From 219afbce685899557c4110eadd95c82243958605 Mon Sep 17 00:00:00 2001
From: Boris <boris@topoteretes.com>
Date: Fri, 3 May 2024 10:35:41 +0200
Subject: [PATCH 1/3] feat: add lancedb vector storage [COG-176] (#90)

* feat: integrate lancedb

* fix: use futures in weaviate adapter to enable async behaviour
---
 .gitignore                                    |   2 +-
 cognee/api/v1/cognify/cognify.py              |  29 +-
 cognee/infrastructure/InfrastructureConfig.py |  32 +-
 .../embeddings/DefaultEmbeddingEngine.py      |   2 -
 .../vector/embeddings/EmbeddingEngine.py      |   4 +-
 .../vector/lancedb/LanceDBAdapter.py          | 187 ++++---
 .../databases/vector/models/DataPoint.py      |  11 +-
 .../databases/vector/models/PayloadSchema.py  |   3 +
 .../databases/vector/qdrant/QDrantAdapter.py  |   7 +-
 .../databases/vector/vector_db_interface.py   |  62 +--
 .../vector/weaviate_db/WeaviateAdapter.py     |  53 +-
 .../graph/add_cognitive_layer_graphs.py       |  13 +-
 .../modules/cognify/graph/add_data_chunks.py  |  49 +-
 .../cognify/graph/add_document_node.py        |  14 +-
 .../modules/cognify/graph/add_label_nodes.py  |  16 +-
 .../extract_knowledge_graph.py                |   4 +-
 cognee/modules/memory/__init__.py             |   0
 cognee/modules/memory/vector/__init__.py      |   1 -
 .../memory/vector/create_vector_memory.py     |   7 -
 .../search/vector/search_similarity.py        |   7 +-
 notebooks/full_run.ipynb                      |  54 +-
 poetry.lock                                   | 469 +++++++-----------
 pyproject.toml                                |  24 +-
 23 files changed, 498 insertions(+), 552 deletions(-)
 create mode 100644 cognee/infrastructure/databases/vector/models/PayloadSchema.py
 delete mode 100644 cognee/modules/memory/__init__.py
 delete mode 100644 cognee/modules/memory/vector/__init__.py
 delete mode 100644 cognee/modules/memory/vector/create_vector_memory.py

diff --git a/.gitignore b/.gitignore
index 86c59647e..558fa4e82 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,7 +160,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 .vscode/
 cognee/data/
diff --git a/cognee/api/v1/cognify/cognify.py b/cognee/api/v1/cognify/cognify.py
index 32fcd9285..3fd0d902d 100644
--- a/cognee/api/v1/cognify/cognify.py
+++ b/cognee/api/v1/cognify/cognify.py
@@ -16,7 +16,6 @@ from cognee.modules.cognify.graph.add_node_connections import group_nodes_by_lay
     graph_ready_output, connect_nodes_in_graph
 from cognee.modules.cognify.llm.resolve_cross_graph_references import resolve_cross_graph_references
 from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
-from cognee.modules.cognify.graph.add_label_nodes import add_label_nodes
 from cognee.modules.cognify.graph.add_cognitive_layers import add_cognitive_layers
 # from cognee.modules.cognify.graph.initialize_graph import initialize_graph
 from cognee.infrastructure.files.utils.guess_file_type import guess_file_type, FileTypeException
@@ -41,7 +40,7 @@ logger = logging.getLogger("cognify")
 async def cognify(datasets: Union[str, List[str]] = None):
     """This function is responsible for the cognitive processing of the content."""
     # Has to be loaded in advance, multithreading doesn't work without it.
-    nltk.download('stopwords', quiet=True)
+    nltk.download("stopwords", quiet=True)
     stopwords.ensure_loaded()
 
     graph_db_type = infrastructure_config.get_config()["graph_engine"]
@@ -84,6 +83,12 @@ async def cognify(datasets: Union[str, List[str]] = None):
         for file_metadata in files:
             with open(file_metadata["file_path"], "rb") as file:
                 try:
+                    document_id = await add_document_node(
+                        graph_client,
+                        parent_node_id = f"DefaultGraphModel__{USER_ID}",
+                        document_metadata = file_metadata,
+                    )
+
                     file_type = guess_file_type(file)
                     text = extract_text_from_file(file, file_type)
                     subchunks = chunk_engine.chunk_data(chunk_strategy, text, config.chunk_size, config.chunk_overlap)
@@ -92,31 +97,25 @@ async def cognify(datasets: Union[str, List[str]] = None):
                         data_chunks[dataset_name] = []
 
                     for subchunk in subchunks:
-                        data_chunks[dataset_name].append(dict(text = subchunk, chunk_id = str(uuid4()), file_metadata = file_metadata))
+                        data_chunks[dataset_name].append(dict(document_id = document_id, chunk_id = str(uuid4()), text = subchunk))
                 except FileTypeException:
                     logger.warning("File (%s) has an unknown file type. We are skipping it.", file_metadata["id"])
 
     added_chunks: list[tuple[str, str, dict]] = await add_data_chunks(data_chunks)
 
     await asyncio.gather(
-        *[process_text(chunk["collection"], chunk["chunk_id"], chunk["text"], chunk["file_metadata"]) for chunk in added_chunks]
+        *[process_text(chunk["document_id"], chunk["chunk_id"], chunk["collection"], chunk["text"]) for chunk in added_chunks]
     )
 
     return graph_client.graph
 
-async def process_text(chunk_collection: str, chunk_id: str, input_text: str, file_metadata: dict):
-    print(f"Processing chunk ({chunk_id}) from document ({file_metadata['id']}).")
+async def process_text(document_id: str, chunk_id: str, chunk_collection: str, input_text: str):
+    raw_document_id = document_id.split("__")[-1]
+
+    print(f"Processing chunk ({chunk_id}) from document ({raw_document_id}).")
 
     graph_client = await get_graph_client(infrastructure_config.get_config()["graph_engine"])
 
-    document_id = await add_document_node(
-        graph_client,
-        parent_node_id = f"DefaultGraphModel__{USER_ID}", #make a param of defaultgraph model to make sure when user passes his stuff, it doesn't break pipeline
-        document_metadata = file_metadata,
-    )
-
-    await add_label_nodes(graph_client, document_id, chunk_id, file_metadata["keywords"].split("|"))
-
     classified_categories = await get_content_categories(input_text)
     await add_classification_nodes(
         graph_client,
@@ -139,7 +138,7 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
 
     if infrastructure_config.get_config()["connect_documents"] is True:
         db_engine = infrastructure_config.get_config()["database_engine"]
-        relevant_documents_to_connect = db_engine.fetch_cognify_data(excluded_document_id = file_metadata["id"])
+        relevant_documents_to_connect = db_engine.fetch_cognify_data(excluded_document_id = raw_document_id)
 
         list_of_nodes = []
 
diff --git a/cognee/infrastructure/InfrastructureConfig.py b/cognee/infrastructure/InfrastructureConfig.py
index 12a21b188..7615b8516 100644
--- a/cognee/infrastructure/InfrastructureConfig.py
+++ b/cognee/infrastructure/InfrastructureConfig.py
@@ -1,7 +1,6 @@
 from cognee.config import Config
 from .databases.relational import DuckDBAdapter, DatabaseEngine
 from .databases.vector.vector_db_interface import VectorDBInterface
-from .databases.vector.qdrant.QDrantAdapter import QDrantAdapter
 from .databases.vector.embeddings.DefaultEmbeddingEngine import DefaultEmbeddingEngine
 from .llm.llm_interface import LLMInterface
 from .llm.openai.adapter import OpenAIAdapter
@@ -81,6 +80,12 @@ class InfrastructureConfig():
         if (config_entity is None or config_entity == "llm_engine") and self.llm_engine is None:
             self.llm_engine = OpenAIAdapter(config.openai_key, config.openai_model)
 
