diff --git a/cognee/api/v1/cognify/cognify.py b/cognee/api/v1/cognify/cognify.py index 474760ebc..b9bcf81cb 100644 --- a/cognee/api/v1/cognify/cognify.py +++ b/cognee/api/v1/cognify/cognify.py @@ -1,4 +1,5 @@ import asyncio +import os from uuid import uuid4 from typing import List, Union import logging @@ -6,7 +7,11 @@ import instructor import nltk from openai import OpenAI from nltk.corpus import stopwords + +from cognee.api.v1.prune import prune from cognee.config import Config +from cognee.infrastructure.data.chunking.LangchainChunkingEngine import LangchainChunkEngine +from cognee.infrastructure.databases.vector.embeddings.DefaultEmbeddingEngine import OpenAIEmbeddingEngine from cognee.modules.cognify.graph.add_data_chunks import add_data_chunks from cognee.modules.cognify.graph.add_document_node import add_document_node from cognee.modules.cognify.graph.add_classification_nodes import add_classification_nodes @@ -14,7 +19,6 @@ from cognee.modules.cognify.graph.add_cognitive_layer_graphs import add_cognitiv from cognee.modules.cognify.graph.add_summary_nodes import add_summary_nodes from cognee.modules.cognify.graph.add_node_connections import group_nodes_by_layer, \ graph_ready_output, connect_nodes_in_graph -from cognee.modules.cognify.graph.initialize_graph import initialize_graph from cognee.modules.cognify.llm.resolve_cross_graph_references import resolve_cross_graph_references from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client from cognee.modules.cognify.graph.add_label_nodes import add_label_nodes @@ -27,6 +31,8 @@ from cognee.modules.data.get_content_categories import get_content_categories from cognee.modules.data.get_content_summary import get_content_summary from cognee.modules.data.get_cognitive_layers import get_cognitive_layers from cognee.modules.data.get_layer_graphs import get_layer_graphs +from cognee.modules.topology.topology import TopologyEngine +from cognee.shared.data_models import ChunkStrategy from cognee.utils import send_telemetry @@ -69,27 +75,12 @@ async def cognify(datasets: Union[str, List[str]] = None): dataset_files = [] # datasets is a dataset name string dataset_name = datasets.replace(".", "_").replace(" ", "_") - for added_dataset in added_datasets: if dataset_name in added_dataset: dataset_files.append((added_dataset, db_engine.get_files_metadata(added_dataset))) + # print("dataset_files", dataset_files) - print(dataset_files) - - # topology can be inferred, loaded, or extrapolated from the data in the end of the flow - # for code generation, we infer the topology from the folder structure as simple step - - - - - - - graph_topology = infrastructure_config.get_config()["graph_topology"] - - - - await initialize_graph(USER_ID, graph_client=graph_client) data_chunks = {} @@ -102,6 +93,8 @@ async def cognify(datasets: Union[str, List[str]] = None): try: file_type = guess_file_type(file) text = extract_text_from_file(file, file_type) + if text is None: + text = "" subchunks = chunk_engine.chunk_data(chunk_strategy, text, config.chunk_size, config.chunk_overlap) if dataset_name not in data_chunks: @@ -125,22 +118,25 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi graph_client = await get_graph_client(infrastructure_config.get_config()["graph_engine"]) + graph_topology = infrastructure_config.get_config()["graph_topology"] + + document_id = await add_document_node( graph_client, - parent_node_id = f"DefaultGraphModel__{USER_ID}", #make a param of defaultgraph model to make sure when user passes his stuff, it doesn't break pipeline + parent_node_id = f"{file_metadata['name']}.{file_metadata['extension']}", #make a param of defaultgraph model to make sure when user passes his stuff, it doesn't break pipeline document_metadata = file_metadata, ) - + # await add_label_nodes(graph_client, document_id, chunk_id, file_metadata["keywords"].split("|")) - classified_categories = await get_content_categories(input_text) - await add_classification_nodes( - graph_client, - parent_node_id = document_id, - categories = classified_categories, - ) + # classified_categories = await get_content_categories(input_text) + # await add_classification_nodes( + # graph_client, + # parent_node_id = document_id, + # categories = classified_categories, + # ) - print(f"Chunk ({chunk_id}) classified.") + # print(f"Chunk ({chunk_id}) classified.") print("document_id", document_id) @@ -149,53 +145,61 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi print(f"Chunk ({chunk_id}) summarized.") - cognitive_layers = await get_cognitive_layers(input_text, classified_categories) - cognitive_layers = (await add_cognitive_layers(graph_client, document_id, cognitive_layers))[:2] - - layer_graphs = await get_layer_graphs(input_text, cognitive_layers) - await add_cognitive_layer_graphs(graph_client, chunk_collection, chunk_id, layer_graphs) - - if infrastructure_config.get_config()["connect_documents"] is True: - db_engine = infrastructure_config.get_config()["database_engine"] - relevant_documents_to_connect = db_engine.fetch_cognify_data(excluded_document_id = file_metadata["id"]) - - list_of_nodes = [] - - relevant_documents_to_connect.append({ - "layer_id": document_id, - }) - - for document in relevant_documents_to_connect: - node_descriptions_to_match = await graph_client.extract_node_description(document["layer_id"]) - list_of_nodes.extend(node_descriptions_to_match) - - nodes_by_layer = await group_nodes_by_layer(list_of_nodes) - - results = await resolve_cross_graph_references(nodes_by_layer) - - relationships = graph_ready_output(results) - - await connect_nodes_in_graph( - graph_client, - relationships, - score_threshold = infrastructure_config.get_config()["intra_layer_score_treshold"] - ) - - send_telemetry("cognee.cognify") - - print(f"Chunk ({chunk_id}) cognified.") + # cognitive_layers = await get_cognitive_layers(input_text, classified_categories) + # cognitive_layers = (await add_cognitive_layers(graph_client, document_id, cognitive_layers))[:2] + # + # layer_graphs = await get_layer_graphs(input_text, cognitive_layers) + # await add_cognitive_layer_graphs(graph_client, chunk_collection, chunk_id, layer_graphs) + # + # if infrastructure_config.get_config()["connect_documents"] is True: + # db_engine = infrastructure_config.get_config()["database_engine"] + # relevant_documents_to_connect = db_engine.fetch_cognify_data(excluded_document_id = file_metadata["id"]) + # + # list_of_nodes = [] + # + # relevant_documents_to_connect.append({ + # "layer_id": document_id, + # }) + # + # for document in relevant_documents_to_connect: + # node_descriptions_to_match = await graph_client.extract_node_description(document["layer_id"]) + # list_of_nodes.extend(node_descriptions_to_match) + # + # nodes_by_layer = await group_nodes_by_layer(list_of_nodes) + # + # results = await resolve_cross_graph_references(nodes_by_layer) + # + # relationships = graph_ready_output(results) + # + # await connect_nodes_in_graph( + # graph_client, + # relationships, + # score_threshold = infrastructure_config.get_config()["intra_layer_score_treshold"] + # ) + # + # send_telemetry("cognee.cognify") + # + # print(f"Chunk ({chunk_id}) cognified.") if __name__ == "__main__": async def test(): + # await prune.prune_system() + # # + # from cognee.api.v1.add import add + # data_directory_path = os.path.abspath("../../../.data") + # # print(data_directory_path) + # # config.data_root_directory(data_directory_path) + # # cognee_directory_path = os.path.abspath("../.cognee_system") + # # config.system_root_directory(cognee_directory_path) # - from cognee.api.v1.add import add + # await add("data://" +data_directory_path, "example") - await add(["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"], "code") + infrastructure_config.set_config( {"chunk_engine": LangchainChunkEngine() , "chunk_strategy": ChunkStrategy.CODE,'embedding_engine': OpenAIEmbeddingEngine()}) graph = await cognify() - + # from cognee.utils import render_graph await render_graph(graph, include_color=True, include_nodes=False, include_size=False) diff --git a/cognee/api/v1/topology/__init__.py b/cognee/api/v1/topology/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cognee/api/v1/topology/add_topology.py b/cognee/api/v1/topology/add_topology.py new file mode 100644 index 000000000..3f5ad8c12 --- /dev/null +++ b/cognee/api/v1/topology/add_topology.py @@ -0,0 +1,91 @@ +from typing import List, Dict, Any, Union, Optional + +from cognee.infrastructure import