DynamicRAG

DynamicRAG is a React-based web application that implements a dynamic Retrieval-Augmented Generation (RAG) system. It allows users to input text, process it into embeddings, and perform question-answering tasks using a combination of vector search and Large Language Models.

Screenshots

Input text based Dynamic RAG

Github Repo to Dynamic RAG

Features

• Real-time text processing and chunking
• Dynamic vector database creation using Qdrant
• Batch processing of large text inputs
• Integration with a local Gaia node
• GitHub repository analysis using GitIngest
• Progress tracking and error handling
• Clean, responsive UI using Tailwind CSS and shadcn/ui components

Prerequisites

• Node.js (v16 or higher)
• Python (for GitIngest installation)
• A running Qdrant instance (local or remote)
• A local LLM server through Gaia running on port 8080 (Tutorial: https://docs.gaianet.ai/node-guide/quick-start)
• The nomic-embed embedding model (auto-downloaded in gaianet folder when the Gaia CLI is installed)
• The llama 3.2 language model (auto-downloaded in gaianet folder when the Gaia CLI is installed. You can replace the model depending on your use-case)

Setup

1. Clone the repository:

git clone https://github.com/harishkotra/dynamic-rag-gaia
cd dynamic-rag

2. Install dependencies:

npm install

3. Install GitIngest:

pip install gitingest

4. Ensure your Qdrant server is running and accessible at http://localhost:6333
5. Ensure your Gaia node is running and accessible at http://localhost:8080 (Tutorial: https://docs.gaianet.ai/node-guide/quick-start)
6. Start the development server:

npm run dev

Usage

1. Choose Input Mode:
   • Text Input: Paste your knowledge base text into the input textarea
   • GitHub Repository: Enter a GitHub repository URL to analyze its contents
2. Ask Questions: Enter your query in the question field.
3. Process: Click "Submit" to process your query. The system will:
   • For text input: Split the input text into manageable chunks
   • For GitHub repos: Fetch and process repository content using GitIngest
   • Create embeddings for each chunk
   • Store embeddings in a temporary Qdrant collection
   • Find relevant context using vector similarity search
   • Generate an answer using the local Gaia Node
4. View Results: The system will display the generated response based on the relevant context found.

Technical Details

Text Processing

• Maximum chunk size: 2000 characters
• Batch processing size: 3 chunks at a time
• Chunks are created based on natural text boundaries (paragraphs and sentences)

Vector Database

• Uses Qdrant for vector storage and similarity search
• Creates temporary collections for each query session
• Automatically cleans up collections after use
• Uses 768-dimensional vectors for embeddings

API Integration

• Compatible with OpenAI-style API endpoints
• Supports both embeddings and chat completion endpoints
• Uses the nomic-embed model for embeddings
• Uses the llama model for text generation
• Integrates with GitIngest for repository analysis
• NextJS API routes for command-line tool integration

Components

• DynamicRAG.js: Main component implementing the RAG system
• app/api/gitingest/route.js: API route for GitHub repository processing
• Input handling and validation
• Progress tracking and error display
• Vector database management
• LLM integration

Error Handling

The system includes comprehensive error handling for:

• Text processing failures
• GitHub repository fetch and analysis errors
• Embedding creation errors
• Vector database operations
• LLM query failures
• Network issues

Dependencies

• React
• Tailwind CSS
• shadcn/ui components
• Lodash for utility functions
• Various UI components (@/components/ui/*)

Contributing

• Fork the repository
• Create a feature branch
• Make your changes
• Submit a pull request