queue.get.aio(block=False) + + if not nodes_and_edges: + continue + + if nodes_and_edges == QueueSignal.STOP: + await add_nodes_and_edges_queue.put.aio(QueueSignal.STOP) + stop_seen = True + break + + if len(nodes_and_edges) == 2: + nodes, edges = nodes_and_edges + all_nodes.extend(nodes) + all_edges.extend(edges) + else: + print("None Type detected.") + + if all_nodes or all_edges: + print(f"Adding {len(all_nodes)} nodes and {len(all_edges)} edges.") + + @retry( + retry=retry_if_exception_type(GraphDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def save_graph_nodes(new_nodes): + try: + await graph_engine.add_nodes(new_nodes, distributed=False) + except Exception as error: + if is_deadlock_error(error): + raise GraphDatabaseDeadlockError() + + @retry( + retry=retry_if_exception_type(GraphDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def save_graph_edges(new_edges): + try: + await graph_engine.add_edges(new_edges, distributed=False) + except Exception as error: + if is_deadlock_error(error): + raise GraphDatabaseDeadlockError() + + if all_nodes: + await save_graph_nodes(all_nodes) + + if all_edges: + await save_graph_edges(all_edges) + + print("Finished adding nodes and edges.") + except modal.exception.DeserializationError as error: logger.error(f"Deserialization error: {str(error)}") continue - if len(nodes_and_edges) == 0: - print("Finished processing all nodes and edges; stopping graph engine queue.") - return True - - if len(nodes_and_edges) == 2: - print( - f"Adding {len(nodes_and_edges[0])} nodes and {len(nodes_and_edges[1])} edges." - ) - nodes = nodes_and_edges[0] - edges = nodes_and_edges[1] - - @retry( - retry=retry_if_exception_type(GraphDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def save_graph_nodes(new_nodes): - try: - await graph_engine.add_nodes(new_nodes, distributed=False) - except Exception as error: - if is_deadlock_error(error): - raise GraphDatabaseDeadlockError() - - @retry( - retry=retry_if_exception_type(GraphDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def save_graph_edges(new_edges): - try: - await graph_engine.add_edges(new_edges, distributed=False) - except Exception as error: - if is_deadlock_error(error): - raise GraphDatabaseDeadlockError() - - if nodes: - await save_graph_nodes(nodes) - - if edges: - await save_graph_edges(edges) - - print("Finished adding nodes and edges.") - else: print("No jobs, go to sleep.") await asyncio.sleep(5) From bd49dd7bb526fdeef2319022d45d1fd166b87982 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 14:01:28 +0100 Subject: [PATCH 106/139] Configure `cognee-cli -ui` mcp to connect to cognee backend --- cognee-mcp/Dockerfile | 3 ++ cognee-mcp/entrypoint.sh | 89 +++++++++++++++++++++++++++------------- cognee-mcp/src/server.py | 4 +- cognee/api/v1/ui/ui.py | 46 +++++++++++++++------ 4 files changed, 98 insertions(+), 44 deletions(-) diff --git a/cognee-mcp/Dockerfile b/cognee-mcp/Dockerfile index 68dce1844..6608102c8 100644 --- a/cognee-mcp/Dockerfile +++ b/cognee-mcp/Dockerfile @@ -65,6 +65,9 @@ ENV PYTHONUNBUFFERED=1 ENV MCP_LOG_LEVEL=DEBUG ENV PYTHONPATH=/app +# Add labels for API mode usage +LABEL org.opencontainers.image.description="Cognee MCP Server with API mode support" + # Use the application name from pyproject.toml for normal operation # For testing, we'll override this with a direct command ENTRYPOINT ["/app/entrypoint.sh"] diff --git a/cognee-mcp/entrypoint.sh b/cognee-mcp/entrypoint.sh index e3ff849e0..2f122bbfd 100644 --- a/cognee-mcp/entrypoint.sh +++ b/cognee-mcp/entrypoint.sh @@ -14,61 +14,94 @@ HTTP_PORT=${HTTP_PORT:-8000} echo "Debug port: $DEBUG_PORT" echo "HTTP port: $HTTP_PORT" -# Run Alembic migrations with proper error handling. -# Note on UserAlreadyExists error handling: -# During database migrations, we attempt to create a default user. If this user -# already exists (e.g., from a previous deployment or migration), it's not a -# critical error and shouldn't prevent the application from starting. This is -# different from other migration errors which could indicate database schema -# inconsistencies and should cause the startup to fail. This check allows for -# smooth redeployments and container restarts while maintaining data integrity. -echo "Running database migrations..." +# Check if API mode is enabled +if [ -n "$API_URL" ]; then + echo "API mode enabled: $API_URL" + echo "Skipping database migrations (API server handles its own database)" +else + echo "Direct mode: Using local cognee instance" + # Run Alembic migrations with proper error handling. + # Note on UserAlreadyExists error handling: + # During database migrations, we attempt to create a default user. If this user + # already exists (e.g., from a previous deployment or migration), it's not a + # critical error and shouldn't prevent the application from starting. This is + # different from other migration errors which could indicate database schema + # inconsistencies and should cause the startup to fail. This check allows for + # smooth redeployments and container restarts while maintaining data integrity. + echo "Running database migrations..." -MIGRATION_OUTPUT=$(alembic upgrade head) -MIGRATION_EXIT_CODE=$? + MIGRATION_OUTPUT=$(alembic upgrade head) + MIGRATION_EXIT_CODE=$? -if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then - if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then - echo "Warning: Default user already exists, continuing startup..." - else - echo "Migration failed with unexpected error." - exit 1 + if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then + if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then + echo "Warning: Default user already exists, continuing startup..." + else + echo "Migration failed with unexpected error." + exit 1 + fi fi -fi -echo "Database migrations done." + echo "Database migrations done." +fi echo "Starting Cognee MCP Server with transport mode: $TRANSPORT_MODE" # Add startup delay to ensure DB is ready sleep 2 +# Build API arguments if API_URL is set +API_ARGS="" +if [ -n "$API_URL" ]; then + # Handle localhost in API_URL - convert to host-accessible address + if echo "$API_URL" | grep -q "localhost" || echo "$API_URL" | grep -q "127.0.0.1"; then + echo "⚠️ Warning: API_URL contains localhost/127.0.0.1" + echo " Original: $API_URL" + + # Try to use host.docker.internal (works on Mac/Windows and recent Linux with Docker Desktop) + FIXED_API_URL=$(echo "$API_URL" | sed 's/localhost/host.docker.internal/g' | sed 's/127\.0\.0\.1/host.docker.internal/g') + + echo " Converted to: $FIXED_API_URL" + echo " This will work on Mac/Windows/Docker Desktop." + echo " On Linux without Docker Desktop, you may need to:" + echo " - Use --network host, OR" + echo " - Set API_URL=http://172.17.0.1:8000 (Docker bridge IP)" + + API_URL="$FIXED_API_URL" + fi + + API_ARGS="--api-url $API_URL" + if [ -n "$API_TOKEN" ]; then + API_ARGS="$API_ARGS --api-token $API_TOKEN" + fi +fi + # Modified startup with transport mode selection and error handling if [ "$ENVIRONMENT" = "dev" ] || [ "$ENVIRONMENT" = "local" ]; then if [ "$DEBUG" = "true" ]; then echo "Waiting for the debugger to attach..." if [ "$TRANSPORT_MODE" = "sse" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport stdio --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport stdio --no-migration $API_ARGS fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec cognee-mcp --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration $API_ARGS fi fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec cognee-mcp --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration $API_ARGS fi fi diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index c390d9ac6..f98800b18 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -18,8 +18,7 @@ from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware import uvicorn -# Import the new CogneeClient abstraction -from cognee_client import CogneeClient +from .cognee_client import CogneeClient try: @@ -38,7 +37,6 @@ mcp = FastMCP("Cognee") logger = get_logger() -# Global CogneeClient instance (will be initialized in main()) cognee_client: Optional[CogneeClient] = None diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index 7711cb6ac..01862c6dc 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -507,19 +507,35 @@ def start_ui( try: image = "cognee/cognee-mcp:main" subprocess.run(["docker", "pull", image], check=True) + + docker_cmd = [ + "docker", + "run", + "-p", + f"{mcp_port}:8000", + "--rm", + "--env-file", + env_file, + "-e", + "TRANSPORT_MODE=sse", + ] + + if start_backend: + docker_cmd.extend( + [ + "-e", + f"API_URL=http://localhost:{backend_port}", + ] + ) + logger.info( + f"Configuring MCP to connect to backend API at http://localhost:{backend_port}" + ) + logger.info("(localhost will be auto-converted to host.docker.internal)") + + docker_cmd.append("cognee/cognee-mcp:daulet-dev") + mcp_process = subprocess.Popen( - [ - "docker", - "run", - "-p", - f"{mcp_port}:8000", - "--rm", - "--env-file", - env_file, - "-e", - "TRANSPORT_MODE=sse", - "cognee/cognee-mcp:main", - ], + docker_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, preexec_fn=os.setsid if hasattr(os, "setsid") else None, @@ -529,7 +545,11 @@ def