Merge remote-tracking branch 'origin/dev' into feature/cog-3014-refactor-delete-feature
This commit is contained in:
Boris Arzentar
commit
ae171877d8
18 changed files with 271 additions and 174 deletions
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@ -133,6 +133,6 @@ def create_vector_engine(
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else:
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raise EnvironmentError(
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f"Unsupported graph database provider: {vector_db_provider}. "
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f"Unsupported vector database provider: {vector_db_provider}. "
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f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['LanceDB', 'PGVector', 'neptune_analytics', 'ChromaDB'])}"
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)
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@ -1,4 +1,4 @@
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from pydantic import BaseModel
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from pydantic import BaseModel, field_validator
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from typing import Optional, Any, Dict
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@ -18,9 +18,21 @@ class Edge(BaseModel):
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# Mixed usage
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has_items: (Edge(weight=0.5, weights={"confidence": 0.9}), list[Item])
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# With edge_text for rich embedding representation
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contains: (Edge(relationship_type="contains", edge_text="relationship_name: contains; entity_description: Alice"), Entity)
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"""
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weight: Optional[float] = None
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weights: Optional[Dict[str, float]] = None
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relationship_type: Optional[str] = None
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properties: Optional[Dict[str, Any]] = None
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edge_text: Optional[str] = None
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@field_validator("edge_text", mode="before")
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@classmethod
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def ensure_edge_text(cls, v, info):
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"""Auto-populate edge_text from relationship_type if not explicitly provided."""
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if v is None and info.data.get("relationship_type"):
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return info.data["relationship_type"]
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return v
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@ -1,6 +1,6 @@
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import io
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import os.path
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from typing import BinaryIO, TypedDict
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from typing import BinaryIO, TypedDict, Optional
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from pathlib import Path
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from cognee.shared.logging_utils import get_logger
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@ -27,7 +27,7 @@ class FileMetadata(TypedDict):
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file_size: int
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async def get_file_metadata(file: BinaryIO) -> FileMetadata:
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async def get_file_metadata(file: BinaryIO, name: Optional[str] = None) -> FileMetadata:
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"""
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Retrieve metadata from a file object.
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@ -53,7 +53,7 @@ async def get_file_metadata(file: BinaryIO) -> FileMetadata:
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except io.UnsupportedOperation as error:
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logger.error(f"Error retrieving content hash for file: {file.name} \n{str(error)}\n\n")
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file_type = guess_file_type(file)
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file_type = guess_file_type(file, name)
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file_path = getattr(file, "name", None) or getattr(file, "full_name", None)
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@ -1,6 +1,9 @@
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from typing import BinaryIO
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import io
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from pathlib import Path
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from typing import BinaryIO, Optional, Any
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import filetype
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from .is_text_content import is_text_content
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from tempfile import SpooledTemporaryFile
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from filetype.types.base import Type
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class FileTypeException(Exception):
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@ -22,7 +25,7 @@ class FileTypeException(Exception):
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self.message = message
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def guess_file_type(file: BinaryIO) -> filetype.Type:
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def guess_file_type(file: BinaryIO, name: Optional[str] = None) -> filetype.Type:
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"""
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Guess the file type from the given binary file stream.
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@ -39,12 +42,23 @@ def guess_file_type(file: BinaryIO) -> filetype.Type:
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- filetype.Type: The guessed file type, represented as filetype.Type.
