Updates MongoDBGraphStore.__init__ signature

libs/langchain-mongodb/CHANGELOG.md

@@ -2,7 +2,7 @@
 
 ---
 
-## Changes in version 0.5 (2025/02/11)
+## Changes in version 0.5 (2025/02/25)
 
 - Added GraphRAG support via `MongoDBGraphStore`
 

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

@@ -87,12 +87,15 @@ class MongoDBGraphStore:
     from completely different sources.
     - "Jane Smith works with John Doe."
     - "Jane Smith works at MongoDB."
-
     """
 
     def __init__(
         self,
-        collection: Collection,
+        *,
+        connection_string: Optional[str] = None,
+        database_name: Optional[str] = None,
+        collection_name: Optional[str] = None,
+        collection: Optional[Collection] = None,
         entity_extraction_model: BaseChatModel,
         entity_prompt: ChatPromptTemplate = None,
         query_prompt: ChatPromptTemplate = None,
@@ -106,7 +109,11 @@ def __init__(
     ):
         """
         Args:
-            collection: Collection representing an Entity Graph.
+            connection_string: A valid MongoDB connection URI.
+            database_name: The name of the database to connect to.
+            collection_name: The name of the collection to connect to.
+            collection: A Collection that will represent a Knowledge Graph.
+                ** One may pass a Collection in lieu of connection_string, database_name, and collection_name.
             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
@@ -122,6 +129,62 @@ def __init__(
               - If "warn", the default, documents will be inserted but errors logged.
               - If "error", an exception will be raised if any document does not match the schema.
         """
+        self._schema = deepcopy(entity_schema)
+        collection_existed = True
+        if connection_string and collection is not None:
+            raise ValueError(
+                "Pass one of: connection_string, database_name, and collection_name"
+                "OR a MongoDB Collection."
+            )
+        if collection is None:  # collection is specified by uri and names
+            client: MongoClient = MongoClient(
+                connection_string,
+                driver=DriverInfo(
+                    name="Langchain", version=version("langchain-mongodb")
+                ),
+            )
+            db = client[database_name]
+            if collection_name not in db.list_collection_names():
+                validator = {"$jsonSchema": self._schema} if validate else None
+                collection = client[database_name].create_collection(
+                    collection_name,
+                    validator=validator,
+                    validationAction=validation_action,
+                )
+                collection_existed = False
+            else:
+                collection = db[collection_name]
+        else:
+            if not isinstance(collection, Collection):
+                raise ValueError(
+                    "collection must be a MongoDB Collection. "
+                    "Consider using connection_string, database_name, and collection_name."
+                )
+
+        if validate and collection_existed:
+            # first check for existing validator
+            collection_info = collection.database.command(
+                "listCollections", filter={"name": collection.name}
+            )
+            collection_options = collection_info.get("cursor", {}).get("firstBatch", [])
+            validator = collection_options[0].get("options", {}).get("validator", None)
+            if not validator:
+                try:
+                    collection.database.command(
+                        "collMod",
+                        collection.name,
+                        validator={"$jsonSchema": self._schema},
+                        validationAction=validation_action,
+                    )
+                except OperationFailure:
+                    logger.warning(
+                        "Validation will NOT be performed. "
+                        "User must be DB Admin to add validation **after** a Collection is created. \n"
+                        "Please add validator when you create collection: "
+                        "db.create_collection.(coll_name, validator={'$jsonSchema': schema.entity_schema})"
+                    )
+        self.collection = collection
+
         self.entity_extraction_model = entity_extraction_model
         self.entity_prompt = (
             prompts.entity_prompt if entity_prompt is None else entity_prompt
@@ -145,20 +208,6 @@ def __init__(
             ] = allowed_relationship_types
         else:
             self.allowed_relationship_types = []
-        if validate:
-            try:
-                collection.database.command(
-                    "collMod",
-                    collection.name,
-                    validator={"$jsonSchema": self._schema},
-                    validationAction=validation_action,
-                )
-            except OperationFailure:
-                logger.warning(
-                    "Validation will NOT be performed. User must be DB Admin to add validation **after** a Collection is created. \n"
-                    "Please add validator when you create collection: db.create_collection.(coll_name, validator={'$jsonSchema': self._schema})"
-                )
-        self.collection = collection
 
