From cd9c4897a4c2f56d7205460cc07787807a539ed3 Mon Sep 17 00:00:00 2001
From: Boris <boris@topoteretes.com>
Date: Wed, 30 Apr 2025 11:11:07 +0200
Subject: [PATCH] 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.
---
 .../vector/chromadb/ChromaDBAdapter.py        |  96 +---
 .../vector/lancedb/LanceDBAdapter.py          |  66 +--
 .../databases/vector/milvus/MilvusAdapter.py  |  31 +-
 .../vector/pgvector/PGVectorAdapter.py        |  83 +--
 .../databases/vector/qdrant/QDrantAdapter.py  |  86 ++--
 .../vector/weaviate_db/WeaviateAdapter.py     | 147 +++---
 .../modules/graph/cognee_graph/CogneeGraph.py |   6 +-
 .../modules/retrieval/exceptions/__init__.py  |   2 +-
 .../retrieval/exceptions/exceptions.py        |  10 -
 .../retrieval/graph_completion_retriever.py   |   6 +-
 .../utils/brute_force_triplet_search.py       |  35 +-
 cognee/tests/test_weaviate.py                 |   2 +-
 .../retrieval/chunks_retriever_test.py        |  21 +-
 .../graph_completion_retriever_test.py        |   9 +-
 .../retrieval/summaries_retriever_test.py     |   2 +-
 .../utils/brute_force_triplet_search_test.py  |  44 --
 poetry.lock                                   | 474 ++++++++++--------
 pyproject.toml                                |   1 +
 uv.lock                                       |  20 +
 19 files changed, 500 insertions(+), 641 deletions(-)
 delete mode 100644 cognee/tests/unit/modules/retrieval/utils/brute_force_triplet_search_test.py

diff --git a/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py b/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py
index 112273bc9..7f8391c1a 100644
--- a/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py
+++ b/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py
@@ -6,8 +6,9 @@ from chromadb import AsyncHttpClient, Settings
 from cognee.exceptions import InvalidValueError
 from cognee.shared.logging_utils import get_logger
 from cognee.modules.storage.utils import get_own_properties
-from cognee.infrastructure.engine.utils import parse_id
 from cognee.infrastructure.engine import DataPoint
+from cognee.infrastructure.engine.utils import parse_id
+from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
 from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
 
 from ..embeddings.EmbeddingEngine import EmbeddingEngine
@@ -108,9 +109,7 @@ class ChromaDBAdapter(VectorDBInterface):
         return await self.embedding_engine.embed_text(data)
 
     async def has_collection(self, collection_name: str) -> bool:
-        client = await self.get_connection()
-        collections = await client.list_collections()
-        # In ChromaDB v0.6.0, list_collections returns collection names directly
+        collections = await self.get_collection_names()
         return collection_name in collections
 
     async def create_collection(self, collection_name: str, payload_schema=None):
@@ -119,13 +118,17 @@ class ChromaDBAdapter(VectorDBInterface):
         if not await self.has_collection(collection_name):
             await client.create_collection(name=collection_name, metadata={"hnsw:space": "cosine"})
 
-    async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
-        client = await self.get_connection()
-
+    async def get_collection(self, collection_name: str) -> AsyncHttpClient:
         if not await self.has_collection(collection_name):
-            await self.create_collection(collection_name)
+            raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
 
-        collection = await client.get_collection(collection_name)
+        client = await self.get_connection()
+        return await client.get_collection(collection_name)
+
+    async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
+        await self.create_collection(collection_name)
+
+        collection = await self.get_collection(collection_name)
 
         texts = [DataPoint.get_embeddable_data(data_point) for data_point in data_points]
         embeddings = await self.embed_data(texts)
@@ -161,8 +164,7 @@ class ChromaDBAdapter(VectorDBInterface):
 
     async def retrieve(self, collection_name: str, data_point_ids: list[str]):
         """Retrieve data points by their IDs from a collection."""
-        client = await self.get_connection()
-        collection = await client.get_collection(collection_name)
+        collection = await self.get_collection(collection_name)
         results = await collection.get(ids=data_point_ids, include=["metadatas"])
 
         return [
@@ -174,62 +176,12 @@ class ChromaDBAdapter(VectorDBInterface):
             for id, metadata in zip(results["ids"], results["metadatas"])
         ]
 
