RAG-based Chat Assistant for FAQ

A specialized Retrieval-Augmented Generation (RAG) system that enhances product support by providing accurate, context-aware responses to customer inquiries. This solution combines the power of large language models with targeted knowledge retrieval from product documentation.

🎯 Project Objective

This project implements a RAG-based chat assistant that helps users find accurate product information by:

• Processing and indexing FAQ documents using advanced embedding models
• Enabling natural language queries about product features and support
• Retrieving relevant context before generating responses
• Providing a user-friendly interface for seamless interaction

🚀 Features

Core Capabilities

• PDF Processing: Extracts and structures FAQ content from product documentation
• Smart Chunking: Breaks down documents into meaningful segments for precise retrieval
• Vector Embeddings: Converts text into high-dimensional vectors for semantic search

Intelligent Retrieval

• Contextual Search: Finds the most relevant FAQ sections for each query
• Semantic Understanding: Matches user questions with document content beyond simple keyword matching
• Efficient Storage: Uses ChromaDB for fast and scalable vector similarity search

Advanced RAG Workflow

• Context-Enriched Generation: Enhances LLM responses with retrieved document excerpts
• Confidence Scoring: Indicates response reliability based on source material relevance
• Source Attribution: Provides references to the original document sections

User Experience

• Natural Interaction: Understands and responds to free-form product questions
• Conversation Flow: Maintains context for follow-up questions
• Clear Feedback: Handles unknown queries gracefully with helpful guidance

Observability & Monitoring

• Observability: Integrated with LangSmith for tracing and debugging
• Modular Design: Easy to extend with additional data sources or LLM providers
• Performance Metrics: Tracks response quality and system performance

Extensibility

• Modular Architecture: Easy integration of additional document sources

🛠️ Tech Stack

• Backend: FastAPI, Uvicorn
• Vector Database: ChromaDB
• LLM Integration: OpenAI
• NLP: LangChain
• Frontend: React
• Environment: Python 3.8+

📦 Installation

1. Clone the repository

   git clone https://github.com/narendra-cs/rag-based-chat-assistent-for-faq.git
   cd rag-based-chat-assistent-for-faq

2. Set up a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables Copy .env.example to .env and update the values:

   cp .env.example .env

   Edit the .env file with your configuration (API keys, model names, etc.)

🚀 Quick Start

1. Start the backend server

   python -m src.main

2. Access the web interface Open your browser and navigate to http://localhost:8000

3. Upload documents : (To be implemented)

   • Use the web interface to upload PDFs or other supported documents
   • The system will automatically process and index the content

4. Start chatting

   • Type your questions in the chat interface
   • The assistant will provide answers based on the uploaded documents

🔧 Configuration

Edit the .env file to customize the application:

# LLM Configuration
LLM_PROVIDER=openai
OPENAI_API_KEY=your_openai_api_key

# LangChain API Key
LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_PROJECT="your_langchain_project"
LANGCHAIN_TRACING_V2=true

# Application Settings
LOG_LEVEL=info
ENVIRONMENT=development

📚 Documentation

For detailed documentation, please refer to the docs directory.

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

📄 License

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

🙏 Acknowledgments

• Built with ❤️ using FastAPI and LangChain
• Thanks to all contributors who have helped improve this project

📞 Contact

For any questions or feedback, please open an issue on GitHub.

UI Snapshots

Home Page

Chat Interface