feat: remove get_distance_from_collection_names and adapt search (#766)
<!-- .github/pull_request_template.md --> ## Description <!-- Provide a clear description of the changes in this PR --> ## 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.
This commit is contained in:
Boris
GitHub
parent
ad943d0680
commit
cd9c4897a4
19 changed files with 500 additions and 641 deletions
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@ -6,8 +6,9 @@ from chromadb import AsyncHttpClient, Settings
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from cognee.exceptions import InvalidValueError
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.storage.utils import get_own_properties
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from cognee.infrastructure.engine.utils import parse_id
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.engine.utils import parse_id
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from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
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from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
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from ..embeddings.EmbeddingEngine import EmbeddingEngine
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@ -108,9 +109,7 @@ class ChromaDBAdapter(VectorDBInterface):
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return await self.embedding_engine.embed_text(data)
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async def has_collection(self, collection_name: str) -> bool:
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client = await self.get_connection()
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collections = await client.list_collections()
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# In ChromaDB v0.6.0, list_collections returns collection names directly
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collections = await self.get_collection_names()
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return collection_name in collections
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async def create_collection(self, collection_name: str, payload_schema=None):
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@ -119,13 +118,17 @@ class ChromaDBAdapter(VectorDBInterface):
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if not await self.has_collection(collection_name):
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await client.create_collection(name=collection_name, metadata={"hnsw:space": "cosine"})
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async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
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client = await self.get_connection()
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async def get_collection(self, collection_name: str) -> AsyncHttpClient:
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if not await self.has_collection(collection_name):
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await self.create_collection(collection_name)
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raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
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collection = await client.get_collection(collection_name)
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client = await self.get_connection()
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return await client.get_collection(collection_name)
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async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
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await self.create_collection(collection_name)
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collection = await self.get_collection(collection_name)
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texts = [DataPoint.get_embeddable_data(data_point) for data_point in data_points]
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embeddings = await self.embed_data(texts)
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@ -161,8 +164,7 @@ class ChromaDBAdapter(VectorDBInterface):
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async def retrieve(self, collection_name: str, data_point_ids: list[str]):
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"""Retrieve data points by their IDs from a collection."""
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client = await self.get_connection()
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collection = await client.get_collection(collection_name)
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collection = await self.get_collection(collection_name)
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results = await collection.get(ids=data_point_ids, include=["metadatas"])
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return [
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@ -174,62 +176,12 @@ class ChromaDBAdapter(VectorDBInterface):
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for id, metadata in zip(results["ids"], results["metadatas"])
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]
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async def get_distance_from_collection_elements(
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self, collection_name: str, query_text: str = None, query_vector: List[float] = None
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):
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"""Calculate distance between query and all elements in a collection."""
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if query_text is None and query_vector is None:
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raise InvalidValueError(message="One of query_text or query_vector must be provided!")
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if query_text and not query_vector:
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query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
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client = await self.get_connection()
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try:
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collection = await client.get_collection(collection_name)
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collection_count = await collection.count()
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results = await collection.query(
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query_embeddings=[query_vector],
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include=["metadatas", "distances"],
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n_results=collection_count,
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)
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result_values = []
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for i, (id, metadata, distance) in enumerate(
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zip(results["ids"][0], results["metadatas"][0], results["distances"][0])
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):
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result_values.append(
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{
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"id": parse_id(id),
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"payload": restore_data_from_chroma(metadata),
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"_distance": distance,
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}
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)
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normalized_values = normalize_distances(result_values)
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scored_results = []
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for i, result in enumerate(result_values):
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scored_results.append(
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ScoredResult(
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id=result["id"],
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payload=result["payload"],
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score=normalized_values[i],
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)
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)
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return scored_results
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except Exception:
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return []
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async def search(
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self,
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collection_name: str,
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query_text: str = None,
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query_vector: List[float] = None,
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limit: int = 5,
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limit: int = 15,
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with_vector: bool = False,
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normalized: bool = True,
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):
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@ -241,8 +193,10 @@ class ChromaDBAdapter(VectorDBInterface):
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query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
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try:
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client = await self.get_connection()
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collection = await client.get_collection(collection_name)
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collection = await self.get_collection(collection_name)
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if limit == 0:
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limit = await collection.count()
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results = await collection.query(
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query_embeddings=[query_vector],
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@ -296,8 +250,7 @@ class ChromaDBAdapter(VectorDBInterface):
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"""Perform multiple searches in a single request for efficiency."""
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query_vectors = await self.embed_data(query_texts)
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client = await self.get_connection()
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collection = await client.get_collection(collection_name)
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collection = await self.get_collection(collection_name)
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results = await collection.query(
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query_embeddings=query_vectors,
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@ -346,15 +299,14 @@ class ChromaDBAdapter(VectorDBInterface):
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async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
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"""Remove data points from a collection by their IDs."""
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client = await self.get_connection()
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collection = await client.get_collection(collection_name)
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collection = await self.get_collection(collection_name)
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await collection.delete(ids=data_point_ids)
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return True
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async def prune(self):
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"""Delete all collections in the ChromaDB database."""
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client = await self.get_connection()
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collections = await client.list_collections()
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collections = await self.list_collections()
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for collection_name in collections:
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await client.delete_collection(collection_name)
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return True
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@ -362,4 +314,8 @@ class ChromaDBAdapter(VectorDBInterface):
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async def get_collection_names(self):
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"""Get a list of all collection names in the database."""
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client = await self.get_connection()
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return await client.list_collections()
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collections = await client.list_collections()
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return [
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collection.name if hasattr(collection, "name") else collection["name"]
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for collection in collections
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]
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@ -1,6 +1,5 @@
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import asyncio
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from typing import Generic, List, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
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import lancedb
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from lancedb.pydantic import LanceModel, Vector
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from pydantic import BaseModel
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@ -76,9 +75,14 @@ class LanceDBAdapter(VectorDBInterface):
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exist_ok=True,
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)
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async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
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connection = await self.get_connection()
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async def get_collection(self, collection_name: str):
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if not await self.has_collection(collection_name):
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raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
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connection = await self.get_connection()
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return await connection.open_table(collection_name)
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async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
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payload_schema = type(data_points[0])
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if not await self.has_collection(collection_name):
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@ -87,7 +91,7 @@ class LanceDBAdapter(VectorDBInterface):
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payload_schema,
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)
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collection = await connection.open_table(collection_name)
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collection = await self.get_collection(collection_name)
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data_vectors = await self.embed_data(
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[DataPoint.get_embeddable_data(data_point) for data_point in data_points]
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@ -125,8 +129,7 @@ class LanceDBAdapter(VectorDBInterface):
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)
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async def retrieve(self, collection_name: str, data_point_ids: list[str]):
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connection = await self.get_connection()
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collection = await connection.open_table(collection_name)
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collection = await self.get_collection(collection_name)
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if len(data_point_ids) == 1:
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results = await collection.query().where(f"id = '{data_point_ids[0]}'").to_pandas()
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@ -142,48 +145,12 @@ class LanceDBAdapter(VectorDBInterface):
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for result in results.to_dict("index").values()
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]
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async def get_distance_from_collection_elements(
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self, collection_name: str, query_text: str = None, query_vector: List[float] = None
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):
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if query_text is None and query_vector is None:
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raise InvalidValueError(message="One of query_text or query_vector must be provided!")
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if query_text and not query_vector:
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query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
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connection = await self.get_connection()
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try:
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collection = await connection.open_table(collection_name)
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collection_size = await collection.count_rows()
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results = (
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await collection.vector_search(query_vector).limit(collection_size).to_pandas()
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)
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result_values = list(results.to_dict("index").values())
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normalized_values = normalize_distances(result_values)
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return [
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ScoredResult(
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id=parse_id(result["id"]),
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payload=result["payload"],
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score=normalized_values[value_index],
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)
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for value_index, result in enumerate(result_values)
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]
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except ValueError:
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# Ignore if collection doesn't exist
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return []
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async def search(
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self,
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collection_name: str,
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query_text: str = None,
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query_vector: List[float] = None,
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limit: int = 5,
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limit: int = 15,
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with_vector: bool = False,
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normalized: bool = True,
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):
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@ -193,12 +160,10 @@ class LanceDBAdapter(VectorDBInterface):
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if query_text and not query_vector:
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query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
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connection = await self.get_connection()
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collection = await self.get_collection(collection_name)
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try:
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collection = await connection.open_table(collection_name)
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except ValueError:
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raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
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if limit == 0:
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limit = await collection.count_rows()
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results = await collection.vector_search(query_vector).limit(limit).to_pandas()
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@ -242,8 +207,7 @@ class LanceDBAdapter(VectorDBInterface):
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def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
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@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
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async def _delete_data_points():
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connection = await self.get_connection()
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collection = await connection.open_table(collection_name)
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collection = await self.get_collection(collection_name)
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# Delete one at a time to avoid commit conflicts
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for data_point_id in data_point_ids:
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@ -288,7 +252,7 @@ class LanceDBAdapter(VectorDBInterface):
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collection_names = await connection.table_names()
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for collection_name in collection_names:
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collection = await connection.open_table(collection_name)
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collection = await self.get_collection(collection_name)
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await collection.delete("id IS NOT NULL")
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await connection.drop_table(collection_name)
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@ -1,11 +1,12 @@
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from __future__ import annotations
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import asyncio
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from cognee.shared.logging_utils import get_logger
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from uuid import UUID
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from typing import List, Optional
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from cognee.shared.logging_utils import get_logger
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.engine.utils import parse_id
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from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
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from ..embeddings.EmbeddingEngine import EmbeddingEngine
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from ..models.ScoredResult import ScoredResult
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@ -96,7 +97,7 @@ class MilvusAdapter(VectorDBInterface):
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raise e
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async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
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from pymilvus import MilvusException
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from pymilvus import MilvusException, exceptions
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client = self.get_milvus_client()
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data_vectors = await self.embed_data(
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@ -118,6 +119,10 @@ class MilvusAdapter(VectorDBInterface):
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f"Inserted {result.get('insert_count', 0)} data points into collection '{collection_name}'."