-    async def get_distance_from_collection_elements(
-        self, collection_name: str, query_text: str = None, query_vector: List[float] = None
-    ):
-        """Calculate distance between query and all elements in a collection."""
-        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_text and not query_vector:
-            query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
-
-        client = await self.get_connection()
-        try:
-            collection = await client.get_collection(collection_name)
-
-            collection_count = await collection.count()
-
-            results = await collection.query(
-                query_embeddings=[query_vector],
-                include=["metadatas", "distances"],
-                n_results=collection_count,
-            )
-
-            result_values = []
-            for i, (id, metadata, distance) in enumerate(
-                zip(results["ids"][0], results["metadatas"][0], results["distances"][0])
-            ):
-                result_values.append(
-                    {
-                        "id": parse_id(id),
-                        "payload": restore_data_from_chroma(metadata),
-                        "_distance": distance,
-                    }
-                )
-
-            normalized_values = normalize_distances(result_values)
-
-            scored_results = []
-            for i, result in enumerate(result_values):
-                scored_results.append(
-                    ScoredResult(
-                        id=result["id"],
-                        payload=result["payload"],
-                        score=normalized_values[i],
-                    )
-                )
-
-            return scored_results
-        except Exception:
-            return []
-
     async def search(
         self,
         collection_name: str,
         query_text: str = None,
         query_vector: List[float] = None,
-        limit: int = 5,
+        limit: int = 15,
         with_vector: bool = False,
         normalized: bool = True,
     ):
@@ -241,8 +193,10 @@ class ChromaDBAdapter(VectorDBInterface):
             query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
 
         try:
-            client = await self.get_connection()
-            collection = await client.get_collection(collection_name)
+            collection = await self.get_collection(collection_name)
+
+            if limit == 0:
+                limit = await collection.count()
 
             results = await collection.query(
                 query_embeddings=[query_vector],
@@ -296,8 +250,7 @@ class ChromaDBAdapter(VectorDBInterface):
         """Perform multiple searches in a single request for efficiency."""
         query_vectors = await self.embed_data(query_texts)
 
-        client = await self.get_connection()
-        collection = await client.get_collection(collection_name)
+        collection = await self.get_collection(collection_name)
 
         results = await collection.query(
             query_embeddings=query_vectors,
@@ -346,15 +299,14 @@ class ChromaDBAdapter(VectorDBInterface):
 
     async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
         """Remove data points from a collection by their IDs."""
-        client = await self.get_connection()
-        collection = await client.get_collection(collection_name)
+        collection = await self.get_collection(collection_name)
         await collection.delete(ids=data_point_ids)
         return True
 
     async def prune(self):
         """Delete all collections in the ChromaDB database."""
         client = await self.get_connection()
-        collections = await client.list_collections()
+        collections = await self.list_collections()
         for collection_name in collections:
             await client.delete_collection(collection_name)
         return True
@@ -362,4 +314,8 @@ class ChromaDBAdapter(VectorDBInterface):
     async def get_collection_names(self):
         """Get a list of all collection names in the database."""
         client = await self.get_connection()
-        return await client.list_collections()
+        collections = await client.list_collections()
+        return [
+            collection.name if hasattr(collection, "name") else collection["name"]
+            for collection in collections
+        ]
diff --git a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
index 48c7dcaa8..4ea28e4ac 100644
--- a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
+++ b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
@@ -1,6 +1,5 @@
 import asyncio
 from typing import Generic, List, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
-
 import lancedb
 from lancedb.pydantic import LanceModel, Vector
 from pydantic import BaseModel
@@ -76,9 +75,14 @@ class LanceDBAdapter(VectorDBInterface):
                 exist_ok=True,
             )
 
-    async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
-        connection = await self.get_connection()
+    async def get_collection(self, collection_name: str):
+        if not await self.has_collection(collection_name):
+            raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
 
+        connection = await self.get_connection()
+        return await connection.open_table(collection_name)
+
+    async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
         payload_schema = type(data_points[0])
 
         if not await self.has_collection(collection_name):
@@ -87,7 +91,7 @@ class LanceDBAdapter(VectorDBInterface):
                 payload_schema,
             )
 
-        collection = await connection.open_table(collection_name)
+        collection = await self.get_collection(collection_name)
 
         data_vectors = await self.embed_data(
             [DataPoint.get_embeddable_data(data_point) for data_point in data_points]
@@ -125,8 +129,7 @@ class LanceDBAdapter(VectorDBInterface):
         )
 
     async def retrieve(self, collection_name: str, data_point_ids: list[str]):
-        connection = await self.get_connection()
-        collection = await connection.open_table(collection_name)
+        collection = await self.get_collection(collection_name)
 
         if len(data_point_ids) == 1:
             results = await collection.query().where(f"id = '{data_point_ids[0]}'").to_pandas()
@@ -142,48 +145,12 @@ class LanceDBAdapter(VectorDBInterface):
             for result in results.to_dict("index").values()
         ]
 