+        if (config_entity is None or config_entity == "database_directory_path") and self.database_directory_path is None:
+            self.database_directory_path = self.system_root_directory + "/" + config.db_path
+
+        if (config_entity is None or config_entity == "database_file_path") and self.database_file_path is None:
+            self.database_file_path = self.system_root_directory + "/" + config.db_path + "/" + config.db_name
+
         if (config_entity is None or config_entity == "vector_engine") and self.vector_engine is None:
             try:
                 from .databases.vector.weaviate_db import WeaviateAdapter
@@ -94,17 +99,24 @@ class InfrastructureConfig():
                     embedding_engine = self.embedding_engine
                 )
             except (EnvironmentError, ModuleNotFoundError):
-                self.vector_engine = QDrantAdapter(
-                    qdrant_url = config.qdrant_url,
-                    qdrant_api_key = config.qdrant_api_key,
-                    embedding_engine = self.embedding_engine
-                )
+                if config.qdrant_url and config.qdrant_api_key:
+                    from .databases.vector.qdrant.QDrantAdapter import QDrantAdapter
 
-        if (config_entity is None or config_entity == "database_directory_path") and self.database_directory_path is None:
-            self.database_directory_path = self.system_root_directory + "/" + config.db_path
+                    self.vector_engine = QDrantAdapter(
+                        qdrant_url = config.qdrant_url,
+                        qdrant_api_key = config.qdrant_api_key,
+                        embedding_engine = self.embedding_engine
+                    )
+                else:
+                    from .databases.vector.lancedb.LanceDBAdapter import LanceDBAdapter
+                    lance_db_path = self.database_directory_path + "/cognee.lancedb"
+                    LocalStorage.ensure_directory_exists(lance_db_path)
 
-        if (config_entity is None or config_entity == "database_file_path") and self.database_file_path is None:
-            self.database_file_path = self.system_root_directory + "/" + config.db_path + "/" + config.db_name
+                    self.vector_engine = LanceDBAdapter(
+                        uri = lance_db_path,
+                        api_key = None,
+                        embedding_engine = self.embedding_engine,
+                    )
 
         if config_entity is not None:
             return getattr(self, config_entity)
diff --git a/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py
index eb23b0509..19d281340 100644
--- a/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py
+++ b/cognee/infrastructure/databases/vector/embeddings/DefaultEmbeddingEngine.py
@@ -3,8 +3,6 @@ from typing import List
 import instructor
 from openai import AsyncOpenAI
 from fastembed import TextEmbedding
-from fastembed import TextEmbedding
-from openai import AsyncOpenAI
 
 from cognee.config import Config
 from cognee.root_dir import get_absolute_path
diff --git a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py
index f28ff4115..bcb99fb07 100644
--- a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py
+++ b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py
@@ -1,7 +1,7 @@
-from typing import List, Protocol
+from typing import Protocol
 
 class EmbeddingEngine(Protocol):
-    async def embed_text(self, text: str) -> List[float]:
+    async def embed_text(self, text: list[str]) -> list[list[float]]:
         raise NotImplementedError()
 
     def get_vector_size(self) -> int:
diff --git a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
index 286706de1..9ffe3d8de 100644
--- a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
+++ b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
@@ -1,74 +1,145 @@
+from typing import List, Optional, get_type_hints, Generic, TypeVar
 import asyncio
+from pydantic import BaseModel, Field
 import lancedb
-from typing import List, Optional
+from lancedb.pydantic import Vector, LanceModel
+from cognee.infrastructure.files.storage import LocalStorage
+from ..models.ScoredResult import ScoredResult
+from ..vector_db_interface import VectorDBInterface, DataPoint
+from ..embeddings.EmbeddingEngine import EmbeddingEngine
 
-import asyncio
-import lancedb
-from pathlib import Path
-import tempfile
+class LanceDBAdapter(VectorDBInterface):
+    connection: lancedb.AsyncConnection = None
 
-class LanceDBAdapter:
-    def __init__(self, uri: Optional[str] = None, api_key: Optional[str] = None):
-        if uri:
-            self.uri = uri
-        else:
-            # Create a temporary directory for the LanceDB 'in-memory' simulation
-            self.temp_dir = tempfile.mkdtemp(suffix='.lancedb')
-            self.uri = f"file://{self.temp_dir}"
+    def __init__(
+        self,
+        uri: Optional[str],
+        api_key: Optional[str],
+        embedding_engine: EmbeddingEngine,
+    ):
+        self.uri = uri
         self.api_key = api_key
-        self.db = None
+        self.embedding_engine = embedding_engine
 
-    async def connect(self):
-        # Asynchronously connect to a LanceDB database, effectively in-memory if no URI is provided
-        self.db = await lancedb.connect_async(self.uri, api_key=self.api_key)
+    async def get_connection(self):
+        if self.connection is None:
+            self.connection = await lancedb.connect_async(self.uri, api_key = self.api_key)
 
-    async def disconnect(self):
-        # Disconnect and clean up the database if it was set up as temporary
-        await self.db.close()
-        if hasattr(self, 'temp_dir'):
-            Path(self.temp_dir).unlink(missing_ok=True)  # Remove the temporary directory
+        return self.connection
 
-    async def create_table(self, table_name: str, schema=None, data=None):
-        if not await self.table_exists(table_name):
-            return await self.db.create_table(name=table_name, schema=schema, data=data)
-        else:
-            raise ValueError(f"Table {table_name} already exists")
+    async def embed_data(self, data: list[str]) -> list[list[float]]:
+        return await self.embedding_engine.embed_text(data)
 
-    async def table_exists(self, table_name: str) -> bool:
-        table_names = await self.db.table_names()
-        return table_name in table_names
+    async def collection_exists(self, collection_name: str) -> bool:
+        connection = await self.get_connection()
+        collection_names = await connection.table_names()
+        return collection_name in collection_names
 
-    async def insert_data(self, table_name: str, data_points: List[dict]):
-        table = await self.db.open_table(table_name)
-        await table.add(data_points)
+    async def create_collection(self, collection_name: str, payload_schema: BaseModel):
+        data_point_types = get_type_hints(DataPoint)
 
-    async def query_data(self, table_name: str, query=None, limit=10):
-        # Asynchronously query data from a table
-        table = await self.db.open_table(table_name)
-        if query:
-            query_result = await table.query().where(query).limit(limit).to_pandas()
-        else:
-            query_result = await table.query().limit(limit).to_pandas()
-        return query_result
+        class LanceDataPoint(LanceModel):
+            id: data_point_types["id"] = Field(...)
+            vector: Vector(self.embedding_engine.get_vector_size())
+            payload: payload_schema
 
