app.py

import logging
import streamlit as st
from dotenv import load_dotenv, find_dotenv
from langgraph.errors import GraphRecursionError
from langchain_groq import ChatGroq
from agent import SQLAgentRAG
from tools import retriever
from constant import GROQ_API_KEY, CONFIG

# Initialize logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv(find_dotenv())

# Initialize the language model
llm = ChatGroq(
    model="llama3-8b-8192",
    api_key=GROQ_API_KEY,
    temperature=0.1,
    verbose=True
)

# Initialize SQL Agent
agent = SQLAgentRAG(llm=llm, tools=retriever)

def query_rag_agent(query: str):
    """
    Handle a query through the RAG Agent, producing an SQL response if applicable.

    Parameters:
        - query (str): The input query to process.

    Returns:
        - Tuple[str, List[str]]: The response content and SQL query if applicable.

    Raises:
        - GraphRecursionError: If there's a recursion limit reached within the agent's graph.
    """
    try:
        output = agent.graph.invoke({"messages": query}, CONFIG)
        response = output["messages"][-1].content
        sql_query = output.get("sql_query", ["No SQL query generated"])[-1]

        logger.info(f"Query processed successfully: {query}")
        return response, sql_query

    except GraphRecursionError:
        logger.error("Graph recursion limit reached; query processing failed.")
        return "Graph recursion limit reached. No SQL result generated.", ""

with st.sidebar:
    st.header("About Project")
    st.markdown(
        """
        RAG (Retrieval-Augmented Generation) Agent SQL is an approach that combines retrieval techniques with text generation to create more relevant and contextualised answers from data, 
        particularly in SQL databases. RAG-Agent SQL uses two main components:
        - Retrieval: Retrieving relevant information from the database based on a given question or input.
        - Augmented Generation: Using natural language models (e.g., LLMs such as GPT or LLaMA) to generate more detailed answers, using information from the retrieval results.

        to see the architecture can be seen here [Github](https://github.com/fahmiaziz98/sql_agent/tree/main/002sql-agent-ra)
        """
    )

    st.header("Example Question")
    st.markdown(
        """
        - How many different aircraft models are there? And what are the models?
        - What is the aircraft model with the longest range?
        - Which airports are located in the city of Basel?
        - Can you please provide information on what I asked before?
        - What are the fare conditions available on Boeing 777-300?
        - What is the total amount of bookings made in April 2024?
        - What is the scheduled arrival time of flight number QR0051?
        - Which car rental services are available in Basel?
        - Which seat was assigned to the boarding pass with ticket number 0060005435212351?
        - Which trip recommendations are related to history in Basel?
        - How many tickets were sold for Business class on flight 30625?
        - Which hotels are located in Zurich?
        """
    )

# Main Application Title
st.title("RAG SQL-Agent")

# Initialize session state for storing chat messages
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display conversation history from session state
for message in st.session_state.messages:
    role = message.get("role", "assistant")
    with st.chat_message(role):
        if "output" in message:
            st.markdown(message["output"])
        if "sql_query" in message and message["sql_query"]:
            with st.expander("SQL Query", expanded=True):
                st.code(message["sql_query"])

# Input form for user prompt
if prompt := st.chat_input("What do you want to know?"):
    st.chat_message("user").markdown(prompt)
    st.session_state.messages.append({"role": "user", "output": prompt})

    # Fetch response from RAG agent function directly
    with st.spinner("Searching for an answer..."):
        output_text, sql_query = query_rag_agent(prompt)

    # Display assistant response and SQL query
    st.chat_message("assistant").markdown(output_text)
    if sql_query:
        with st.expander("SQL Query", expanded=True):
            st.code(sql_query)

    # Append assistant response to session state
    st.session_state.messages.append(
        {
            "role": "assistant",
            "output": output_text,
            "sql_query": sql_query,
        }
    )