-    async def get_distance_from_collection_elements(
-        self, collection_name: str, query_text: str = None, query_vector: List[float] = None
-    ):
-        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_text and not query_vector:
-            query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
-
-        connection = await self.get_connection()
-
-        try:
-            collection = await connection.open_table(collection_name)
-
-            collection_size = await collection.count_rows()
-
-            results = (
-                await collection.vector_search(query_vector).limit(collection_size).to_pandas()
-            )
-
-            result_values = list(results.to_dict("index").values())
-
-            normalized_values = normalize_distances(result_values)
-
-            return [
-                ScoredResult(
-                    id=parse_id(result["id"]),
-                    payload=result["payload"],
-                    score=normalized_values[value_index],
-                )
-                for value_index, result in enumerate(result_values)
-            ]
-        except ValueError:
-            # Ignore if collection doesn't exist
-            return []
-
     async def search(
         self,
         collection_name: str,
         query_text: str = None,
         query_vector: List[float] = None,
-        limit: int = 5,
+        limit: int = 15,
         with_vector: bool = False,
         normalized: bool = True,
     ):
@@ -193,12 +160,10 @@ class LanceDBAdapter(VectorDBInterface):
         if query_text and not query_vector:
             query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
 
-        connection = await self.get_connection()
+        collection = await self.get_collection(collection_name)
 
-        try:
-            collection = await connection.open_table(collection_name)
-        except ValueError:
-            raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
+        if limit == 0:
+            limit = await collection.count_rows()
 
         results = await collection.vector_search(query_vector).limit(limit).to_pandas()
 
@@ -242,8 +207,7 @@ class LanceDBAdapter(VectorDBInterface):
     def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
         @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
         async def _delete_data_points():
-            connection = await self.get_connection()
-            collection = await connection.open_table(collection_name)
+            collection = await self.get_collection(collection_name)
 
             # Delete one at a time to avoid commit conflicts
             for data_point_id in data_point_ids:
@@ -288,7 +252,7 @@ class LanceDBAdapter(VectorDBInterface):
         collection_names = await connection.table_names()
 
         for collection_name in collection_names:
-            collection = await connection.open_table(collection_name)
+            collection = await self.get_collection(collection_name)
             await collection.delete("id IS NOT NULL")
             await connection.drop_table(collection_name)
 
diff --git a/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py b/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py
index 5247b1730..9daebb760 100644
--- a/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py
+++ b/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py
@@ -1,11 +1,12 @@
 from __future__ import annotations
-
 import asyncio
-from cognee.shared.logging_utils import get_logger
+from uuid import UUID
 from typing import List, Optional
 
+from cognee.shared.logging_utils import get_logger
 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
@@ -96,7 +97,7 @@ class MilvusAdapter(VectorDBInterface):
             raise e
 
     async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
-        from pymilvus import MilvusException
+        from pymilvus import MilvusException, exceptions
 
         client = self.get_milvus_client()
         data_vectors = await self.embed_data(
@@ -118,6 +119,10 @@ class MilvusAdapter(VectorDBInterface):
                 f"Inserted {result.get('insert_count', 0)} data points into collection '{collection_name}'."
             )
             return result
+        except exceptions.CollectionNotExistException as error:
+            raise CollectionNotFoundError(
+                f"Collection '{collection_name}' does not exist!"
+            ) from error
         except MilvusException as e:
             logger.error(
                 f"Error inserting data points into collection '{collection_name}': {str(e)}"
@@ -140,8 +145,8 @@ class MilvusAdapter(VectorDBInterface):
         collection_name = f"{index_name}_{index_property_name}"
         await self.create_data_points(collection_name, formatted_data_points)
 
