📘 Dev Docs Chat

A powerful RAG (Retrieval-Augmented Generation) system that allows you to upload documents, ingest content from URLs, and ask questions about your knowledge base with AI-powered answers.

🚀 Features

📁 Document Support

• PDF Files: Extract and process PDF documents
• Text Files: Plain text document processing
• Markdown Files: Structured markdown with proper parsing
• URL Ingestion: Fetch and process content from web URLs

🎯 Core Functionality

• Smart Search: Vector-based semantic search across your documents
• AI-Powered Q&A: Get intelligent answers based on your content
• Conversational Memory: Maintains context across multiple questions

🗂️ Data Management

• File Upload: Drag-and-drop interface for document ingestion
• URL Ingestion: Process web content with progress indicators
• Delete Operations: Remove files, URLs, and their embeddings
• Bulk Clear: Reset entire knowledge base with one click

🛠️ Installation

Prerequisites

• Python 3.10+
• pip package manager

Setup Instructions

1. Clone the repository

   git clone https://github.com/GovindKurapati/dev_docs_chat.git
   cd dev_docs_chat

2. Create 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 Create a .env file in the project root:

   GROQ_API_KEY=your_groq_api_key_here
   GROQ_API_BASE=https://api.groq.com/openai/v1

5. Get API Key

   • Sign up at Groq
   • Generate an API key
   • Add it to your .env file

🚀 Usage

Starting the Application

python app.py

The application will be available at http://127.0.0.1:7860

📸 Screenshots

Document Upload

Upload and process PDF, TXT, and Markdown files with drag-and-drop interface.

URL Ingestion

Fetch and process content from web URLs with progress indicators.

Data Management

Manage your uploaded files and ingested URLs with easy deletion options.

Q&A Interface

Ask questions about your documents and get AI-powered answers with markdown formatting.

📁 Project Structure

dev-docs-chat/
├── app.py                 # Main Gradio application
├── qa_pipeline.py        # Question-answering logic
├── ingestion.py          # Document ingestion logic
├── requirements.txt      # Python dependencies
├── .env                 # Environment variables (create this)
├── chroma_db/          # Vector database storage
├── uploads/            # Uploaded file storage
├── ingested_urls.txt   # List of ingested URLs
└── README.md           # This file

🔧 Technical Details

Architecture

• Vector Database: ChromaDB for efficient similarity search
• Embeddings: HuggingFace sentence-transformers
• LLM: Groq's fast LLM for quick responses
• Framework: Gradio for web interface

🎯 Use Cases

📚 Documentation Assistant

• Upload project documentation and README files
• Ask questions about implementation details
• Get instant answers about your codebase

🔍 Research Tool

• Ingest research papers and technical articles
• Ask questions about new technologies
• Stay updated with industry trends

📖 Learning Platform

• Upload tutorials and educational content
• Ask questions about complex topics
• Get personalized explanations

📈 Future Enhancements

• Streaming Responses: Real-time answer generation
• File Type Support: Excel, Word, PowerPoint documents
• Advanced Search: Filters and date-based search
• Export Features: Save conversations and answers
• User Authentication: Multi-user support
• API Endpoints: REST API for integration

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

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

🙏 Acknowledgments

• LangChain: For the RAG framework
• ChromaDB: For vector storage
• Gradio: For the web interface
• Groq: For fast LLM inference
• HuggingFace: For embedding models

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Made with ❤️ by Govind Kurapati