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"""
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# Note: If file has .txt or .text extension, consider it a plain text file as filetype.guess may not detect it properly
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# as it contains no magic number encoding
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ext = None
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if isinstance(file, str):
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ext = Path(file).suffix
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elif name is not None:
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ext = Path(name).suffix
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if ext in [".txt", ".text"]:
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file_type = Type("text/plain", "txt")
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return file_type
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file_type = filetype.guess(file)
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# If file type could not be determined consider it a plain text file as they don't have magic number encoding
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if file_type is None:
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from filetype.types.base import Type
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file_type = Type("text/plain", "txt")
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if file_type is None:
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@ -42,6 +42,7 @@ class AudioLoader(LoaderInterface):
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"audio/wav",
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"audio/amr",
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"audio/aiff",
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"audio/x-wav",
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]
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@property
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@ -1,6 +1,7 @@
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from typing import List, Union
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.engine.models.Edge import Edge
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from cognee.modules.data.processing.document_types import Document
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from cognee.modules.engine.models import Entity
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from cognee.tasks.temporal_graph.models import Event
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@ -31,6 +32,6 @@ class DocumentChunk(DataPoint):
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chunk_index: int
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cut_type: str
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is_part_of: Document
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contains: List[Union[Entity, Event]] = None
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contains: List[Union[Entity, Event, tuple[Edge, Entity]]] = None
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metadata: dict = {"index_fields": ["text"]}
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@ -171,8 +171,10 @@ class CogneeGraph(CogneeAbstractGraph):
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embedding_map = {result.payload["text"]: result.score for result in edge_distances}
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for edge in self.edges:
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relationship_type = edge.attributes.get("relationship_type")
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distance = embedding_map.get(relationship_type, None)
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edge_key = edge.attributes.get("edge_text") or edge.attributes.get(
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"relationship_type"
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)
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distance = embedding_map.get(edge_key, None)
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if distance is not None:
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edge.attributes["vector_distance"] = distance
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@ -1,5 +1,6 @@
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from typing import Optional
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from cognee.infrastructure.engine.models.Edge import Edge
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from cognee.modules.chunking.models import DocumentChunk
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from cognee.modules.engine.models import Entity, EntityType
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from cognee.modules.engine.utils import (
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@ -243,10 +244,26 @@ def _process_graph_nodes(
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ontology_relationships,
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)
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# Add entity to data chunk
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if data_chunk.contains is None:
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data_chunk.contains = []
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data_chunk.contains.append(entity_node)
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edge_text = "; ".join(
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[
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"relationship_name: contains",
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f"entity_name: {entity_node.name}",
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f"entity_description: {entity_node.description}",
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]
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)
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data_chunk.contains.append(
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(
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Edge(
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relationship_type="contains",
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edge_text=edge_text,
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),
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entity_node,
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)
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)
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def _process_graph_edges(
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@ -1,71 +1,70 @@
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import string
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from typing import List
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from collections import Counter
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from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
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from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
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def _get_top_n_frequent_words(
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text: str, stop_words: set = None, top_n: int = 3, separator: str = ", "
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) -> str:
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"""Concatenates the top N frequent words in text."""
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if stop_words is None:
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stop_words = DEFAULT_STOP_WORDS
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words = [word.lower().strip(string.punctuation) for word in text.split()]
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words = [word for word in words if word and word not in stop_words]
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top_words = [word for word, freq in Counter(words).most_common(top_n)]
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return separator.join(top_words)
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def _create_title_from_text(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
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"""Creates a title by combining first words with most frequent words from the text."""
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first_words = text.split()[:first_n_words]
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top_words = _get_top_n_frequent_words(text, top_n=top_n_words)
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return f"{' '.join(first_words)}... [{top_words}]"
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def _extract_nodes_from_edges(retrieved_edges: List[Edge]) -> dict:
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"""Creates a dictionary of nodes with their names and content."""
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nodes = {}
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for edge in retrieved_edges:
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for node in (edge.node1, edge.node2):
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if node.id in nodes:
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continue
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text = node.attributes.get("text")
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if text:
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name = _create_title_from_text(text)
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content = text
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else:
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name = node.attributes.get("name", "Unnamed Node")
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content = node.attributes.get("description", name)
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nodes[node.id] = {"node": node, "name": name, "content": content}
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return nodes
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async def resolve_edges_to_text(retrieved_edges: List[Edge]) -> str:
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"""
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Converts retrieved graph edges into a human-readable string format.
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"""Converts retrieved graph edges into a human-readable string format."""
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nodes = _extract_nodes_from_edges(retrieved_edges)
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Parameters:
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-----------
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- retrieved_edges (list): A list of edges retrieved from the graph.
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Returns:
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--------
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- str: A formatted string representation of the nodes and their connections.