-    async def retrieve(self, collection_name: str, data_point_ids: list[str]):
-        from pymilvus import MilvusException
+    async def retrieve(self, collection_name: str, data_point_ids: list[UUID]):
+        from pymilvus import MilvusException, exceptions
 
         client = self.get_milvus_client()
         try:
@@ -153,6 +158,10 @@ class MilvusAdapter(VectorDBInterface):
                 output_fields=["*"],
             )
             return results
+        except exceptions.CollectionNotExistException as error:
+            raise CollectionNotFoundError(
+                f"Collection '{collection_name}' does not exist!"
+            ) from error
         except MilvusException as e:
             logger.error(
                 f"Error retrieving data points from collection '{collection_name}': {str(e)}"
@@ -164,10 +173,10 @@ class MilvusAdapter(VectorDBInterface):
         collection_name: str,
         query_text: Optional[str] = None,
         query_vector: Optional[List[float]] = None,
-        limit: int = 5,
+        limit: int = 15,
         with_vector: bool = False,
     ):
-        from pymilvus import MilvusException
+        from pymilvus import MilvusException, exceptions
 
         client = self.get_milvus_client()
         if query_text is None and query_vector is None:
@@ -184,7 +193,7 @@ class MilvusAdapter(VectorDBInterface):
                 collection_name=collection_name,
                 data=[query_vector],
                 anns_field="vector",
-                limit=limit,
+                limit=limit if limit > 0 else None,
                 output_fields=output_fields,
                 search_params={
                     "metric_type": "COSINE",
@@ -199,6 +208,10 @@ class MilvusAdapter(VectorDBInterface):
                 )
                 for result in results[0]
             ]
+        except exceptions.CollectionNotExistException as error:
+            raise CollectionNotFoundError(
+                f"Collection '{collection_name}' does not exist!"
+            ) from error
         except MilvusException as e:
             logger.error(f"Error during search in collection '{collection_name}': {str(e)}")
             raise e
@@ -220,7 +233,7 @@ class MilvusAdapter(VectorDBInterface):
             ]
         )
 
-    async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
+    async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]):
         from pymilvus import MilvusException
 
         client = self.get_milvus_client()
diff --git a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py
index ae4739184..5796a4619 100644
--- a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py
+++ b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py
@@ -7,19 +7,18 @@ from sqlalchemy import JSON, Column, Table, select, delete, MetaData
 from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
 
 from cognee.exceptions import InvalidValueError
-from cognee.infrastructure.databases.exceptions import EntityNotFoundError
-from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
 from cognee.infrastructure.engine import DataPoint
 from cognee.infrastructure.engine.utils import parse_id
 from cognee.infrastructure.databases.relational import get_relational_engine
 
 from ...relational.ModelBase import Base
 from ...relational.sqlalchemy.SqlAlchemyAdapter import SQLAlchemyAdapter
-from ..embeddings.EmbeddingEngine import EmbeddingEngine
-from ..models.ScoredResult import ScoredResult
-from ..vector_db_interface import VectorDBInterface
-from .serialize_data import serialize_data
 from ..utils import normalize_distances
+from ..models.ScoredResult import ScoredResult
+from ..exceptions import CollectionNotFoundError
+from ..vector_db_interface import VectorDBInterface
+from ..embeddings.EmbeddingEngine import EmbeddingEngine
+from .serialize_data import serialize_data
 
 
 class IndexSchema(DataPoint):
@@ -203,60 +202,12 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
                 for result in results
             ]
 
-    async def get_distance_from_collection_elements(
-        self,
-        collection_name: str,
-        query_text: str = None,
-        query_vector: List[float] = None,
-        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)):
diff --git a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
index 48148c9c8..72d66e2de 100644
--- a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
+++ b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
@@ -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,
diff --git a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
index 200f828db..dac6bc83f 100644
--- a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
+++ b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
@@ -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()
diff --git a/cognee/modules/graph/cognee_graph/CogneeGraph.py b/cognee/modules/graph/cognee_graph/CogneeGraph.py
index fa1edca1b..f79bbd010 100644
--- a/cognee/modules/graph/cognee_graph/CogneeGraph.py
+++ b/cognee/modules/graph/cognee_graph/CogneeGraph.py
@@ -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}
diff --git a/cognee/modules/retrieval/exceptions/__init__.py b/cognee/modules/retrieval/exceptions/__init__.py
index feb84a962..1b98cddcd 100644
--- a/cognee/modules/retrieval/exceptions/__init__.py
+++ b/cognee/modules/retrieval/exceptions/__init__.py
@@ -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
diff --git a/cognee/modules/retrieval/exceptions/exceptions.py b/cognee/modules/retrieval/exceptions/exceptions.py
index acd9c1fa8..7e9ebd65b 100644
--- a/cognee/modules/retrieval/exceptions/exceptions.py
+++ b/cognee/modules/retrieval/exceptions/exceptions.py
@@ -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,
diff --git a/cognee/modules/retrieval/graph_completion_retriever.py b/cognee/modules/retrieval/graph_completion_retriever.py
index 3d41444c2..98ac5dd61 100644
--- a/cognee/modules/retrieval/graph_completion_retriever.py
+++ b/cognee/modules/retrieval/graph_completion_retriever.py
@@ -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 ""
diff --git a/cognee/modules/retrieval/utils/brute_force_triplet_search.py b/cognee/modules/retrieval/utils/brute_force_triplet_search.py
index fcec0edd3..5e08eb9ac 100644
--- a/cognee/modules/retrieval/utils/brute_force_triplet_search.py
+++ b/cognee/modules/retrieval/utils/brute_force_triplet_search.py
@@ -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",
diff --git a/cognee/tests/test_weaviate.py b/cognee/tests/test_weaviate.py
index 3f67695c7..174b6b4f8 100644
--- a/cognee/tests/test_weaviate.py
+++ b/cognee/tests/test_weaviate.py
@@ -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"
 