start_ui( _stream_process_output(mcp_process, "stderr", "[MCP]", "\033[34m") # Blue pid_callback(mcp_process.pid) - logger.info(f"✓ Cognee MCP server starting on http://127.0.0.1:{mcp_port}/sse") + + mode_info = "API mode" if start_backend else "direct mode" + logger.info( + f"✓ Cognee MCP server starting on http://127.0.0.1:{mcp_port}/sse ({mode_info})" + ) except Exception as e: logger.error(f"Failed to start MCP server with Docker: {str(e)}") # Start backend server if requested From 67306786f19e26f4680fb1628a3c042d3dbae0f3 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 14:03:26 +0100 Subject: [PATCH 107/139] docs: update README.md --- cognee-mcp/README.md | 133 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 131 insertions(+), 2 deletions(-) diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index 8d973725b..13b730908 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -38,7 +38,8 @@ Build memory for Agents and query from any client that speaks MCP – in your t ## ✨ Features - Multiple transports – choose Streamable HTTP --transport http (recommended for web deployments), SSE --transport sse (real‑time streaming), or stdio (classic pipe, default) -- Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev +- **API Mode** – connect to an already running Cognee FastAPI server instead of using cognee directly (see [API Mode](#-api-mode) below) +- Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev - Local file ingestion – feed .md, source files, Cursor rule‑sets, etc. straight from disk - Background pipelines – long‑running cognify & codify jobs spawn off‑thread; check progress with status tools - Developer rules bootstrap – one call indexes .cursorrules, .cursor/rules, AGENT.md, and friends into the developer_rules nodeset @@ -91,7 +92,7 @@ To use different LLM providers / database configurations, and for more info chec ## 🐳 Docker Usage -If you’d rather run cognee-mcp in a container, you have two options: +If you'd rather run cognee-mcp in a container, you have two options: 1. **Build locally** 1. Make sure you are in /cognee root directory and have a fresh `.env` containing only your `LLM_API_KEY` (and your chosen settings). @@ -128,6 +129,64 @@ If you’d rather run cognee-mcp in a container, you have two options: - ✅ Direct: `python src/server.py --transport http` - ❌ Direct: `-e TRANSPORT_MODE=http` (won't work) +### **Docker API Mode** + +To connect the MCP Docker container to a Cognee API server running on your host machine: + +#### **Simple Usage (Automatic localhost handling):** +```bash +# Start your Cognee API server on the host +python -m cognee.api.client + +# Run MCP container in API mode - localhost is automatically converted! +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` +**Note:** The container will automatically convert `localhost` to `host.docker.internal` on Mac/Windows/Docker Desktop. You'll see a message in the logs showing the conversion. + +#### **Explicit host.docker.internal (Mac/Windows):** +```bash +# Or explicitly use host.docker.internal +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://host.docker.internal:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` + +#### **On Linux (use host network or container IP):** +```bash +# Option 1: Use host network (simplest) +docker run \ + --network host \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=your_auth_token \ + --rm -it cognee/cognee-mcp:main + +# Option 2: Use host IP address +# First, get your host IP: ip addr show docker0 +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://172.17.0.1:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` + +**Environment variables for API mode:** +- `API_URL`: URL of the running Cognee API server +- `API_TOKEN`: Authentication token (optional, if API requires auth) + +**Note:** When running in API mode: +- Database migrations are automatically skipped (API server handles its own DB) +- Some features are limited (see [API Mode Limitations](#-api-mode)) + ## 🔗 MCP Client Configuration @@ -255,6 +314,76 @@ You can configure both transports simultaneously for testing: **Note:** Only enable the server you're actually running to avoid connection errors. +## 🌐 API Mode + +The MCP server can operate in two modes: + +### **Direct Mode** (Default) +The MCP server directly imports and uses the cognee library. This is the default mode with full feature support. + +### **API Mode** +The MCP server connects to an already running Cognee FastAPI server via HTTP requests. This is useful when: +- You have a centralized Cognee API server running +- You want to separate the MCP server from the knowledge graph backend +- You need multiple MCP servers to share the same knowledge graph + +**Starting the MCP server in API mode:** +```bash +# Start your Cognee FastAPI server first (default port 8000) +cd /path/to/cognee +python -m cognee.api.client + +# Then start the MCP server in API mode +cd cognee-mcp +python src/server.py --api-url http://localhost:8000 --api-token YOUR_AUTH_TOKEN +``` + +**API Mode with different transports:** +```bash +# With SSE transport +python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN + +# With HTTP transport +python src/server.py --transport http --api-url http://localhost:8000 --api-token YOUR_TOKEN +``` + +**API Mode with Docker:** +```bash +# On Mac/Windows (use host.docker.internal to access host) +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://host.docker.internal:8000 \ + -e API_TOKEN=YOUR_TOKEN \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main + +# On Linux (use host network) +docker run \ + --network host \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=YOUR_TOKEN \ + --rm -it cognee/cognee-mcp:main +``` + +**Command-line arguments for API mode:** +- `--api-url`: Base URL of the running Cognee FastAPI server (e.g., `http://localhost:8000`) +- `--api-token`: Authentication token for the API (optional, required if API has authentication enabled) + +**Docker environment variables for API mode:** +- `API_URL`: Base URL of the running Cognee FastAPI server +- `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) + +**API Mode limitations:** +Some features are only available in direct mode: +- `codify` (code graph pipeline) +- `cognify_status` / `codify_status` (pipeline status tracking) +- `prune` (data reset) +- `get_developer_rules` (developer rules retrieval) +- `list_data` with specific dataset_id (detailed data listing) + +Basic operations like `cognify`, `search`, `delete`, and `list_data` (all datasets) work in both modes. + ## 💻 Basic Usage The MCP server exposes its functionality through tools. Call them from any MCP client (Cursor, Claude Desktop, Cline, Roo and more). From 79fb0595d890e592da0561d7d3b48b9fb87bc8a2 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 15:22:23 +0100 Subject: [PATCH 108/139] feat: fix zoomToFit method usage GraphVisualization --- .../src/app/(graph)/GraphVisualization.tsx | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/cognee-frontend/src/app/(graph)/GraphVisualization.tsx b/cognee-frontend/src/app/(graph)/GraphVisualization.tsx index 6f66264ca..6846e3aee 100644 --- a/cognee-frontend/src/app/(graph)/GraphVisualization.tsx +++ b/cognee-frontend/src/app/(graph)/GraphVisualization.tsx @@ -217,10 +217,24 @@ export default function GraphVisualization({ ref, data, graphControls, className const [graphShape, setGraphShape] = useState(); + const zoomToFit: ForceGraphMethods["zoomToFit"] = ( + durationMs?: number, + padding?: number, + nodeFilter?: (node: NodeObject) => boolean + ) => { + if (!graphRef.current) { + console.warn("GraphVisualization: graphRef not ready yet"); + return undefined as any; + } + + return graphRef.current.zoomToFit?.(durationMs, padding, nodeFilter); + }; + useImperativeHandle(ref, () => ({ - zoomToFit: graphRef.current!.zoomToFit, - setGraphShape: setGraphShape, + zoomToFit, + setGraphShape, })); + return (
