Cleanup

agent.py

@@ -6,10 +6,6 @@ from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
 
 from tools.math_tools import add, subtract, multiply, divide, modulus, power, sqrt
 from tools.search_tools import search_wikipedia, web_search, arxiv_search
@@ -18,22 +14,6 @@ from tools.file_tools import analyze_excel_file, execute_python_code, analyze_cs
 
 system_prompt = Path("system_prompt.txt").read_text()
 
-# 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",
-# )
-# retriever_tool = create_retriever_tool(
-#     retriever=vector_store.as_retriever(),
-#     name="Question Search",
-#     description="A tool to retrieve similar questions from a vector store.",
-# )
-
 def build_graph():
     llm = ChatGoogleGenerativeAI(
         model="gemini-2.0-flash-001",
@@ -59,20 +39,6 @@ def build_graph():
         response = llm_with_tools.invoke(messages)
         return {"messages": [response]}
     
-    # def retriever(state: MessagesState):
-    #     """Retriever node"""
-    #     # Add system message if not present
-    #     messages = state["messages"]
-    #     if not any(isinstance(m, SystemMessage) for m in messages):
-    #         messages = [SystemMessage(content="You are a helpful AI assistant. Use the available tools to answer questions accurately. When providing your final answer, use the format: FINAL ANSWER: [your answer]")] + messages
-    #     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": messages + [example_msg]}
-
 
     builder = StateGraph(MessagesState)
     # builder.add_node("retriever", retriever)

tools/file_tools.py

@@ -7,7 +7,6 @@ import uuid
 import pandas as pd
 import contextlib
 from langchain_core.tools import tool
-from langchain_google_genai import ChatGoogleGenerativeAI
 import requests
 from PIL import Image
 import pytesseract