Remove retriever from Agent
Browse files
agents.py
CHANGED
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@@ -13,8 +13,6 @@ from langchain_community.document_loaders import ArxivLoader
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from langchain_community.vectorstores import SupabaseVectorStore
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain_core.tools import tool
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from langchain.tools.retriever import create_retriever_tool
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from supabase.client import Client, create_client
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load_dotenv()
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@@ -133,24 +131,6 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
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# System message
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sys_msg = SystemMessage(content=system_prompt)
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# build a retriever
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768
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supabase: Client = create_client(
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os.environ.get("SUPABASE_URL"),
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os.environ.get("SUPABASE_SERVICE_KEY"))
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vector_store = SupabaseVectorStore(
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client=supabase,
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embedding= embeddings,
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table_name="documents",
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query_name="match_documents_langchain",
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)
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create_retriever_tool = create_retriever_tool(
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retriever=vector_store.as_retriever(),
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name="Question Search",
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description="A tool to retrieve similar questions from a vector store.",
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)
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tools = [
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multiply,
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@@ -190,21 +170,10 @@ def build_graph(provider: str = "groq"):
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def assistant(state: MessagesState):
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"""Assistant node"""
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return {"messages": [llm_with_tools.invoke(state["messages"])]}
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def retriever(state: MessagesState):
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"""Retriever node"""
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similar_question = vector_store.similarity_search(state["messages"][0].content)
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example_msg = HumanMessage(
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content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
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)
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return {"messages": [sys_msg] + state["messages"] + [example_msg]}
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builder = StateGraph(MessagesState)
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builder.add_node("retriever", retriever)
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builder.add_node("assistant", assistant)
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builder.add_node("tools", ToolNode(tools))
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builder.add_edge(START, "retriever")
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builder.add_edge("retriever", "assistant")
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builder.add_conditional_edges(
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"assistant",
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tools_condition,
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from langchain_community.vectorstores import SupabaseVectorStore
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain_core.tools import tool
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load_dotenv()
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# System message
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sys_msg = SystemMessage(content=system_prompt)
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tools = [
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multiply,
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def assistant(state: MessagesState):
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"""Assistant node"""
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return {"messages": [llm_with_tools.invoke(state["messages"])]}
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builder = StateGraph(MessagesState)
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builder.add_node("assistant", assistant)
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builder.add_node("tools", ToolNode(tools))
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builder.add_conditional_edges(
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"assistant",
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tools_condition,
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