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)
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return result
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except exceptions.CollectionNotExistException as error:
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raise CollectionNotFoundError(
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f"Collection '{collection_name}' does not exist!"
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) from error
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except MilvusException as e:
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logger.error(
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f"Error inserting data points into collection '{collection_name}': {str(e)}"
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@ -140,8 +145,8 @@ class MilvusAdapter(VectorDBInterface):
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collection_name = f"{index_name}_{index_property_name}"
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await self.create_data_points(collection_name, formatted_data_points)
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async def retrieve(self, collection_name: str, data_point_ids: list[str]):
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from pymilvus import MilvusException
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async def retrieve(self, collection_name: str, data_point_ids: list[UUID]):
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from pymilvus import MilvusException, exceptions
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client = self.get_milvus_client()
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try:
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@ -153,6 +158,10 @@ class MilvusAdapter(VectorDBInterface):
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output_fields=["*"],
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)
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return results
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except exceptions.CollectionNotExistException as error:
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raise CollectionNotFoundError(
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f"Collection '{collection_name}' does not exist!"
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) from error
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except MilvusException as e:
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logger.error(
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f"Error retrieving data points from collection '{collection_name}': {str(e)}"
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@ -164,10 +173,10 @@ class MilvusAdapter(VectorDBInterface):
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collection_name: str,
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query_text: Optional[str] = None,
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query_vector: Optional[List[float]] = None,
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limit: int = 5,
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limit: int = 15,
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with_vector: bool = False,
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):
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from pymilvus import MilvusException
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from pymilvus import MilvusException, exceptions
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client = self.get_milvus_client()
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if query_text is None and query_vector is None:
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@ -184,7 +193,7 @@ class MilvusAdapter(VectorDBInterface):
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collection_name=collection_name,
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data=[query_vector],
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anns_field="vector",
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limit=limit,
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limit=limit if limit > 0 else None,
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output_fields=output_fields,
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search_params={
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"metric_type": "COSINE",
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@ -199,6 +208,10 @@ class MilvusAdapter(VectorDBInterface):
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)
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for result in results[0]
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]
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except exceptions.CollectionNotExistException as error:
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raise CollectionNotFoundError(
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f"Collection '{collection_name}' does not exist!"
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) from error
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except MilvusException as e:
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logger.error(f"Error during search in collection '{collection_name}': {str(e)}")
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raise e
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@ -220,7 +233,7 @@ class MilvusAdapter(VectorDBInterface):
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]
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)
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async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
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async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]):
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from pymilvus import MilvusException
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client = self.get_milvus_client()
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|
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@ -7,19 +7,18 @@ from sqlalchemy import JSON, Column, Table, select, delete, MetaData
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from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
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from cognee.exceptions import InvalidValueError
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from cognee.infrastructure.databases.exceptions import EntityNotFoundError
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from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.engine.utils import parse_id
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from cognee.infrastructure.databases.relational import get_relational_engine
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from ...relational.ModelBase import Base
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from ...relational.sqlalchemy.SqlAlchemyAdapter import SQLAlchemyAdapter
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from ..embeddings.EmbeddingEngine import EmbeddingEngine
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from ..models.ScoredResult import ScoredResult
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from ..vector_db_interface import VectorDBInterface
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from .serialize_data import serialize_data
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from ..utils import normalize_distances
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from ..models.ScoredResult import ScoredResult
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from ..exceptions import CollectionNotFoundError
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from ..vector_db_interface import VectorDBInterface
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from ..embeddings.EmbeddingEngine import EmbeddingEngine
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from .serialize_data import serialize_data
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class IndexSchema(DataPoint):
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@ -203,60 +202,12 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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for result in results
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]
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async def get_distance_from_collection_elements(
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self,
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collection_name: str,
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query_text: str = None,
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query_vector: List[float] = None,
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with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_text and not query_vector:
|
||||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
try:
|
||||
# Get PGVectorDataPoint Table from database
|
||||
PGVectorDataPoint = await self.get_table(collection_name)
|
||||
|
||||
# Use async session to connect to the database
|
||||
async with self.get_async_session() as session:
|
||||
# Find closest vectors to query_vector
|
||||
closest_items = await session.execute(
|
||||
select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label(
|
||||
"similarity"
|
||||
),
|
||||
).order_by("similarity")
|
||||
)
|
||||
|
||||
vector_list = []
|
||||
|
||||
# Extract distances and find min/max for normalization
|
||||
for vector in closest_items:
|
||||
# TODO: Add normalization of similarity score
|
||||
vector_list.append(vector)
|
||||
|
||||
# Create and return ScoredResult objects
|
||||
return [
|
||||
ScoredResult(id=parse_id(str(row.id)), payload=row.payload, score=row.similarity)
|
||||
for row in vector_list
|
||||
]
|
||||
except EntityNotFoundError:
|
||||
# Ignore if collection does not exist
|
||||
return []
|
||||
except CollectionNotFoundError:
|
||||
# Ignore if collection does not exist
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 5,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
|
|
@ -273,20 +224,21 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
|
||||
# Use async session to connect to the database
|
||||
async with self.get_async_session() as session:
|
||||
query = select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label("similarity"),
|
||||
).order_by("similarity")
|
||||
|
||||
if limit > 0:
|
||||
query = query.limit(limit)
|
||||
|
||||
# Find closest vectors to query_vector
|
||||
closest_items = await session.execute(
|
||||
select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label("similarity"),
|
||||
)
|
||||
.order_by("similarity")
|
||||
.limit(limit)
|
||||
)
|
||||
closest_items = await session.execute(query)
|
||||
|
||||
vector_list = []
|
||||
|
||||
# Extract distances and find min/max for normalization
|
||||
for vector in closest_items:
|
||||
for vector in closest_items.all():
|
||||
vector_list.append(
|
||||
{
|
||||
"id": parse_id(str(vector.id)),
|
||||
|
|
@ -295,6 +247,9 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
}
|
||||
)
|
||||
|
||||
if len(vector_list) == 0:
|
||||
return []
|
||||
|
||||
# Normalize vector distance and add this as score information to vector_list
|
||||
normalized_values = normalize_distances(vector_list)
|
||||
for i in range(0, len(normalized_values)):
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
from cognee.shared.logging_utils import get_logger
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from qdrant_client import AsyncQdrantClient, models
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..vector_db_interface import VectorDBInterface
|
||||
|
|
@ -97,6 +97,8 @@ class QDrantAdapter(VectorDBInterface):
|
|||
await client.close()
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
|
||||
from qdrant_client.http.exceptions import UnexpectedResponse
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
data_vectors = await self.embed_data(
|
||||
|
|
@ -114,6 +116,13 @@ class QDrantAdapter(VectorDBInterface):
|
|||
|
||||
try:
|
||||
client.upload_points(collection_name=collection_name, points=points)
|
||||
except UnexpectedResponse as error:
|
||||
if "Collection not found" in str(error):
|
||||
raise CollectionNotFoundError(
|
||||
message=f"Collection {collection_name} not found!"
|
||||
) from error
|
||||
else:
|
||||
raise error
|
||||
except Exception as error:
|
||||
logger.error("Error uploading data points to Qdrant: %s", str(error))
|
||||
raise error
|
||||
|
|
@ -143,19 +152,22 @@ class QDrantAdapter(VectorDBInterface):
|
|||
await client.close()
|
||||
return results
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
):
|
||||
from qdrant_client.http.exceptions import UnexpectedResponse
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
try:
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
results = await client.search(
|
||||
collection_name=collection_name,
|
||||
query_vector=models.NamedVector(
|
||||
|
|
@ -164,9 +176,12 @@ class QDrantAdapter(VectorDBInterface):
|
|||
if query_vector is not None
|
||||
else (await self.embed_data([query_text]))[0],
|
||||
),
|
||||
limit=limit if limit > 0 else None,
|
||||
with_vectors=with_vector,
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(result.id),
|
||||
|
|
@ -178,51 +193,16 @@ class QDrantAdapter(VectorDBInterface):
|
|||
)
|
||||
for result in results
|
||||
]
|
||||
except ValueError:
|
||||
# Ignore if the collection doesn't exist
|
||||
return []
|
||||
except UnexpectedResponse as error:
|
||||
if "Collection not found" in str(error):
|
||||
raise CollectionNotFoundError(
|
||||
message=f"Collection {collection_name} not found!"
