📁 Project Structure smart-faq-bot/ ├── app.py # Flask app with API endpoints ├── chains.py # LangChain Gemini integration ├── db.py # SQLite database functions ├── templates/ │ └── index.html # Simple HTML frontend ├── .env # Environment variables (example) ├── requirements.txt # Python dependencies └── logs.db # SQLite DB (auto-created) 🚀 Setup Instructions

Create the project directory and files: bashmkdir smart-faq-bot cd smart-faq-bot mkdir templates

Copy all the code files from the artifacts above into their respective locations. Install dependencies: bashpip install -r requirements.txt

Get Google API Key:

Go to Google AI Studio Create a new API key Copy your actual API key and replace your_google_api_key_here in the .env file

Run the application: bashpython app.py

Open your browser:

Go to http://localhost:5000/ Start asking questions!

✅ Features Implemented Backend (Flask):

POST /ask - Accepts questions, gets Gemini responses, saves to DB GET /logs - Returns all Q&A history as JSON Proper error handling and validation Environment variable loading with python-dotenv

LangChain Integration:

Uses langchain-google-genai for Gemini Pro access System prompt for FAQ assistant behavior Temperature control for response consistency

Database (SQLite):

Auto-creates logs.db on first run Stores questions, answers, and timestamps Helper functions for database operations

Frontend:

Clean, responsive HTML interface Real-time question submission via fetch API Loading states and error handling Q&A history viewer with toggle functionality Enter key support for quick submission

🔧 API Usage Ask a question: bashcurl -X POST http://localhost:5000/ask
-H "Content-Type: application/json"
-d '{"question": "What is artificial intelligence?"}' Get logs: bashcurl http://localhost:5000/logs The system is production-ready with proper error handling, database logging, and a user-friendly interface. Just add your Google API key and you're ready to go!