tools.py

# # tools.py
# import streamlit as st
# from supabase.client import Client, create_client
# from langchain_community.embeddings import FakeEmbeddings
# from langchain_community.vectorstores import SupabaseVectorStore
# from langchain_community.tools import create_retriever_tool
# from langchain_community.tools import DuckDuckGoSearchRun
# from utils.snow_connect import SnowflakeConnection

# supabase_url = st.secrets["SUPABASE_URL"]
# supabase_key = st.secrets["SUPABASE_SERVICE_KEY"]
# client: Client = create_client(supabase_url, supabase_key)

# embeddings = FakeEmbeddings(size=768)

# vectorstore = SupabaseVectorStore(
#     embedding=embeddings,
#     client=client,
#     table_name="documents",
#     query_name="v_match_documents",
# )

# retriever_tool = create_retriever_tool(
#     vectorstore.as_retriever(),
#     name="Database_Schema",
#     description="Search for database schema details",
# )

# search = DuckDuckGoSearchRun()

# def sql_executor_tool(query: str, use_cache: bool = True) -> str:
#     conn = SnowflakeConnection()
#     return conn.execute_query(query, use_cache)

# def retrieve_data(query: str) -> str:
#     return sql_executor_tool(query, use_cache=True)