|
||||
) from error
|
||||
else:
|
||||
raise error
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 5,
|
||||
with_vector: bool = False,
|
||||
):
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
results = await client.search(
|
||||
collection_name=collection_name,
|
||||
query_vector=models.NamedVector(
|
||||
name="text",
|
||||
vector=query_vector
|
||||
if query_vector is not None
|
||||
else (await self.embed_data([query_text]))[0],
|
||||
),
|
||||
limit=limit,
|
||||
with_vectors=with_vector,
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(result.id),
|
||||
payload={
|
||||
**result.payload,
|
||||
"id": parse_id(result.id),
|
||||
},
|
||||
score=1 - result.score,
|
||||
)
|
||||
for result in results
|
||||
]
|
||||
|
||||
async def batch_search(
|
||||
self,
|
||||
collection_name: str,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from typing import List, Optional
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
|
|
@ -34,21 +34,23 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
|
||||
self.embedding_engine = embedding_engine
|
||||
|
||||
self.client = weaviate.connect_to_wcs(
|
||||
self.client = weaviate.use_async_with_weaviate_cloud(
|
||||
cluster_url=url,
|
||||
auth_credentials=weaviate.auth.AuthApiKey(api_key),
|
||||
additional_config=wvc.init.AdditionalConfig(timeout=wvc.init.Timeout(init=30)),
|
||||
)
|
||||
|
||||
async def get_client(self):
|
||||
await self.client.connect()
|
||||
|
||||
return self.client
|
||||
|
||||
async def embed_data(self, data: List[str]) -> List[float]:
|
||||
return await self.embedding_engine.embed_text(data)
|
||||
|
||||
async def has_collection(self, collection_name: str) -> bool:
|
||||
future = asyncio.Future()
|
||||
|
||||
future.set_result(self.client.collections.exists(collection_name))
|
||||
|
||||
return await future
|
||||
client = await self.get_client()
|
||||
return await client.collections.exists(collection_name)
|
||||
|
||||
async def create_collection(
|
||||
self,
|
||||
|
|
@ -57,26 +59,25 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
):
|
||||
import weaviate.classes.config as wvcc
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
if not self.client.collections.exists(collection_name):
|
||||
future.set_result(
|
||||
self.client.collections.create(
|
||||
name=collection_name,
|
||||
properties=[
|
||||
wvcc.Property(
|
||||
name="text", data_type=wvcc.DataType.TEXT, skip_vectorization=True
|
||||
)
|
||||
],
|
||||
)
|
||||
if not await self.has_collection(collection_name):
|
||||
client = await self.get_client()
|
||||
return await client.collections.create(
|
||||
name=collection_name,
|
||||
properties=[
|
||||
wvcc.Property(
|
||||
name="text", data_type=wvcc.DataType.TEXT, skip_vectorization=True
|
||||
)
|
||||
],
|
||||
)
|
||||
else:
|
||||
future.set_result(self.get_collection(collection_name))
|
||||
return await self.get_collection(collection_name)
|
||||
|
||||
return await future
|
||||
async def get_collection(self, collection_name: str):
|
||||
if not await self.has_collection(collection_name):
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found.")
|
||||
|
||||
def get_collection(self, collection_name: str):
|
||||
return self.client.collections.get(collection_name)
|
||||
client = await self.get_client()
|
||||
return client.collections.get(collection_name)
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
|
||||
from weaviate.classes.data import DataObject
|
||||
|
|
@ -97,29 +98,30 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
|
||||
data_points = [convert_to_weaviate_data_points(data_point) for data_point in data_points]
|
||||
|
||||
collection = self.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
try:
|
||||
if len(data_points) > 1:
|
||||
with collection.batch.dynamic() as batch:
|
||||
for data_point in data_points:
|
||||
batch.add_object(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
references=data_point.references,
|
||||
)
|
||||
return await collection.data.insert_many(data_points)
|
||||
# with collection.batch.dynamic() as batch:
|
||||
# for data_point in data_points:
|
||||
# batch.add_object(
|
||||
# uuid=data_point.uuid,
|
||||
# vector=data_point.vector,
|
||||
# properties=data_point.properties,
|
||||
# references=data_point.references,
|
||||
# )
|
||||
else:
|
||||
data_point: DataObject = data_points[0]
|
||||
if collection.data.exists(data_point.uuid):
|
||||
return collection.data.update(
|
||||
return await collection.data.update(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
references=data_point.references,
|
||||
)
|
||||
else:
|
||||
return collection.data.insert(
|
||||
return await collection.data.insert(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
|
|
@ -130,12 +132,12 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
raise error
|
||||
|
||||
async def create_vector_index(self, index_name: str, index_property_name: str):
|
||||
await self.create_collection(f"{index_name}_{index_property_name}")
|
||||
return await self.create_collection(f"{index_name}_{index_property_name}")
|
||||
|
||||
async def index_data_points(
|
||||
self, index_name: str, index_property_name: str, data_points: list[DataPoint]
|
||||
):
|
||||
await self.create_data_points(
|
||||
return await self.create_data_points(
|
||||
f"{index_name}_{index_property_name}",
|
||||
[
|
||||
IndexSchema(
|
||||
|
|
@ -149,9 +151,8 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
|
||||
from weaviate.classes.query import Filter
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
data_points = self.get_collection(collection_name).query.fetch_objects(
|
||||
collection = await self.get_collection(collection_name)
|
||||
data_points = await collection.query.fetch_objects(
|
||||
filters=Filter.by_id().contains_any(data_point_ids)
|
||||
)
|
||||
|
||||
|
|
@ -160,30 +161,32 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
data_point.id = data_point.uuid
|
||||
del data_point.properties
|
||||
|
||||
future.set_result(data_points.objects)
|
||||
return data_points.objects
|
||||
|
||||
return await future
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
):
|
||||
import weaviate.classes as wvc
|
||||
import weaviate.exceptions
|
||||
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_vector is None:
|
||||
query_vector = (await self.embed_data([query_text]))[0]
|
||||
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
try:
|
||||
search_result = self.get_collection(collection_name).query.hybrid(
|
||||
search_result = await collection.query.hybrid(
|
||||
query=None,
|
||||
vector=query_vector,
|
||||
limit=limit if limit > 0 else None,
|
||||
include_vector=with_vector,
|
||||
return_metadata=wvc.query.MetadataQuery(score=True),
|
||||
)
|
||||
|
|
@ -196,43 +199,10 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
)
|
||||
for result in search_result.objects
|
||||
]
|
||||
except weaviate.exceptions.UnexpectedStatusCodeError:
|
||||
except weaviate.exceptions.WeaviateInvalidInputError:
|
||||
# Ignore if the collection doesn't exist
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = None,
|
||||
with_vector: bool = False,
|
||||
):
|
||||
import weaviate.classes as wvc
|
||||
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_vector is None:
|
||||
query_vector = (await self.embed_data([query_text]))[0]
|
||||
|
||||
search_result = self.get_collection(collection_name).query.hybrid(
|
||||
query=None,
|
||||
vector=query_vector,
|
||||
limit=limit,
|
||||
include_vector=with_vector,
|
||||
return_metadata=wvc.query.MetadataQuery(score=True),
|
||||
)
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(str(result.uuid)),
|
||||
payload=result.properties,
|
||||
score=1 - float(result.metadata.score),
|
||||
)
|
||||
for result in search_result.objects
|
||||
]
|
||||
|
||||
async def batch_search(
|
||||
self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False
|
||||
):
|
||||
|
|
@ -248,14 +218,13 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
|
||||
from weaviate.classes.query import Filter
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
result = self.get_collection(collection_name).data.delete_many(
|
||||
collection = await self.get_collection(collection_name)
|
||||
result = await collection.data.delete_many(
|
||||
filters=Filter.by_id().contains_any(data_point_ids)
|
||||
)
|
||||
future.set_result(result)
|
||||
|
||||
return await future
|
||||
return result
|
||||
|
||||
async def prune(self):
|
||||
self.client.collections.delete_all()
|
||||
client = await self.get_client()
|
||||
await client.collections.delete_all()
|
||||
|
|
|
|||
|
|
@ -128,8 +128,10 @@ class CogneeGraph(CogneeAbstractGraph):
|
|||
if query_vector is None or len(query_vector) == 0:
|
||||
raise ValueError("Failed to generate query embedding.")
|
||||
|
||||
edge_distances = await vector_engine.get_distance_from_collection_elements(
|
||||
"EdgeType_relationship_name", query_text=query
|
||||
edge_distances = await vector_engine.search(
|
||||
collection_name="EdgeType_relationship_name",
|
||||
query_text=query,
|
||||
limit=0,
|
||||
)
|
||||
|
||||
embedding_map = {result.payload["text"]: result.score for result in edge_distances}
|
||||
|
|
|
|||
|
|
@ -4,4 +4,4 @@ Custom exceptions for the Cognee API.
|
|||
This module defines a set of exceptions for handling various data errors
|
||||
"""
|
||||
|
||||
from .exceptions import SearchTypeNotSupported, CypherSearchError, CollectionDistancesNotFoundError
|
||||
from .exceptions import SearchTypeNotSupported, CypherSearchError
|
||||
|
|
|
|||
|
|
@ -2,16 +2,6 @@ from fastapi import status
|
|||
from cognee.exceptions import CogneeApiError, CriticalError
|
||||
|
||||
|
||||
class CollectionDistancesNotFoundError(CogneeApiError):
|
||||
def __init__(
|
||||
self,
|
||||
message: str = "No distances found between the query and collections. It is possible that the given collection names don't exist.",
|
||||
name: str = "CollectionDistancesNotFoundError",
|
||||
status_code: int = status.HTTP_404_NOT_FOUND,
|
||||
):
|
||||
super().__init__(message, name, status_code)
|
||||
|
||||
|
||||
class SearchTypeNotSupported(CogneeApiError):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ from collections import Counter
|
|||
import string
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError
|
||||
from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
|
||||
from cognee.modules.retrieval.base_retriever import BaseRetriever
|
||||
from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
|
||||
|
|
@ -76,10 +75,7 @@ class GraphCompletionRetriever(BaseRetriever):
|
|||
|
||||
async def get_context(self, query: str) -> str:
|
||||
"""Retrieves and resolves graph triplets into context."""