From c58d9ad12f0645562b8f78282e461840643ca59d Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 14:22:29 +0100 Subject: [PATCH 109/139] switch docker image from dev to main --- cognee/api/v1/ui/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index 01862c6dc..2c3687e16 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -532,7 +532,7 @@ def start_ui( ) logger.info("(localhost will be auto-converted to host.docker.internal)") - docker_cmd.append("cognee/cognee-mcp:daulet-dev") + docker_cmd.append("cognee/cognee-mcp:main") mcp_process = subprocess.Popen( docker_cmd, From 0031a13f44c85c4ffe80a4fc9f5396c7af2cf372 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 14:28:24 +0100 Subject: [PATCH 110/139] Refactor initialization of CogneeClient to use bool for API mode determination --- cognee-mcp/src/cognee_client.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee-mcp/src/cognee_client.py b/cognee-mcp/src/cognee_client.py index e22b8c060..9242aa6b2 100644 --- a/cognee-mcp/src/cognee_client.py +++ b/cognee-mcp/src/cognee_client.py @@ -32,7 +32,7 @@ class CogneeClient: def __init__(self, api_url: Optional[str] = None, api_token: Optional[str] = None): self.api_url = api_url.rstrip("/") if api_url else None self.api_token = api_token - self.use_api = api_url is not None + self.use_api = bool(api_url) if self.use_api: logger.info(f"Cognee client initialized in API mode: {self.api_url}") From 079a5528b0a99e979d549baf8170814396691f30 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 15:56:37 +0100 Subject: [PATCH 111/139] handle mcp shutdown cleanly, and remove the need for .env if in standalone mode --- cognee/api/v1/ui/ui.py | 17 +++++++++++----- cognee/cli/_cognee.py | 44 +++++++++++++++++++++++++++++++++++++----- 2 files changed, 51 insertions(+), 10 deletions(-) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index 2c3687e16..d0f109393 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -502,20 +502,22 @@ def start_ui( if start_mcp: logger.info("Starting Cognee MCP server with Docker...") - cwd = os.getcwd() - env_file = os.path.join(cwd, ".env") try: image = "cognee/cognee-mcp:main" subprocess.run(["docker", "pull", image], check=True) + import uuid + + container_name = f"cognee-mcp-{uuid.uuid4().hex[:8]}" + docker_cmd = [ "docker", "run", + "--name", + container_name, "-p", f"{mcp_port}:8000", "--rm", - "--env-file", - env_file, "-e", "TRANSPORT_MODE=sse", ] @@ -531,6 +533,10 @@ def start_ui( f"Configuring MCP to connect to backend API at http://localhost:{backend_port}" ) logger.info("(localhost will be auto-converted to host.docker.internal)") + else: + cwd = os.getcwd() + env_file = os.path.join(cwd, ".env") + docker_cmd.extend(["--env-file", env_file]) docker_cmd.append("cognee/cognee-mcp:main") @@ -544,7 +550,8 @@ def start_ui( _stream_process_output(mcp_process, "stdout", "[MCP]", "\033[34m") # Blue _stream_process_output(mcp_process, "stderr", "[MCP]", "\033[34m") # Blue - pid_callback(mcp_process.pid) + # Pass both PID and container name using a tuple + pid_callback((mcp_process.pid, container_name)) mode_info = "API mode" if start_backend else "direct mode" logger.info( diff --git a/cognee/cli/_cognee.py b/cognee/cli/_cognee.py index b257d37de..9008d68e3 100644 --- a/cognee/cli/_cognee.py +++ b/cognee/cli/_cognee.py @@ -175,12 +175,40 @@ def main() -> int: # Handle UI flag if hasattr(args, "start_ui") and args.start_ui: spawned_pids = [] + docker_container = None def signal_handler(signum, frame): """Handle Ctrl+C and other termination signals""" - nonlocal spawned_pids + nonlocal spawned_pids, docker_container fmt.echo("\nShutting down UI server...") + # First, stop Docker container if running + if docker_container: + try: + fmt.echo(f"Stopping Docker container {docker_container}...") + result = subprocess.run( + ["docker", "stop", docker_container], + capture_output=True, + timeout=10, + check=False, + ) + if result.returncode == 0: + fmt.success(f"✓ Docker container {docker_container} stopped.") + else: + fmt.warning( + f"Could not stop container {docker_container}: {result.stderr.decode()}" + ) + except subprocess.TimeoutExpired: + fmt.warning( + f"Timeout stopping container {docker_container}, forcing removal..." + ) + subprocess.run( + ["docker", "rm", "-f", docker_container], capture_output=True, check=False + ) + except Exception as e: + fmt.warning(f"Could not stop Docker container {docker_container}: {e}") + + # Then, stop regular processes for pid in spawned_pids: try: if hasattr(os, "killpg"): @@ -209,10 +237,16 @@ def main() -> int: fmt.echo("Starting cognee UI...") - # Callback to capture PIDs of all spawned processes - def pid_callback(pid): - nonlocal spawned_pids - spawned_pids.append(pid) + # Callback to capture PIDs and Docker container of all spawned processes + def pid_callback(pid_or_tuple): + nonlocal spawned_pids, docker_container + # Handle both regular PIDs and (PID, container_name) tuples + if isinstance(pid_or_tuple, tuple): + pid, container_name = pid_or_tuple + spawned_pids.append(pid) + docker_container = container_name + else: + spawned_pids.append(pid_or_tuple) frontend_port = 3000 start_backend, backend_port = True, 8000 From 9a3343987656d215edb30155385c5219a18e7cac Mon Sep 17 00:00:00 2001 From: Igor Ilic Date: Thu, 9 Oct 2025 19:10:31 +0200 Subject: [PATCH 112/139] refactor: Change default PDF loader --- cognee/infrastructure/loaders/LoaderEngine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/infrastructure/loaders/LoaderEngine.py b/cognee/infrastructure/loaders/LoaderEngine.py index d4be2aab5..87a008660 100644 --- a/cognee/infrastructure/loaders/LoaderEngine.py +++ b/cognee/infrastructure/loaders/LoaderEngine.py @@ -27,11 +27,11 @@ class LoaderEngine: self.default_loader_priority = [ "text_loader", - "advanced_pdf_loader", "pypdf_loader", "image_loader", "audio_loader", "unstructured_loader", + "advanced_pdf_loader", ] def register_loader(self, loader: LoaderInterface) -> bool: From 2c8d8e805800f4a93e9e4a0a9bb1ce26e31ad787 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Thu, 9 Oct 2025 19:47:52 +0100 Subject: [PATCH 113/139] feat: handle cli processes cleanup when terminal window is closed --- cognee/cli/_cognee.py | 51 ++++++++++++++++++++++++++++--------------- 1 file changed, 34 insertions(+), 17 deletions(-) diff --git a/cognee/cli/_cognee.py b/cognee/cli/_cognee.py index 9008d68e3..1539d1acf 100644 --- a/cognee/cli/_cognee.py +++ b/cognee/cli/_cognee.py @@ -180,33 +180,42 @@ def main() -> int: def signal_handler(signum, frame): """Handle Ctrl+C and other termination signals""" nonlocal spawned_pids, docker_container - fmt.echo("\nShutting down UI server...") + + try: + fmt.echo("\nShutting down UI server...") + except (BrokenPipeError, OSError): + pass # First, stop Docker container if running if docker_container: try: - fmt.echo(f"Stopping Docker container {docker_container}...") result = subprocess.run( ["docker", "stop", docker_container], capture_output=True, timeout=10, check=False, ) - if result.returncode == 0: - fmt.success(f"✓ Docker container {docker_container} stopped.") - else: - fmt.warning( - f"Could not stop container {docker_container}: {result.stderr.decode()}" - ) + try: + if result.returncode == 0: + fmt.success(f"✓ Docker container {docker_container} stopped.") + else: + fmt.warning( + f"Could not stop container {docker_container}: {result.stderr.decode()}" + ) + except (BrokenPipeError, OSError): + pass except subprocess.TimeoutExpired: - fmt.warning( - f"Timeout stopping container {docker_container}, forcing removal..." - ) + try: + fmt.warning( + f"Timeout stopping container {docker_container}, forcing removal..." + ) + except (BrokenPipeError, OSError): + pass subprocess.run( ["docker", "rm", "-f", docker_container], capture_output=True, check=False ) - except Exception as e: - fmt.warning(f"Could not stop Docker container {docker_container}: {e}") + except Exception: + pass # Then, stop regular processes for pid in spawned_pids: @@ -215,7 +224,10 @@ def main() -> int: # Unix-like systems: Use process groups pgid = os.getpgid(pid) os.killpg(pgid, signal.SIGTERM) - fmt.success(f"✓ Process group {pgid} (PID {pid}) terminated.") + try: + fmt.success(f"✓ Process group {pgid} (PID {pid}) terminated.") + except (BrokenPipeError, OSError): + pass else: # Windows: Use taskkill to terminate process and its children subprocess.run( @@ -223,14 +235,19 @@ def main() -> int: capture_output=True, check=False, ) - fmt.success(f"✓ Process {pid} and its children terminated.") - except (OSError, ProcessLookupError, subprocess.SubprocessError) as e: - fmt.warning(f"Could not terminate process {pid}: {e}") + try: + fmt.success(f"✓ Process {pid} and its children terminated.") + except (BrokenPipeError, OSError): + pass + except (OSError, ProcessLookupError, subprocess.SubprocessError): + pass sys.exit(0) signal.signal(signal.SIGINT, signal_handler) # Ctrl+C signal.signal(signal.SIGTERM, signal_handler) # Termination request + if hasattr(signal, "SIGHUP"): + signal.signal(signal.SIGHUP, signal_handler) try: from cognee import start_ui From f82dfbe3718d64cd935998f3534b66f400f78b85 Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Fri, 10 Oct 2025 02:34:04 +0530 Subject: [PATCH 114/139] solved nitpick comments --- cognee/api/v1/add/add.