-    async def vector_search(self, table_name: str, query_vector: List[float], limit=10):
-        # Perform an asynchronous vector search
-        table = await self.db.open_table(table_name)
-        query_result = await table.vector_search().nearest_to(query_vector).limit(limit).to_pandas()
-        return query_result
+        if not await self.collection_exists(collection_name):
+            connection = await self.get_connection()
+            return await connection.create_table(
+                name = collection_name,
+                schema = LanceDataPoint,
+                exist_ok = True,
+            )
 
+    async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
+        connection = await self.get_connection()
 
-async def main():
-    # Example without providing a URI, simulates in-memory behavior
-    adapter = LanceDBAdapter()
-    await adapter.connect()
+        if not await self.collection_exists(collection_name):
+            await self.create_collection(
+                collection_name,
+                payload_schema = type(data_points[0].payload),
+            )
 
-    try:
-        await adapter.create_table("my_table")
-        data_points = [{"id": 1, "text": "example", "vector": [0.1, 0.2, 0.3]}]
-        await adapter.insert_data("my_table", data_points)
-    finally:
-        await adapter.disconnect()
+        collection = await connection.open_table(collection_name)
 
-if __name__ == "__main__":
-    asyncio.run(main())
\ No newline at end of file
+        data_vectors = await self.embed_data(
+            [data_point.get_embeddable_data() for data_point in data_points]
+        )
+
+        IdType = TypeVar("IdType")
+        PayloadSchema = TypeVar("PayloadSchema")
+
+        class LanceDataPoint(LanceModel, Generic[IdType, PayloadSchema]):
+            id: IdType
+            vector: Vector(self.embedding_engine.get_vector_size())
+            payload: PayloadSchema
+
+        lance_data_points = [
+            LanceDataPoint[type(data_point.id), type(data_point.payload)](
+                id = data_point.id,
+                vector = data_vectors[data_index],
+                payload = data_point.payload,
+            ) for (data_index, data_point) in enumerate(data_points)
+        ]
+
+        await collection.add(lance_data_points)
+
+    async def retrieve(self, collection_name: str, data_point_id: str):
+        connection = await self.get_connection()
+        collection = await connection.open_table(collection_name)
+        results = await collection.query().where(f"id = '{data_point_id}'").to_pandas()
+        result = results.to_dict("index")[0]
+
+        return ScoredResult(
+            id = result["id"],
+            payload = result["payload"],
+            score = 1,
+        )
+
+    async def search(
+        self,
+        collection_name: str,
+        query_text: str = None,
+        query_vector: List[float] = None,
+        limit: int = 10,
+        with_vector: bool = False,
+    ):
+        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]
+
+        connection = await self.get_connection()
+        collection = await connection.open_table(collection_name)
+
+        results = await collection.vector_search(query_vector).limit(limit).to_pandas()
+
+        return [ScoredResult(
+            id = str(result["id"]),
+            score = float(result["_distance"]),
+            payload = result["payload"],
+        ) for result in results.to_dict("index").values()]
+
+    async def batch_search(
+        self,
+        collection_name: str,
+        query_texts: List[str],
+        limit: int = None,
+        with_vector: bool = False,
+    ):
+        query_vectors = await self.embedding_engine.embed_text(query_texts)
+
+        return asyncio.gather(
+            *[self.search(
+                collection_name = collection_name,
+                query_vector = query_vector,
+                limit = limit,
+                with_vector = with_vector,
+            ) for query_vector in query_vectors]
+        )
+
+    async def prune(self):
+        # Clean up the database if it was set up as temporary
+        if self.uri.startswith("/"):
+            LocalStorage.remove_all(self.uri) # Remove the temporary directory and files inside
diff --git a/cognee/infrastructure/databases/vector/models/DataPoint.py b/cognee/infrastructure/databases/vector/models/DataPoint.py
index bdbba9276..5ad870b65 100644
--- a/cognee/infrastructure/databases/vector/models/DataPoint.py
+++ b/cognee/infrastructure/databases/vector/models/DataPoint.py
@@ -1,10 +1,13 @@
-from typing import Union
+from typing import Generic, TypeVar
 from pydantic import BaseModel
 
-class DataPoint(BaseModel):
+PayloadSchema = TypeVar("PayloadSchema", bound = BaseModel)
+
+class DataPoint(BaseModel, Generic[PayloadSchema]):
     id: str
-    payload: dict[str, Union[str, dict[str, str]]]
+    payload: PayloadSchema
     embed_field: str = "value"
 
     def get_embeddable_data(self):
-        return self.payload[self.embed_field]
+        if hasattr(self.payload, self.embed_field):
+            return getattr(self.payload, self.embed_field)
diff --git a/cognee/infrastructure/databases/vector/models/PayloadSchema.py b/cognee/infrastructure/databases/vector/models/PayloadSchema.py
new file mode 100644
index 000000000..1b0f2ed66
--- /dev/null
+++ b/cognee/infrastructure/databases/vector/models/PayloadSchema.py
@@ -0,0 +1,3 @@
+from typing import TypeVar
+
+PayloadSchema = TypeVar("PayloadSchema")
diff --git a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
index 2d994d3ee..d7ddd51a7 100644
--- a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
+++ b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
@@ -68,6 +68,7 @@ class QDrantAdapter(VectorDBInterface):
     async def create_collection(
       self,
       collection_name: str,
+      payload_schema = None,
     ):
         client = self.get_qdrant_client()
 
@@ -93,7 +94,7 @@ class QDrantAdapter(VectorDBInterface):
         def convert_to_qdrant_point(data_point: DataPoint):
             return models.PointStruct(
                 id = data_point.id,
-                payload = data_point.payload,
+                payload = data_point.payload.dict(),
                 vector = {
                     "text": data_vectors[data_points.index(data_point)]
                 }
@@ -110,9 +111,9 @@ class QDrantAdapter(VectorDBInterface):
 
         return result
 
-    async def retrieve(self, collection_name: str, data_id: str):
+    async def retrieve(self, collection_name: str, data_point_id: str):
         client = self.get_qdrant_client()
-        results = await client.retrieve(collection_name, [data_id], with_payload = True)
+        results = await client.retrieve(collection_name, [data_point_id], with_payload = True)
         await client.close()
         return results[0] if len(results) > 0 else None
 
diff --git a/cognee/infrastructure/databases/vector/vector_db_interface.py b/cognee/infrastructure/databases/vector/vector_db_interface.py
index 53fa1aab9..3960d9269 100644
--- a/cognee/infrastructure/databases/vector/vector_db_interface.py
+++ b/cognee/infrastructure/databases/vector/vector_db_interface.py
@@ -1,42 +1,21 @@
 from typing import List, Protocol, Optional
 from abc import abstractmethod
 from .models.DataPoint import DataPoint
+from .models.PayloadSchema import PayloadSchema
 
 class VectorDBInterface(Protocol):
     """ Collections """
+    @abstractmethod
+    async def collection_exists(self, collection_name: str) -> bool:
+        raise NotImplementedError
+
     @abstractmethod
     async def create_collection(
         self,
-        collection_name: str
+        collection_name: str,
+        payload_schema: Optional[PayloadSchema] = None,
     ): raise NotImplementedError
 