|
||||
try:
|
||||
triplets = await self.get_triplets(query)
|
||||
except EntityNotFoundError:
|
||||
return ""
|
||||
triplets = await self.get_triplets(query)
|
||||
|
||||
if len(triplets) == 0:
|
||||
return ""
|
||||
|
|
|
|||
|
|
@ -1,14 +1,15 @@
|
|||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger, ERROR
|
||||
from typing import List, Optional
|
||||
|
||||
from cognee.shared.logging_utils import get_logger, ERROR
|
||||
from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
from cognee.infrastructure.databases.vector import get_vector_engine
|
||||
from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.shared.utils import send_telemetry
|
||||
from cognee.modules.retrieval.exceptions import CollectionDistancesNotFoundError
|
||||
|
||||
logger = get_logger(level=ERROR)
|
||||
|
||||
|
|
@ -62,11 +63,14 @@ async def get_memory_fragment(
|
|||
if properties_to_project is None:
|
||||
properties_to_project = ["id", "description", "name", "type", "text"]
|
||||
|
||||
await memory_fragment.project_graph_from_db(
|
||||
graph_engine,
|
||||
node_properties_to_project=properties_to_project,
|
||||
edge_properties_to_project=["relationship_name"],
|
||||
)
|
||||
try:
|
||||
await memory_fragment.project_graph_from_db(
|
||||
graph_engine,
|
||||
node_properties_to_project=properties_to_project,
|
||||
edge_properties_to_project=["relationship_name"],
|
||||
)
|
||||
except EntityNotFoundError:
|
||||
pass
|
||||
|
||||
return memory_fragment
|
||||
|
||||
|
|
@ -139,16 +143,21 @@ async def brute_force_search(
|
|||
|
||||
send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
|
||||
|
||||
async def search_in_collection(collection_name: str):
|
||||
try:
|
||||
return await vector_engine.search(
|
||||
collection_name=collection_name, query_text=query, limit=top_k
|
||||
)
|
||||
except CollectionNotFoundError:
|
||||
return []
|
||||
|
||||
try:
|
||||
results = await asyncio.gather(
|
||||
*[
|
||||
vector_engine.get_distance_from_collection_elements(collection, query_text=query)
|
||||
for collection in collections
|
||||
]
|
||||
*[search_in_collection(collection_name) for collection_name in collections]
|
||||
)
|
||||
|
||||
if all(not item for item in results):
|
||||
raise CollectionDistancesNotFoundError()
|
||||
return []
|
||||
|
||||
node_distances = {collection: result for collection, result in zip(collections, results)}
|
||||
|
||||
|
|
@ -161,6 +170,8 @@ async def brute_force_search(
|
|||
|
||||
return results
|
||||
|
||||
except CollectionNotFoundError:
|
||||
return []
|
||||
except Exception as error:
|
||||
logger.error(
|
||||
"Error during brute force search for user: %s, query: %s. Error: %s",
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ async def main():
|
|||
assert not os.path.isdir(data_directory_path), "Local data files are not deleted"
|
||||
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
collections = get_vector_engine().client.collections.list_all()
|
||||
collections = await get_vector_engine().client.collections.list_all()
|
||||
assert len(collections) == 0, "Weaviate vector database is not empty"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -16,11 +16,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_simple(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_rag_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunks_context_simple"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_rag_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunks_context_simple"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -73,11 +73,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_complex(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context_complex"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context_complex"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -162,11 +162,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_on_empty_graph(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context_empty"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context_empty"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -190,6 +190,9 @@ if __name__ == "__main__":
|
|||
|
||||
test = TestChunksRetriever()
|
||||
|
||||
run(test.test_chunk_context_simple())
|
||||
run(test.test_chunk_context_complex())
|
||||
run(test.test_chunk_context_on_empty_graph())
|
||||
async def main():
|
||||
await test.test_chunk_context_simple()
|
||||
await test.test_chunk_context_complex()
|
||||
await test.test_chunk_context_on_empty_graph()
|
||||
|
||||
run(main())
|
||||
|
|
|
|||
|
|
@ -154,6 +154,9 @@ if __name__ == "__main__":
|
|||
|
||||
test = TestGraphCompletionRetriever()
|
||||
|
||||
run(test.test_graph_completion_context_simple())
|
||||
run(test.test_graph_completion_context_complex())
|
||||
run(test.test_get_graph_completion_context_on_empty_graph())
|
||||
async def main():
|
||||
await test.test_graph_completion_context_simple()
|
||||
await test.test_graph_completion_context_complex()
|
||||
await test.test_get_graph_completion_context_on_empty_graph()
|
||||
|
||||
run(main())
|
||||
|
|
|
|||
|
|
@ -127,7 +127,7 @@ class TextSummariesRetriever:
|
|||
|
||||
await add_data_points(entities)
|
||||
|
||||
retriever = SummariesRetriever(limit=20)
|
||||
retriever = SummariesRetriever(top_k=20)
|
||||
|
||||
context = await retriever.get_context("Christina")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,44 +0,0 @@
|
|||
import pytest
|
||||
from unittest.mock import AsyncMock, patch
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.modules.retrieval.exceptions import CollectionDistancesNotFoundError
|
||||
from cognee.modules.retrieval.utils.brute_force_triplet_search import (
|
||||
brute_force_search,
|
||||
brute_force_triplet_search,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch("cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine")
|
||||
async def test_brute_force_search_collection_not_found(mock_get_vector_engine):
|
||||
user = User(id="test_user")
|
||||
query = "test query"
|
||||
collections = ["nonexistent_collection"]
|
||||
top_k = 5
|
||||
mock_memory_fragment = AsyncMock()
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.get_distance_from_collection_elements.return_value = []
|
||||
mock_get_vector_engine.return_value = mock_vector_engine
|
||||
|
||||
with pytest.raises(CollectionDistancesNotFoundError):
|
||||
await brute_force_search(
|
||||
query, user, top_k, collections=collections, memory_fragment=mock_memory_fragment
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch("cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine")
|
||||
async def test_brute_force_triplet_search_collection_not_found(mock_get_vector_engine):
|
||||
user = User(id="test_user")
|
||||
query = "test query"
|
||||
collections = ["nonexistent_collection"]
|
||||
top_k = 5
|
||||
mock_memory_fragment = AsyncMock()
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.get_distance_from_collection_elements.return_value = []
|
||||
mock_get_vector_engine.return_value = mock_vector_engine
|
||||
|
||||
with pytest.raises(CollectionDistancesNotFoundError):
|
||||
await brute_force_triplet_search(
|
||||
query, user, top_k, collections=collections, memory_fragment=mock_memory_fragment
|
||||
)
|
||||
474
poetry.lock
generated
474
poetry.lock
generated
|
|
@ -857,14 +857,14 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.1.31"
|
||||
version = "2025.4.26"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
|
||||
{file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
|
||||
{file = "certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"},
|
||||
{file = "certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1225,7 +1225,7 @@ 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 = "(sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\") and (platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\" or extra == \"codegraph\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\" or extra == \"chromadb\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\" or extra == \"chromadb\" or extra == \"codegraph\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\") and (python_version < \"3.13\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\")"
|
||||
markers = "(sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\") and (platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\" or extra == \"codegraph\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\" or extra == \"chromadb\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\") and (sys_platform == \"win32\" or platform_system == \"Windows\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"dev\" or extra == \"chromadb\" or extra == \"codegraph\") and (python_version < \"3.13\" or platform_system == \"Windows\" or extra == \"notebook\" or extra == \"dev\" or extra == \"llama-index\" or extra == \"deepeval\" or extra == \"chromadb\")"
|
||||
files = [
|
||||
{file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
|
||||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
|
|
@ -1238,7 +1238,7 @@ description = "Colored terminal output for Python's logging module"
|
|||
optional = true
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or extra == \"chromadb\" or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
markers = "python_version == \"3.10\" and (extra == \"codegraph\" or extra == \"chromadb\") or extra == \"chromadb\" or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
files = [
|
||||
