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# This example does not use a langchain agent,
# The langchain sql chain has knowledge of the database, but doesn't interact with it becond intialization.
# The output of the sql chain is parsed seperately and passed to `duckdb.sql()` by streamlit
import streamlit as st
## Database connection
from sqlalchemy import create_engine
from langchain.sql_database import SQLDatabase
db_uri = "duckdb:///pad.duckdb"
engine = create_engine(db_uri, connect_args={'read_only': True})
db = SQLDatabase(engine, view_support=True)
import duckdb
con = duckdb.connect("pad.duckdb", read_only=True)
con.install_extension("spatial")
con.load_extension("spatial")
## ChatGPT Connection
from langchain_openai import ChatOpenAI
chatgpt_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, api_key=st.secrets["OPENAI_API_KEY"])
chatgpt4_llm = ChatOpenAI(model="gpt-4", temperature=0, api_key=st.secrets["OPENAI_API_KEY"])
# Requires ollama server running locally
from langchain_community.llms import Ollama
## # from langchain_community.llms import ChatOllama
models = {"duckdb-nsql": Ollama(model="duckdb-nsql", temperature=0),
"sqlcoder": Ollama(model="sqlcoder", temperature=0),
"zephyr": Ollama(model="zephyr", temperature=0),
"gemma:7b": Ollama(model="gemma:7b", temperature=0),
"codegemma": Ollama(model="codegemma", temperature=0),
"llama2:70b": Ollama(model="llama2:70b", temperature=0),
"chatgpt3.5": chatgpt_llm,
"chatgpt4": chatgpt4_llm}
with st.sidebar:
choice = st.radio("Select an LLM:", models)
llm = models[choice]
## A SQL Chain
from langchain.chains import create_sql_query_chain
chain = create_sql_query_chain(llm, db)
# agent does not work
# agent = create_sql_agent(llm, db=db, verbose=True)
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
response = chain.invoke({"question": prompt})
st.write(response)
tbl = con.sql(response).to_df()
st.dataframe(tbl)
# duckdb_sql fails but chatgpt3.5 succeeds with a query like:
# use the st_area function and st_GeomFromWKB functions to compute the area of the Shape column in the fee table, and then use that to compute the total area under each GAP_Sts category
# For most queries, duckdb_sql does much better than alternative open models though
# Federal agencies are identified as 'FED' in the Mang_Type column in the 'combined' data table. The Mang_Name column indicates the different agencies. Which federal agencies manage the greatest area of GAP_Sts 1 or 2 land?
# Federal agencies are identified as 'FED' in the Mang_Type column in the table named "fee". The Mang_Name column indicates the different agencies. List which managers manage the largest total areas that identified as GAP_Sts '1' or '2' ? |