         # Include examples
         if entity_examples is None:

libs/langchain-mongodb/pyproject.toml

@@ -36,6 +36,7 @@ dev = [
     "langchain-openai>=0.2.14",
     "langchain-community>=0.3.14",
     "pypdf>=5.0.1",
+    "flaky>=3.8.1",
 ]
 
 [tool.pytest.ini_options]

libs/langchain-mongodb/tests/integration_tests/test_graphrag.py

@@ -1,6 +1,7 @@
 import os
 
 import pytest
+from flaky import flaky
 from langchain_core.documents import Document
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import AIMessage
@@ -117,7 +118,10 @@ def entity_example():
 @pytest.fixture(scope="module")
 def graph_store(collection, entity_extraction_model, documents) -> MongoDBGraphStore:
     store = MongoDBGraphStore(
-        collection, entity_extraction_model, entity_prompt, query_prompt
+        collection=collection,
+        entity_extraction_model=entity_extraction_model,
+        entity_prompt=entity_prompt,
+        query_prompt=query_prompt,
     )
     bulkwrite_results = store.add_documents(documents)
     assert len(bulkwrite_results) == len(documents)
@@ -136,6 +140,7 @@ def test_add_docs_store(graph_store):
     assert 4 <= len(extracted_entities) < 8
 
 
+@flaky
 def test_extract_entity_names(graph_store, query_connection):
     query_entity_names = graph_store.extract_entity_names(query_connection)
     assert set(query_entity_names) == {"John Doe", "Jane Smith"}
@@ -145,6 +150,7 @@ def test_extract_entity_names(graph_store, query_connection):
     assert len(no_names) == 0
 
 
+@flaky
 def test_related_entities(graph_store):
     entity_names = ["John Doe", "Jane Smith"]
     related_entities = graph_store.related_entities(entity_names)
@@ -155,29 +161,39 @@ def test_related_entities(graph_store):
     assert len(no_entities) == 0
 
 
+@flaky
 def test_additional_entity_examples(entity_extraction_model, entity_example, documents):
-    # Test additional examples
+    # First, create one client just to drop any existing collections
     client = MongoClient(MONGODB_URI)
-    db = client[DB_NAME]
     clxn_name = f"{COLLECTION_NAME}_addl_examples"
-    db[clxn_name].drop()
-    collection = db.create_collection(clxn_name)
+    client[DB_NAME][clxn_name].drop()
+    # Test additional examples
     store_with_addl_examples = MongoDBGraphStore(
-        collection, entity_extraction_model, entity_examples=entity_example
+        connection_string=MONGODB_URI,
+        database_name=DB_NAME,
+        collection_name=clxn_name,
+        entity_extraction_model=entity_extraction_model,
+        entity_prompt=entity_prompt,
+        query_prompt=query_prompt,
+        entity_examples=entity_example,
     )
+    store_with_addl_examples.collection.drop()
+
     store_with_addl_examples.add_documents(documents)
     entity_names = ["ACME Corporation", "GreenTech Ltd."]
     new_entities = store_with_addl_examples.related_entities(entity_names)
     assert len(new_entities) >= 2
 
 
+@flaky
 def test_chat_response(graph_store, query_connection):
     """Displays querying an existing Knowledge Graph Database"""
     answer = graph_store.chat_response(query_connection)
     assert isinstance(answer, AIMessage)
     assert "acme corporation" in answer.content.lower()
 
 
+@flaky
 def test_similarity_search(graph_store, query_connection):
     docs = graph_store.similarity_search(query_connection)
     assert len(docs) >= 4
@@ -186,33 +202,78 @@ def test_similarity_search(graph_store, query_connection):
     assert any("attributes" in d.keys() for d in docs)
 
