from flask import Flask, render_template, request, redirect, url_for, send_from_directory, flash from flask_socketio import SocketIO import threading import os from dotenv import load_dotenv import sqlite3 from werkzeug.utils import secure_filename import traceback # LangChain and agent imports from langchain_community.chat_models.huggingface import ChatHuggingFace # if needed later from langchain.agents import Tool from langchain.agents.format_scratchpad import format_log_to_str from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser from langchain_core.callbacks import CallbackManager, BaseCallbackHandler from langchain_community.agent_toolkits.load_tools import load_tools from langchain_core.tools import tool from langchain_community.agent_toolkits import SQLDatabaseToolkit from langchain.chains import LLMMathChain from langchain import hub from langchain_community.tools import DuckDuckGoSearchRun # Agent requirements and type hints from typing import Annotated, Literal, TypedDict, Any from langchain_core.messages import AIMessage, ToolMessage from pydantic import BaseModel, Field from typing_extensions import TypedDict from langgraph.graph import END, StateGraph, START from langgraph.graph.message import AnyMessage, add_messages from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks from langgraph.prebuilt import ToolNode from langchain_core.prompts import ChatPromptTemplate from langchain_community.utilities import SQLDatabase # Load environment variables load_dotenv() # Global configuration variables UPLOAD_FOLDER = os.path.join(os.getcwd(), "uploads") os.makedirs(UPLOAD_FOLDER, exist_ok=True) BASE_DIR = os.path.abspath(os.path.dirname(__file__)) # API Keys from .env file os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY") os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL_API_KEY") # Global state: dynamic agent and DB file path agent_app = None abs_file_path = None db_path = None # ============================================================================= # create_agent_app: Given a database path, initialize the agent workflow. # ============================================================================= def create_agent_app(db_path: str): # Use ChatGroq as our LLM here; swap to ChatMistralAI if preferred. from langchain_groq import ChatGroq llm = ChatGroq(model="llama3-70b-8192") # ------------------------------------------------------------------------- # Define a tool for executing SQL queries, with an explicit description. # ------------------------------------------------------------------------- @tool(description="Executes a SQL query on the connected SQLite database and returns the result.") def db_query_tool(query: str) -> str: """ Executes a SQL query on the connected SQLite database. """ try: result = db_instance.run_no_throw(query) return result if result else "Error: Query failed. Please rewrite your query and try again." except Exception as e: return f"Error: {str(e)}" # ------------------------------------------------------------------------- # Pydantic model for final answer. # ------------------------------------------------------------------------- class SubmitFinalAnswer(BaseModel): final_answer: str = Field(..., description="The final answer to the user") # ------------------------------------------------------------------------- # Define state type for our workflow. # ------------------------------------------------------------------------- class State(TypedDict): messages: Annotated[list[AnyMessage], add_messages] # ------------------------------------------------------------------------- # Set up prompt templates for query checking and generation. # ------------------------------------------------------------------------- query_check_system = ( "You are a SQL expert with a strong attention to detail.\n" "Double check the SQLite query for common mistakes, including:\n" "- Using NOT IN with NULL values\n" "- Using UNION when UNION ALL should have been used\n" "- Using BETWEEN for exclusive ranges\n" "- Data type mismatch in predicates\n" "- Properly quoting identifiers\n" "- Using the correct number of arguments for functions\n" "- Casting to the correct data type\n" "- Using the proper columns for joins\n\n" "If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n" "You will call the appropriate tool to execute the query after running this check." ) query_check_prompt = ChatPromptTemplate.from_messages([ ("system", query_check_system), ("placeholder", "{messages}") ]) query_check = query_check_prompt | llm.bind_tools([db_query_tool]) query_gen_system = ( "You are a SQL expert with a strong attention to detail.\n\n" "Given an input question, output a syntactically correct SQLite query to run, then look at the results of the query and return the answer.\n\n" "DO NOT call any tool besides SubmitFinalAnswer to submit the final answer.\n\n" "When generating the query:\n" "Output the SQL query that answers the input question without a tool call.\n" "Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.