Update agent.py

agent.py

@@ -155,25 +155,6 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
 # System message
 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",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-"""
-
 
 tools = [
     multiply,
@@ -229,26 +210,17 @@ def build_graph(provider: str = "huggingface"):
         
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
-
+    
     # Node
     def assistant(state: MessagesState):
         """Assistant node"""      
         return {"messages": [llm_with_tools.invoke([sys_msg] + 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]}
- 
-    """
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        return {"messages": [sys_msg] + state["messages"]}
     
+    #def retriever(state: MessagesState):
+    #    """Retriever node"""
+    #    return {"messages": [sys_msg] + state["messages"]}
+   
 
     builder = StateGraph(MessagesState)
     #builder.add_node("retriever", retriever)