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.

info

The complete source code and detailed instructions for setting up and running this example can be found in the Gaia Cookbook repository.

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/*)

info

The complete source code and detailed instructions for setting up and running this example can be found in the Gaia Cookbook repository.