{file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
|
||||
{file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
|
||||
|
|
@ -1420,6 +1420,9 @@ files = [
|
|||
{file = "coverage-7.8.0.tar.gz", hash = "sha256:7a3d62b3b03b4b6fd41a085f3574874cf946cb4604d2b4d3e8dca8cd570ca501"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
|
||||
|
||||
[package.extras]
|
||||
toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
|
||||
|
||||
|
|
@ -2295,7 +2298,7 @@ description = "The FlatBuffers serialization format for Python"
|
|||
optional = true
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or extra == \"chromadb\" or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
markers = "python_version == \"3.10\" and (extra == \"codegraph\" or extra == \"chromadb\") or extra == \"chromadb\" or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
files = [
|
||||
{file = "flatbuffers-25.2.10-py2.py3-none-any.whl", hash = "sha256:ebba5f4d5ea615af3f7fd70fc310636fbb2bbd1f566ac0a23d98dd412de50051"},
|
||||
{file = "flatbuffers-25.2.10.tar.gz", hash = "sha256:97e451377a41262f8d9bd4295cc836133415cc03d8cb966410a4af92eb00d26e"},
|
||||
|
|
@ -2690,15 +2693,15 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
|
|||
|
||||
[[package]]
|
||||
name = "google-api-python-client"
|
||||
version = "2.167.0"
|
||||
version = "2.169.0"
|
||||
description = "Google API Client Library for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "google_api_python_client-2.167.0-py2.py3-none-any.whl", hash = "sha256:ce25290cc229505d770ca5c8d03850e0ae87d8e998fc6dd743ecece018baa396"},
|
||||
{file = "google_api_python_client-2.167.0.tar.gz", hash = "sha256:a458d402572e1c2caf9db090d8e7b270f43ff326bd9349c731a86b19910e3995"},
|
||||
{file = "google_api_python_client-2.169.0-py3-none-any.whl", hash = "sha256:dae3e882dc0e6f28e60cf09c1f13fedfd881db84f824dd418aa9e44def2fe00d"},
|
||||
{file = "google_api_python_client-2.169.0.tar.gz", hash = "sha256:0585bb97bd5f5bf3ed8d4bf624593e4c5a14d06c811d1952b07a1f94b4d12c51"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3164,14 +3167,14 @@ tornado = ["tornado (>=0.2)"]
|
|||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.14.0"
|
||||
version = "0.16.0"
|
||||
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
|
||||
{file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
|
||||
{file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
|
||||
{file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3273,19 +3276,19 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.8"
|
||||
version = "1.0.9"
|
||||
description = "A minimal low-level HTTP client."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "httpcore-1.0.8-py3-none-any.whl", hash = "sha256:5254cf149bcb5f75e9d1b2b9f729ea4a4b883d1ad7379fc632b727cec23674be"},
|
||||
{file = "httpcore-1.0.8.tar.gz", hash = "sha256:86e94505ed24ea06514883fd44d2bc02d90e77e7979c8eb71b90f41d364a1bad"},
|
||||
{file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
|
||||
{file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = "*"
|
||||
h11 = ">=0.13,<0.15"
|
||||
h11 = ">=0.16"
|
||||
|
||||
[package.extras]
|
||||
asyncio = ["anyio (>=4.0,<5.0)"]
|
||||
|
|
@ -3448,7 +3451,7 @@ description = "Human friendly output for text interfaces using Python"
|
|||
optional = true
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or extra == \"chromadb\" or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
markers = "python_version == \"3.10\" and (extra == \"codegraph\" or extra == \"chromadb\") or extra == \"chromadb\" or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
files = [
|
||||
{file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
|
||||
{file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
|
||||
|
|
@ -3625,15 +3628,15 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
|
|||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "8.35.0"
|
||||
version = "8.36.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
optional = true
|
||||
python-versions = ">=3.10"
|
||||
groups = ["main"]
|
||||
markers = "python_version < \"3.11\" and (extra == \"notebook\" or extra == \"dev\")"
|
||||
files = [
|
||||
{file = "ipython-8.35.0-py3-none-any.whl", hash = "sha256:e6b7470468ba6f1f0a7b116bb688a3ece2f13e2f94138e508201fad677a788ba"},
|
||||
{file = "ipython-8.35.0.tar.gz", hash = "sha256:d200b7d93c3f5883fc36ab9ce28a18249c7706e51347681f80a0aef9895f2520"},
|
||||
{file = "ipython-8.36.0-py3-none-any.whl", hash = "sha256:12b913914d010dcffa2711505ec8be4bf0180742d97f1e5175e51f22086428c1"},
|
||||
{file = "ipython-8.36.0.tar.gz", hash = "sha256:24658e9fe5c5c819455043235ba59cfffded4a35936eefceceab6b192f7092ff"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3665,15 +3668,15 @@ test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "n
|
|||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "9.1.0"
|
||||
version = "9.2.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
optional = true
|
||||
python-versions = ">=3.11"
|
||||
groups = ["main"]
|
||||
markers = "python_version >= \"3.11\" and (extra == \"notebook\" or extra == \"dev\")"
|
||||
files = [
|
||||
{file = "ipython-9.1.0-py3-none-any.whl", hash = "sha256:2df07257ec2f84a6b346b8d83100bcf8fa501c6e01ab75cd3799b0bb253b3d2a"},
|
||||
{file = "ipython-9.1.0.tar.gz", hash = "sha256:a47e13a5e05e02f3b8e1e7a0f9db372199fe8c3763532fe7a1e0379e4e135f16"},
|
||||
{file = "ipython-9.2.0-py3-none-any.whl", hash = "sha256:fef5e33c4a1ae0759e0bba5917c9db4eb8c53fee917b6a526bd973e1ca5159f6"},
|
||||
{file = "ipython-9.2.0.tar.gz", hash = "sha256:62a9373dbc12f28f9feaf4700d052195bf89806279fc8ca11f3f54017d04751b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4593,15 +4596,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.55"
|
||||
version = "0.3.56"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"langchain\" or extra == \"deepeval\""
|
||||
files = [
|
||||
{file = "langchain_core-0.3.55-py3-none-any.whl", hash = "sha256:b3cb36bf37755a616158a79866657c6697b43a2f7c69dd723ce425f1c76c1baa"},
|
||||
{file = "langchain_core-0.3.55.tar.gz", hash = "sha256:0f2b3e311621116a83510c70b0ac9d959030a0a457a69483535cff18501fedc9"},
|
||||
{file = "langchain_core-0.3.56-py3-none-any.whl", hash = "sha256:a20c6aca0fa0da265d96d3b14a5a01828ac5d2d9d27516434873d76f2d4839ed"},
|
||||
{file = "langchain_core-0.3.56.tar.gz", hash = "sha256:de896585bc56e12652327dcd195227c3739a07e86e587c91a07101e0df11dffe"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4752,14 +4755,13 @@ valkey = ["valkey (>=6)"]
|
|||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.67.2"
|
||||
version = "1.67.4.post1"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
optional = false
|
||||
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "litellm-1.67.2-py3-none-any.whl", hash = "sha256:32df4d17b3ead17d04793311858965e41e83a7bdf9bd661895c0e6bc9c78dc8b"},
|
||||
{file = "litellm-1.67.2.tar.gz", hash = "sha256:9e108827bff16af04fd4c35b0c1a1d6c7746c96db3870189a60141d449797487"},
|
||||
{file = "litellm-1.67.4.post1.tar.gz", hash = "sha256:057f2505f82d8c3f83d705c375b0d1931de998b13e239a6b06e16ee351fda648"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4777,19 +4779,19 @@ tokenizers = "*"
|
|||
|
||||
[package.extras]
|
||||
extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "redisvl (>=0.4.1,<0.5.0) ; python_version >= \"3.9\" and python_version < \"3.14\"", "resend (>=0.8.0,<0.9.0)"]
|
||||
proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "boto3 (==1.34.34)", "cryptography (>=43.0.1,<44.0.0)", "fastapi (>=0.115.5,<0.116.0)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=23.0.0,<24.0.0)", "litellm-proxy-extras (==0.1.11)", "mcp (==1.5.0) ; python_version >= \"3.10\"", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.18,<0.0.19)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.29.0,<0.30.0)", "uvloop (>=0.21.0,<0.22.0)", "websockets (>=13.1.0,<14.0.0)"]
|
||||
proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "boto3 (==1.34.34)", "cryptography (>=43.0.1,<44.0.0)", "fastapi (>=0.115.5,<0.116.0)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=23.0.0,<24.0.0)", "litellm-proxy-extras (==0.1.13)", "mcp (==1.5.0) ; python_version >= \"3.10\"", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.18,<0.0.19)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.29.0,<0.30.0)", "uvloop (>=0.21.0,<0.22.0)", "websockets (>=13.1.0,<14.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "llama-cloud"
|
||||
version = "0.1.18"
|
||||
version = "0.1.19"
|
||||
description = ""
|
||||
optional = true
|
||||
python-versions = "<4,>=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"deepeval\""
|
||||
files = [
|
||||
{file = "llama_cloud-0.1.18-py3-none-any.whl", hash = "sha256:5842722a0c3033afa930b4a50d43e6f1e77ff1dab12383a769dc51a15fb87c9b"},
|
||||
{file = "llama_cloud-0.1.18.tar.gz", hash = "sha256:65cb88b1cb1a3a0e63e4438e8c8a2e6013dfdafbb4201d274c0459e5d04fb328"},
|
||||
{file = "llama_cloud-0.1.19-py3-none-any.whl", hash = "sha256:d2d551baa4b63f7717f8e04cbb81b0f817e5450a66870c5487dd371f81dab8ec"},
|
||||
{file = "llama_cloud-0.1.19.tar.gz", hash = "sha256:b0a5424ae0099ca27df2a2d7e5aec99066de9ca860ab65987c9f931f1ea7abff"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4799,20 +4801,20 @@ pydantic = ">=1.10"
|
|||
|
||||
[[package]]
|
||||
name = "llama-cloud-services"
|
||||
version = "0.6.15"
|
||||
version = "0.6.20"
|
||||
description = "Tailored SDK clients for LlamaCloud services."