 
+@flaky
 def test_validator(documents, entity_extraction_model):
+    # Case 1. No existing collection.
     client = MongoClient(MONGODB_URI)
-    clxn_name = "langchain_test_graphrag_validation"
+    clxn_name = f"{COLLECTION_NAME}_validation"
     client[DB_NAME][clxn_name].drop()
-    clxn = client[DB_NAME].create_collection(clxn_name)
+    # now we call with validation that can be added without db admin privileges
     store = MongoDBGraphStore(
-        clxn, entity_extraction_model, validate=True, validation_action="error"
+        connection_string=MONGODB_URI,
+        database_name=DB_NAME,
+        collection_name=clxn_name,
+        entity_extraction_model=entity_extraction_model,
+        validate=True,
+        validation_action="error",
     )
     bulkwrite_results = store.add_documents(documents)
     assert len(bulkwrite_results) == len(documents)
     entities = store.collection.find({}).to_list()
     # Using subset because SolarGrid Initiative is not always considered an entity
     assert {"Person", "Organization"}.issubset(set(e["type"] for e in entities))
+    client.close()
 
+    # Case 2: Existing collection with a validator
+    client = MongoClient(MONGODB_URI)
+    clxn_name = f"{COLLECTION_NAME}_validation"
+    collection = client[DB_NAME][clxn_name]
+    collection.delete_many({})
 
+    store = MongoDBGraphStore(
+        collection=collection,
+        entity_extraction_model=entity_extraction_model,
+        validate=True,
+        validation_action="error",
+    )
+    bulkwrite_results = store.add_documents(documents)
+    assert len(bulkwrite_results) == len(documents)
+    collection.drop()
+    client.close()
+
+    # Case 3: Existing collection without a validator
+    client = MongoClient(MONGODB_URI)
+    clxn_name = f"{COLLECTION_NAME}_validation"
+    collection = client[DB_NAME].create_collection(clxn_name)
+    store = MongoDBGraphStore(
+        collection=collection,
+        entity_extraction_model=entity_extraction_model,
+        validate=True,
+        validation_action="error",
+    )
+    bulkwrite_results = store.add_documents(documents)
+    assert len(bulkwrite_results) == len(documents)
+    client.close()
+
+
+@flaky
 def test_allowed_entity_types(documents, entity_extraction_model):
     """Add allowed_entity_types. Use the validator to confirm behaviour."""
     allowed_entity_types = ["Person"]
+    # drop collection
     client = MongoClient(MONGODB_URI)
     collection_name = f"{COLLECTION_NAME}_allowed_entity_types"
     client[DB_NAME][collection_name].drop()
-    collection = client[DB_NAME].create_collection(collection_name)
+    # create knowledge graph with only allowed_entity_types
+    # this changes the schema at runtime
     store = MongoDBGraphStore(
-        collection,
-        entity_extraction_model,
         allowed_entity_types=allowed_entity_types,
         validate=True,
+        validation_action="error",
+        connection_string=MONGODB_URI,
+        database_name=DB_NAME,
+        collection_name=collection_name,
+        entity_extraction_model=entity_extraction_model,
     )
     bulkwrite_results = store.add_documents(documents)
     assert len(bulkwrite_results) == len(documents)
@@ -223,16 +284,24 @@ def test_allowed_entity_types(documents, entity_extraction_model):
     all([len(e["relationships"].get("attributes", [])) == 0 for e in entities])
 
 
+@flaky
 def test_allowed_relationship_types(documents, entity_extraction_model):
+    # drop collection
     client = MongoClient(MONGODB_URI)
-    collection_name = f"{COLLECTION_NAME}_allowed_relationship_types"
-    client[DB_NAME][collection_name].drop()
-    collection = client[DB_NAME].create_collection(collection_name)
+    clxn_name = f"{COLLECTION_NAME}_allowed_relationship_types"
+    client[DB_NAME][clxn_name].drop()
+    collection = client[DB_NAME].create_collection(clxn_name)
+    collection.drop()
+    # create knowledge graph with only allowed_relationship_types=["partner"]
+    # this changes the schema at runtime
     store = MongoDBGraphStore(
-        collection,
-        entity_extraction_model,
         allowed_relationship_types=["partner"],
         validate=True,
+        validation_action="error",
+        connection_string=MONGODB_URI,
+        database_name=DB_NAME,
+        collection_name=clxn_name,
+        entity_extraction_model=entity_extraction_model,
     )
     bulkwrite_results = store.add_documents(documents)
     assert len(bulkwrite_results) == len(documents)