Add support for Langchain VectorStores (#2518)

mem0/vector_stores/configs.py

@@ -25,6 +25,7 @@ class VectorStoreConfig(BaseModel):
         "supabase": "SupabaseConfig",
         "weaviate": "WeaviateConfig",
         "faiss": "FAISSConfig",
+        "langchain": "LangchainConfig",
     }
 
     @model_validator(mode="after")

mem0/vector_stores/langchain.py

@@ -0,0 +1,161 @@
+from typing import Dict, List, Optional
+
+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'.")
+
+from mem0.vector_stores.base import VectorStoreBase
+
+
+class OutputData(BaseModel):
+    id: Optional[str]  # memory id
+    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.
+
+        Args:
+            data (Dict): Output data or list of Document objects.
+
+        Returns:
+            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__')):
+            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", {})
+                )
+                result.append(entry)
+            return result
+            
+        # Original format handling
+        keys = ["ids", "distances", "metadatas"]
+        values = []
+
+        for key in keys:
+            value = data.get(key, [])
+            if isinstance(value, list) and value and isinstance(value[0], list):
+                value = value[0]
+            values.append(value)
+
+        ids, distances, metadatas = values
+        max_length = max(len(v) for v in values if isinstance(v, list) and v is not None)
+
+        result = []
+        for i in range(max_length):
+            entry = OutputData(
+                id=ids[i] if isinstance(ids, list) and ids and i < len(ids) else None,
+                score=(distances[i] if isinstance(distances, list) and distances and i < len(distances) else None),
+                payload=(metadatas[i] if isinstance(metadatas, list) and metadatas and i < len(metadatas) else None),
+            )
+            result.append(entry)
+
+        return result
+
+    def create_col(self, name, vector_size=None, distance=None):
+        self.collection_name = name
+        return self.client
+
+    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
+            )
+        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
+            )
+
+    def search(self, query: str, vectors: List[List[float]], limit: int = 5, filters: Optional[Dict] = None):
+        """
+        Search for similar vectors in LangChain.
+        """
+        # For each vector, perform a similarity search
+        if 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
+            )
+
+        final_results = self._parse_output(results)
+        return final_results
+
+    def delete(self, vector_id):
+        """
+        Delete a vector by ID.
+        """
+        self.client.delete(ids=[vector_id])
+
+    def update(self, vector_id, vector=None, payload=None):
+        """
+        Update a vector and its payload.
+        """
+        self.delete(vector_id)
+        self.insert(vector, payload, [vector_id])
+
+    def get(self, vector_id):
+        """
+        Retrieve a vector by ID.
+        """
+        docs = self.client.get_by_ids([vector_id])
+        if docs and len(docs) > 0:
+            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.
+        """
+        # This would require implementation-specific access to the underlying store
+        raise NotImplementedError("Listing all vectors not directly supported by LangChain vectorstores")