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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,
}
)
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