\n" "You can order the results by a relevant column to return the most interesting examples in the database.\n" "Never query for all the columns from a specific table, only ask for the relevant columns given the question.\n\n" "If you get an error while executing a query, rewrite the query and try again.\n" "If you get an empty result set, you should try to rewrite the query to get a non-empty result set.\n" "NEVER make stuff up if you don't have enough information to answer the query... just say you don't have enough information.\n\n" "If you have enough information to answer the input question, simply invoke the appropriate tool to submit the final answer to the user.\n" "DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database. Do not return any SQL query except answer." ) query_gen_prompt = ChatPromptTemplate.from_messages([ ("system", query_gen_system), ("placeholder", "{messages}") ]) query_gen = query_gen_prompt | llm.bind_tools([SubmitFinalAnswer]) # ------------------------------------------------------------------------- # Create SQLDatabase connection. # ------------------------------------------------------------------------- db_uri = f"sqlite:///{os.path.abspath(db_path)}" print("db_uri", db_uri) try: db_instance = SQLDatabase.from_uri(db_uri) except Exception as e: raise Exception(f"Failed to create SQLDatabase connection: {e}") print("db_instance----->", db_instance) # ------------------------------------------------------------------------- # Create SQL toolkit. # ------------------------------------------------------------------------- toolkit_instance = SQLDatabaseToolkit(db=db_instance, llm=llm) tools_instance = toolkit_instance.get_tools() # ------------------------------------------------------------------------- # Define workflow nodes and fallback functions. # ------------------------------------------------------------------------- def first_tool_call(state: State): return {"messages": [AIMessage(content="", tool_calls=[{"name": "sql_db_list_tables", "args": {}, "id": "tool_abcd123"}])]} def handle_tool_error(state: State): tool_calls = state["messages"][-1].tool_calls return {"messages": [ ToolMessage(content="Error occurred. Please revise.", tool_call_id=tc["id"]) for tc in tool_calls ]} def create_tool_node_with_fallback(tools_list): return ToolNode(tools_list).with_fallbacks([RunnableLambda(handle_tool_error)], exception_key="error") def query_gen_node(state: State): try: message = query_gen.invoke(state) except Exception as e: raise Exception(f"Exception in query_gen_node: {e}") tool_messages = [] if message.tool_calls: for tc in message.tool_calls: if tc["name"] != "SubmitFinalAnswer": tool_messages.append(ToolMessage( content=f"Error: Wrong tool called: {tc['name']}", tool_call_id=tc["id"] )) return {"messages": [message] + tool_messages} def should_continue(state: State) -> Literal[END, "correct_query", "query_gen"]: messages = state["messages"] last_message = messages[-1] if getattr(last_message, "tool_calls", None): return END if last_message.content.startswith("Error:"): return "query_gen" return "correct_query" def model_check_query(state: State): return {"messages": [query_check.invoke({"messages": [state["messages"][-1]]})]} # ------------------------------------------------------------------------- # Get tools for listing tables and fetching schema. # ------------------------------------------------------------------------- list_tables_tool = next((t for t in tools_instance if t.name == "sql_db_list_tables"), None) schema_tool = next((t for t in tools_instance if t.name == "sql_db_schema"), None) model_get_schema = llm.bind_tools([schema_tool]) workflow = StateGraph(State) workflow.add_node("first_tool_call", first_tool_call) workflow.add_node("list_tables_tool", create_tool_node_with_fallback([list_tables_tool])) workflow.add_node("get_schema_tool", create_tool_node_with_fallback([schema_tool])) workflow.add_node("model_get_schema", lambda state: {"messages": [model_get_schema.invoke(state["messages"])]}) workflow.add_node("query_gen", query_gen_node) workflow.add_node("correct_query", model_check_query) workflow.add_node("execute_query", create_tool_node_with_fallback([db_query_tool])) workflow.add_edge(START, "first_tool_call") workflow.add_edge("first_tool_call", "list_tables_tool") workflow.add_edge("list_tables_tool", "model_get_schema") workflow.add_edge("model_get_schema", "get_schema_tool") workflow.add_edge("get_schema_tool", "query_gen") workflow.add_conditional_edges("query_gen", should_continue) workflow.add_edge("correct_query", "execute_query") workflow.add_edge("execute_query", "query_gen") return workflow.compile() # ============================================================================= # create_app: The application factory. # ============================================================================= def create_app(): flask_app = Flask(__name__, static_url_path='/uploads', static_folder='uploads') socketio = SocketIO(flask_app, cors_allowed_origins="*") if not os.path.exists(UPLOAD_FOLDER): os.makedirs(UPLOAD_FOLDER) flask_app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER flask_app.config['SECRET_KEY'] = os.getenv("FLASK_SECRET_KEY", "mysecretkey") @flask_app.route("/files/") def uploaded_file(filename): try: return send_from_directory(flask_app.config['UPLOAD_FOLDER'], filename) except Exception as e: flash(f"Could not send file: {str(e)}", "error") return redirect(url_for("index")) def run_agent(prompt, socketio): global agent_app, abs_file_path, db_path if not abs_file_path: socketio.emit("log", {"message": "[ERROR]: No DB file uploaded."