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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 | |
| ollama_llm = Ollama(model="duckdb-nsql", temperature=0) | |
| models = {"ollama": ollama_llm, "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 | |
| # 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? | |