infrastructure_config +from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client + +from cognee.modules.topology.topology import TopologyEngine, GitHubRepositoryModel +import pandas as pd +from pydantic import BaseModel + +USER_ID = "default_user" +async def add_topology(directory="example", model=GitHubRepositoryModel): + graph_db_type = infrastructure_config.get_config()["graph_engine"] + + graph_client = await get_graph_client(graph_db_type) + + graph_topology = infrastructure_config.get_config()["graph_topology"] + + engine = TopologyEngine() + topology = await engine.infer_from_directory_structure(node_id =USER_ID , repository = directory, model=model) + + def flatten_model(model: BaseModel, parent_id: Optional[str] = None) -> Dict[str, Any]: + """Flatten a single Pydantic model to a dictionary handling nested structures.""" + result = {**model.dict(), 'parent_id': parent_id} + if hasattr(model, 'default_relationship') and model.default_relationship: + result.update({ + 'relationship_type': model.default_relationship.type, + 'relationship_source': model.default_relationship.source, + 'relationship_target': model.default_relationship.target + }) + return result + + def recursive_flatten(items: Union[List[Any], BaseModel], parent_id: Optional[str] = None) -> List[Dict[str, Any]]: + """Recursively flatten nested Pydantic models or lists of models.""" + if isinstance(items, list): + return [entry for item in items for entry in recursive_flatten(item, parent_id)] + elif isinstance(items, BaseModel): + flat = [flatten_model(items, parent_id)] + for field, value in items: + if isinstance(value, (BaseModel, list)): + flat.extend(recursive_flatten(value, items.dict().get('node_id', None))) + return flat + else: + return [] + + def flatten_repository(repo_model): + """ Flatten the entire repository model, starting with the top-level model """ + return recursive_flatten(repo_model) + + flt_topology = flatten_repository(topology) + + df =pd.DataFrame(flt_topology) + + print(df.head(10)) + + + for _, row in df.iterrows(): + node_data = row.to_dict() + node_id = node_data.pop('node_id') + + # Remove 'node_id' and get its value + await graph_client.add_node(node_id, node_data) + if pd.notna(row['relationship_source']) and pd.notna(row['relationship_target']): + await graph_client.add_edge(row['relationship_source'], row['relationship_target'], relationship_name=row['relationship_type']) + + return graph_client.graph + +if __name__ == "__main__": + async def test(): + # await prune.prune_system() + # # + # from cognee.api.v1.add import add + # data_directory_path = os.path.abspath("../../../.data") + # # print(data_directory_path) + # # config.data_root_directory(data_directory_path) + # # cognee_directory_path = os.path.abspath("../.cognee_system") + # # config.system_root_directory(cognee_directory_path) + # + # await add("data://" +data_directory_path, "example") + + graph = await add_topology() + + graph_db_type = infrastructure_config.get_config()["graph_engine"] + + graph_client = await get_graph_client(graph_db_type) + # + from cognee.utils import render_graph + + await render_graph(graph_client.graph, include_color=True, include_nodes=False, include_size=False) + + import asyncio + asyncio.run(test()) \ No newline at end of file diff --git a/cognee/infrastructure/data/chunking/DefaultChunkEngine.py b/cognee/infrastructure/data/chunking/DefaultChunkEngine.py index b486a5faa..0aca8da52 100644 --- a/cognee/infrastructure/data/chunking/DefaultChunkEngine.py +++ b/cognee/infrastructure/data/chunking/DefaultChunkEngine.py @@ -52,6 +52,7 @@ class DefaultChunkEngine(): elif chunk_strategy == ChunkStrategy.EXACT: chunked_data = DefaultChunkEngine.chunk_data_exact(source_data, chunk_size, chunk_overlap) + return chunked_data diff --git a/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py index eb23b0509..8ced98728 100644 --- a/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py @@ -1,3 +1,4 @@ +import asyncio from typing import List import instructor @@ -8,8 +9,8 @@ from openai import AsyncOpenAI from cognee.config import Config from cognee.root_dir import get_absolute_path -from .EmbeddingEngine import EmbeddingEngine - +from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine +from litellm import aembedding config = Config() config.load() @@ -27,16 +28,28 @@ class DefaultEmbeddingEngine(EmbeddingEngine): class OpenAIEmbeddingEngine(EmbeddingEngine): async def embed_text(self, text: List[str]) -> List[float]: - OPENAI_API_KEY = config.openai_key - aclient = instructor.patch(AsyncOpenAI()) - text = text.replace("\n", " ") - response = await aclient.embeddings.create(input = text, model = config.openai_embedding_model) - embedding = response.data[0].embedding + response = await aembedding(config.openai_embedding_model, input=text) + + + # embedding = response.data[0].embedding # embeddings_list = list(map(lambda embedding: embedding.tolist(), embedding_model.embed(text))) - return embedding + return response.data[0]['embedding'] def get_vector_size(self) -> int: return config.openai_embedding_dimensions + +if __name__ == "__main__": + async def gg(): + openai_embedding_engine = OpenAIEmbeddingEngine() + # print(openai_embedding_engine.embed_text(["Hello, how are you?"])) + # print(openai_embedding_engine.get_vector_size()) + # default_embedding_engine = DefaultEmbeddingEngine() + sds = await openai_embedding_engine.embed_text(["Hello, sadasdas are you?"]) + print(sds) + # print(default_embedding_engine.get_vector_size()) + + asyncio.run(gg()) + diff --git a/cognee/infrastructure/databases/vector/lancedb/__init__.py b/cognee/infrastructure/databases/vector/lancedb/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cognee/infrastructure/llm/prompts/extract_topology.txt b/cognee/infrastructure/llm/prompts/extract_topology.txt index f3b21077f..2ef2c38f9 100644 --- a/cognee/infrastructure/llm/prompts/extract_topology.txt +++ b/cognee/infrastructure/llm/prompts/extract_topology.txt @@ -1 +1,4 @@ -You are a topology master and need to extract the following topology information from the text provided to you \ No newline at end of file +You are a topology master and need to extract the following topology information from the text provided to you. +Relationship part'S can't be empty, and have to be logical AND CONNECTING ELEMENTS OF THE TOPOLOGY +The source is the parent of the target. And the target is the child of the source. +Have in mind this model needs to become a graph later. \ No newline at end of file diff --git a/cognee/modules/cognify/graph/add_label_nodes.py b/cognee/modules/cognify/graph/add_label_nodes.py index 245814f7c..8a8d9b846 100644 --- a/cognee/modules/cognify/graph/add_label_nodes.py +++ b/cognee/modules/cognify/graph/add_label_nodes.py @@ -37,24 +37,24 @@ async def add_label_nodes(graph_client, parent_node_id: str, chunk_id: str, keyw ]) # Add data to vector - keyword_data_points = [ - DataPoint( - id = str(uuid4()), - payload = dict( - value = keyword_data["keyword"], - references = dict( - node_id = keyword_node_id, - cognitive_layer = parent_node_id, - ), - ), - embed_field = "value" - ) for (keyword_node_id, keyword_data) in keyword_nodes - ] - - try: - await vector_client.create_collection(parent_node_id) - except Exception: - # It's ok if the collection already exists. - pass - - await vector_client.create_data_points(parent_node_id, keyword_data_points) + # keyword_data_points = [ + # DataPoint( + # id = str(uuid4()), + # payload = dict( + # value = keyword_data["keyword"], + # references = dict( + # node_id = keyword_node_id, + # cognitive_layer = parent_node_id, + # ), + # ), + # embed_field = "value" + # ) for (keyword_node_id, keyword_data) in keyword_nodes + # ] + # + # try: + # await vector_client.create_collection(parent_node_id) + # except Exception: + # # It's ok if the collection already exists. + # pass + # + # await vector_client.create_data_points(parent_node_id, keyword_data_points) diff --git a/cognee/modules/cognify/graph/create.py b/cognee/modules/cognify/graph/create.py index e6bbd8868..dc1c03181 100644 --- a/cognee/modules/cognify/graph/create.py +++ b/cognee/modules/cognify/graph/create.py @@ -40,12 +40,20 @@ async def add_node(client, parent_id: Optional[str], node_id: str, node_data: di if node_id != "Relationship_default": try: # Attempt to add the node to the graph database + # print('NODE ID', node_id) + # print('NODE DATA', node_data) result = await client.add_node(node_id, node_properties = node_data) + print("added node", result) # Add an edge if a parent ID is provided and the graph engine is NETWORKX if parent_id and "default_relationship" in node_data and infrastructure_config.get_config()["graph_engine"] == GraphDBType.NETWORKX: - print("Node id", node_id) - await client.add_edge(parent_id, node_id, relationship_name = node_data["default_relationship"]["type"], edge_properties = node_data) + + try: + await client.add_edge(parent_id, node_id, relationship_name = node_data["default_relationship"]["type"], edge_properties = node_data) + print("added edge") + except Exception as e: + print(f"Error adding edge: {e}") + pass except Exception as e: # Log the exception; consider a logging framework for production use print(f"Error adding node or edge: {e}") @@ -139,39 +147,67 @@ async def add_edge(client, parent_id: Optional[str], node_id: str, node_data: di await client.add_edge(relationship_details['source'], relationship_details['target'], relationship_name = relationship_details['type']) - - async def process_attribute(graph_client, parent_id: Optional[str], attribute: str, value: Any, created_node_ids=None): - if created_node_ids is None: created_node_ids = [] + if isinstance(value, BaseModel): node_id = await generate_node_id(value) node_data = value.model_dump() - # Use the specified default relationship for the edge between the parent node and the current node - + # Add node to the graph created_node_id = await add_node(graph_client, parent_id, node_id, node_data) + created_node_ids.append(node_id) - created_node_ids.append(created_node_id) - - # await add_edge(graph_client, parent_id, node_id, node_data, relationship_data,created_node_ids) + # Add an edge if a default_relationship exists + if hasattr(value, 'default_relationship') and value.default_relationship: + await add_edge(graph_client, parent_id, node_id, value.default_relationship.dict()) # Recursively process nested attributes to ensure all nodes and relationships are added to the graph - for sub_attr, sub_val in value.__dict__.items(): # Access attributes and their values directly - - out = await process_attribute(graph_client, node_id, sub_attr, sub_val) - - created_node_ids.extend(out) + for sub_attr, sub_val in value.dict().items(): + if isinstance(sub_val, (BaseModel, list)): # Check if the value is a model or list + await process_attribute(graph_client, node_id, sub_attr, sub_val, created_node_ids) elif isinstance(value, list) and all(isinstance(item, BaseModel) for item in value): # For lists of BaseModel instances, process each item in the list for item in value: - out = await process_attribute(graph_client, parent_id, attribute, item, created_node_ids) - created_node_ids.extend(out) + await process_attribute(graph_client, parent_id, attribute, item, created_node_ids) return created_node_ids +# async def process_attribute(graph_client, parent_id: Optional[str], attribute: str, value: Any, created_node_ids=None): +# +# if created_node_ids is None: +# created_node_ids = [] +# if isinstance(value, BaseModel): +# node_id = await generate_node_id(value) +# node_data = value.model_dump() +# +# # Use the specified default relationship for the edge between the parent node and the current node +# +# created_node_id = await add_node(graph_client, parent_id, node_id, node_data) +# +# created_node_ids.append(created_node_id) +# +# # await add_edge(graph_client, parent_id, node_id, node_data, relationship_data,created_node_ids) +# +# # Recursively process nested attributes to ensure all nodes and relationships are added to the graph +# # for sub_attr, sub_val in value.