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"""
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def _get_nodes(retrieved_edges: List[Edge]) -> dict:
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def _get_title(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
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def _top_n_words(text, stop_words=None, top_n=3, separator=", "):
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"""Concatenates the top N frequent words in text."""
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if stop_words is None:
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from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
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stop_words = DEFAULT_STOP_WORDS
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import string
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words = [word.lower().strip(string.punctuation) for word in text.split()]
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if stop_words:
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words = [word for word in words if word and word not in stop_words]
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from collections import Counter
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top_words = [word for word, freq in Counter(words).most_common(top_n)]
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return separator.join(top_words)
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"""Creates a title, by combining first words with most frequent words from the text."""
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first_words = text.split()[:first_n_words]
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top_words = _top_n_words(text, top_n=first_n_words)
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return f"{' '.join(first_words)}... [{top_words}]"
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"""Creates a dictionary of nodes with their names and content."""
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nodes = {}
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for edge in retrieved_edges:
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for node in (edge.node1, edge.node2):
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if node.id not in nodes:
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text = node.attributes.get("text")
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if text:
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name = _get_title(text)
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content = text
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else:
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name = node.attributes.get("name", "Unnamed Node")
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content = node.attributes.get("description", name)
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nodes[node.id] = {"node": node, "name": name, "content": content}
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return nodes
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nodes = _get_nodes(retrieved_edges)
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node_section = "\n".join(
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f"Node: {info['name']}\n__node_content_start__\n{info['content']}\n__node_content_end__\n"
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for info in nodes.values()
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)
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connection_section = "\n".join(
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f"{nodes[edge.node1.id]['name']} --[{edge.attributes['relationship_type']}]--> {nodes[edge.node2.id]['name']}"
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for edge in retrieved_edges
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)
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connections = []
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for edge in retrieved_edges:
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source_name = nodes[edge.node1.id]["name"]
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target_name = nodes[edge.node2.id]["name"]
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edge_label = edge.attributes.get("edge_text") or edge.attributes.get("relationship_type")
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connections.append(f"{source_name} --[{edge_label}]--> {target_name}")
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connection_section = "\n".join(connections)
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return f"Nodes:\n{node_section}\n\nConnections:\n{connection_section}"
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|
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@ -30,7 +30,7 @@ class BinaryData(IngestionData):
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async def ensure_metadata(self):
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if self.metadata is None:
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self.metadata = await get_file_metadata(self.data)
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self.metadata = await get_file_metadata(self.data, name=self.name)
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if self.metadata["name"] is None:
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self.metadata["name"] = self.name
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|
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@ -71,7 +71,7 @@ async def get_memory_fragment(
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await memory_fragment.project_graph_from_db(
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graph_engine,
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node_properties_to_project=properties_to_project,
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edge_properties_to_project=["relationship_name"],
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edge_properties_to_project=["relationship_name", "edge_text"],
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node_type=node_type,
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node_name=node_name,
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)
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|
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@ -8,47 +8,58 @@ logger = get_logger("index_data_points")
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async def index_data_points(data_points: list[DataPoint]):
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created_indexes = {}
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index_points = {}
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"""Index data points in the vector engine by creating embeddings for specified fields.
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Process:
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1. Groups data points into a nested dict: {type_name: {field_name: [points]}}
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2. Creates vector indexes for each (type, field) combination on first encounter
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3. Batches points per (type, field) and creates async indexing tasks
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4. Executes all indexing tasks in parallel for efficient embedding generation
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Args:
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data_points: List of DataPoint objects to index. Each DataPoint's metadata must
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contain an 'index_fields' list specifying which fields to embed.
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Returns:
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The original data_points list.
