fix(langchain): search() crashes with TypeError when score is None (#5072)

Co-authored-by: Kartik <kartik.labhshetwar@mem0.ai>

mem0/utils/scoring.py

@@ -107,7 +107,7 @@ def score_and_rank(
         if mem_id is None:
             continue
 
-        semantic_score = result.get("score", 0.0)
+        semantic_score = result.get("score") or 0.0
         if semantic_score < threshold:
             continue
 

mem0/vector_stores/langchain.py

@@ -21,6 +21,16 @@ class OutputData(BaseModel):
     payload: Optional[Dict]  # metadata
 
 
+# Methods that accept a pre-computed embedding and return (Document, float) pairs.
+# Tried in order; first match wins. Not part of the base VectorStore contract,
+# but exposed by several concrete implementations.
+_SCORED_BY_VECTOR_METHODS = [
+    "similarity_search_by_vector_with_relevance_scores",  # Chroma
+    "similarity_search_with_score_by_vector",             # FAISS, Qdrant
+    "similarity_search_by_vector_with_score",             # Pinecone, YDB
+]
+
+
 class Langchain(VectorStoreBase):
     def __init__(self, client: VectorStore, collection_name: str = "mem0"):
         self.client = client
@@ -95,14 +105,40 @@ class Langchain(VectorStoreBase):
         """
         Search for similar vectors in LangChain.
         """
-        # For each vector, perform a similarity search
+        kwargs = {"embedding": vectors, "k": top_k}
         if filters:
-            results = self.client.similarity_search_by_vector(embedding=vectors, k=top_k, filter=filters)
-        else:
-            results = self.client.similarity_search_by_vector(embedding=vectors, k=top_k)
+            kwargs["filter"] = filters
 
-        final_results = self._parse_output(results)
-        return final_results
+        # Try methods that return (Document, float) pairs — not in the base contract
+        # but available on several concrete implementations.
+        for method_name in _SCORED_BY_VECTOR_METHODS:
+            method = getattr(self.client, method_name, None)
+            if method is None:
+                continue
+            try:
+                results = method(**kwargs)
+                return [
+                    OutputData(
+                        id=getattr(doc, "id", None),
+                        score=float(score),
+                        payload=getattr(doc, "metadata", {}),
+                    )
+                    for doc, score in results
+                ]
+            except (NotImplementedError, TypeError):
+                continue
+
+        # Fallback: similarity_search_by_vector returns List[Document] with no scores.
+        # Assign 1.0 so score_and_rank never receives None (None < threshold crashes).
+        docs = self.client.similarity_search_by_vector(**kwargs)
+        return [
+            OutputData(
+                id=getattr(doc, "id", None),
+                score=1.0,
+                payload=getattr(doc, "metadata", {}),
+            )
+            for doc in docs
+        ]
 
     def delete(self, vector_id):
         """