Update agent.py

agent.py

@@ -13,13 +13,15 @@ from langchain_community.document_loaders import ArxivLoader
 from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
+
 from supabase.client import Client, create_client
 # --- langchain create_retriever_tool fallback (paste near other imports) ---
+# NOTE: removed the unconditional import that caused ModuleNotFoundError.
 import traceback
 
 try:
-    from langchain.tools.retriever import create_retriever_tool
+    # Prefer the real helper if available
+    from langchain.tools.retriever import create_retriever_tool  # type: ignore
     HAS_CREATE_RETRIEVER_TOOL = True
 except Exception:
     HAS_CREATE_RETRIEVER_TOOL = False
@@ -55,18 +57,18 @@ except Exception:
                         text = d.get("page_content") or d.get("text") or str(d)
                     else:
                         text = str(d)
-                out_texts.append(text.strip())
+                if text:
+                    out_texts.append(text.strip())
             # return compact result
             return "\n\n".join(t for t in out_texts if t)
 
     def create_retriever_tool(retriever, name: str = "retriever", description: str = ""):
         """
-        Minimal drop-in fallback that returns an object with .run(query).
-        Use this if the langchain helper isn't available in the installed package.
+        Minimal drop-in fallback returning an object with .run(query).
+        Replace with the real langchain helper later once you pin the package.
         """
         return _SimpleRetrieverTool(retriever, name=name, description=description)
 
-
 load_dotenv()
 
 @tool
@@ -182,7 +184,7 @@ vector_store = SupabaseVectorStore(
     table_name="documents",
     query_name="match_documents_langchain",
 )
-create_retriever_tool = create_retriever_tool(
+retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),
     name="Question Search",
     description="A tool to retrieve similar questions from a vector store.",
@@ -190,6 +192,7 @@ create_retriever_tool = create_retriever_tool(
 
 
 
+
 tools = [
     multiply,
     add,