Update agent.py

agent.py

@@ -20,6 +20,7 @@ from langchain_community.document_loaders import TextLoader
 from langchain_community.vectorstores import FAISS
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import CharacterTextSplitter
+from supabase.client import Client, create_client
 
 
 load_dotenv()
@@ -128,15 +129,20 @@ sys_msg = SystemMessage(content=system_prompt)
 
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
 
-
-def create_retriever_tool(persist_directory="vector_store"):
-    documents = TextLoader("state_of_the_union.txt").load()
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    texts = text_splitter.split_documents(documents)
-    retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()
-    
-    docs = retriever.invoke(sys_msg)
-    #pretty_print_docs(docs)
+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 = [
@@ -178,47 +184,25 @@ 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]}
-
-    
-    from langchain_core.messages import AIMessage
-
     def retriever(state: MessagesState):
-        query = state["messages"][-1].content
-        similar_doc = vector_store.similarity_search(query, k=1)[0]
-
-        content = similar_doc.page_content
-        if "Final answer :" in content:
-            answer = content.split("Final answer :")[-1].strip()
-        else:
-            answer = content.strip()
-
-        return {"messages": [AIMessage(content=answer)]}
-
-   # builder = StateGraph(MessagesState)
-    #builder.add_node("retriever", retriever)
-    #builder.add_node("assistant", assistant)
-    #builder.add_node("tools", ToolNode(tools))
-    #builder.add_edge(START, "retriever")
-    #builder.add_edge("retriever", "assistant")
-    #builder.add_conditional_edges(
-     #   "assistant",
-      #  tools_condition,
-    #)
-    #builder.add_edge("tools", "assistant")
+        """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]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
-    
-    # Retriever ist Start und Endpunkt
-    builder.set_entry_point("retriever")
-    builder.set_finish_point("retriever")
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
 
     # Compile graph
     return builder.compile()