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"""
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data_points_by_type = {}
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vector_engine = get_vector_engine()
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for data_point in data_points:
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data_point_type = type(data_point)
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type_name = data_point_type.__name__
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for field_name in data_point.metadata["index_fields"]:
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if getattr(data_point, field_name, None) is None:
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continue
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index_name = f"{data_point_type.__name__}_{field_name}"
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if type_name not in data_points_by_type:
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data_points_by_type[type_name] = {}
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if index_name not in created_indexes:
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await vector_engine.create_vector_index(data_point_type.__name__, field_name)
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created_indexes[index_name] = True
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if index_name not in index_points:
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index_points[index_name] = []
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if field_name not in data_points_by_type[type_name]:
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await vector_engine.create_vector_index(type_name, field_name)
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data_points_by_type[type_name][field_name] = []
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indexed_data_point = data_point.model_copy()
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indexed_data_point.metadata["index_fields"] = [field_name]
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index_points[index_name].append(indexed_data_point)
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data_points_by_type[type_name][field_name].append(indexed_data_point)
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tasks: list[asyncio.Task] = []
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batch_size = vector_engine.embedding_engine.get_batch_size()
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for index_name_and_field, points in index_points.items():
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first = index_name_and_field.index("_")
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index_name = index_name_and_field[:first]
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field_name = index_name_and_field[first + 1 :]
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batches = (
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(type_name, field_name, points[i : i + batch_size])
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for type_name, fields in data_points_by_type.items()
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for field_name, points in fields.items()
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for i in range(0, len(points), batch_size)
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)
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# Create embedding requests per batch to run in parallel later
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for i in range(0, len(points), batch_size):
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batch = points[i : i + batch_size]
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tasks.append(
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asyncio.create_task(vector_engine.index_data_points(index_name, field_name, batch))
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)
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tasks = [
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asyncio.create_task(vector_engine.index_data_points(type_name, field_name, batch_points))
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for type_name, field_name, batch_points in batches
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]
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# Run all embedding requests in parallel
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await asyncio.gather(*tasks)
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return data_points
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|
|
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|
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@ -1,17 +1,44 @@
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import asyncio
|
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from collections import Counter
|
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from typing import Optional, Dict, Any, List, Tuple, Union
|
||||
|
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from cognee.modules.engine.utils.generate_edge_id import generate_edge_id
|
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from cognee.shared.logging_utils import get_logger
|
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from collections import Counter
|
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from typing import Optional, Dict, Any, List, Tuple, Union
|
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from cognee.infrastructure.databases.vector import get_vector_engine
|
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from cognee.infrastructure.databases.graph import get_graph_engine
|
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from cognee.modules.graph.models.EdgeType import EdgeType
|
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from cognee.infrastructure.databases.graph.graph_db_interface import EdgeData
|
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from cognee.tasks.storage.index_data_points import index_data_points
|
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|
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logger = get_logger()
|
||||
|
||||
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def _get_edge_text(item: dict) -> str:
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||||
"""Extract edge text for embedding - prefers edge_text field with fallback."""
|
||||
if "edge_text" in item:
|
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return item["edge_text"]
|
||||
|
||||
if "relationship_name" in item:
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return item["relationship_name"]
|
||||
|
||||
return ""
|
||||
|
||||
|
||||
def create_edge_type_datapoints(edges_data) -> list[EdgeType]:
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||||
"""Transform raw edge data into EdgeType datapoints."""
|
||||
edge_texts = [
|
||||
_get_edge_text(item)
|
||||
for edge in edges_data
|
||||
for item in edge
|
||||
if isinstance(item, dict) and "relationship_name" in item
|
||||
]
|
||||
|
||||
edge_types = Counter(edge_texts)
|
||||
|
||||
return [
|
||||
EdgeType(id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count)
|
||||
for text, count in edge_types.items()
|
||||
]
|
||||
|
||||
|
||||
async def index_graph_edges(
|
||||
edges_data: Union[List[EdgeData], List[Tuple[str, str, str, Optional[Dict[str, Any]]]]] = None,
|
||||
):
|
||||
|
|
@ -23,24 +50,17 @@ async def index_graph_edges(
|
|||
the `relationship_name` field.