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index c3751da4b..c67afe7de 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -150,7 +150,7 @@ async def add( await cognee.add("https://example.com",extraction_rules=extraction_rules) # Add a single url and tavily extract ingestion method - Make sure to set TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable + Make sure to set TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable await cognee.add("https://example.com") # Add multiple urls From 4058d6303621ded36d8b8668c720ffe6f47a64cf Mon Sep 17 00:00:00 2001 From: Geoff-Robin Date: Fri, 10 Oct 2025 02:37:03 +0530 Subject: [PATCH 115/139] Added better selectors for testing --- cognee/tests/tasks/web_scraping/web_scraping_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py index 7ba1b9956..bf66b5155 100644 --- a/cognee/tests/tasks/web_scraping/web_scraping_test.py +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -122,8 +122,8 @@ async def test_cron_web_scraper(): await cognee.prune.prune_system(metadata=True) urls = ["https://quotes.toscrape.com/", "https://books.toscrape.com/"] extraction_rules = { - "quotes": {"selector": ".quote span.text", "all": True}, - "authors": {"selector": ".quote small", "all": True}, + "quotes": ".quote .text", + "authors": ".quote .author", "titles": "article.product_pod h3 a", "prices": "article.product_pod p.price_color", } From b75aa6f9ad43ec6af0515c5e650472acc70575f8 Mon Sep 17 00:00:00 2001 From: Andrej Milicevic Date: Fri, 10 Oct 2025 12:59:28 +0200 Subject: [PATCH 116/139] refactor: Keep code DRY in health endpoint --- cognee/api/health.py | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/cognee/api/health.py b/cognee/api/health.py index 0241a0712..ac30f3bf4 100644 --- a/cognee/api/health.py +++ b/cognee/api/health.py @@ -241,16 +241,6 @@ class HealthChecker: """Get comprehensive health status.""" components = {} - # Critical services - critical_components = [ - "relational_db", - "vector_db", - "graph_db", - "file_storage", - "llm_provider", - "embedding_service", - ] - critical_checks = [ ("relational_db", self.check_relational_db()), ("vector_db", self.check_vector_db()), @@ -296,11 +286,11 @@ class HealthChecker: else: components[name] = result + critical_comps = [check[0] for check in critical_checks] # Determine overall status critical_unhealthy = any( - comp.status == HealthStatus.UNHEALTHY + comp.status == HealthStatus.UNHEALTHY and name in critical_comps for name, comp in components.items() - if name in critical_components ) has_degraded = any(comp.status == HealthStatus.DEGRADED for comp in components.values()) From 2f9c3f030bd3dc9e5f627597984b0a2b1f2b1129 Mon Sep 17 00:00:00 2001 From: Igor Ilic <30923996+dexters1@users.noreply.github.com> Date: Fri, 10 Oct 2025 15:16:10 +0200 Subject: [PATCH 117/139] =?UTF-8?q?refactor:=20user=20different=20top=5Fk?= =?UTF-8?q?=20number=20to=20reflect=20upgrades=20made=20to=20rel=E2=80=A6?= =?UTF-8?q?=20(#1527)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit … db migration ## Description ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) ## Pre-submission Checklist - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --- examples/python/relational_database_migration_example.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/python/relational_database_migration_example.py b/examples/python/relational_database_migration_example.py index 6a5c3b78b..7e87347bc 100644 --- a/examples/python/relational_database_migration_example.py +++ b/examples/python/relational_database_migration_example.py @@ -70,23 +70,23 @@ async def main(): search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What kind of data do you contain?", - top_k=1000, + top_k=200, ) print(f"Search results: {search_results}") # Having a top_k value set to too high might overwhelm the LLM context when specific questions need to be answered. - # For this kind of question we've set the top_k to 30 + # For this kind of question we've set the top_k to 50 search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What invoices are related to Leonie Köhler?", - top_k=30, + top_k=50, ) print(f"Search results: {search_results}") search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What invoices are related to Luís Gonçalves?", - top_k=30, + top_k=50, ) print(f"Search results: {search_results}") From 446a378b03d3e91cbb554bb93e7b89c6064a8656 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Fri, 10 Oct 2025 14:39:08 +0100 Subject: [PATCH 118/139] chore: update Docker image for MCP server to feature branch for testing --- cognee/api/v1/ui/ui.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index d0f109393..2a5746f8c 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -503,7 +503,7 @@ def start_ui( if start_mcp: logger.info("Starting Cognee MCP server with Docker...") try: - image = "cognee/cognee-mcp:main" + image = "cognee/cognee-mcp:feature-standalone-mcp" # TODO: change to main right before merging into main subprocess.run(["docker", "pull", image], check=True) import uuid @@ -538,7 +538,9 @@ def start_ui( env_file = os.path.join(cwd, ".env") docker_cmd.extend(["--env-file", env_file]) - docker_cmd.append("cognee/cognee-mcp:main") + docker_cmd.append( + "cognee/cognee-mcp:feature-standalone-mcp" + ) # TODO: change to main right before merging into main mcp_process = subprocess.Popen( docker_cmd, From c7ca4c6782cc8052ff3e6c5b741b35996a569b85 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Fri, 10 Oct 2025 16:02:58 +0100 Subject: [PATCH 119/139] chore: update Docker image comment and improve import handling for CogneeClient --- cognee-mcp/src/__init__.py | 5 ++++- cognee-mcp/src/server.py | 7 +++++-- cognee/api/v1/ui/ui.py | 6 +++--- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 933bcd73f..f1c3706c6 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -1,4 +1,7 @@ -from .server import main as server_main +try: + from .server import main as server_main +except ImportError: + from server import main as server_main import warnings import sys diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index f98800b18..f2b309fa5 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -18,7 +18,10 @@ from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware import uvicorn -from .cognee_client import CogneeClient +try: + from .cognee_client import CogneeClient +except ImportError: + from cognee_client import CogneeClient try: @@ -1088,7 +1091,7 @@ async def main(): parser.add_argument( "--api-token", default=None, - help="Authentication token for the Cognee API. Required if --api-url is provided.", + help="Authentication token for the API (optional, required if API has authentication enabled).", ) args = parser.parse_args() diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index 2a5746f8c..51088c3e1 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -503,7 +503,7 @@ def start_ui( if start_mcp: logger.info("Starting Cognee MCP server with Docker...") try: - image = "cognee/cognee-mcp:feature-standalone-mcp" # TODO: change to main right before merging into main + image = "cognee/cognee-mcp:feature-standalone-mcp" # TODO: change to "cognee/cognee-mcp:main" right before merging into main subprocess.run(["docker", "pull", image], check=True) import uuid @@ -539,8 +539,8 @@ def start_ui( docker_cmd.extend(["--env-file", env_file]) docker_cmd.append( - "cognee/cognee-mcp:feature-standalone-mcp" - ) # TODO: change to main right before merging into main + image + ) # TODO: change to "cognee/cognee-mcp:main" right before merging into main mcp_process = subprocess.Popen( docker_cmd, From d480ea23a1a0fd0c40de36cc625b6576b5daa3a5 Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Fri, 10 Oct 2025 16:03:16 +0100 Subject: [PATCH 120/139] docs: clarify API_TOKEN requirement in README and improve JSON handling in CogneeClient --- cognee-mcp/README.md | 2 +- cognee-mcp/src/cognee_client.py | 13 ++++++------- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index 13b730908..00ec5a80f 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -181,7 +181,7 @@ docker run \ **Environment variables for API mode:** - `API_URL`: URL of the running Cognee API server -- `API_TOKEN`: Authentication token (optional, if API requires auth) +- `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) **Note:** When running in API mode: - Database migrations are automatically skipped (API server handles its own DB) diff --git a/cognee-mcp/src/cognee_client.py b/cognee-mcp/src/cognee_client.py index 9242aa6b2..a2fd3345f 100644 --- a/cognee-mcp/src/cognee_client.py +++ b/cognee-mcp/src/cognee_client.py @@ -12,6 +12,7 @@ from uuid import UUID from contextlib import redirect_stdout import httpx from cognee.shared.logging_utils import get_logger +import json logger = get_logger() @@ -26,7 +27,7 @@ class CogneeClient: Base URL of the Cognee API server (e.g., "http://localhost:8000"). If None, uses direct cognee function calls. api_token : str, optional - Authentication token for API requests. Required if api_url is provided. + Authentication token for the API (optional, required if API has authentication enabled). """ def __init__(self, api_url: Optional[str] = None, api_token: Optional[str] = None): @@ -72,17 +73,14 @@ class CogneeClient: Result of the add operation """ if self.use_api: - # API mode: Make HTTP request endpoint = f"{self.api_url}/api/v1/add" - # For API mode, we need to handle file uploads differently - # For now, we'll assume data is text content files = {"data": ("data.txt", str(data), "text/plain")} form_data = { "datasetName": dataset_name, } - if node_set: - form_data["node_set"] = node_set + if node_set is not None: + form_data["node_set"] = json.dumps(node_set) response = await self.client.post( endpoint, @@ -93,7 +91,6 @@ class CogneeClient: response.raise_for_status() return response.json() else: - # Direct mode: Call cognee directly with redirect_stdout(sys.stderr): await self.cognee.add(data, dataset_name=dataset_name, node_set=node_set) return {"status": "success", "message": "Data added successfully"} @@ -138,6 +135,8 @@ class CogneeClient: # Direct mode: Call cognee directly with redirect_stdout(sys.stderr): kwargs = {} + if datasets: + kwargs["datasets"] = datasets if custom_prompt: kwargs["custom_prompt"] = custom_prompt if graph_model: From e445dd7f8b1d9f08ca70db72265552b55552b46e Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 10:44:08 +0200 Subject: [PATCH 121/139] ugly fix for the scraper --- cognee/api/v1/add/add.py | 48 +++++++++++-------- .../ingestion/save_data_item_to_storage.py | 3 +- 2 files changed, 