-    # @abstractmethod
-    # async def update_collection(
-    #     self,
-    #     collection_name: str,
-    #     collection_config: object
-    # ): raise NotImplementedError
-
-    # @abstractmethod
-    # async def delete_collection(
-    #     self,
-    #     collection_name: str
-    # ): raise NotImplementedError
-
-    # @abstractmethod
-    # async def create_vector_index(
-    #     self,
-    #     collection_name: str,
-    #     vector_index_config: object
-    # ): raise NotImplementedError
-
-    # @abstractmethod
-    # async def create_data_index(
-    #     self,
-    #     collection_name: str,
-    #     vector_index_config: object
-    # ): raise NotImplementedError
-
     """ Data points """
     @abstractmethod
     async def create_data_points(
@@ -45,27 +24,12 @@ class VectorDBInterface(Protocol):
         data_points: List[DataPoint]
     ): raise NotImplementedError
 
-    # @abstractmethod
-    # async def get_data_point(
-    #     self,
-    #     collection_name: str,
-    #     data_point_id: str
-    # ): raise NotImplementedError
-
-    # @abstractmethod
-    # async def update_data_point(
-    #     self,
-    #     collection_name: str,
-    #     data_point_id: str,
-    #     payload: object
-    # ): raise NotImplementedError
-
-    # @abstractmethod
-    # async def delete_data_point(
-    #     self,
-    #     collection_name: str,
-    #     data_point_id: str
-    # ): raise NotImplementedError
+    @abstractmethod
+    async def retrieve(
+        self,
+        collection_name: str,
+        data_point_id: str
+    ): raise NotImplementedError
 
     """ Search """
     @abstractmethod
diff --git a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
index c0bba0710..861508dff 100644
--- a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
+++ b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
@@ -21,9 +21,6 @@ class WeaviateAdapter(VectorDBInterface):
         self.client = weaviate.connect_to_wcs(
             cluster_url=url,
             auth_credentials=weaviate.auth.AuthApiKey(api_key),
-            # headers = {
-            #     "X-OpenAI-Api-Key": openai_api_key
-            # },
             additional_config=wvc.init.AdditionalConfig(timeout=wvc.init.Timeout(init=30))
         )
 
@@ -31,20 +28,23 @@ class WeaviateAdapter(VectorDBInterface):
         return await self.embedding_engine.embed_text(data)
 
     async def collection_exists(self, collection_name: str) -> bool:
-        event_loop = asyncio.get_event_loop()
+        future = asyncio.Future()
 
-        def sync_collection_exists():
-            return self.client.collections.exists(collection_name)
+        future.set_result(self.client.collections.exists(collection_name))
 
-        return await event_loop.run_in_executor(None, sync_collection_exists)
+        return await future
 
-    async def create_collection(self, collection_name: str):
+    async def create_collection(
+        self,
+        collection_name: str,
+        payload_schema = None,
+    ):
         import weaviate.classes.config as wvcc
 
-        event_loop = asyncio.get_event_loop()
+        future = asyncio.Future()
 
-        def sync_create_collection():
-            return self.client.collections.create(
+        future.set_result(
+            self.client.collections.create(
                 name=collection_name,
                 properties=[
                     wvcc.Property(
@@ -54,13 +54,9 @@ class WeaviateAdapter(VectorDBInterface):
                     )
                 ]
             )
+        )
 
-        # try:
-        result = await event_loop.run_in_executor(None, sync_create_collection)
-        # finally:
-        #     event_loop.shutdown_executor()
-
-        return result
+        return await future
 
     def get_collection(self, collection_name: str):
         return self.client.collections.get(collection_name)
@@ -73,30 +69,26 @@ class WeaviateAdapter(VectorDBInterface):
 
         def convert_to_weaviate_data_points(data_point: DataPoint):
             return DataObject(
-                uuid=data_point.id,
-                properties=data_point.payload,
-                vector=data_vectors[data_points.index(data_point)]
+                uuid = data_point.id,
+                properties = data_point.payload.dict(),
+                vector = data_vectors[data_points.index(data_point)]
             )
 
         objects = list(map(convert_to_weaviate_data_points, data_points))
 
         return self.get_collection(collection_name).data.insert_many(objects)
 
-    async def retrieve(self, collection_name: str, data_id: str):
-        def sync_retrieve():
-            return self.get_collection(collection_name).query.fetch_object_by_id(UUID(data_id))
+    async def retrieve(self, collection_name: str, data_point_id: str):
+        future = asyncio.Future()
 
-        event_loop = asyncio.get_event_loop()
-
-        # try:
-        data_point = await event_loop.run_in_executor(None, sync_retrieve)
-        # finally:
-            # event_loop.shutdown_executor()
+        data_point = self.get_collection(collection_name).query.fetch_object_by_id(UUID(data_point_id))
 
         data_point.payload = data_point.properties
         del data_point.properties
 
-        return data_point
+        future.set_result(data_point)
+
+        return await future
 
     async def search(
             self,
@@ -114,7 +106,6 @@ class WeaviateAdapter(VectorDBInterface):
         if query_vector is None:
             query_vector = (await self.embed_data([query_text]))[0]
 
-        # def sync_search():
         search_result = self.get_collection(collection_name).query.hybrid(
             query = None,
             vector = query_vector,
diff --git a/cognee/modules/cognify/graph/add_cognitive_layer_graphs.py b/cognee/modules/cognify/graph/add_cognitive_layer_graphs.py
index 11cc83a19..a2a64bbec 100644
--- a/cognee/modules/cognify/graph/add_cognitive_layer_graphs.py
+++ b/cognee/modules/cognify/graph/add_cognitive_layer_graphs.py
@@ -1,6 +1,7 @@
 from datetime import datetime
 from uuid import uuid4
 from typing import List, Tuple, TypedDict
+from pydantic import BaseModel
 from cognee.infrastructure import infrastructure_config
 from cognee.infrastructure.databases.vector import DataPoint
 from cognee.shared.data_models import KnowledgeGraph
@@ -106,14 +107,22 @@ async def add_cognitive_layer_graphs(
 
         await graph_client.add_edges(graph_edges)
 
+        class References(BaseModel):
+            node_id: str
+            cognitive_layer: str
+
+        class PayloadSchema(BaseModel):
+            value: str
+            references: References
+
         try:
-            await vector_client.create_collection(layer_id)
+            await vector_client.create_collection(layer_id, payload_schema = PayloadSchema)
         except Exception:
             # It's ok if the collection already exists.
             pass
 
         data_points = [
-            DataPoint(
+            DataPoint[PayloadSchema](
                 id = str(uuid4()),
                 payload = dict(
                     value = node_data["name"],
diff --git a/cognee/modules/cognify/graph/add_data_chunks.py b/cognee/modules/cognify/graph/add_data_chunks.py
index a40f6dd52..d4482c651 100644
--- a/cognee/modules/cognify/graph/add_data_chunks.py
+++ b/cognee/modules/cognify/graph/add_data_chunks.py
@@ -1,5 +1,5 @@
-import logging
 from typing import TypedDict
+from pydantic import BaseModel, Field
 from cognee.infrastructure import infrastructure_config
 from cognee.infrastructure.databases.vector import DataPoint
 