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"deepeval\""
|
||||
files = [
|
||||
{file = "llama_cloud_services-0.6.15-py3-none-any.whl", hash = "sha256:c4e24dd41f2cde17eeba7750d41cc70fe26e1179c03ae832122d762572e53de6"},
|
||||
{file = "llama_cloud_services-0.6.15.tar.gz", hash = "sha256:912799d9cdcf48074145c6781f40a6dd7dadb6344ecb30b715407db85a0e675e"},
|
||||
{file = "llama_cloud_services-0.6.20-py3-none-any.whl", hash = "sha256:f6878f602551112b0d7520b891dc88281ba6135285aaa4aaa89e55d684e7fb1f"},
|
||||
{file = "llama_cloud_services-0.6.20.tar.gz", hash = "sha256:1cba4d33e9c40eaa3cbf50e924c7b6371ba73be02830a615fcf86245c4f3a142"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
click = ">=8.1.7,<9.0.0"
|
||||
llama-cloud = ">=0.1.18,<0.2.0"
|
||||
llama-cloud = "0.1.19"
|
||||
llama-index-core = ">=0.11.0"
|
||||
platformdirs = ">=4.3.7,<5.0.0"
|
||||
pydantic = "!=2.10"
|
||||
|
|
@ -5065,19 +5067,19 @@ llama-parse = ">=0.5.0"
|
|||
|
||||
[[package]]
|
||||
name = "llama-parse"
|
||||
version = "0.6.12"
|
||||
version = "0.6.20"
|
||||
description = "Parse files into RAG-Optimized formats."
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"deepeval\""
|
||||
files = [
|
||||
{file = "llama_parse-0.6.12-py3-none-any.whl", hash = "sha256:2dd1c74b0cba1a2bc300286f6b91a650f6ddc396acfce3497ba3d72d43c53fac"},
|
||||
{file = "llama_parse-0.6.12.tar.gz", hash = "sha256:c99593fb955c338a69e64a2ec449e09753afe6dcff239ab050989fda74839867"},
|
||||
{file = "llama_parse-0.6.20-py3-none-any.whl", hash = "sha256:7f99d42a0fa70530d0560d1235bba14a7c198c8db8c0fddd80c32ad61b1d5b10"},
|
||||
{file = "llama_parse-0.6.20.tar.gz", hash = "sha256:73aabf19229a6d8f77c79864c39e8bda0b0acef6eb1f8f65fed656d875259e7c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
llama-cloud-services = ">=0.6.12"
|
||||
llama-cloud-services = ">=0.6.20"
|
||||
|
||||
[[package]]
|
||||
name = "loguru"
|
||||
|
|
@ -5952,7 +5954,7 @@ description = "Python library for arbitrary-precision floating-point arithmetic"
|
|||
optional = true
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or extra == \"chromadb\" or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
markers = "python_version == \"3.10\" and (extra == \"codegraph\" or extra == \"chromadb\") or extra == \"chromadb\" or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
files = [
|
||||
{file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
|
||||
{file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
|
||||
|
|
@ -6151,15 +6153,15 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "narwhals"
|
||||
version = "1.36.0"
|
||||
version = "1.37.1"
|
||||
description = "Extremely lightweight compatibility layer between dataframe libraries"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"evals\""
|
||||
files = [
|
||||
{file = "narwhals-1.36.0-py3-none-any.whl", hash = "sha256:e3c50dd1d769bc145f57ae17c1f0f0da6c3d397d62cdd0bb167e9b618e95c9d6"},
|
||||
{file = "narwhals-1.36.0.tar.gz", hash = "sha256:7cd860e7e066609bd8a042bb5b8e4193275532114448210a91cbd5c622b6e5eb"},
|
||||
{file = "narwhals-1.37.1-py3-none-any.whl", hash = "sha256:6f358a23b7351897d6efb45496dc0528918ce4ca6c8f9631594885cd873576a7"},
|
||||
{file = "narwhals-1.37.1.tar.gz", hash = "sha256:1eb8f17ff00e6c471d5afb704e9068f41657234eb73bde2ee66ad975a170015b"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -6172,6 +6174,7 @@ pandas = ["pandas (>=0.25.3)"]
|
|||
polars = ["polars (>=0.20.3)"]
|
||||
pyarrow = ["pyarrow (>=11.0.0)"]
|
||||
pyspark = ["pyspark (>=3.5.0)"]
|
||||
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|
||||
sqlframe = ["sqlframe (>=3.22.0)"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -6533,7 +6536,7 @@ description = "ONNX Runtime is a runtime accelerator for Machine Learning models
|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
|
|
@ -6565,14 +6568,14 @@ sympy = "*"
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.76.0"
|
||||
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|
||||
description = "The official Python library for the openai API"
|
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[package.dependencies]
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|
@ -6730,81 +6733,85 @@ files = [
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||||
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||||
{file = "orjson-3.10.17.tar.gz", hash = "sha256:28eeae6a15243966962b658dfcf7bae9e7bb1f3260dfcf0370dbd41f5ff6058b"},
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||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -6929,8 +6936,8 @@ files = [
|
|||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
|
|
@ -7549,7 +7556,7 @@ description = ""
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and extra == \"codegraph\" or (extra == \"chromadb\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"gemini\" or extra == \"deepeval\" or extra == \"milvus\") and python_version < \"3.11\" or (python_version == \"3.12\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\" or extra == \"chromadb\") and (extra == \"codegraph\" or extra == \"chromadb\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\") and python_version >= \"3.12\" or python_version == \"3.11\" and (extra == \"codegraph\" or extra == \"chromadb\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\")"
|
||||
markers = "python_version == \"3.10\" and extra == \"codegraph\" or (extra == \"chromadb\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"gemini\" or extra == \"deepeval\" or extra == \"milvus\") and python_version < \"3.11\" or python_version == \"3.11\" and (extra == \"codegraph\" or extra == \"chromadb\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\") or (python_version == \"3.12\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\" or extra == \"chromadb\") and (extra == \"codegraph\" or extra == \"chromadb\" or extra == \"gemini\" or extra == \"weaviate\" or extra == \"qdrant\" or extra == \"deepeval\" or extra == \"milvus\") and python_version >= \"3.12\""
|
||||
files = [
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win32.whl", hash = "sha256:13eb236f8eb9ec34e63fc8b1d6efd2777d062fa6aaa68268fb67cf77f6839ad7"},
|
||||
{file = "protobuf-5.29.4-cp310-abi3-win_amd64.whl", hash = "sha256:bcefcdf3976233f8a502d265eb65ea740c989bacc6c30a58290ed0e519eb4b8d"},
|
||||
|
|
@ -7785,54 +7792,67 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "pyarrow"
|
||||
version = "19.0.1"
|
||||
version = "20.0.0"
|
||||
description = "Python library for Apache Arrow"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
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||||
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[package.extras]
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dill = [
|
||||
{version = ">=0.2", markers = "python_version < \"3.11\""},
|
||||
{version = ">=0.3.6", markers = "python_version >= \"3.11\""},
|
||||
{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=0.3.6", markers = "python_version == \"3.11\""},
|
||||
]
|
||||
isort = ">=4.2.5,<5.13 || >5.13,<7"
|
||||
mccabe = ">=0.6,<0.8"
|
||||
|
|
@ -8135,15 +8155,15 @@ testutils = ["gitpython (>3)"]
|
|||
|
||||
[[package]]
|
||||
name = "pymdown-extensions"
|
||||
version = "10.14.3"
|
||||
version = "10.15"
|
||||
description = "Extension pack for Python Markdown."
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"dev\""
|
||||
files = [
|
||||
{file = "pymdown_extensions-10.14.3-py3-none-any.whl", hash = "sha256:05e0bee73d64b9c71a4ae17c72abc2f700e8bc8403755a00580b49a4e9f189e9"},
|
||||
{file = "pymdown_extensions-10.14.3.tar.gz", hash = "sha256:41e576ce3f5d650be59e900e4ceff231e0aed2a88cf30acaee41e02f063a061b"},
|
||||
{file = "pymdown_extensions-10.15-py3-none-any.whl", hash = "sha256:46e99bb272612b0de3b7e7caf6da8dd5f4ca5212c0b273feb9304e236c484e5f"},
|
||||
{file = "pymdown_extensions-10.15.tar.gz", hash = "sha256:0e5994e32155f4b03504f939e501b981d306daf7ec2aa1cd2eb6bd300784f8f7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8155,15 +8175,15 @@ extra = ["pygments (>=2.19.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "pymilvus"
|
||||
version = "2.5.7"
|
||||
version = "2.5.8"
|
||||
description = "Python Sdk for Milvus"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"milvus\""
|
||||
files = [
|
||||
{file = "pymilvus-2.5.7-py3-none-any.whl", hash = "sha256:91373cb1a9576ceccd422182e50bdd29f7cc9228447bdf73e22c10bd225536d3"},
|
||||
{file = "pymilvus-2.5.7.tar.gz", hash = "sha256:4c092a01d847eb704b122625261e9db167c050b4a383d1259f1988bf6287dcf3"},
|
||||
{file = "pymilvus-2.5.8-py3-none-any.whl", hash = "sha256:6f33c9e78c041373df6a94724c90ca83448fd231aa33d6298a7a84ed2a5a0236"},
|
||||
{file = "pymilvus-2.5.8.tar.gz", hash = "sha256:48923e7efeebcc366d32b644772796f60484e0ca1a5afc1606d21a10ed98133c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8250,7 +8270,7 @@ description = "A python implementation of GNU readline."
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform == \"win32\" and (python_version == \"3.10\" or extra == \"chromadb\" or extra == \"codegraph\") and (extra == \"chromadb\" or python_version == \"3.12\" or python_version == \"3.10\" or python_version == \"3.11\") and (extra == \"codegraph\" or extra == \"chromadb\")"
|
||||
markers = "sys_platform == \"win32\" and python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or sys_platform == \"win32\" and extra == \"chromadb\" or sys_platform == \"win32\" and (extra == \"chromadb\" or extra == \"codegraph\") and python_version == \"3.11\" or sys_platform == \"win32\" and (extra == \"chromadb\" or extra == \"codegraph\") and python_version == \"3.12\""
|
||||
files = [
|
||||
{file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
|
||||
{file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
|
||||
|
|
@ -8377,6 +8397,26 @@ pytest = ">=7.0.0"
|
|||
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
|
||||
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-cov"
|
||||
version = "6.1.1"
|
||||
description = "Pytest plugin for measuring coverage."