30 insertions(+), 21 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index c67afe7de..731895201 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -12,11 +12,15 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( ) from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories -from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig -from cognee.context_global_variables import ( - tavily_config as tavily, - soup_crawler_config as soup_crawler, -) +try: + from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig + from cognee.context_global_variables import ( + tavily_config as tavily, + soup_crawler_config as soup_crawler, + ) +except: + pass +from pydantic import BaseModel from urllib.parse import urlparse @@ -31,8 +35,8 @@ async def add( preferred_loaders: List[str] = None, incremental_loading: bool = True, extraction_rules: Optional[Dict[str, Any]] = None, - tavily_config: Optional[TavilyConfig] = None, - soup_crawler_config: Optional[SoupCrawlerConfig] = None, + tavily_config: Optional[BaseModel] = None, + soup_crawler_config: Optional[BaseModel] = None, ): """ Add data to Cognee for knowledge graph processing. @@ -172,23 +176,27 @@ async def add( """ - if not soup_crawler_config and extraction_rules: - soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) - if not tavily_config and os.getenv("TAVILY_API_KEY"): - tavily_config = TavilyConfig(api_key=os.getenv("TAVILY_API_KEY")) + try: - soup_crawler.set(soup_crawler_config) - tavily.set(tavily_config) + if not soup_crawler_config and extraction_rules: + soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) + if not tavily_config and os.getenv("TAVILY_API_KEY"): + tavily_config = TavilyConfig(api_key=os.getenv("TAVILY_API_KEY")) - http_schemes = {"http", "https"} + soup_crawler.set(soup_crawler_config) + tavily.set(tavily_config) - def _is_http_url(item: Union[str, BinaryIO]) -> bool: - return isinstance(item, str) and urlparse(item).scheme in http_schemes + http_schemes = {"http", "https"} - if _is_http_url(data): - node_set = ["web_content"] if not node_set else node_set + ["web_content"] - elif isinstance(data, list) and any(_is_http_url(item) for item in data): - node_set = ["web_content"] if not node_set else node_set + ["web_content"] + def _is_http_url(item: Union[str, BinaryIO]) -> bool: + return isinstance(item, str) and urlparse(item).scheme in http_schemes + + if _is_http_url(data): + node_set = ["web_content"] if not node_set else node_set + ["web_content"] + elif isinstance(data, list) and any(_is_http_url(item) for item in data): + node_set = ["web_content"] if not node_set else node_set + ["web_content"] + except: + pass tasks = [ Task(resolve_data_directories, include_subdirectories=True), diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 105f17c0d..9df5e6e57 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -7,7 +7,7 @@ from cognee.modules.ingestion.exceptions import IngestionError from cognee.modules.ingestion import save_data_to_file from cognee.shared.logging_utils import get_logger from pydantic_settings import BaseSettings, SettingsConfigDict -from cognee.context_global_variables import tavily_config, soup_crawler_config + logger = get_logger() @@ -59,6 +59,7 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str elif parsed_url.scheme == "http" or parsed_url.scheme == "https": # Validate URL by sending a HEAD request try: + from cognee.context_global_variables import tavily_config, soup_crawler_config from cognee.tasks.web_scraper import fetch_page_content tavily = tavily_config.get() From 988a196479f681c3f6280f8cd0ed1defd9a63f25 Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 10:54:14 +0200 Subject: [PATCH 122/139] added fixes --- cognee/api/v1/add/add.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 731895201..30b323ab2 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -12,6 +12,7 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( ) from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories + try: from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig from cognee.context_global_variables import ( @@ -177,7 +178,6 @@ async def add( """ try: - if not soup_crawler_config and extraction_rules: soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) if not tavily_config and os.getenv("TAVILY_API_KEY"): From 4c5de735989b871f095cead92a83cda40b9dcdc9 Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:03:19 +0200 Subject: [PATCH 123/139] small linting issues --- cognee/api/v1/add/add.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 30b323ab2..35746cc6b 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -1,7 +1,8 @@ from uuid import UUID import os from typing import Union, BinaryIO, List, Optional, Dict, Any - +from pydantic import BaseModel +from urllib.parse import urlparse from cognee.modules.users.models import User from cognee.modules.pipelines import Task, run_pipeline from cognee.modules.pipelines.layers.resolve_authorized_user_dataset import ( @@ -12,6 +13,9 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( ) from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories +from cognee.shared.logging_utils import get_logger + +logger = get_logger() try: from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig @@ -19,10 +23,9 @@ try: tavily_config as tavily, soup_crawler_config as soup_crawler, ) -except: +except ImportError: + logger.debug(f"Unable to import {str(ImportError)}") pass -from pydantic import BaseModel -from urllib.parse import urlparse async def add( @@ -195,7 +198,8 @@ async def add( node_set = ["web_content"] if not node_set else node_set + ["web_content"] elif isinstance(data, list) and any(_is_http_url(item) for item in data): node_set = ["web_content"] if not node_set else node_set + ["web_content"] - except: + except ImportError: + logger.debug(f"Unable to import {str(ImportError)}") pass tasks = [ From 0d726c0e324dcb3604dff72d9857135863b3e74e Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:09:43 +0200 Subject: [PATCH 124/139] small linting issues --- cognee/api/v1/add/add.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index 35746cc6b..cbb1c8fd5 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -198,7 +198,7 @@ async def add( node_set = ["web_content"] if not node_set else node_set + ["web_content"] elif isinstance(data, list) and any(_is_http_url(item) for item in data): node_set = ["web_content"] if not node_set else node_set + ["web_content"] - except ImportError: + except NameError: logger.debug(f"Unable to import {str(ImportError)}") pass From 19431a41d6a3e32d938b4ad89743617328eec5ad Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:25:41 +0200 Subject: [PATCH 125/139] tried adding cli fix --- cognee/cli/commands/add_command.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee/cli/commands/add_command.py b/cognee/cli/commands/add_command.py index d3fea7f80..e05998ec5 100644 --- a/cognee/cli/commands/add_command.py +++ b/cognee/cli/commands/add_command.py @@ -77,4 +77,4 @@ After adding data, use `cognee cognify` to process it into knowledge graphs. except Exception as e: if isinstance(e, CliCommandInnerException): raise CliCommandException(str(e), error_code=1) from e - raise CliCommandException(f"Error adding data: {str(e)}", error_code=1) from e + raise CliCommandException(f"Failed to add data: {str(e)}", error_code=1) from e From 8f619a7b8d24ad82d2e7eef31b84a669610d721a Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:27:20 +0200 Subject: [PATCH 126/139] added poetry lock --- poetry.lock | 385 ++++++++++++++++++++++++++++++++-------------------- 1 file changed, 235 insertions(+), 150 deletions(-) diff --git a/poetry.lock b/poetry.lock index 551295733..9cfa408f4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. 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+markers = "(platform_system == \"Windows\" or extra == \"docs\" or extra == \"evals\" or extra == \"dlt\") and (extra == \"scraping\" or extra == \"docs\" or extra == \"evals\" or extra == \"dlt\")" files = [ {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"}, {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"}, ] +[[package]] +name = "tzlocal" +version = "5.3.1" +description = "tzinfo object for the local timezone" +optional = true +python-versions = ">=3.9" +groups = ["main"] +markers = "extra == \"scraping\"" +files = [ + {file = "tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d"}, + {file = "tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd"}, +] + +[package.dependencies] +tzdata = {version = "*", markers = "platform_system == \"Windows\""} + +[package.extras] +devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"] + [[package]] name = "unstructured" version = "0.18.15" @@ -12734,8 +12818,9 @@ ollama = ["transformers"] postgres = ["asyncpg", "pgvector", "psycopg2"] postgres-binary = ["asyncpg", "pgvector", "psycopg2-binary"] posthog = ["posthog"] +scraping = ["APScheduler", "beautifulsoup4", "lxml", "playwright", "protego", "tavily-python"] [metadata] lock-version = "2.1" python-versions = ">=3.10,<=3.13" -content-hash = "38353807b06e5c06caaa107979529937b978204f0f405c6b38cee283f4a49d3c" +content-hash = "fcedd3f087a5f9626a9a720ced791bebd799c4e63352ae430803dfb21258c16e" From 694ed54d0739ddc50ee5d46f5cf5d183d259bddf Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:41:18 +0200 Subject: [PATCH 127/139] regen lock --- poetry.lock | 1023 +++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 996 insertions(+), 27 deletions(-) diff --git a/poetry.lock b/poetry.lock index d88b94e5d..9db2a4889 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,38 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. + +[[package]] +name = "accelerate" +version = "1.10.1" +description = "Accelerate" +optional = true +python-versions = ">=3.9.0" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11"}, + {file = "accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8"}, +] + +[package.dependencies] +huggingface_hub = ">=0.21.0" +numpy = ">=1.17,<3.0.0" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +safetensors = ">=0.4.3" +torch = ">=2.0.0" + +[package.extras] +deepspeed = ["deepspeed"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.11.2,<0.12.