__dict__.items(): # Access attributes and their values directly +# # print('SUB ATTR', sub_attr) +# # print('SUB VAL', sub_val) +# # +# # out = await process_attribute(graph_client, node_id, sub_attr, sub_val) +# # +# # created_node_ids.extend(out) +# +# elif isinstance(value, list) and all(isinstance(item, BaseModel) for item in value): +# # For lists of BaseModel instances, process each item in the list +# for item in value: +# out = await process_attribute(graph_client, parent_id, attribute, item, created_node_ids) +# created_node_ids.extend(out) +# +# return created_node_ids + async def process_attribute_edge(graph_client, parent_id: Optional[str], attribute: str, value: Any, created_node_ids=None): @@ -212,20 +248,22 @@ async def create_dynamic(graph_model, graph_client) : created_node_ids.append(out) + print('CREATED NODE IDS', created_node_ids) + for attribute_name, attribute_value in graph_model: ids = await process_attribute(graph_client, root_id, attribute_name, attribute_value) - created_node_ids.extend(ids) - - flattened_and_deduplicated = list({ - item["nodeId"]: item - # Use the 'nodeId' as the unique key in the dictionary comprehension - for sublist in created_node_ids if sublist # Ensure sublist is not None - for item in sublist # Iterate over items in the sublist - }.values()) - - for attribute_name, attribute_value in graph_model: - ids = await process_attribute_edge(graph_client, root_id, attribute_name, attribute_value, flattened_and_deduplicated) - + # created_node_ids.extend(ids) + # + # flattened_and_deduplicated = list({ + # item["nodeId"]: item + # # Use the 'nodeId' as the unique key in the dictionary comprehension + # for sublist in created_node_ids if sublist # Ensure sublist is not None + # for item in sublist # Iterate over items in the sublist + # }.values()) + # + # for attribute_name, attribute_value in graph_model: + # ids = await process_attribute_edge(graph_client, root_id, attribute_name, attribute_value, flattened_and_deduplicated) + # return graph_client diff --git a/cognee/modules/cognify/graph/initialize_graph.py b/cognee/modules/cognify/graph/initialize_graph.py deleted file mode 100644 index dcb3517d8..000000000 --- a/cognee/modules/cognify/graph/initialize_graph.py +++ /dev/null @@ -1,43 +0,0 @@ -from datetime import datetime -from cognee.shared.data_models import DefaultGraphModel, Relationship, UserProperties, UserLocation -from cognee.modules.cognify.graph.create import create_semantic_graph - -async def initialize_graph(root_id: str, graphdatamodel=None, graph_client=None): - if graphdatamodel: - graph = graphdatamodel(node_id= root_id) - graph_ = await create_semantic_graph(graph, graph_client) - return graph_ - else: - print("Creating default graph") - - graph = DefaultGraphModel( - node_id = root_id, - user_properties = UserProperties( - custom_properties = { "age": "30" }, - location = UserLocation( - location_id = "ny", - description = "New York", - default_relationship = Relationship( - type = "located_in", - source = "UserProperties", - target = "ny", - ) - ), - default_relationship = Relationship( - type = "has_properties", - source = root_id, - target = "UserProperties", - ) - ), - default_relationship = Relationship( - type = "has_properties", - source = root_id, - target = "UserProperties" - ), - default_fields = { - "created_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S") - } - ) - - return await create_semantic_graph(graph, graph_client) diff --git a/cognee/modules/topology/topology.py b/cognee/modules/topology/topology.py index c65a13272..d2ed8565c 100644 --- a/cognee/modules/topology/topology.py +++ b/cognee/modules/topology/topology.py @@ -10,7 +10,7 @@ from datetime import datetime from cognee import config from cognee.infrastructure import infrastructure_config -from infer_data_topology import infer_data_topology +from cognee.modules.topology.infer_data_topology import infer_data_topology @@ -31,66 +31,124 @@ from infer_data_topology import infer_data_topology # default_relationship: Relationship = Relationship(type = "has_properties") # class Relationship(BaseModel): - type: str - source: Optional[str] = None - target: Optional[str] = None - properties: Optional[Dict[str, Any]] = None + type: str = Field(..., description="The type of relationship, e.g., 'belongs_to'.") + source: Optional[str] = Field(None, description="The identifier of the source id of in the relationship being a directory or subdirectory") + target: Optional[str] = Field(None, description="The identifier of the target id in the relationship being the directory, subdirectory or file") + properties: Optional[Dict[str, Any]] = Field(None, description="A dictionary of additional properties and values related to the relationship.") class Document(BaseModel): - id: str + node_id: str title: str description: Optional[str] = None - default_relationship: Relationship = Field(default_factory=lambda: Relationship(type="belongs_to")) + default_relationship: Relationship class DirectoryModel(BaseModel): - name: str + node_id: str path: str summary: str documents: List[Document] = [] subdirectories: List['DirectoryModel'] = [] - default_relationship: Relationship = Field(default_factory=lambda: Relationship(type="belongs_to")) + default_relationship: Relationship DirectoryModel.update_forward_refs() -class RepositoryMetadata(BaseModel): - name: str +class DirMetadata(BaseModel): + node_id: str summary: str owner: str description: Optional[str] = None directories: List[DirectoryModel] = [] documents: List[Document] = [] - default_relationship: Relationship = Field(default_factory=lambda: Relationship(type="belongs_to")) + default_relationship: Relationship class GitHubRepositoryModel(BaseModel): - metadata: RepositoryMetadata + node_id: str + metadata: DirMetadata root_directory: DirectoryModel + class TopologyEngine: def __init__(self): self.models: Dict[str, Type[BaseModel]] = {} - async def infer(self, repository: str): + async def populate_model(self, directory_path, file_structure, parent_id=None): + directory_id = os.path.basename(directory_path) or "root" + directory = DirectoryModel( + node_id=directory_id, + path=directory_path, + summary=f"Contents of {directory_id}", + default_relationship=Relationship(type="contains", source=parent_id, target=directory_id) + ) + + for key, value in file_structure.items(): + if isinstance(value, dict): + # Recurse into subdirectory + subdirectory_path = os.path.join(directory_path, key) + subdirectory = await self.populate_model(subdirectory_path, value, parent_id=directory_id) + directory.subdirectories.append(subdirectory) + elif isinstance(value, tuple) and value[0] == 'file': + # Handle file + document = Document( + node_id=key, + title=key, + default_relationship=Relationship(type="contained_by", source=key, target=directory_id) + ) + directory.documents.append(document) + + return directory + + async def infer_from_directory_structure(self, node_id:str, repository: str, model): + """ Infer the topology of a repository from its file structure """ path = infrastructure_config.get_config()["data_root_directory"] path = path +"/"+ str(repository) print(path) + if not os.path.exists(path): raise FileNotFoundError(f"No such directory: {path}") - file_structure = {} + root = {} for filename in glob.glob(f"{path}/**", recursive=True): + parts = os.path.relpath(filename, start=path).split(os.path.sep) + current = root + for part in parts[:-1]: # Traverse/create to the last directory + if part not in current: + current[part] = {} + current = current[part] + last_part = parts[-1] if os.path.isfile(filename): - key = os.path.relpath(filename, start=path).replace(os.path.sep, "__") - file_structure[key] = (str, ...) # Assuming content as string for simplicity + current[last_part] = ("file", ...) # Placeholder for file content or metadata + elif os.path.isdir(filename): + if last_part not in current: # Only create a new directory entry if it doesn't exist + current[last_part] = {} + root_directory = await self.populate_model('/', root) - result = await infer_data_topology(str(file_structure), GitHubRepositoryModel) + # repository_metadata = await infer_data_topology(str(root), DirMetadata) + + repository_metadata = DirMetadata( + node_id="repo1", + summary="Example repository", + owner="user1", + directories=[root_directory], + documents=[], + default_relationship=Relationship(type="contained_by", source="repo1", target=node_id) + ) + + active_model = GitHubRepositoryModel( + node_id=node_id, + metadata=repository_metadata, + root_directory=root_directory + ) + + return active_model + + # print(github_repo_model) - return result def load(self, model_name: str): return self.models.get(model_name) diff --git a/poetry.lock b/poetry.lock index 53879c375..46a96ece4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -473,13 +473,13 @@ cryptography = "*" [[package]] name = "babel" -version = "2.14.0" +version = "2.15.0" description = "Internationalization utilities" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" 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