|
||||
|
||||
Steps:
|
||||
1. Initialize the vector engine and graph engine.
|
||||
2. Retrieve graph edge data and count relationship types (`relationship_name`).
|
||||
3. Create vector indexes for `relationship_name` if they don't exist.
|
||||
4. Transform the counted relationships into `EdgeType` objects.
|
||||
5. Index the transformed data points in the vector engine.
|
||||
1. Initialize the graph engine if needed and retrieve edge data.
|
||||
2. Transform edge data into EdgeType datapoints.
|
||||
3. Index the EdgeType datapoints using the standard indexing function.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If initialization of the vector engine or graph engine fails.
|
||||
RuntimeError: If initialization of the graph engine fails.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
try:
|
||||
created_indexes = {}
|
||||
index_points = {}
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
|
||||
if edges_data is None:
|
||||
graph_engine = await get_graph_engine()
|
||||
_, edges_data = await graph_engine.get_graph_data()
|
||||
|
|
@ -51,47 +71,7 @@ async def index_graph_edges(
|
|||
logger.error("Failed to initialize engines: %s", e)
|
||||
raise RuntimeError("Initialization error") from e
|
||||
|
||||
edge_types = Counter(
|
||||
item.get("relationship_name")
|
||||
for edge in edges_data
|
||||
for item in edge
|
||||
if isinstance(item, dict) and "relationship_name" in item
|
||||
)
|
||||
|
||||
for text, count in edge_types.items():
|
||||
edge = EdgeType(
|
||||
id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count
|
||||
)
|
||||
data_point_type = type(edge)
|
||||
|
||||
for field_name in edge.metadata["index_fields"]:
|
||||
index_name = f"{data_point_type.__name__}.{field_name}"
|
||||
|
||||
if index_name not in created_indexes:
|
||||
await vector_engine.create_vector_index(data_point_type.__name__, field_name)
|
||||
created_indexes[index_name] = True
|
||||
|
||||
if index_name not in index_points:
|
||||
index_points[index_name] = []
|
||||
|
||||
indexed_data_point = edge.model_copy()
|
||||
indexed_data_point.metadata["index_fields"] = [field_name]
|
||||
index_points[index_name].append(indexed_data_point)
|
||||
|
||||
# Get maximum batch size for embedding model
|
||||
batch_size = vector_engine.embedding_engine.get_batch_size()
|
||||
tasks: list[asyncio.Task] = []
|
||||
|
||||
for index_name, indexable_points in index_points.items():
|
||||
index_name, field_name = index_name.split(".")
|
||||
|
||||
# Create embedding tasks to run in parallel later
|
||||
for start in range(0, len(indexable_points), batch_size):
|
||||
batch = indexable_points[start : start + batch_size]
|
||||
|
||||
tasks.append(vector_engine.index_data_points(index_name, field_name, batch))
|
||||
|
||||
# Start all embedding tasks and wait for completion
|
||||
await asyncio.gather(*tasks)
|
||||
edge_type_datapoints = create_edge_type_datapoints(edges_data)
|
||||
await index_data_points(edge_type_datapoints)
|
||||
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -52,6 +52,33 @@ async def test_edge_ingestion():
|
|||
|
||||
edge_type_counts = Counter(edge_type[2] for edge_type in graph[1])
|
||||
|
||||
"Tests edge_text presence and format"
|
||||
contains_edges = [edge for edge in graph[1] if edge[2] == "contains"]
|
||||
assert len(contains_edges) > 0, "Expected at least one contains edge for edge_text verification"
|
||||
|
||||
edge_properties = contains_edges[0][3]
|
||||
assert "edge_text" in edge_properties, "Expected edge_text in edge properties"
|
||||
|
||||
edge_text = edge_properties["edge_text"]
|
||||
assert "relationship_name: contains" in edge_text, (
|
||||
f"Expected 'relationship_name: contains' in edge_text, got: {edge_text}"
|
||||
)
|
||||
assert "entity_name:" in edge_text, f"Expected 'entity_name:' in edge_text, got: {edge_text}"
|
||||
assert "entity_description:" in edge_text, (
|
||||
f"Expected 'entity_description:' in edge_text, got: {edge_text}"
|
||||
)
|
||||
|
||||
all_edge_texts = [
|
||||
edge[3].get("edge_text", "") for edge in contains_edges if "edge_text" in edge[3]
|
||||
]
|
||||
expected_entities = ["dave", "ana", "bob", "dexter", "apples", "cognee"]
|
||||
found_entity = any(
|
||||
any(entity in text.lower() for entity in expected_entities) for text in all_edge_texts
|
||||
)
|
||||
assert found_entity, (
|
||||
f"Expected to find at least one entity name in edge_text: {all_edge_texts[:3]}"
|
||||
)
|
||||
|
||||
"Tests the presence of basic nested edges"
|
||||
for basic_nested_edge in basic_nested_edges:
|
||||
assert edge_type_counts.get(basic_nested_edge, 0) >= 1, (
|
||||
|
|
|
|||
|
|
@ -0,0 +1,27 @@
|
|||
import pytest
|
||||
from unittest.mock import AsyncMock, patch, MagicMock
|
||||
from cognee.tasks.storage.index_data_points import index_data_points
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
|
||||
|
||||
class TestDataPoint(DataPoint):
|
||||
name: str
|
||||
metadata: dict = {"index_fields": ["name"]}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_index_data_points_calls_vector_engine():
|
||||
"""Test that index_data_points creates vector index and indexes data."""