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,<0.12.0)"] +rich = ["rich"] +sagemaker = ["sagemaker"] +test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +test-fp8 = ["torchao"] +test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"] +test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "trackio", "wandb"] +testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] [[package]] name = "aiobotocore" @@ -274,6 +308,18 @@ aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.8)"] bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"] vertex = ["google-auth[requests] (>=2,<3)"] +[[package]] +name = "antlr4-python3-runtime" +version = "4.9.3" +description = "ANTLR 4.9.3 runtime for Python 3.7" +optional = true +python-versions = "*" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, +] + [[package]] name = "anyio" version = "4.10.0" @@ -832,7 +878,7 @@ description = "Extensible memoizing collections and decorators" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"}, {file = "cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4"}, @@ -1157,7 +1203,7 @@ description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" groups = ["main"] -markers = "(platform_system == \"Windows\" or sys_platform == \"win32\" or extra == \"llama-index\" or extra == \"dev\" or os_name == \"nt\") and (platform_system == \"Windows\" or sys_platform == \"win32\" or extra == \"llama-index\" or extra == \"dev\" or extra == \"chromadb\")" +markers = "(platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"dev\" or extra == \"chromadb\" or sys_platform == \"win32\") and (platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"dev\" or os_name == \"nt\" or sys_platform == \"win32\")" files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, @@ -1204,7 +1250,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = true python-versions = ">=3.10" groups = ["main"] -markers = "python_version == \"3.10\" and extra == \"evals\"" +markers = "python_version == \"3.10\" and (extra == \"docs\" or extra == \"evals\")" files = [ {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"}, {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"}, @@ -1282,7 +1328,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = true python-versions = ">=3.11" groups = ["main"] -markers = "python_version >= \"3.11\" and extra == \"evals\"" +markers = "python_version >= \"3.11\" and (extra == \"docs\" or extra == \"evals\")" files = [ {file = "contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1"}, {file = "contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381"}, @@ -1586,7 +1632,7 @@ description = "Composable style cycles" optional = true python-versions = ">=3.8" groups = ["main"] -markers = "extra == \"evals\"" +markers = "extra == \"docs\" or extra == \"evals\"" files = [ {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, @@ -2006,6 +2052,26 @@ files = [ {file = "durationpy-0.10.tar.gz", hash = "sha256:1fa6893409a6e739c9c72334fc65cca1f355dbdd93405d30f726deb5bde42fba"}, ] +[[package]] +name = "effdet" +version = "0.4.1" +description = "EfficientDet for PyTorch" +optional = true +python-versions = ">=3.7" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "effdet-0.4.1-py3-none-any.whl", hash = "sha256:10889a226228d515c948e3fcf811e64c0d78d7aa94823a300045653b9c284cb7"}, + {file = "effdet-0.4.1.tar.gz", hash = "sha256:ac5589fd304a5650c201986b2ef5f8e10c111093a71b1c49fa6b8817710812b5"}, +] + +[package.dependencies] +omegaconf = ">=2.0" +pycocotools = ">=2.0.2" +timm = ">=0.9.2" +torch = ">=1.12.1" +torchvision = "*" + [[package]] name = "email-validator" version = "2.2.0" @@ -2051,6 +2117,21 @@ files = [ {file = "et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54"}, ] +[[package]] +name = "eval-type-backport" +version = "0.2.2" +description = "Like `typing._eval_type`, but lets older Python versions use newer typing features." +optional = false +python-versions = ">=3.8" +groups = ["main"] +files = [ + {file = "eval_type_backport-0.2.2-py3-none-any.whl", hash = "sha256:cb6ad7c393517f476f96d456d0412ea80f0a8cf96f6892834cd9340149111b0a"}, + {file = "eval_type_backport-0.2.2.tar.gz", hash = "sha256:f0576b4cf01ebb5bd358d02314d31846af5e07678387486e2c798af0e7d849c1"}, +] + +[package.extras] +tests = ["pytest"] + [[package]] name = "exceptiongroup" version = "1.3.0" @@ -2309,7 +2390,7 @@ description = "Tools to manipulate font files" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"evals\"" +markers = "extra == \"docs\" or extra == \"evals\"" files = [ {file = "fonttools-4.60.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:151282a235c36024168c21c02193e939e8b28c73d5fa0b36ae1072671d8fa134"}, {file = "fonttools-4.60.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3f32cc42d485d9b1546463b9a7a92bdbde8aef90bac3602503e04c2ddb27e164"}, @@ -2670,7 +2751,7 @@ description = "Google API client core library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\"" +markers = "extra == \"gemini\" or extra == \"docs\"" files = [ {file = "google_api_core-2.25.1-py3-none-any.whl", hash = "sha256:8a2a56c1fef82987a524371f99f3bd0143702fecc670c72e600c1cda6bf8dbb7"}, {file = "google_api_core-2.25.1.tar.gz", hash = "sha256:d2aaa0b13c78c61cb3f4282c464c046e45fbd75755683c9c525e6e8f7ed0a5e8"}, @@ -2727,7 +2808,7 @@ description = "Google Authentication Library" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "google_auth-2.40.3-py2.py3-none-any.whl", hash = "sha256:1370d4593e86213563547f97a92752fc658456fe4514c809544f330fed45a7ca"}, {file = "google_auth-2.40.3.tar.gz", hash = "sha256:500c3a29adedeb36ea9cf24b8d10858e152f2412e3ca37829b3fa18e33d63b77"}, @@ -2765,6 +2846,28 @@ files = [ google-auth = "*" httplib2 = ">=0.19.0" +[[package]] +name = "google-cloud-vision" +version = "3.10.2" +description = "Google Cloud Vision API client library" +optional = true +python-versions = ">=3.7" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "google_cloud_vision-3.10.2-py3-none-any.whl", hash = "sha256:42a17fbc2219b0a88e325e2c1df6664a8dafcbae66363fb37ebcb511b018fc87"}, + {file = "google_cloud_vision-3.10.2.tar.gz", hash = "sha256:649380faab8933440b632bf88072c0c382a08d49ab02bc0b4fba821882ae1765"}, +] + +[package.dependencies] +google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0", extras = ["grpc"]} +google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0" +proto-plus = [ + {version = ">=1.22.3,<2.0.0"}, + {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""}, +] +protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" + [[package]] name = "google-genai" version = "1.38.0" @@ -2824,7 +2927,7 @@ description = "Common protobufs used in Google APIs" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"}, {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"}, @@ -2974,7 +3077,7 @@ description = "HTTP/2-based RPC framework" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"gemini\" or extra == \"deepeval\" or extra == \"chromadb\"" +markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\"" files = [ {file = "grpcio-1.75.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:1ec9cbaec18d9597c718b1ed452e61748ac0b36ba350d558f9ded1a94cc15ec7"}, {file = "grpcio-1.75.