This repository contains a web scraping and data processing project designed to collect restaurant data from various sources, construct a knowledge base using Retrieval-Augmented Generation (RAG), and provide a user-friendly interface for querying this data. The project utilizes Python, FastAPI for the backend, and React for the frontend.

• Web Scraping: Collects data from specified restaurant sources.
• Knowledge Base Construction: Processes scraped data and builds a vector store for efficient querying using RAG techniques.
• FastAPI Backend: Serves the processed data and handles RAG-based queries via API endpoints, including WebSocket support for chat.
• React Frontend: Provides an interactive user interface for asking questions, chatting with the bot (with session management), viewing restaurant details, and browsing menus.
• RAG Implementation: Allows users to ask natural language questions about restaurants (general or specific), compare prices, check for dietary options (e.g., gluten-free), and more, leveraging the constructed knowledge base.
• Docker Support: Includes Docker configuration for simplified setup and deployment.
• Data Processing & Storage: Manages the storage and retrieval of restaurant information and menu items.
• Logging & Error Handling: Implements basic logging and error management.
• Configuration Management: Uses configuration files for managing settings.

Access the live application here: https://restaurant-scraper-rag-bot.onrender.com/

Demo Video provides a walkthrough of the application, showcasing its features and functionality. (Please use headphones for a better experience.)

1. Ask General Questions: Query the entire restaurant database. The RAG bot responds with relevant information based on the knowledge base. Compare prices, check dietary options, etc.

2. General ChatBot: Engage in a conversation with the chatbot using WebSockets and session management. The bot maintains context and answers based on the knowledge base.

3. Ask Specific Restaurant Questions: Focus queries on a single restaurant.

4. Specific Restaurant ChatBot: Chat specifically about one restaurant, maintaining context via WebSockets.

5. View Menu: Browse the menu of a specific restaurant with details and images.

6. View Restaurant Details: Access comprehensive information about a specific restaurant (address, phone, website, etc.).

SETUP.md provides a detailed setup guide for running the project locally, including instructions for installing dependencies, configuring the environment, and launching the application.

ARCHITECTURE.md provides an overview of the system architecture, detailing the data flow, components (scraper, backend, frontend, knowledge base), and their interactions.

Contributions are welcome! Please follow standard GitHub practices: open an issue to discuss changes or submit a pull request with your improvements. Adhere to the project's coding standards and guidelines.

This project is licensed under the MIT License. See the LICENSE file for details.

This project was inspired by the need for a comprehensive tool for restaurant data scraping, analysis, and querying using modern AI techniques. Thanks to the open-source community.

For inquiries or feedback, contact the project maintainer via their website: prashantyadav.site.

• Prashant Yadav
  • GitHub: 0PrashantYadav0
  • LinkedIn: prashantyadav097