 
diff --git a/cognee/tests/unit/modules/retrieval/chunks_retriever_test.py b/cognee/tests/unit/modules/retrieval/chunks_retriever_test.py
index 0050228e9..a28885e8c 100644
--- a/cognee/tests/unit/modules/retrieval/chunks_retriever_test.py
+++ b/cognee/tests/unit/modules/retrieval/chunks_retriever_test.py
@@ -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())
diff --git a/cognee/tests/unit/modules/retrieval/graph_completion_retriever_test.py b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_test.py
index ad9a6eb52..2dc6ca6ce 100644
--- a/cognee/tests/unit/modules/retrieval/graph_completion_retriever_test.py
+++ b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_test.py
@@ -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())
diff --git a/cognee/tests/unit/modules/retrieval/summaries_retriever_test.py b/cognee/tests/unit/modules/retrieval/summaries_retriever_test.py
index 69a53194a..e12979caa 100644
--- a/cognee/tests/unit/modules/retrieval/summaries_retriever_test.py
+++ b/cognee/tests/unit/modules/retrieval/summaries_retriever_test.py
@@ -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")
 
diff --git a/cognee/tests/unit/modules/retrieval/utils/brute_force_triplet_search_test.py b/cognee/tests/unit/modules/retrieval/utils/brute_force_triplet_search_test.py
deleted file mode 100644
index cb4d8de8c..000000000
--- a/cognee/tests/unit/modules/retrieval/utils/brute_force_triplet_search_test.py
+++ /dev/null
@@ -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
-        )
diff --git a/poetry.lock b/poetry.lock
index 603d93da6..f88d77218 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -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 = [
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-    {file = "llama_parse-0.6.12.tar.gz", hash = "sha256:c99593fb955c338a69e64a2ec449e09753afe6dcff239ab050989fda74839867"},
+    {file = "llama_parse-0.6.20-py3-none-any.whl", hash = "sha256:7f99d42a0fa70530d0560d1235bba14a7c198c8db8c0fddd80c32ad61b1d5b10"},
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 ]
 
 [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 = [
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@@ -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 = [
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+    {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)"]
+pyspark-connect = ["pyspark[connect] (>=3.5.0)"]
 sqlframe = ["sqlframe (>=3.22.0)"]
 
 [[package]]
@@ -6533,7 +6536,7 @@ description = "ONNX Runtime is a runtime accelerator for Machine Learning models
 optional = true
 python-versions = ">=3.10"
 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\")"
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@@ -6565,14 +6568,14 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.76.0"
+version = "1.76.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -6730,81 +6733,85 @@ files = [
 
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 name = "orjson"
-version = "3.10.16"
+version = "3.10.17"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "platform_python_implementation != \"PyPy\""
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 [[package]]
@@ -6929,8 +6936,8 @@ files = [
 [package.dependencies]
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-    {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\""},
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -7549,7 +7556,7 @@ description = ""
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 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.9"
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 [package.extras]
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     {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 = [
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     {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 = [
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+    {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 = [
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-    {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"},
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@@ -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"
diff --git a/pyproject.toml b/pyproject.toml
index dcefadc76..7ca75d318 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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",
diff --git a/uv.lock b/uv.lock
index e864fa770..00d716e41 100644
--- a/uv.lock
+++ b/uv.lock
@@ -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"