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:7ee5ee42bfae8238b66a275f9ebcf6f295724375f2fa6f3b52188008b6380faf"}, @@ -3042,7 +3145,7 @@ description = "Status proto mapping for gRPC" optional = true python-versions = ">=3.9" groups = ["main"] -markers = "extra == \"gemini\"" +markers = "extra == \"gemini\" or extra == \"docs\"" files = [ {file = "grpcio_status-1.71.2-py3-none-any.whl", hash = "sha256:803c98cb6a8b7dc6dbb785b1111aed739f241ab5e9da0bba96888aa74704cfd3"}, {file = "grpcio_status-1.71.2.tar.gz", hash = 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= ["cmake (>=3.20,<4.0)", "lit"] +tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"] +tutorials = ["matplotlib", "pandas", "tabulate"] + [[package]] name = "tweepy" version = "4.16.0" @@ -10898,8 +11808,10 @@ backoff = "*" beautifulsoup4 = "*" charset-normalizer = "*" dataclasses-json = "*" +effdet = {version = "*", optional = true, markers = "extra == \"pdf\""} emoji = "*" filetype = "*" +google-cloud-vision = {version = "*", optional = true, markers = "extra == \"pdf\""} html5lib = "*" langdetect = "*" lxml = "*" @@ -10908,10 +11820,17 @@ msoffcrypto-tool = {version = "*", optional = true, markers = "extra == \"xlsx\" networkx = {version = "*", optional = true, markers = "extra == \"xlsx\""} nltk = "*" numpy = "*" +onnx = {version = ">=1.17.0", optional = true, markers = "extra == \"pdf\""} +onnxruntime = {version = ">=1.19.0", optional = true, markers = "extra == \"pdf\""} openpyxl = {version = "*", optional = true, markers = "extra == \"xlsx\""} pandas = {version = "*", optional = true, markers = "extra == \"csv\" or extra == \"tsv\" or extra == \"xlsx\""} +pdf2image = {version = "*", optional = true, markers = "extra == \"pdf\""} +"pdfminer.six" = {version = "*", optional = true, markers = "extra == \"pdf\""} +pi-heif = {version = "*", optional = true, markers = "extra == \"pdf\""} +pikepdf = {version = "*", optional = true, markers = "extra == \"pdf\""} psutil = "*" pypandoc = {version = "*", optional = true, markers = "extra == \"epub\" or extra == \"odt\" or extra == \"org\" or extra == \"rst\" or extra == \"rtf\""} +pypdf = {version = "*", optional = true, markers = "extra == \"pdf\""} python-docx = {version = ">=1.1.2", optional = true, markers = "extra == \"doc\" or extra == \"docx\" or extra == \"odt\""} python-iso639 = "*" python-magic = "*" @@ -10922,6 +11841,8 @@ requests = "*" tqdm = "*" typing-extensions = "*" unstructured-client = "*" +unstructured-inference = {version = ">=1.0.5", optional = true, markers = "extra == \"pdf\""} +"unstructured.pytesseract" = {version = ">=0.3.12", optional = true, markers = "extra == \"pdf\""} wrapt = "*" xlrd = {version = "*", optional = true, markers = "extra == \"xlsx\""} @@ -10984,6 +11905,54 @@ urllib3 = ">=1.26.18" [package.extras] dev = ["pylint (==3.1.0)"] +[[package]] +name = "unstructured-inference" +version = "1.0.5" +description = "A library for performing inference using trained models." +optional = true +python-versions = ">=3.7.0" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "unstructured_inference-1.0.5-py3-none-any.whl", hash = "sha256:ecbe385a6c58ca6b68b5723ed3cb540b70fd6317eecd1d5e6541516edf7071d0"}, + {file = "unstructured_inference-1.0.5.tar.gz", hash = "sha256:ccd6881b0f03c533418bde6c9bd178a6660da8efbbe8c06a08afda9f25fe732b"}, +] + +[package.dependencies] +accelerate = "*" +huggingface-hub = "*" +matplotlib = "*" +numpy = "*" +onnx = "*" +onnxruntime = ">=1.18.0" +opencv-python = "!=4.7.0.68" +pandas = "*" +pdfminer-six = "*" +pypdfium2 = "*" +python-multipart = "*" +rapidfuzz = "*" +scipy = "*" +timm = "*" +torch = "*" +transformers = ">=4.25.1" + +[[package]] +name = "unstructured-pytesseract" +version = "0.3.15" +description = "Python-tesseract is a python wrapper for Google's Tesseract-OCR" +optional = true +python-versions = ">=3.8" +groups = ["main"] +markers = "extra == \"docs\"" +files = [ + {file = "unstructured.pytesseract-0.3.15-py3-none-any.whl", hash = "sha256:a3f505c5efb7ff9f10379051a7dd6aa624b3be6b0f023ed6767cc80d0b1613d1"}, + {file = "unstructured.pytesseract-0.3.15.tar.gz", hash = "sha256:4b81bc76cfff4e2ef37b04863f0e48bd66184c0b39c3b2b4e017483bca1a7394"}, +] + +[package.dependencies] +packaging = ">=21.3" +Pillow = ">=8.0.0" + [[package]] name = "uri-template" version = "1.3.0" @@ -11065,7 +12034,7 @@ description = "Fast implementation of asyncio event loop on top of libuv" optional = true python-versions = ">=3.8.0" groups = ["main"] -markers = "platform_python_implementation != \"PyPy\" and extra == \"chromadb\" and sys_platform != \"win32\" and sys_platform != \"cygwin\"" +markers = "platform_python_implementation != \"PyPy\" and sys_platform != \"win32\" and sys_platform != \"cygwin\" and extra == \"chromadb\"" files = [ {file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ec7e6b09a6fdded42403182ab6b832b71f4edaf7f37a9a0e371a01db5f0cb45f"}, {file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:196274f2adb9689a289ad7d65700d37df0c0930fd8e4e743fa4834e850d7719d"}, @@ -11898,4 +12867,4 @@ posthog = ["posthog"] [metadata] lock-version = "2.1" python-versions = ">=3.10,<=3.13" -content-hash = "49d2d5207803b6b56e0bbf2be871941f4fc4535d0bc823815e0a7cbe61e12574" +content-hash = "8cd22d0a48dec9e2accc42159a55cf175041522fdabfeaa46c09ab1fcfdc1956" From 617c1f0d71fcafe82ceb47afea8f1a07bec77086 Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Tue, 23 Sep 2025 13:16:44 +0530 Subject: [PATCH 128/139] Added Mistral support as LLM provider using litellm --- cognee/api/v1/add/add.py | 2 +- .../settings/routers/get_settings_router.py | 8 +- .../litellm_instructor/llm/get_llm_client.py | 17 +++ .../llm/mistral/__init__.py | 0 .../litellm_instructor/llm/mistral/adapter.py | 113 ++++++++++++++++++ cognee/modules/settings/get_settings.py | 23 ++++ pyproject.toml | 3 +- 7 files changed, 163 insertions(+), 3 deletions(-) create mode 100644 cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py create mode 100644 cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index cbb1c8fd5..65394f1ec 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -170,7 +170,7 @@ async def add( - LLM_API_KEY: API key for your LLM provider (OpenAI, Anthropic, etc.) Optional: - - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama" + - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral" - LLM_MODEL: Model name (default: "gpt-5-mini") - DEFAULT_USER_EMAIL: Custom default user email - DEFAULT_USER_PASSWORD: Custom default user password diff --git a/cognee/api/v1/settings/routers/get_settings_router.py b/cognee/api/v1/settings/routers/get_settings_router.py index c85352746..c64ce2072 100644 --- a/cognee/api/v1/settings/routers/get_settings_router.py +++ b/cognee/api/v1/settings/routers/get_settings_router.py @@ -21,7 +21,13 @@ class SettingsDTO(OutDTO): class LLMConfigInputDTO(InDTO): - provider: Union[Literal["openai"], Literal["ollama"], Literal["anthropic"], Literal["gemini"]] + provider: Union[ + Literal["openai"], + Literal["ollama"], + Literal["anthropic"], + Literal["gemini"], + Literal["mistral"], + ] model: str api_key: str diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py index 0ae621428..bbdfe49e9 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py @@ -23,6 +23,7 @@ class LLMProvider(Enum): - ANTHROPIC: Represents the Anthropic provider. - CUSTOM: Represents a custom provider option. - GEMINI: Represents the Gemini provider. + - MISTRAL: Represents the Mistral AI provider. """ OPENAI = "openai" @@ -30,6 +31,7 @@ class LLMProvider(Enum): ANTHROPIC = "anthropic" CUSTOM = "custom" GEMINI = "gemini" + MISTRAL = "mistral" def get_llm_client(raise_api_key_error: bool = True): @@ -145,5 +147,20 @@ def get_llm_client(raise_api_key_error: bool = True): api_version=llm_config.llm_api_version, ) + elif provider == LLMProvider.MISTRAL: + if llm_config.llm_api_key is None: + raise LLMAPIKeyNotSetError() + + from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import ( + MistralAdapter, + ) + + return MistralAdapter( + api_key=llm_config.llm_api_key, + model=llm_config.llm_model, + max_completion_tokens=max_completion_tokens, + endpoint=llm_config.llm_endpoint, + ) + else: raise UnsupportedLLMProviderError(provider) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py new file mode 100644 index 000000000..a9cf68d6e --- /dev/null +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -0,0 +1,113 @@ +import litellm +import instructor +from pydantic import BaseModel +from typing import Type, Optional +from litellm import acompletion, JSONSchemaValidationError + +from cognee.shared.logging_utils import get_logger +from cognee.modules.observability.get_observe import get_observe +from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( + LLMInterface, +) +from cognee.infrastructure.llm.LLMGateway import LLMGateway +from cognee.infrastructure.llm.config import get_llm_config +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( + rate_limit_async, + sleep_and_retry_async, +) + +logger = get_logger() +observe = get_observe() + + +class MistralAdapter(LLMInterface): + """ + Adapter for Mistral AI API, for structured output generation and prompt display. + + Public methods: + - acreate_structured_output + - show_prompt + """ + + name = "Mistral" + model: str + api_key: str + max_completion_tokens: int + + def __init__(self, api_key: str, model: str, max_completion_tokens: int, endpoint: str = None): + from mistralai import Mistral + + self.model = model + self.max_completion_tokens = max_completion_tokens + + self.aclient = instructor.from_litellm( + litellm.acompletion, + mode=instructor.Mode.MISTRAL_TOOLS, + api_key=get_llm_config().llm_api_key, + ) + + @sleep_and_retry_async() + @rate_limit_async + async def acreate_structured_output( + self, text_input: str, system_prompt: str, response_model: Type[BaseModel] + ) -> BaseModel: + """ + Generate a response from the user query. + + Parameters: + ----------- + - text_input (str): The input text from the user to be processed. + - system_prompt (str): A prompt that sets the context for the query. + - response_model (Type[BaseModel]): The model to structure the response according to + its format. + + Returns: + -------- + - BaseModel: An instance of BaseModel containing the structured response. + """ + return await