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"dev\""
|
||||
files = [
|
||||
{file = "pytest_cov-6.1.1-py3-none-any.whl", hash = "sha256:bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde"},
|
||||
{file = "pytest_cov-6.1.1.tar.gz", hash = "sha256:46935f7aaefba760e716c2ebfbe1c216240b9592966e7da99ea8292d4d3e2a0a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
coverage = {version = ">=7.5", extras = ["toml"]}
|
||||
pytest = ">=4.6"
|
||||
|
||||
[package.extras]
|
||||
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-repeat"
|
||||
version = "0.9.4"
|
||||
|
|
@ -8870,15 +8910,15 @@ fastembed-gpu = ["fastembed-gpu (==0.3.6) ; python_version < \"3.13\""]
|
|||
|
||||
[[package]]
|
||||
name = "qdrant-client"
|
||||
version = "1.14.1"
|
||||
version = "1.14.2"
|
||||
description = "Client library for the Qdrant vector search engine"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version < \"3.13\" and extra == \"qdrant\""
|
||||
files = [
|
||||
{file = "qdrant_client-1.14.1-py3-none-any.whl", hash = "sha256:1c4d5ed791873698da8b5df68df16bb203ec1b0cd6cec0fd6002572a06291a1b"},
|
||||
{file = "qdrant_client-1.14.1.tar.gz", hash = "sha256:75352057ea59fdd7987313dc9cef4d83953591d083028d94eac99cd0e5e2f607"},
|
||||
{file = "qdrant_client-1.14.2-py3-none-any.whl", hash = "sha256:7c283b1f0e71db9c21b85d898fb395791caca2a6d56ee751da96d797b001410c"},
|
||||
{file = "qdrant_client-1.14.2.tar.gz", hash = "sha256:da5cab4d367d099d1330b6f30d45aefc8bd76f8b8f9d8fa5d4f813501b93af0d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -9464,31 +9504,31 @@ pyasn1 = ">=0.1.3"
|
|||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.11.6"
|
||||
version = "0.11.7"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"dev\""
|
||||
files = [
|
||||
{file = "ruff-0.11.6-py3-none-linux_armv6l.whl", hash = "sha256:d84dcbe74cf9356d1bdb4a78cf74fd47c740bf7bdeb7529068f69b08272239a1"},
|
||||
{file = "ruff-0.11.6-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:9bc583628e1096148011a5d51ff3c836f51899e61112e03e5f2b1573a9b726de"},
|
||||
{file = "ruff-0.11.6-py3-none-macosx_11_0_arm64.whl", hash = "sha256:f2959049faeb5ba5e3b378709e9d1bf0cab06528b306b9dd6ebd2a312127964a"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:63c5d4e30d9d0de7fedbfb3e9e20d134b73a30c1e74b596f40f0629d5c28a193"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:26a4b9a4e1439f7d0a091c6763a100cef8fbdc10d68593df6f3cfa5abdd9246e"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b5edf270223dd622218256569636dc3e708c2cb989242262fe378609eccf1308"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:f55844e818206a9dd31ff27f91385afb538067e2dc0beb05f82c293ab84f7d55"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d8f782286c5ff562e4e00344f954b9320026d8e3fae2ba9e6948443fafd9ffc"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:01c63ba219514271cee955cd0adc26a4083df1956d57847978383b0e50ffd7d2"},
|
||||
{file = "ruff-0.11.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15adac20ef2ca296dd3d8e2bedc6202ea6de81c091a74661c3666e5c4c223ff6"},
|
||||
{file = "ruff-0.11.6-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:4dd6b09e98144ad7aec026f5588e493c65057d1b387dd937d7787baa531d9bc2"},
|
||||
{file = "ruff-0.11.6-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:45b2e1d6c0eed89c248d024ea95074d0e09988d8e7b1dad8d3ab9a67017a5b03"},
|
||||
{file = "ruff-0.11.6-py3-none-musllinux_1_2_i686.whl", hash = "sha256:bd40de4115b2ec4850302f1a1d8067f42e70b4990b68838ccb9ccd9f110c5e8b"},
|
||||
{file = "ruff-0.11.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:77cda2dfbac1ab73aef5e514c4cbfc4ec1fbef4b84a44c736cc26f61b3814cd9"},
|
||||
{file = "ruff-0.11.6-py3-none-win32.whl", hash = "sha256:5151a871554be3036cd6e51d0ec6eef56334d74dfe1702de717a995ee3d5b287"},
|
||||
{file = "ruff-0.11.6-py3-none-win_amd64.whl", hash = "sha256:cce85721d09c51f3b782c331b0abd07e9d7d5f775840379c640606d3159cae0e"},
|
||||
{file = "ruff-0.11.6-py3-none-win_arm64.whl", hash = "sha256:3567ba0d07fb170b1b48d944715e3294b77f5b7679e8ba258199a250383ccb79"},
|
||||
{file = "ruff-0.11.6.tar.gz", hash = "sha256:bec8bcc3ac228a45ccc811e45f7eb61b950dbf4cf31a67fa89352574b01c7d79"},
|
||||
{file = "ruff-0.11.7-py3-none-linux_armv6l.whl", hash = "sha256:d29e909d9a8d02f928d72ab7837b5cbc450a5bdf578ab9ebee3263d0a525091c"},
|
||||
{file = "ruff-0.11.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:dd1fb86b168ae349fb01dd497d83537b2c5541fe0626e70c786427dd8363aaee"},
|
||||
{file = "ruff-0.11.7-py3-none-macosx_11_0_arm64.whl", hash = "sha256:d3d7d2e140a6fbbc09033bce65bd7ea29d6a0adeb90b8430262fbacd58c38ada"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4809df77de390a1c2077d9b7945d82f44b95d19ceccf0c287c56e4dc9b91ca64"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f3a0c2e169e6b545f8e2dba185eabbd9db4f08880032e75aa0e285a6d3f48201"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:49b888200a320dd96a68e86736cf531d6afba03e4f6cf098401406a257fcf3d6"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:2b19cdb9cf7dae00d5ee2e7c013540cdc3b31c4f281f1dacb5a799d610e90db4"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:64e0ee994c9e326b43539d133a36a455dbaab477bc84fe7bfbd528abe2f05c1e"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bad82052311479a5865f52c76ecee5d468a58ba44fb23ee15079f17dd4c8fd63"},
|
||||
{file = "ruff-0.11.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7940665e74e7b65d427b82bffc1e46710ec7f30d58b4b2d5016e3f0321436502"},
|
||||
{file = "ruff-0.11.7-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:169027e31c52c0e36c44ae9a9c7db35e505fee0b39f8d9fca7274a6305295a92"},
|
||||
{file = "ruff-0.11.7-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:305b93f9798aee582e91e34437810439acb28b5fc1fee6b8205c78c806845a94"},
|
||||
{file = "ruff-0.11.7-py3-none-musllinux_1_2_i686.whl", hash = "sha256:a681db041ef55550c371f9cd52a3cf17a0da4c75d6bd691092dfc38170ebc4b6"},
|
||||
{file = "ruff-0.11.7-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:07f1496ad00a4a139f4de220b0c97da6d4c85e0e4aa9b2624167b7d4d44fd6b6"},
|
||||
{file = "ruff-0.11.7-py3-none-win32.whl", hash = "sha256:f25dfb853ad217e6e5f1924ae8a5b3f6709051a13e9dad18690de6c8ff299e26"},
|
||||
{file = "ruff-0.11.7-py3-none-win_amd64.whl", hash = "sha256:0a931d85959ceb77e92aea4bbedfded0a31534ce191252721128f77e5ae1f98a"},
|
||||
{file = "ruff-0.11.7-py3-none-win_arm64.whl", hash = "sha256:778c1e5d6f9e91034142dfd06110534ca13220bfaad5c3735f6cb844654f6177"},
|
||||
{file = "ruff-0.11.7.tar.gz", hash = "sha256:655089ad3224070736dc32844fde783454f8558e71f501cb207485fe4eee23d4"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -9859,14 +9899,14 @@ unleash = ["UnleashClient (>=6.0.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "79.0.1"
|
||||
version = "80.0.0"
|
||||
description = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "setuptools-79.0.1-py3-none-any.whl", hash = "sha256:e147c0549f27767ba362f9da434eab9c5dc0045d5304feb602a0af001089fc51"},
|
||||
{file = "setuptools-79.0.1.tar.gz", hash = "sha256:128ce7b8f33c3079fd1b067ecbb4051a66e8526e7b65f6cec075dfc650ddfa88"},
|
||||
{file = "setuptools-80.0.0-py3-none-any.whl", hash = "sha256:a38f898dcd6e5380f4da4381a87ec90bd0a7eec23d204a5552e80ee3cab6bd27"},
|
||||
{file = "setuptools-80.0.0.tar.gz", hash = "sha256:c40a5b3729d58dd749c0f08f1a07d134fb8a0a3d7f87dc33e7c5e1f762138650"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -10173,14 +10213,14 @@ sqlcipher = ["sqlcipher3_binary"]
|
|||
|
||||
[[package]]
|
||||
name = "sqlglot"
|
||||
version = "26.16.1"
|
||||
version = "26.16.2"
|
||||
description = "An easily customizable SQL parser and transpiler"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "sqlglot-26.16.1-py3-none-any.whl", hash = "sha256:496cb742da55d491ae0c5b38d84e498362ad17a1eef1009d9b336b108a9ee636"},
|
||||
{file = "sqlglot-26.16.1.tar.gz", hash = "sha256:cced52b35bebb828722f2f4ae4d677d840470ef348f160945ae0ef3d4e457ef8"},
