INTPYTHON-515 Release GraphRAG langchain-mongodb 0.5.0 (#80)

Author: caseyclements

README.md

@@ -29,6 +29,8 @@ It contains the following packages.
     - [MongoDBRecordManager](https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/indexes/langchain_mongodb.indexes.MongoDBRecordManager.html#langchain_mongodb.indexes.MongoDBRecordManager)
 - Loading
     - [MongoDBLoader](https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/loaders/langchain_mongodb.loaders.MongoDBLoader.html#langchain_mongodb.loaders.MongoDBLoader)
+- GraphRAG
+    - [MongoDBGraphStore](https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/graphrag/langchain_mongodb.graphrag.graph.MongoDBGraphStore.html)
 
 ### LangGraph
 

libs/langchain-mongodb/CHANGELOG.md

@@ -2,9 +2,9 @@
 
 ---
 
-## Changes in version 0.5 (YYYY/MM/DD)
+## Changes in version 0.5 (2025/02/11)
 
-- TBD
+- Added GraphRAG support via `MongoDBGraphStore`
 
 ## Changes in version 0.4 (2025/01/09)
 

libs/langchain-mongodb/README.md

@@ -1,3 +1,5 @@
+from libs.community.tests.unit_tests.chains.test_pebblo_retrieval import retriever
+
 # langchain-mongodb
 
 # Installation
@@ -6,34 +8,31 @@ pip install -U langchain-mongodb
 ```
 
 # Usage
-- See [Getting Started with the LangChain Integration](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
+- [Integrate Atlas Vector Search with LangChain](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
 
 ## Using MongoDBAtlasVectorSearch
 ```python
+import os
 from langchain_mongodb import MongoDBAtlasVectorSearch
+from langchain_openai import OpenAIEmbeddings
 
 # Pull MongoDB Atlas URI from environment variables
-MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
-
+MONGODB_ATLAS_CONNECTION_STRING = os.environ["MONGODB_CONNECTION_STRING"]
 DB_NAME = "langchain_db"
 COLLECTION_NAME = "test"
-ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
-MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
-
-# Create the vector search via `from_connection_string`
-vector_search = MongoDBAtlasVectorSearch.from_connection_string(
-    MONGODB_ATLAS_CLUSTER_URI,
-    DB_NAME + "." + COLLECTION_NAME,
-    OpenAIEmbeddings(disallowed_special=()),
-    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
-)
+VECTOR_SEARCH_INDEX_NAME = "index_name"
 
-# Initialize MongoDB python client
-client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
-# Create the vector search via instantiation
-vector_search_2 = MongoDBAtlasVectorSearch(
-    collection=MONGODB_COLLECTION,
-    embeddings=OpenAIEmbeddings(disallowed_special=()),
-    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
+MODEL_NAME = "text-embedding-3-large"
+OPENAI_API_KEY =  os.environ["OPENAI_API_KEY"]
+
+
+vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
+    connection_string=MONGODB_ATLAS_CONNECTION_STRING,
+    namespace=DB_NAME + "." + COLLECTION_NAME,
+    embedding=OpenAIEmbeddings(model=MODEL_NAME),
+    index_name=VECTOR_SEARCH_INDEX_NAME,
 )
+
+retrieved_docs = vectorstore.similarity_search(
+    "How do I deploy MongoDBAtlasVectorSearch in our production environment?")
 ```

libs/langchain-mongodb/langchain_mongodb/graphrag/graph.py

@@ -93,8 +93,8 @@ def __init__(
         self,
         collection: Collection,
         entity_extraction_model: BaseChatModel,
-        entity_prompt: ChatPromptTemplate = prompts.entity_prompt,
-        query_prompt: ChatPromptTemplate = prompts.query_prompt,
+        entity_prompt: ChatPromptTemplate = None,
+        query_prompt: ChatPromptTemplate = None,
         max_depth: int = 2,
         allowed_entity_types: List[str] = None,
         allowed_relationship_types: List[str] = None,
@@ -108,7 +108,9 @@ def __init__(
             collection: Collection representing an Entity Graph.
             entity_extraction_model: LLM for converting documents into Graph of Entities and Relationships.
             entity_prompt: Prompt to fill graph store with entities following schema.
+                Defaults to .prompts.ENTITY_EXTRACTION_INSTRUCTIONS
             query_prompt: Prompt extracts entities and relationships as search starting points.
+                Defaults to .prompts.NAME_EXTRACTION_INSTRUCTIONS
             max_depth: Maximum recursion depth in graph traversal.
             allowed_entity_types: If provided, constrains search to these types.
             allowed_relationship_types: If provided, constrains search to these types.
@@ -120,8 +122,12 @@ def __init__(
               - If "error", an exception will be raised if any document does not match the schema.
         """
         self.entity_extraction_model = entity_extraction_model
-        self.entity_prompt = entity_prompt
-        self.query_prompt = query_prompt
+        self.entity_prompt = (
+            prompts.entity_prompt if entity_prompt is None else entity_prompt
+        )
+        self.query_prompt = (
+            prompts.query_prompt if query_prompt is None else query_prompt
+        )
         self.max_depth = max_depth
         self._schema = deepcopy(entity_schema)
         if allowed_entity_types:
@@ -138,7 +144,6 @@ def __init__(
             ] = allowed_relationship_types
         else:
             self.allowed_relationship_types = []
-
         if validate:
             collection.database.command(
                 "collMod",
@@ -155,7 +160,6 @@ def __init__(
             1,
             SystemMessagePromptTemplate.from_template(entity_examples),
         )
-
         if entity_name_examples:
             self.query_prompt.messages.insert(
                 1,

libs/langchain-mongodb/pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langchain-mongodb"
-version = "0.4.0"
+version = "0.5.0"
 description = "An integration package connecting MongoDB and LangChain"
 readme = "README.md"
 requires-python = ">=3.9"