@@ -13,12 +13,15 @@ async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
 
     identified_chunks = []
 
+    class PayloadSchema(BaseModel):
+        text: str = Field(...)
+
     for (dataset_name, chunks) in dataset_data_chunks.items():
         try:
-        # if not await vector_client.collection_exists(dataset_name):
-        #     logging.error(f"Creating collection {str(dataset_name)}")
-            await vector_client.create_collection(dataset_name)
-        except Exception:
+
+            await vector_client.create_collection(dataset_name, payload_schema = PayloadSchema)
+        except Exception as error:
+            print(error)
             pass
 
         dataset_chunks = [
@@ -26,35 +29,21 @@ async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
                 chunk_id = chunk["chunk_id"],
                 collection = dataset_name,
                 text = chunk["text"],
-                file_metadata = chunk["file_metadata"],
+                document_id = chunk["document_id"],
             ) for chunk in chunks
         ]
 
         identified_chunks.extend(dataset_chunks)
 
-        # if not await vector_client.collection_exists(dataset_name):
-        try:
-            logging.error("Collection still not found. Creating collection again.")
-            await vector_client.create_collection(dataset_name)
-        except:
-            pass
-
-        async def create_collection_retry(dataset_name, dataset_chunks):
-            await vector_client.create_data_points(
-                dataset_name,
-                [
-                    DataPoint(
-                        id = chunk["chunk_id"],
-                        payload = dict(text = chunk["text"]),
-                        embed_field = "text"
-                    ) for chunk in dataset_chunks
-                ],
-            )
-
-        try:
-            await create_collection_retry(dataset_name, dataset_chunks)
-        except Exception:
-            logging.error("Collection not found in create data points.")
-            await create_collection_retry(dataset_name, dataset_chunks)
+        await vector_client.create_data_points(
+            dataset_name,
+            [
+                DataPoint[PayloadSchema](
+                    id = chunk["chunk_id"],
+                    payload = PayloadSchema.parse_obj(dict(text = chunk["text"])),
+                    embed_field = "text",
+                ) for chunk in dataset_chunks
+            ],
+        )
 
     return identified_chunks
diff --git a/cognee/modules/cognify/graph/add_document_node.py b/cognee/modules/cognify/graph/add_document_node.py
index 1e93ae60a..0d85ad1a7 100644
--- a/cognee/modules/cognify/graph/add_document_node.py
+++ b/cognee/modules/cognify/graph/add_document_node.py
@@ -1,13 +1,10 @@
 from cognee.shared.data_models import Document
+from cognee.modules.cognify.graph.add_label_nodes import add_label_nodes
 from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
 
 async def add_document_node(graph_client: GraphDBInterface, parent_node_id, document_metadata):
     document_id = f"DOCUMENT__{document_metadata['id']}"
 
-
-
-
-
     document = await graph_client.extract_node(document_id)
 
     if not document:
@@ -21,6 +18,13 @@ async def add_document_node(graph_client: GraphDBInterface, parent_node_id, docu
 
     await graph_client.add_node(document_id, document)
 
-    await graph_client.add_edge(parent_node_id, document_id, "has_document", dict(relationship_name = "has_document"))
+    await graph_client.add_edge(
+        parent_node_id,
+        document_id,
+        "has_document",
+        dict(relationship_name = "has_document"),
+    )
+
+    await add_label_nodes(graph_client, document_id, document_metadata["keywords"].split("|"))
 
     return document_id
diff --git a/cognee/modules/cognify/graph/add_label_nodes.py b/cognee/modules/cognify/graph/add_label_nodes.py
index 572475060..0ff235462 100644
--- a/cognee/modules/cognify/graph/add_label_nodes.py
+++ b/cognee/modules/cognify/graph/add_label_nodes.py
@@ -1,10 +1,11 @@
 from uuid import uuid4
 from typing import List
 from datetime import datetime
+from pydantic import BaseModel
 from cognee.infrastructure import infrastructure_config
 from cognee.infrastructure.databases.vector import DataPoint
 
-async def add_label_nodes(graph_client, parent_node_id: str, chunk_id: str, keywords: List[str]) -> None:
+async def add_label_nodes(graph_client, parent_node_id: str, keywords: List[str]) -> None:
     vector_client = infrastructure_config.get_config("vector_engine")
 
     keyword_nodes = []
@@ -16,7 +17,6 @@ async def add_label_nodes(graph_client, parent_node_id: str, chunk_id: str, keyw
             keyword_id,
             dict(
                 id = keyword_id,
-                chunk_id = chunk_id,
                 name = keyword.lower().capitalize(),
                 keyword = keyword.lower(),
                 entity_type = "Keyword",
@@ -36,9 +36,17 @@ async def add_label_nodes(graph_client, parent_node_id: str, chunk_id: str, keyw
         ) for (keyword_id, __) in keyword_nodes
     ])
 
+    class References(BaseModel):
+        node_id: str
+        cognitive_layer: str
+
+    class PayloadSchema(BaseModel):
+        value: str
+        references: References
+
     # Add data to vector
     keyword_data_points = [
-        DataPoint(
+        DataPoint[PayloadSchema](
             id = str(uuid4()),
             payload = dict(
                 value = keyword_data["keyword"],
@@ -52,7 +60,7 @@ async def add_label_nodes(graph_client, parent_node_id: str, chunk_id: str, keyw
     ]
 
     try:
-        await vector_client.create_collection(parent_node_id)
+        await vector_client.create_collection(parent_node_id, payload_schema = PayloadSchema)
     except Exception:
         # It's ok if the collection already exists.
         pass
diff --git a/cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph.py b/cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph.py
index 24c57a40e..65c0da228 100644
--- a/cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph.py
+++ b/cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph.py
@@ -19,6 +19,6 @@ async def extract_knowledge_graph(text: str, cognitive_layer, graph_model):
         return (await event_loop.run_in_executor(None, sync_extract_knowledge_graph)).graph
         # return compiled_extract_knowledge_graph(text, question = "").graph
     except Exception as error:
-        logger.error("Error extracting graph from content: %s", error, exc_info = True)
-        
+        # TODO: Log error to Sentry
+
         return await extract_content_graph(text, cognitive_layer, graph_model)
diff --git a/cognee/modules/memory/__init__.py b/cognee/modules/memory/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/cognee/modules/memory/vector/__init__.py b/cognee/modules/memory/vector/__init__.py
deleted file mode 100644
index 23a274d96..000000000
--- a/cognee/modules/memory/vector/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .create_vector_memory import create_vector_memory
diff --git a/cognee/modules/memory/vector/create_vector_memory.py b/cognee/modules/memory/vector/create_vector_memory.py
deleted file mode 100644
index 46fc2d299..000000000
--- a/cognee/modules/memory/vector/create_vector_memory.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from cognee.infrastructure.databases.vector.qdrant.adapter import CollectionConfig
-from cognee.infrastructure.databases.vector.get_vector_database import get_vector_database
-
-async def create_vector_memory(memory_name: str, collection_config: CollectionConfig):
-    vector_db = get_vector_database()
-
-    return await vector_db.create_collection(memory_name, collection_config)
diff --git a/cognee/modules/search/vector/search_similarity.py b/cognee/modules/search/vector/search_similarity.py
index 0098f7910..2b288996c 100644
--- a/cognee/modules/search/vector/search_similarity.py
+++ b/cognee/modules/search/vector/search_similarity.py
@@ -25,7 +25,7 @@ async def search_similarity(query: str, graph):
                     layer_id = result.payload["references"]["cognitive_layer"],
                     node_id = result.payload["references"]["node_id"],
                     score = result.score,
-                ) for result in results if result.score > 0.5
+                ) for result in results if result.score > 0.8
             ])
 
     if len(graph_nodes) == 0:
@@ -39,7 +39,10 @@ async def search_similarity(query: str, graph):
         if "chunk_collection" not in graph_node and "chunk_id" not in graph_node:
             continue
 