|
||||
data_points = [TestDataPoint(name="test1")]
|
||||
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
|
||||
|
||||
with patch.dict(
|
||||
index_data_points.__globals__,
|
||||
{"get_vector_engine": lambda: mock_vector_engine},
|
||||
):
|
||||
await index_data_points(data_points)
|
||||
|
||||
assert mock_vector_engine.create_vector_index.await_count >= 1
|
||||
assert mock_vector_engine.index_data_points.await_count >= 1
|
||||
|
|
@ -5,8 +5,7 @@ from cognee.tasks.storage.index_graph_edges import index_graph_edges
|
|||
|
||||
@pytest.mark.asyncio
|
||||
async def test_index_graph_edges_success():
|
||||
"""Test that index_graph_edges uses the index datapoints and creates vector index."""
|
||||
# Create the mocks for the graph and vector engines.
|
||||
"""Test that index_graph_edges retrieves edges and delegates to index_data_points."""
|
||||
mock_graph_engine = AsyncMock()
|
||||
mock_graph_engine.get_graph_data.return_value = (
|
||||
None,
|
||||
|
|
@ -15,26 +14,23 @@ async def test_index_graph_edges_success():
|
|||
[{"relationship_name": "rel2"}],
|
||||
],
|
||||
)
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
|
||||
mock_index_data_points = AsyncMock()
|
||||
|
||||
# Patch the globals of the function so that when it does:
|
||||
# vector_engine = get_vector_engine()
|
||||
# graph_engine = await get_graph_engine()
|
||||
# it uses the mocked versions.
|
||||
with patch.dict(
|
||||
index_graph_edges.__globals__,
|
||||
{
|
||||
"get_graph_engine": AsyncMock(return_value=mock_graph_engine),
|
||||
"get_vector_engine": lambda: mock_vector_engine,
|
||||
"index_data_points": mock_index_data_points,
|
||||
},
|
||||
):
|
||||
await index_graph_edges()
|
||||
|
||||
# Assertions on the mock calls.
|
||||
mock_graph_engine.get_graph_data.assert_awaited_once()
|
||||
assert mock_vector_engine.create_vector_index.await_count == 1
|
||||
assert mock_vector_engine.index_data_points.await_count == 1
|
||||
mock_index_data_points.assert_awaited_once()
|
||||
|
||||
call_args = mock_index_data_points.call_args[0][0]
|
||||
assert len(call_args) == 2
|
||||
assert all(hasattr(item, "relationship_name") for item in call_args)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
@ -42,20 +38,22 @@ async def test_index_graph_edges_no_relationships():
|
|||
"""Test that index_graph_edges handles empty relationships correctly."""
