Update agent.py

agent.py

@@ -121,21 +121,33 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
 sys_msg = SystemMessage(content=system_prompt)
 
 # build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
+try:
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-mpnet-base-v2"
+    )  #  dim=768
+    supabase: Client = create_client(
+        os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY")
+    )
+    vector_store = SupabaseVectorStore(
+        client=supabase,
+        embedding=embeddings,
+        table_name="documents",
+        query_name="match_documents_langchain",
+    )
+    
+    # Test the connection
+    test_results = vector_store.similarity_search("test query", k=1)
+    print(f"Vector store initialized successfully. Test returned {len(test_results)} results.")
+    
+except Exception as e:
+    print(f"Warning: Vector store initialization failed: {e}")
+    vector_store = None
+
 create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
+    retriever=vector_store.as_retriever() if vector_store else None,
     name="Question Search",
     description="A tool to retrieve similar questions from a vector store.",
-)
+) if vector_store else None
 
 
 
@@ -152,18 +164,19 @@ tools = [
 
 # Build graph function
 def build_graph(provider: str = "huggingface"):
-    """Build the graph"""
+    """Build the graph with improved error handling"""
 
     if provider == "groq":
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+        llm = ChatGroq(
+            model="qwen-qwq-32b", temperature=0
+        )  # optional : qwen-qwq-32b gemma2-9b-it
     elif provider == "huggingface":
         llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
-                repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
-            ),
+            llm=HuggingFaceEndpoint(repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
         )
     else:
         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
@@ -171,14 +184,32 @@ def build_graph(provider: str = "huggingface"):
     def assistant(state: MessagesState):
         """Assistant node"""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
+
     def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        """Retriever node with error handling"""
+        try:
+            # Check if vector_store is available
+            if vector_store is None:
+                print("Vector store not available, proceeding without retrieval")
+                return {"messages": [sys_msg] + state["messages"]}
+                
+            similar_question = vector_store.similarity_search(state["messages"][0].content)
+            
+            # Check if we have results before accessing them
+            if similar_question and len(similar_question) > 0:
+                example_msg = HumanMessage(
+                    content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+                )
+                return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+            else:
+                # No similar questions found, proceed without reference
+                print("No similar questions found in vector store")
+                return {"messages": [sys_msg] + state["messages"]}
+                
+        except Exception as e:
+            print(f"Error in retriever: {e}")
+            # Fallback: continue without retrieval
+            return {"messages": [sys_msg] + state["messages"]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
@@ -194,6 +225,27 @@ def build_graph(provider: str = "huggingface"):
 
     # Compile graph
     return builder.compile()
+    
+def retriever(state: MessagesState):
+    """Retriever node with error handling"""
+    try:
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        
+        # Check if we have results before accessing them
+        if similar_question and len(similar_question) > 0:
+            example_msg = HumanMessage(
+                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+            )
+            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        else:
+            # No similar questions found, proceed without reference
+            print("No similar questions found in vector store")
+            return {"messages": [sys_msg] + state["messages"]}
+            
+    except Exception as e:
+        print(f"Error in retriever: {e}")
+        # Fallback: continue without retrieval
+        return {"messages": [sys_msg] + state["messages"]}
 
 # test
 if __name__ == "__main__":