self.aclient.chat.completions.create( + model=self.model, + max_tokens=self.max_completion_tokens, + max_retries=5, + messages=[ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information + from the following input: {text_input}""", + }, + ], + response_model=response_model, + ) + + def show_prompt(self, text_input: str, system_prompt: str) -> str: + """ + Format and display the prompt for a user query. + + Parameters: + ----------- + - text_input (str): Input text from the user to be included in the prompt. + - system_prompt (str): The system prompt that will be shown alongside the user input. + + Returns: + -------- + - str: The formatted prompt string combining system prompt and user input. + """ + if not text_input: + text_input = "No user input provided." + if not system_prompt: + raise MissingSystemPromptPathError() + + system_prompt = LLMGateway.read_query_prompt(system_prompt) + + formatted_prompt = ( + f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" + if system_prompt + else None + ) + + return formatted_prompt diff --git a/cognee/modules/settings/get_settings.py b/cognee/modules/settings/get_settings.py index fa92c8043..071bcca36 100644 --- a/cognee/modules/settings/get_settings.py +++ b/cognee/modules/settings/get_settings.py @@ -15,6 +15,7 @@ class ModelName(Enum): ollama = "ollama" anthropic = "anthropic" gemini = "gemini" + mistral = "mistral" class LLMConfig(BaseModel): @@ -72,6 +73,10 @@ def get_settings() -> SettingsDict: "value": "gemini", "label": "Gemini", }, + { + "value": "mistral", + "label": "Mistral", + }, ] return SettingsDict.model_validate( @@ -134,6 +139,24 @@ def get_settings() -> SettingsDict: "label": "Gemini 2.0 Flash", }, ], + "mistral": [ + { + "value": "mistral-medium-2508", + "label": "Mistral Medium 3.1", + }, + { + "value": "magistral-medium-2509", + "label": "Magistral Medium 1.2", + }, + { + "value": "magistral-medium-2507", + "label": "Magistral Medium 1.1", + }, + { + "value": "mistral-large-2411", + "label": "Mistral Large 2.1", + }, + ], }, }, vector_db={ diff --git a/pyproject.toml b/pyproject.toml index 4baf40e2f..aa5e693c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,7 +54,8 @@ dependencies = [ "networkx>=3.4.2,<4", "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", - "websockets>=15.0.1,<16.0.0" + "websockets>=15.0.1,<16.0.0", + "mistralai>=1.9.10", ] [project.optional-dependencies] From 06dca5bf26e0e3abc28e9a6f5f78932d84c81001 Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Wed, 24 Sep 2025 10:40:17 +0530 Subject: [PATCH 129/139] Enhance MistralAdapter error handling and response validation in chat completion method --- .../litellm_instructor/llm/mistral/adapter.py | 50 ++++++++++++------- pyproject.toml | 4 +- 2 files changed, 35 insertions(+), 19 deletions(-) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py index a9cf68d6e..dbf0322b1 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -66,23 +66,39 @@ class MistralAdapter(LLMInterface): -------- - BaseModel: An instance of BaseModel containing the structured response. """ - return await self.aclient.chat.completions.create( - model=self.model, - max_tokens=self.max_completion_tokens, - max_retries=5, - messages=[ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": f"""Use the given format to extract information - from the following input: {text_input}""", - }, - ], - response_model=response_model, - ) + try: + messages = [ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information + from the following input: {text_input}""", + }, + ] + try: + response = await self.aclient.chat.completions.create( + model=self.model, + max_tokens=self.max_completion_tokens, + max_retries=5, + messages=messages, + response_model=response_model, + ) + if response.choices and response.choices[0].message.content: + content = response.choices[0].message.content + return response_model.model_validate_json(content) + else: + raise ValueError("Failed to get valid response after retries") + except litellm.exceptions.BadRequestError as e: + logger.error(f"Bad request error: {str(e)}") + raise ValueError(f"Invalid request: {str(e)}") + + except JSONSchemaValidationError as e: + logger.error(f"Schema validation failed: {str(e)}") + logger.debug(f"Raw response: {e.raw_response}") + raise ValueError(f"Response failed schema validation: {str(e)}") def show_prompt(self, text_input: str, system_prompt: str) -> str: """ diff --git a/pyproject.toml b/pyproject.toml index aa5e693c1..0e754914b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - "mistralai>=1.9.10", + ] [project.optional-dependencies] @@ -92,7 +92,7 @@ langchain = [ llama-index = ["llama-index-core>=0.12.11,<0.13"] huggingface = ["transformers>=4.46.3,<5"] ollama = ["transformers>=4.46.3,<5"] -mistral = ["mistral-common>=1.5.2,<2"] +mistral = ["mistral-common>=1.5.2,<2","mistralai>=1.9.10"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] From 19fcbba7cc0f98ff9e1b654047415c6de0e98934 Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Wed, 1 Oct 2025 00:22:28 +0530 Subject: [PATCH 130/139] chore: update dependencies in pyproject.toml and uv.lock - Added "mistralai==1.9.10" to the dependencies in pyproject.toml. - Updated sdist entries in uv.lock to remove unnecessary upload-time fields for various packages. - Ensured consistency in package specifications across the project files. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0e754914b..0b3d14189 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - + "mistralai==1.9.10", ] [project.optional-dependencies] From 9229e1154de66e608569678632fea2ee8c39d869 Mon Sep 17 00:00:00 2001 From: Aniruddha Mandal Date: Wed, 1 Oct 2025 00:33:05 +0530 Subject: [PATCH 131/139] chore: update dependency specifications in pyproject.toml - Changed "mistralai==1.9.10" to "mistralai>=1.9.10" for more flexible versioning. - Removed "mistralai" from the optional dependencies under "mistral". - Expanded the "docs" dependency to include "pdf" support. --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0b3d14189..23c001d96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ dependencies = [ "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", "websockets>=15.0.1,<16.0.0", - "mistralai==1.9.10", + "mistralai>=1.9.10", ] [project.optional-dependencies] @@ -92,7 +92,7 @@ langchain = [ llama-index = ["llama-index-core>=0.12.11,<0.13"] huggingface = ["transformers>=4.46.3,<5"] ollama = ["transformers>=4.46.3,<5"] -mistral = ["mistral-common>=1.5.2,<2","mistralai>=1.9.10"] +mistral = ["mistral-common>=1.5.2,<2"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] @@ -148,6 +148,7 @@ Homepage = "https://www.cognee.ai" Repository = "https://github.com/topoteretes/cognee" [project.scripts] +cognee = "cognee.cli._cognee:main" cognee-cli = "cognee.cli._cognee:main" [build-system] From 5527b6e0b339f7e3f4fab57a380d43ea5549652c Mon Sep 17 00:00:00 2001 From: vasilije Date: Sun, 12 Oct 2025 11:47:40 +0200 Subject: [PATCH 132/139] format done --- .../litellm_instructor/llm/mistral/adapter.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py index dbf0322b1..c4e51b70b 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -68,16 +68,16 @@ class MistralAdapter(LLMInterface): """ try: messages = [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": f"""Use the given format to extract information + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information from the following input: {text_input}""", - }, - ] + }, + ] try: response = await self.aclient.chat.completions.create( model=self.model, @@ -94,7 +94,7 @@ class MistralAdapter(LLMInterface): except litellm.exceptions.BadRequestError as e: logger.error(f"Bad request error: {str(e)}") raise ValueError(f"Invalid request: {str(e)}") - + except JSONSchemaValidationError as e: logger.error(f"Schema validation failed: {str(e)}") logger.debug(f"Raw response: {e.raw_response}") From 64b43cfadd5529a0042f25f6f7e0dca1e2e51d5c Mon Sep 17 00:00:00 2001 From: hajdul88 <52442977+hajdul88@users.noreply.github.com> Date: Tue, 14 Oct 2025 09:55:02 +0200 Subject: [PATCH 133/139] chore: deletes toml and lock files from distributed directory --- distributed/poetry.lock | 12238 ----------------------------------- distributed/pyproject.toml | 185 - 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a/cognee/tasks/web_scraper/bs4_crawler.py +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -20,7 +20,7 @@ logger = get_logger(__name__) try: from playwright.async_api import async_playwright except ImportError: - logger.error( + logger.warning( "Failed to import playwright, make sure to install using pip install playwright>=1.9.0" ) async_playwright = None @@ -28,7 +28,7 @@ except ImportError: try: from protego import Protego except ImportError: - logger.error("Failed to import protego, make sure to install using pip install protego>=0.1") + logger.warning("Failed to import protego, make sure to install using pip install protego>=0.1") Protego = None From 42ca782e59d994a3b6b65bf7d53c7626a9027de9 Mon Sep 17 00:00:00 2001 From: Igor Ilic Date: Tue, 14 Oct 2025 15:44:21 +0200 Subject: [PATCH 139/139] fix: Resolve issue with MCP --- cognee-mcp/pyproject.toml | 3 +- cognee-mcp/uv.lock | 2857 ++++++++++++++++++++----------------- 2 files changed, 1589 insertions(+), 1271 deletions(-) diff 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