|
||||
mock_graph_engine = AsyncMock()
|
||||
mock_graph_engine.get_graph_data.return_value = (None, [])
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_index_data_points = AsyncMock()
|
||||
|
||||
with patch.dict(
|
||||
index_graph_edges.__globals__,
|
||||
{
|
||||
"get_graph_engine": AsyncMock(return_value=mock_graph_engine),
|
||||
"get_vector_engine": lambda: mock_vector_engine,
|
||||
"index_data_points": mock_index_data_points,
|
||||
},
|
||||
):
|
||||
await index_graph_edges()
|
||||
|
||||
mock_graph_engine.get_graph_data.assert_awaited_once()
|
||||
mock_vector_engine.create_vector_index.assert_not_awaited()
|
||||
mock_vector_engine.index_data_points.assert_not_awaited()
|
||||
mock_index_data_points.assert_awaited_once()
|
||||
|
||||
call_args = mock_index_data_points.call_args[0][0]
|
||||
assert len(call_args) == 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
[project]
|
||||
name = "cognee"
|
||||
|
||||
version = "0.3.8"
|
||||
version = "0.3.9"
|
||||
description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
|
||||
authors = [
|
||||
{ name = "Vasilije Markovic" },
|
||||
|
|
|
|||
10
uv.lock
generated
10
uv.lock
generated
|
|
@ -929,7 +929,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "cognee"
|
||||
version = "0.3.8"
|
||||
version = "0.3.9"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "aiofiles" },
|
||||
|
|
@ -6226,8 +6226,10 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/8c/df/16848771155e7c419c60afeb24950b8aaa3ab09c0a091ec3ccca26a574d0/psycopg2_binary-2.9.11-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c47676e5b485393f069b4d7a811267d3168ce46f988fa602658b8bb901e9e64d", size = 4410873, upload-time = "2025-10-10T11:10:38.951Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/43/79/5ef5f32621abd5a541b89b04231fe959a9b327c874a1d41156041c75494b/psycopg2_binary-2.9.11-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:a28d8c01a7b27a1e3265b11250ba7557e5f72b5ee9e5f3a2fa8d2949c29bf5d2", size = 4468016, upload-time = "2025-10-10T11:10:43.319Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f0/9b/d7542d0f7ad78f57385971f426704776d7b310f5219ed58da5d605b1892e/psycopg2_binary-2.9.11-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5f3f2732cf504a1aa9e9609d02f79bea1067d99edf844ab92c247bbca143303b", size = 4164996, upload-time = "2025-10-10T11:10:46.705Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/14/ed/e409388b537fa7414330687936917c522f6a77a13474e4238219fcfd9a84/psycopg2_binary-2.9.11-cp310-cp310-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:865f9945ed1b3950d968ec4690ce68c55019d79e4497366d36e090327ce7db14", size = 3981881, upload-time = "2025-10-30T02:54:57.182Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/30/50e330e63bb05efc6fa7c1447df3e08954894025ca3dcb396ecc6739bc26/psycopg2_binary-2.9.11-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:91537a8df2bde69b1c1db01d6d944c831ca793952e4f57892600e96cee95f2cd", size = 3650857, upload-time = "2025-10-10T11:10:50.112Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f0/e0/4026e4c12bb49dd028756c5b0bc4c572319f2d8f1c9008e0dad8cc9addd7/psycopg2_binary-2.9.11-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:4dca1f356a67ecb68c81a7bc7809f1569ad9e152ce7fd02c2f2036862ca9f66b", size = 3296063, upload-time = "2025-10-10T11:10:54.089Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2c/34/eb172be293c886fef5299fe5c3fcf180a05478be89856067881007934a7c/psycopg2_binary-2.9.11-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:0da4de5c1ac69d94ed4364b6cbe7190c1a70d325f112ba783d83f8440285f152", size = 3043464, upload-time = "2025-10-30T02:55:02.483Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/18/1c/532c5d2cb11986372f14b798a95f2eaafe5779334f6a80589a68b5fcf769/psycopg2_binary-2.9.11-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37d8412565a7267f7d79e29ab66876e55cb5e8e7b3bbf94f8206f6795f8f7e7e", size = 3345378, upload-time = "2025-10-10T11:11:01.039Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/70/e7/de420e1cf16f838e1fa17b1120e83afff374c7c0130d088dba6286fcf8ea/psycopg2_binary-2.9.11-cp310-cp310-win_amd64.whl", hash = "sha256:c665f01ec8ab273a61c62beeb8cce3014c214429ced8a308ca1fc410ecac3a39", size = 2713904, upload-time = "2025-10-10T11:11:04.81Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c7/ae/8d8266f6dd183ab4d48b95b9674034e1b482a3f8619b33a0d86438694577/psycopg2_binary-2.9.11-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0e8480afd62362d0a6a27dd09e4ca2def6fa50ed3a4e7c09165266106b2ffa10", size = 3756452, upload-time = "2025-10-10T11:11:11.583Z" },
|
||||
|
|
@ -6235,8 +6237,10 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/48/89/3fdb5902bdab8868bbedc1c6e6023a4e08112ceac5db97fc2012060e0c9a/psycopg2_binary-2.9.11-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2e164359396576a3cc701ba8af4751ae68a07235d7a380c631184a611220d9a4", size = 4410955, upload-time = "2025-10-10T11:11:21.21Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ce/24/e18339c407a13c72b336e0d9013fbbbde77b6fd13e853979019a1269519c/psycopg2_binary-2.9.11-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:d57c9c387660b8893093459738b6abddbb30a7eab058b77b0d0d1c7d521ddfd7", size = 4468007, upload-time = "2025-10-10T11:11:24.831Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/91/7e/b8441e831a0f16c159b5381698f9f7f7ed54b77d57bc9c5f99144cc78232/psycopg2_binary-2.9.11-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2c226ef95eb2250974bf6fa7a842082b31f68385c4f3268370e3f3870e7859ee", size = 4165012, upload-time = "2025-10-10T11:11:29.51Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/0d/61/4aa89eeb6d751f05178a13da95516c036e27468c5d4d2509bb1e15341c81/psycopg2_binary-2.9.11-cp311-cp311-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a311f1edc9967723d3511ea7d2708e2c3592e3405677bf53d5c7246753591fbb", size = 3981881, upload-time = "2025-10-30T02:55:07.332Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/76/a1/2f5841cae4c635a9459fe7aca8ed771336e9383b6429e05c01267b0774cf/psycopg2_binary-2.9.11-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ebb415404821b6d1c47353ebe9c8645967a5235e6d88f914147e7fd411419e6f", size = 3650985, upload-time = "2025-10-10T11:11:34.975Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/84/74/4defcac9d002bca5709951b975173c8c2fa968e1a95dc713f61b3a8d3b6a/psycopg2_binary-2.9.11-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:f07c9c4a5093258a03b28fab9b4f151aa376989e7f35f855088234e656ee6a94", size = 3296039, upload-time = "2025-10-10T11:11:40.432Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/6d/c2/782a3c64403d8ce35b5c50e1b684412cf94f171dc18111be8c976abd2de1/psycopg2_binary-2.9.11-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:00ce1830d971f43b667abe4a56e42c1e2d594b32da4802e44a73bacacb25535f", size = 3043477, upload-time = "2025-10-30T02:55:11.182Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/c8/31/36a1d8e702aa35c38fc117c2b8be3f182613faa25d794b8aeaab948d4c03/psycopg2_binary-2.9.11-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:cffe9d7697ae7456649617e8bb8d7a45afb71cd13f7ab22af3e5c61f04840908", size = 3345842, upload-time = "2025-10-10T11:11:45.366Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6e/b4/a5375cda5b54cb95ee9b836930fea30ae5a8f14aa97da7821722323d979b/psycopg2_binary-2.9.11-cp311-cp311-win_amd64.whl", hash = "sha256:304fd7b7f97eef30e91b8f7e720b3db75fee010b520e434ea35ed1ff22501d03", size = 2713894, upload-time = "2025-10-10T11:11:48.775Z" },
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||||
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@ -6244,8 +6248,10 @@ wheels = [
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@ -6253,8 +6259,10 @@ wheels = [
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]
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|
|
|
|||
Loading…
Add table
Reference in a new issue