-        vector_point = await vector_engine.retrieve(graph_node["chunk_collection"], graph_node["chunk_id"])
+        vector_point = await vector_engine.retrieve(
+            graph_node["chunk_collection"],
+            graph_node["chunk_id"],
+        )
 
         relevant_context.append(vector_point.payload["text"])
 
diff --git a/notebooks/full_run.ipynb b/notebooks/full_run.ipynb
index 878346fdf..06ba70145 100644
--- a/notebooks/full_run.ipynb
+++ b/notebooks/full_run.ipynb
@@ -47,7 +47,12 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "44603a2a",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-30T16:53:16.917678Z",
+     "start_time": "2024-04-30T16:53:14.700232Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from os import path\n",
@@ -69,7 +74,12 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "65bfaf09",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-30T16:55:30.886217Z",
+     "start_time": "2024-04-30T16:53:19.164943Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from os import path\n",
@@ -78,26 +88,29 @@
     "\n",
     "logging.basicConfig(level = logging.INFO)\n",
     "\n",
-    "await cognee.prune.prune_system()\n",
-    "\n",
     "data_directory_path = path.abspath(\"../.data\")\n",
     "cognee.config.data_root_directory(data_directory_path)\n",
     "\n",
     "cognee_directory_path = path.abspath(\"../.cognee_system\")\n",
     "cognee.config.system_root_directory(cognee_directory_path)\n",
     "\n",
-    "await cognee.cognify('short_stories')"
+    "await cognee.prune.prune_system()\n",
+    "\n",
+    "await cognee.cognify(\"short_stories\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "a514cf38",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-30T16:55:39.819971Z",
+     "start_time": "2024-04-30T16:55:35.627964Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
     "import graphistry\n",
     "from cognee.config import Config\n",
     "from cognee.utils import render_graph\n",
@@ -115,22 +128,7 @@
     "graph_client = await get_graph_client(GraphDBType.NETWORKX, \"cognee_graph.pkl\")\n",
     "graph = graph_client.graph\n",
     "\n",
-    "await render_graph(graph)\n",
-    "\n",
-    "# edges = nx.to_pandas_edgelist(graph)\n",
-    "\n",
-    "# nodes_data = [{\n",
-    "#     \"id\": node_id,\n",
-    "#     \"label\": node[\"name\"] if \"name\" in node else node_id,\n",
-    "# } for (node_id, node) in graph.nodes(data = True)]\n",
-    "\n",
-    "# nodes = pd.DataFrame(nodes_data)\n",
-    "\n",
-    "# plotter = graphistry.edges(edges, source = \"source\", destination = \"target\").nodes(nodes, \"id\")\n",
-    "\n",
-    "# plotter.bind(edge_title = \"relationship_name\", edge_label = \"relationship_name\", point_title = \"name\", point_label = \"name\")\n",
-    "# url = plotter.plot(render = False, as_files = True)\n",
-    "# print(f\"Graph is visualized at: {url}\")\n"
+    "await render_graph(graph)\n"
    ]
   },
   {
@@ -160,6 +158,14 @@
     "    print(\"French girls\" in result)\n",
     "    print(result)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5e44018878d383f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/poetry.lock b/poetry.lock
index 3711a9966..1355e482a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -374,59 +374,6 @@ files = [
     {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
 ]
 
-[[package]]
-name = "asyncpg"
-version = "0.28.0"
-description = "An asyncio PostgreSQL driver"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "asyncpg-0.28.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a6d1b954d2b296292ddff4e0060f494bb4270d87fb3655dd23c5c6096d16d83"},
-    {file = "asyncpg-0.28.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0740f836985fd2bd73dca42c50c6074d1d61376e134d7ad3ad7566c4f79f8184"},
-    {file = "asyncpg-0.28.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e907cf620a819fab1737f2dd90c0f185e2a796f139ac7de6aa3212a8af96c050"},
-    {file = "asyncpg-0.28.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86b339984d55e8202e0c4b252e9573e26e5afa05617ed02252544f7b3e6de3e9"},
-    {file = "asyncpg-0.28.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0c402745185414e4c204a02daca3d22d732b37359db4d2e705172324e2d94e85"},
-    {file = "asyncpg-0.28.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c88eef5e096296626e9688f00ab627231f709d0e7e3fb84bb4413dff81d996d7"},
-    {file = "asyncpg-0.28.0-cp310-cp310-win32.whl", hash = "sha256:90a7bae882a9e65a9e448fdad3e090c2609bb4637d2a9c90bfdcebbfc334bf89"},
-    {file = "asyncpg-0.28.0-cp310-cp310-win_amd64.whl", hash = "sha256:76aacdcd5e2e9999e83c8fbcb748208b60925cc714a578925adcb446d709016c"},
-    {file = "asyncpg-0.28.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a0e08fe2c9b3618459caaef35979d45f4e4f8d4f79490c9fa3367251366af207"},
-    {file = "asyncpg-0.28.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b24e521f6060ff5d35f761a623b0042c84b9c9b9fb82786aadca95a9cb4a893b"},
-    {file = "asyncpg-0.28.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:99417210461a41891c4ff301490a8713d1ca99b694fef05dabd7139f9d64bd6c"},
-    {file = "asyncpg-0.28.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f029c5adf08c47b10bcdc857001bbef551ae51c57b3110964844a9d79ca0f267"},
-    {file = "asyncpg-0.28.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:ad1d6abf6c2f5152f46fff06b0e74f25800ce8ec6c80967f0bc789974de3c652"},
-    {file = "asyncpg-0.28.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d7fa81ada2807bc50fea1dc741b26a4e99258825ba55913b0ddbf199a10d69d8"},
-    {file = "asyncpg-0.28.0-cp311-cp311-win32.whl", hash = "sha256:f33c5685e97821533df3ada9384e7784bd1e7865d2b22f153f2e4bd4a083e102"},
-    {file = "asyncpg-0.28.0-cp311-cp311-win_amd64.whl", hash = "sha256:5e7337c98fb493079d686a4a6965e8bcb059b8e1b8ec42106322fc6c1c889bb0"},
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 name = "attrs"
 version = "23.2.0"
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 [package.dependencies]
@@ -572,17 +519,17 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "boto3"
-version = "1.34.91"
+version = "1.34.94"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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+botocore = ">=1.34.94,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -591,13 +538,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.91"
+version = "1.34.94"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -624,13 +571,13 @@ files = [
 