|
||||
{file = "sqlglot-26.16.2-py3-none-any.whl", hash = "sha256:0162f6c651f5786e2c0a6a1399c07967d8dfef61d9dde1858d58d4903c649ef1"},
|
||||
{file = "sqlglot-26.16.2.tar.gz", hash = "sha256:81278c5dcbc4935fe233d6d492ea2e991ba6d03c6609ac49a4d2e373cfa77898"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -10253,14 +10293,14 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "structlog"
|
||||
version = "25.2.0"
|
||||
version = "25.3.0"
|
||||
description = "Structured Logging for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "structlog-25.2.0-py3-none-any.whl", hash = "sha256:0fecea2e345d5d491b72f3db2e5fcd6393abfc8cd06a4851f21fcd4d1a99f437"},
|
||||
{file = "structlog-25.2.0.tar.gz", hash = "sha256:d9f9776944207d1035b8b26072b9b140c63702fd7aa57c2f85d28ab701bd8e92"},
|
||||
{file = "structlog-25.3.0-py3-none-any.whl", hash = "sha256:a341f5524004c158498c3127eecded091eb67d3a611e7a3093deca30db06e172"},
|
||||
{file = "structlog-25.3.0.tar.gz", hash = "sha256:8dab497e6f6ca962abad0c283c46744185e0c9ba900db52a423cb6db99f7abeb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -10274,15 +10314,15 @@ typing = ["mypy (>=1.4)", "rich", "twisted"]
|
|||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.13.3"
|
||||
version = "1.14.0"
|
||||
description = "Computer algebra system (CAS) in Python"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "python_version == \"3.10\" and (extra == \"chromadb\" or extra == \"codegraph\") or extra == \"chromadb\" or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
markers = "python_version == \"3.10\" and (extra == \"codegraph\" or extra == \"chromadb\") or extra == \"chromadb\" or python_version == \"3.11\" and (extra == \"chromadb\" or extra == \"codegraph\") or python_version == \"3.12\" and (extra == \"chromadb\" or extra == \"codegraph\")"
|
||||
files = [
|
||||
{file = "sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73"},
|
||||
{file = "sympy-1.13.3.tar.gz", hash = "sha256:b27fd2c6530e0ab39e275fc9b683895367e51d5da91baa8d3d64db2565fec4d9"},
|
||||
{file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"},
|
||||
{file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -10466,7 +10506,7 @@ description = "A lil' TOML parser"
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "python_version < \"3.11\" and (extra == \"notebook\" or extra == \"dev\" or extra == \"deepeval\")"
|
||||
markers = "python_version < \"3.11\" and (extra == \"dev\" or extra == \"notebook\" or extra == \"deepeval\")"
|
||||
files = [
|
||||
{file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
|
||||
|
|
@ -10766,14 +10806,14 @@ urllib3 = ">=1.26.0"
|
|||
|
||||
[[package]]
|
||||
name = "typer"
|
||||
version = "0.15.2"
|
||||
version = "0.15.3"
|
||||
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "typer-0.15.2-py3-none-any.whl", hash = "sha256:46a499c6107d645a9c13f7ee46c5d5096cae6f5fc57dd11eccbbb9ae3e44ddfc"},
|
||||
{file = "typer-0.15.2.tar.gz", hash = "sha256:ab2fab47533a813c49fe1f16b1a370fd5819099c00b119e0633df65f22144ba5"},
|
||||
{file = "typer-0.15.3-py3-none-any.whl", hash = "sha256:c86a65ad77ca531f03de08d1b9cb67cd09ad02ddddf4b34745b5008f43b239bd"},
|
||||
{file = "typer-0.15.3.tar.gz", hash = "sha256:818873625d0569653438316567861899f7e9972f2e6e0c16dab608345ced713c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -10797,14 +10837,14 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-setuptools"
|
||||
version = "79.0.0.20250422"
|
||||
version = "80.0.0.20250429"
|
||||
description = "Typing stubs for setuptools"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "types_setuptools-79.0.0.20250422-py3-none-any.whl", hash = "sha256:55238c0b18cdc08dd26c32d6d8385ca1ea59b93dde760dae96d15868b7911990"},
|
||||
{file = "types_setuptools-79.0.0.20250422.tar.gz", hash = "sha256:9c9f699a5914d2ed97f02ee749fb2c7bc2898f8dad03b5dd74b74d4f80e29972"},
|
||||
{file = "types_setuptools-80.0.0.20250429-py3-none-any.whl", hash = "sha256:9bbcdcea88d1fda4b0f1371488333f606c78a8b10154a42530ed926ecf3242cb"},
|
||||
{file = "types_setuptools-80.0.0.20250429.tar.gz", hash = "sha256:a4de44f1110f531e7f9453d72999437a1caa6052609e2c7c859dd6613ab0d593"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -11947,7 +11987,7 @@ api = ["gunicorn", "uvicorn"]
|
|||
chromadb = ["chromadb", "pypika"]
|
||||
codegraph = ["fastembed", "transformers", "tree-sitter", "tree-sitter-python"]
|
||||
deepeval = ["deepeval"]
|
||||
dev = ["coverage", "debugpy", "deptry", "gitpython", "mkdocs-material", "mkdocs-minify-plugin", "mkdocstrings", "mypy", "notebook", "pylance", "pylint", "pytest", "pytest-asyncio", "ruff", "tweepy"]
|
||||
dev = ["coverage", "debugpy", "deptry", "gitpython", "mkdocs-material", "mkdocs-minify-plugin", "mkdocstrings", "mypy", "notebook", "pylance", "pylint", "pytest", "pytest-asyncio", "pytest-cov", "ruff", "tweepy"]
|
||||
docs = ["unstructured"]
|
||||
evals = ["gdown", "plotly"]
|
||||
falkordb = ["falkordb"]
|
||||
|
|
@ -11972,4 +12012,4 @@ weaviate = ["weaviate-client"]
|
|||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.10,<=3.13"
|
||||
content-hash = "3bceceedcc14d649181c255eae55057103e8a3ec55df3250988249fd0176cb78"
|
||||
content-hash = "7a300700800488853f060001d915b84d108461a6a39ec8299dcb6390fa78ea41"
|
||||
|
|
|
|||
|
|
@ -112,6 +112,7 @@ gui = [
|
|||
graphiti = ["graphiti-core>=0.7.0,<0.8"]
|
||||
dev = [
|
||||
"pytest>=7.4.0,<8",
|
||||
"pytest-cov>=6.1.1",
|
||||
"pytest-asyncio>=0.21.1,<0.22",
|
||||
"coverage>=7.3.2,<8",
|
||||
"mypy>=1.7.1,<2",
|
||||
|
|
|
|||
20
uv.lock
generated
20
uv.lock
generated
|
|
@ -932,6 +932,7 @@ dev = [
|
|||
{ name = "pylint" },
|
||||
{ name = "pytest" },
|
||||
{ name = "pytest-asyncio" },
|
||||
{ name = "pytest-cov" },
|
||||
{ name = "ruff" },
|
||||
{ name = "tweepy" },
|
||||
]
|
||||
|
|
@ -1069,6 +1070,7 @@ requires-dist = [
|
|||
{ name = "pyside6", marker = "extra == 'gui'", specifier = ">=6.8.3,<7" },
|
||||
{ name = "pytest", marker = "extra == 'dev'", specifier = ">=7.4.0,<8" },
|
||||
{ name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.21.1,<0.22" },
|
||||
{ name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.1.1" },
|
||||
{ name = "python-dotenv", specifier = "==1.0.1" },
|
||||
{ name = "python-multipart", specifier = "==0.0.20" },
|
||||
{ name = "qasync", marker = "extra == 'gui'", specifier = ">=0.27.1,<0.28" },
|
||||
|
|
@ -1252,6 +1254,11 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/59/f1/4da7717f0063a222db253e7121bd6a56f6fb1ba439dcc36659088793347c/coverage-7.8.0-py3-none-any.whl", hash = "sha256:dbf364b4c5e7bae9250528167dfe40219b62e2d573c854d74be213e1e52069f7", size = 203435 },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
toml = [
|
||||
{ name = "tomli", marker = "python_full_version <= '3.11'" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cryptography"
|
||||
version = "44.0.2"
|
||||
|
|
@ -6075,6 +6082,19 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/9c/ce/1e4b53c213dce25d6e8b163697fbce2d43799d76fa08eea6ad270451c370/pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b", size = 13368 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-cov"
|
||||
version = "6.1.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "coverage", extra = ["toml"] },
|
||||
{ name = "pytest" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/25/69/5f1e57f6c5a39f81411b550027bf72842c4567ff5fd572bed1edc9e4b5d9/pytest_cov-6.1.1.tar.gz", hash = "sha256:46935f7aaefba760e716c2ebfbe1c216240b9592966e7da99ea8292d4d3e2a0a", size = 66857 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/28/d0/def53b4a790cfb21483016430ed828f64830dd981ebe1089971cd10cab25/pytest_cov-6.1.1-py3-none-any.whl", hash = "sha256:bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde", size = 23841 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-repeat"
|
||||
version = "0.9.4"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue