commit

agent.py

@@ -121,21 +121,21 @@ 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",
-)
-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.",
-)
+# 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.",
+# )
 
 
 
@@ -178,20 +178,20 @@ def build_graph(provider: str = "groq"):
         """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]}
 
     builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
+    # 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_edge(START, "assistant")
+    # builder.add_edge("retriever", "assistant")
     builder.add_conditional_edges(
         "assistant",
         tools_condition,