Docs update (#1875)

docs/open-source/graph_memory/overview.mdx

@@ -3,11 +3,11 @@ title: Overview
 description: 'Enhance your memory system with graph-based knowledge representation and retrieval'
 ---
 
-Mem0 now supports **Graph Memory**. 
+Mem0 now supports **Graph Memory**.
 With Graph Memory, users can now create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. 
 This integration enables users to leverage the strengths of both vector-based and graph-based approaches, resulting in more accurate and comprehensive information retrieval and generation.
 
-Try Graph Memory on Google Colab. 
+Try Graph Memory on Google Colab.
 <a target="_blank" href="https://colab.research.google.com/drive/1PfIGVHnliIlG2v8cx0g45TF0US-jRPZ1?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
@@ -27,7 +27,10 @@ allowfullscreen
 
 To initialize Graph Memory you'll need to set up your configuration with graph store providers.
 Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
-Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*). 
+Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
+
+<Note>If you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>
+
 
 User can also customize the LLM for Graph Memory from the [Supported LLM list](https://docs.mem0.ai/components/llms/overview) with three levels of configuration:
 

mem0/configs/vector_stores/chroma.py

@@ -9,7 +9,7 @@ class ChromaDbConfig(BaseModel):
     try:
         from chromadb.api.client import Client
     except ImportError:
-       raise ImportError("The 'chromadb' library is required. Please install it using 'pip install chromadb'.")
+        raise ImportError("The 'chromadb' library is required. Please install it using 'pip install chromadb'.")
     Client: ClassVar[type] = Client
 
     collection_name: str = Field("mem0", description="Default name for the collection")

mem0/memory/graph_memory.py

@@ -3,14 +3,19 @@ import logging
 from langchain_community.graphs import Neo4jGraph
 from rank_bm25 import BM25Okapi
 
-from mem0.graphs.tools import (ADD_MEMORY_STRUCT_TOOL_GRAPH,
+from mem0.graphs.tools import (
-                               ADD_MEMORY_TOOL_GRAPH, ADD_MESSAGE_STRUCT_TOOL,
+    ADD_MEMORY_STRUCT_TOOL_GRAPH,
-                               ADD_MESSAGE_TOOL, NOOP_STRUCT_TOOL, NOOP_TOOL,
+    ADD_MEMORY_TOOL_GRAPH,
-                               SEARCH_STRUCT_TOOL, SEARCH_TOOL,
+    ADD_MESSAGE_STRUCT_TOOL,
-                               UPDATE_MEMORY_STRUCT_TOOL_GRAPH,
+    ADD_MESSAGE_TOOL,
-                               UPDATE_MEMORY_TOOL_GRAPH)
+    NOOP_STRUCT_TOOL,
-from mem0.graphs.utils import (EXTRACT_ENTITIES_PROMPT,
+    NOOP_TOOL,
-                               get_update_memory_messages)
+    SEARCH_STRUCT_TOOL,
+    SEARCH_TOOL,
+    UPDATE_MEMORY_STRUCT_TOOL_GRAPH,
+    UPDATE_MEMORY_TOOL_GRAPH,
+)
+from mem0.graphs.utils import EXTRACT_ENTITIES_PROMPT, get_update_memory_messages
 from mem0.utils.factory import EmbedderFactory, LlmFactory
 
 logger = logging.getLogger(__name__)

mem0/memory/telemetry.py

@@ -63,7 +63,9 @@ def capture_event(event_name, memory_instance, additional_data=None):
         "collection": memory_instance.collection_name,
         "vector_size": memory_instance.embedding_model.config.embedding_dims,
         "history_store": "sqlite",
-        "graph_store": f"{memory_instance.graph.__class__.__module__}.{memory_instance.graph.__class__.__name__}" if memory_instance.config.graph_store.config else None,
+        "graph_store": f"{memory_instance.graph.__class__.__module__}.{memory_instance.graph.__class__.__name__}"
+        if memory_instance.config.graph_store.config
+        else None,
         "vector_store": f"{memory_instance.vector_store.__class__.__module__}.{memory_instance.vector_store.__class__.__name__}",
         "llm": f"{memory_instance.llm.__class__.__module__}.{memory_instance.llm.__class__.__name__}",
         "embedding_model": f"{memory_instance.embedding_model.__class__.__module__}.{memory_instance.embedding_model.__class__.__name__}",

mem0/vector_stores/qdrant.py

@@ -3,9 +3,16 @@ import os
 import shutil
 
 from qdrant_client import QdrantClient
-from qdrant_client.models import (Distance, FieldCondition, Filter, MatchValue,
+from qdrant_client.models import (
-                                  PointIdsList, PointStruct, Range,
+    Distance,
-                                  VectorParams)
+    FieldCondition,
+    Filter,
+    MatchValue,
+    PointIdsList,
+    PointStruct,
+    Range,
+    VectorParams,
+)
 
 from mem0.vector_stores.base import VectorStoreBase