}) socketio.emit("final", {"message": "No database available. Please upload one and try again."}) return try: if agent_app is None: socketio.emit("log", {"message": "[INFO]: Initializing agent for the first time..."}) try: agent_app = create_agent_app(abs_file_path) socketio.emit("log", {"message": "[INFO]: Agent initialized."}) except Exception as e: error_message = f"Agent initialization failed: {str(e)}" socketio.emit("log", {"message": f"[ERROR]: {error_message}"}) socketio.emit("final", {"message": "Agent initialization failed."}) return query = {"messages": [("user", prompt)]} result = agent_app.invoke(query) try: # Attempt to extract the final answer from the tool call arguments if result and result["messages"] and len(result["messages"]) > 0 and result["messages"][-1].tool_calls and len(result["messages"][-1].tool_calls) > 0: result = result["messages"][-1].tool_calls[0]["args"]["final_answer"] else: result = "Query execution did not return a valid final answer." with app.app_context(): flash("Query execution did not return a valid final answer.", "warning") except KeyError: result = "The agent's response did not contain the expected 'final_answer' key." with app.app_context(): flash("Unexpected agent response format.", "warning") except Exception as e: result = f"An error occurred while processing the agent's response: {str(e)}" with app.app_context(): flash("Error processing agent response.", "error") print("final_answer------>", result) socketio.emit("final", {"message": result}) except Exception as e: error_message = f"Generation failed: {str(e)}" socketio.emit("log", {"message": f"[ERROR]: {error_message}"}) socketio.emit("final", {"message": "Generation failed."}) with app.app_context(): flash(error_message, "error") traceback.print_exc() @flask_app.route("/") def index(): return render_template("index.html") @flask_app.route("/generate", methods=["POST"]) def generate(): try: socketio.emit("log", {"message": "[STEP]: Entering query generation..."}) data = request.json prompt = data.get("prompt", "") socketio.emit("log", {"message": f"[INFO]: Received prompt: {prompt}"}) thread = threading.Thread(target=run_agent, args=(prompt, socketio)) socketio.emit("log", {"message": f"[INFO]: Starting thread: {thread}"}) thread.start() with flask_app.app_context(): flash("Query submitted successfully.", "info") return "OK", 200 except Exception as e: error_message = f"[ERROR]: {str(e)}" socketio.emit("log", {"message": error_message}) with flask_app.app_context(): flash(error_message, "error") return "ERROR", 500 def is_sqlite_db(file_path): try: with open(file_path, 'rb') as f: header = f.read(16) return header[:16] == b'SQLite format 3\x00' except Exception: return False @flask_app.route("/upload", methods=["GET", "POST"]) def upload(): global abs_file_path, agent_app, db_path try: if request.method == "POST": file = request.files.get("file") if not file: flash("No file uploaded.", "error") return "No file uploaded", 400 filename = secure_filename(file.filename) if filename.endswith('.db'): db_path = os.path.join(flask_app.config['UPLOAD_FOLDER'], "uploaded.db") print("Saving file to:", db_path) file.save(db_path) if not is_sqlite_db(db_path): os.remove(db_path) flash("Uploaded file is not a valid SQLite database.", "error") socketio.emit("log", {"message": "[ERROR]: Invalid database file uploaded."}) return render_template("upload.html") abs_file_path = os.path.abspath(db_path) print(f"[INFO]: File '{filename}' uploaded. Agent will be initialized on first query.") socketio.emit("log", {"message": f"[INFO]: Database file '{filename}' uploaded."}) flash(f"Database file '{filename}' uploaded successfully.", "info") agent_app = None # Reset the agent to be lazily reinitialized on next query. return redirect(url_for("index")) else: flash("Invalid file format. Please upload a .db file.", "error") return render_template("upload.html") return render_template("upload.html") except Exception as e: error_message = f"[ERROR]: {str(e)}" print(error_message) flash(error_message, "error") socketio.emit("log", {"message": error_message}) return render_template("upload.html") return flask_app, socketio # ============================================================================= # Create the app for Gunicorn compatibility. # ============================================================================= app, socketio_instance = create_app() if __name__ == "__main__": socketio_instance.run(app, debug=True)