 [[package]]
 name = "cairocffi"
-version = "1.6.1"
+version = "1.7.0"
 description = "cffi-based cairo bindings for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -638,7 +585,7 @@ cffi = ">=1.1.0"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "numpy", "pikepdf", "pytest"]
+test = ["numpy", "pikepdf", "pytest", "ruff"]
 xcb = ["xcffib (>=1.4.0)"]
 
 [[package]]
@@ -1548,13 +1495,13 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.13.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1883,22 +1830,6 @@ test = ["flake8 (>=5.0)", "ipython", "mock", "mypy", "pandas-stubs", "pytest", "
 testai = ["numba (>=0.57.1)"]
 umap-learn = ["dirty-cat (==0.2.0)", "scikit-learn (>=1.0)", "umap-learn"]
 
-[[package]]
-name = "graphviz"
-version = "0.20.3"
-description = "Simple Python interface for Graphviz"
-optional = false
-python-versions = ">=3.8"
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-
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-test = ["coverage", "pytest (>=7,<8.1)", "pytest-cov", "pytest-mock (>=3)"]
-
 [[package]]
 name = "greenlet"
 version = "3.0.3"
@@ -2804,13 +2735,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.1.6"
+version = "4.1.8"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.6-py3-none-any.whl", hash = "sha256:cf3e862bc10dbf4331e4eb37438634f813c238cfc62c71c640b3b3b2caa089a8"},
-    {file = "jupyterlab-4.1.6.tar.gz", hash = "sha256:7935f36ba26eb615183a4f5c2bbca5791b5108ce2a00b5505f8cfd100d53648e"},
+    {file = "jupyterlab-4.1.8-py3-none-any.whl", hash = "sha256:c3baf3a2f91f89d110ed5786cd18672b9a357129d4e389d2a0dead15e11a4d2c"},
+    {file = "jupyterlab-4.1.8.tar.gz", hash = "sha256:3384aded8680e7ce504fd63b8bb89a39df21c9c7694d9e7dc4a68742cdb30f9b"},
 ]
 
 [package.dependencies]
@@ -2822,7 +2753,7 @@ jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.19.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
 tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
@@ -3017,17 +2948,17 @@ files = [
 
 [[package]]
 name = "lancedb"
-version = "0.6.10"
+version = "0.6.11"
 description = "lancedb"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "lancedb-0.6.10-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:3a1548bf3820ff606e4efce840bd0e979206eb1815915fff9f738c1d8e337293"},
-    {file = "lancedb-0.6.10-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3874f8aee55c5b4f41b953f4eac28d69c8a9bc10ad992fc7fd2b699a1f4b88ed"},
-    {file = "lancedb-0.6.10-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:73c0909ac7ae95d8128d25936fc0ff670cf6d9ae1b44e06ec48f3a60081d61ca"},
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-    {file = "lancedb-0.6.10-cp38-abi3-win_amd64.whl", hash = "sha256:06a299d91e1d9d2663fea7086efc784bf8643f51ff1532617c4910ec63cd2004"},
+    {file = "lancedb-0.6.11-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:79dbc2a79dac7b843e328a3b7eecb1b42f38ad524742111a38efbeaa1f58ddfe"},
+    {file = "lancedb-0.6.11-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:bfc6661e1fe5dd75346b4a1980243b4ccecd2b0ad991f8becc6e506a608c2d8a"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0998c2dae676c24b95d492358f80ef6e5100b86335b96bf402af3f28f7ec4f3b"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:b6b289599c4c6a61a70da89d4c3201abec7483d0e03573506014af2dcddfe0c4"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:d789d5a181c4bc42650567cb63ce2772ed902046b08e8e401970df284a4df1c1"},
+    {file = "lancedb-0.6.11-cp38-abi3-win_amd64.whl", hash = "sha256:ea12fc94545afb03933d080cce593491e593ab95cbfddf05ab7183bce2bc8d62"},
 ]
 
 [package.dependencies]
@@ -3420,13 +3351,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.19"
+version = "9.5.20"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.19-py3-none-any.whl", hash = "sha256:ea96e150b6c95f5e4ffe47d78bb712c7bacdd91d2a0bec47f46b6fa0705a86ec"},
-    {file = "mkdocs_material-9.5.19.tar.gz", hash = "sha256:7473e06e17e23af608a30ef583fdde8f36389dd3ef56b1d503eed54c89c9618c"},
+    {file = "mkdocs_material-9.5.20-py3-none-any.whl", hash = "sha256:ad0094a7597bcb5d0cc3e8e543a10927c2581f7f647b9bb4861600f583180f9b"},
+    {file = "mkdocs_material-9.5.20.tar.gz", hash = "sha256:986eef0250d22f70fb06ce0f4eac64cc92bd797a589ec3892ce31fad976fe3da"},
 ]
 
 [package.dependencies]
@@ -3773,13 +3704,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.3"
+version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "nbconvert-7.16.3.tar.gz", hash = "sha256:a6733b78ce3d47c3f85e504998495b07e6ea9cf9bf6ec1c98dda63ec6ad19142"},
+    {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
+    {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
 ]
 
 [package.dependencies]
@@ -3801,9 +3732,9 @@ tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
 docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
 test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
@@ -3832,12 +3763,12 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "neo4j"
-version = "5.19.0"
+version = "5.20.0"
 description = "Neo4j Bolt driver for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "neo4j-5.19.0.tar.gz", hash = "sha256:23704f604214174f3b7d15a38653a1462809986019dfdaf773ff7ca4e1b9e2de"},
+    {file = "neo4j-5.20.0.tar.gz", hash = "sha256:c59e54a0c0fa1f109f1d2fa5293c29c2bb30ba388b4f9dd9656919793c10063a"},
 ]
 
 [package.dependencies]
@@ -4236,7 +4167,6 @@ optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"},
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     {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8635c16bf3d99040fdf3ca3db669a7250ddf49c55dc4aa8fe0ae0fa8d6dcc1f0"},
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@@ -4257,7 +4187,6 @@ files = [
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     {file = "pandas-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23"},
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     {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863"},
     {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921"},
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@@ -4999,13 +4928,13 @@ testutils = ["gitpython (>3)"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.8"
+version = "10.8.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pymdown_extensions-10.8-py3-none-any.whl", hash = "sha256:3539003ff0d5e219ba979d2dc961d18fcad5ac259e66c764482e8347b4c0503c"},
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+    {file = "pymdown_extensions-10.8.1-py3-none-any.whl", hash = "sha256:f938326115884f48c6059c67377c46cf631c733ef3629b6eed1349989d1b30cb"},
+    {file = "pymdown_extensions-10.8.1.tar.gz", hash = "sha256:3ab1db5c9e21728dabf75192d71471f8e50f216627e9a1fa9535ecb0231b9940"},
 ]
 
 [package.dependencies]
@@ -5085,13 +5014,13 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
 
 [[package]]
 name = "pytest-asyncio"
-version = "0.21.1"
+version = "0.21.2"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-asyncio-0.21.1.tar.gz", hash = "sha256:40a7eae6dded22c7b604986855ea48400ab15b069ae38116e8c01238e9eeb64d"},
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+    {file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
 ]
 
