Fix user_id functionality (#2548)

mem0/vector_stores/langchain.py

@@ -5,7 +5,9 @@ from pydantic import BaseModel
 try:
     from langchain_community.vectorstores import VectorStore
 except ImportError:
-    raise ImportError("The 'langchain_community' library is required. Please install it using 'pip install langchain_community'.")
+    raise ImportError(
+        "The 'langchain_community' library is required. Please install it using 'pip install langchain_community'."
+    )
 
 from mem0.vector_stores.base import VectorStoreBase
 
@@ -15,11 +17,12 @@ class OutputData(BaseModel):
     score: Optional[float]  # distance
     payload: Optional[Dict]  # metadata
 
+
 class Langchain(VectorStoreBase):
     def __init__(self, client: VectorStore, collection_name: str = "mem0"):
         self.client = client
         self.collection_name = collection_name
-    
+
     def _parse_output(self, data: Dict) -> List[OutputData]:
         """
         Parse the output data.
@@ -31,17 +34,17 @@ class Langchain(VectorStoreBase):
             List[OutputData]: Parsed output data.
         """
         # Check if input is a list of Document objects
-        if isinstance(data, list) and all(hasattr(doc, 'metadata') for doc in data if hasattr(doc, '__dict__')):
+        if isinstance(data, list) and all(hasattr(doc, "metadata") for doc in data if hasattr(doc, "__dict__")):
             result = []
             for doc in data:
                 entry = OutputData(
                     id=getattr(doc, "id", None),
                     score=None,  # Document objects typically don't include scores
-                    payload=getattr(doc, "metadata", {})
+                    payload=getattr(doc, "metadata", {}),
                 )
                 result.append(entry)
             return result
-            
+
         # Original format handling
         keys = ["ids", "distances", "metadatas"]
         values = []
@@ -70,26 +73,20 @@ class Langchain(VectorStoreBase):
         self.collection_name = name
         return self.client
 
-    def insert(self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None):
+    def insert(
+        self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None
+    ):
         """
         Insert vectors into the LangChain vectorstore.
         """
         # Check if client has add_embeddings method
         if hasattr(self.client, "add_embeddings"):
             # Some LangChain vectorstores have a direct add_embeddings method
-            self.client.add_embeddings(
-                embeddings=vectors,
-                metadatas=payloads,
-                ids=ids
-            )
+            self.client.add_embeddings(embeddings=vectors, metadatas=payloads, ids=ids)
         else:
             # Fallback to add_texts method
             texts = [payload.get("data", "") for payload in payloads] if payloads else [""] * len(vectors)
-            self.client.add_texts(
-                texts=texts,
-                metadatas=payloads,
-                ids=ids
-            )
+            self.client.add_texts(texts=texts, metadatas=payloads, ids=ids)
 
     def search(self, query: str, vectors: List[List[float]], limit: int = 5, filters: Optional[Dict] = None):
         """
@@ -97,16 +94,9 @@ class Langchain(VectorStoreBase):
         """
         # For each vector, perform a similarity search
         if filters:
-            results = self.client.similarity_search_by_vector(
-                embedding=vectors,
-                k=limit,
-                filter=filters
-            )
+            results = self.client.similarity_search_by_vector(embedding=vectors, k=limit, filter=filters)
         else:
-            results = self.client.similarity_search_by_vector(
-                embedding=vectors,
-                k=limit
-            )
+            results = self.client.similarity_search_by_vector(embedding=vectors, k=limit)
 
         final_results = self._parse_output(results)
         return final_results
@@ -133,26 +123,26 @@ class Langchain(VectorStoreBase):
             doc = docs[0]
             return self._parse_output([doc])[0]
         return None
-    
+
     def list_cols(self):
         """
         List all collections.
         """
         # LangChain doesn't have collections
         return [self.collection_name]
-    
+
     def delete_col(self):
         """
         Delete a collection.
         """
         self.client.delete(ids=None)
-    
+
     def col_info(self):
         """
         Get information about a collection.
         """
         return {"name": self.collection_name}
-    
+
     def list(self, filters=None, limit=None):
         """
         List all vectors in a collection.