Update agent.py

agent.py

@@ -3,19 +3,19 @@
 import os
 from dotenv import load_dotenv
 from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
+from langgraph.prebuilt import tools_condition, ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
 from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
+from langchain_community.document_loaders import WikipediaLoader, 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
+from typing import TypedDict, List, Annotated
+import operator
 
 load_dotenv()
 
@@ -129,7 +129,7 @@ supabase = create_client(
 vector_store = SupabaseVectorStore(
     client=supabase,
     embedding= embeddings,
-    table_name="documents",
+    table_name="docs",
     query_name="match_docs",
 )
 create_retriever_tool = create_retriever_tool(
@@ -181,14 +181,15 @@ def build_graph(provider: str = "google"):
         """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]}
+    #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]}
 
+    
     from langchain_core.messages import AIMessage
 
     def retriever(state: MessagesState):
@@ -216,8 +217,8 @@ def build_graph(provider: str = "google"):
     #builder.add_edge("tools", "assistant")
 
     builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
     builder.add_node("assistant", assistant)
+    builder.add_node("retriever", retriever)
     
     # Retriever ist Start und Endpunkt
     builder.set_entry_point("retriever")