 [package.dependencies]
@@ -5230,6 +5159,7 @@ files = [
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+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
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@@ -5415,24 +5345,6 @@ files = [
 [package.extras]
 test = ["pytest (>=3.0)", "pytest-asyncio"]
 
-[[package]]
-name = "redis"
-version = "5.0.4"
-description = "Python client for Redis database and key-value store"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.dependencies]
-async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\""}
-
-[package.extras]
-hiredis = ["hiredis (>=1.0.0)"]
-ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
-
 [[package]]
 name = "referencing"
 version = "0.35.0"
@@ -5450,104 +5362,90 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
-version = "2024.4.16"
+version = "2024.4.28"
 description = "Alternative regular expression module, to replace re."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [[package]]
@@ -6970,17 +6906,6 @@ files = [
 [package.extras]
 dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 
-[[package]]
-name = "xmltodict"
-version = "0.13.0"
-description = "Makes working with XML feel like you are working with JSON"
-optional = false
-python-versions = ">=3.4"
-files = [
-    {file = "xmltodict-0.13.0-py2.py3-none-any.whl", hash = "sha256:aa89e8fd76320154a40d19a0df04a4695fb9dc5ba977cbb68ab3e4eb225e7852"},
-    {file = "xmltodict-0.13.0.tar.gz", hash = "sha256:341595a488e3e01a85a9d8911d8912fd922ede5fecc4dce437eb4b6c8d037e56"},
-]
-
 [[package]]
 name = "xxhash"
 version = "3.4.1"
@@ -7217,31 +7142,17 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
-athena = ["pyarrow"]
-az = []
-bigquery = ["pyarrow"]
 cli = []
-databricks = []
-dbt = []
 duckdb = ["duckdb"]
 filesystem = []
-gcp = []
-gs = []
-lancedb = []
 motherduck = ["duckdb", "pyarrow"]
-mssql = []
 neo4j = ["neo4j"]
 notebook = ["overrides"]
 parquet = ["pyarrow"]
-pinecone = []
-postgres = []
-redshift = []
-s3 = []
-snowflake = []
-synapse = ["pyarrow"]
+qdrant = ["qdrant-client"]
 weaviate = ["weaviate-client"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "9609dfc41209efd1fd5e53fd1b77353cb5bce3244ffdb65f81eee686a68b6fc5"
+content-hash = "a374c7915e291ac7e68fe13c2d340c2f534decaa37f407286dae18a3c94bd759"
diff --git a/pyproject.toml b/pyproject.toml
index d3d5a2da6..e5a7d4688 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,10 +27,8 @@ uvicorn = "0.22.0"
 boto3 = "^1.26.125"
 gunicorn = "^20.1.0"
 sqlalchemy = "^2.0.21"
-asyncpg = "^0.28.0"
 instructor = "1.2.1"
 networkx = "^3.2.1"
-graphviz = "^0.20.1"
 debugpy = "^1.8.0"
 pyarrow = "^15.0.0"
 pylint = "^3.0.3"
@@ -53,7 +51,6 @@ scikit-learn = "^1.4.1.post1"
 fastembed = "^0.2.5"
 pypdf = "^4.1.0"
 anthropic = "^0.21.3"
-xmltodict = "^0.13.0"
 neo4j = "^5.18.0"
 jinja2 = "^3.1.3"
 matplotlib = "^3.8.3"
@@ -63,33 +60,19 @@ tiktoken = "^0.6.0"
 dspy-ai = "2.4.3"
 posthog = "^3.5.0"
 lancedb = "^0.6.10"
+tantivy = "^0.21.0"
 
 
 [tool.poetry.extras]
-dbt = ["dbt-core", "dbt-redshift", "dbt-bigquery", "dbt-duckdb", "dbt-snowflake", "dbt-athena-community", "dbt-databricks"]
-gcp = ["grpcio", "google-cloud-bigquery", "db-dtypes", "gcsfs"]
-# bigquery is alias on gcp extras
-bigquery = ["grpcio", "google-cloud-bigquery", "pyarrow", "db-dtypes", "gcsfs"]
-postgres = ["psycopg2-binary", "psycopg2cffi"]
-redshift = ["psycopg2-binary", "psycopg2cffi"]
 parquet = ["pyarrow"]
 duckdb = ["duckdb"]
 filesystem = ["s3fs", "botocore"]
-s3 = ["s3fs", "botocore"]
-gs = ["gcsfs"]
-az = ["adlfs"]
-snowflake = ["snowflake-connector-python"]
 motherduck = ["duckdb", "pyarrow"]
 cli = ["pipdeptree", "cron-descriptor"]
-athena = ["pyathena", "pyarrow", "s3fs", "botocore"]
 weaviate = ["weaviate-client"]
-mssql = ["pyodbc"]
-synapse = ["pyodbc", "adlfs", "pyarrow"]
-databricks = ["databricks-sql-connector"]
-lancedb = ["lancedb"]
-pinecone = ["pinecone-client"]
+qdrant = ["qdrant-client"]
 neo4j = ["neo4j", "py2neo"]
-notebook =[ "ipykernel","overrides", "ipywidgets", "jupyterlab", "jupyterlab_widgets", "jupyterlab-server", "jupyterlab-git"]
+notebook = ["ipykernel","overrides", "ipywidgets", "jupyterlab", "jupyterlab_widgets", "jupyterlab-server", "jupyterlab-git"]
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.0"
@@ -110,7 +93,6 @@ mkdocs-redirects = "^1.2.1"
 
 [tool.poetry.group.test-docs.dependencies]
 fastapi = "^0.109.2"
-redis = "^5.0.1"
 diskcache = "^5.6.3"
 pandas = "^2.2.0"
 tabulate = "^0.9.0"

From 0f9fac11601e91cee94e1d6edb677c47063191c9 Mon Sep 17 00:00:00 2001
From: Boris Arzentar <borisarzentar@gmail.com>
Date: Fri, 3 May 2024 10:43:21 +0200
Subject: [PATCH 2/3] chore: update version to 0.1.7

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index e5a7d4688..6a17883e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cognee"
-version = "0.1.6"
+version = "0.1.7"
 description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
 authors = ["Vasilije Markovic", "Boris Arzentar"]
 readme = "README.md"

From a5966a5dc0fc3d49b0b7c3be5f3fc4ca97d053e5 Mon Sep 17 00:00:00 2001
From: Boris Arzentar <borisarzentar@gmail.com>
Date: Fri, 3 May 2024 10:48:03 +0200
Subject: [PATCH 3/3] docs: update install instructions

---
 README.md | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index d991a0095..7aa2f845b 100644
--- a/README.md
+++ b/README.md
@@ -41,14 +41,24 @@ Join our  <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community
 
 ## 📦 Installation
 
-With pip:
+### With pip
 
+```bash
+pip install cognee
+```
+
+Use Weaviate vector storage:
 ```bash
 pip install "cognee[weaviate]"
 ```
 
-With poetry:
+### With poetry
 
+```bash
+poetry add cognee
+```
+
+Use Weaviate vector storage:
 ```bash
 poetry add "cognee[weaviate]"
 ```