Building a Document QA System with Supavec and Gaia

In this article, I'll share my experience building a document-aware chat system using Supavec and Gaia, complete with code examples and practical insights.

The Challenge

Every developer who has tried to build a document Q&A system knows the pain points:

• Complex document processing pipelines
• Managing chunking and embeddings
• Implementing efficient vector search
• Maintaining context across conversations
• Handling multiple file formats

Why Gaia + Supavec?

The breakthrough came when I discovered the power of combining two specialized tools:

• Supavec: Handles document processing infrastructure
• Gaia: Provides advanced language understanding

Here's a comparison of the traditional approach versus using Supavec:

// Traditional approach - complex and error-prone const processDocumentTraditional = async (file) => { const text = await extractText(file); const chunks = await splitIntoChunks(text); const embeddings = await generateEmbeddings(chunks); await storeInVectorDB(embeddings); // Plus hundreds of lines handling edge cases }; // With Supavec - clean and efficient const uploadDocument = async (file) => { const formData = new FormData(); formData.append("file", file); const response = await fetch("https://api.supavec.com/upload_file", { method: "POST", headers: { authorization: apiKey }, body: formData }); return response.json(); }; 

System Architecture

The system consists of four main components:

1. Frontend (React)

   • File upload interface
   • Real-time chat UI
   • Document selection
   • Response rendering

2. Backend (Express)

   • Request orchestration
   • File handling
   • API integration

3. Supavec Integration

   • Document processing
   • Semantic search
   • Context retrieval

4. Gaia Integration

   • Natural language understanding
   • Response generation
   • Context synthesis

Core Implementation

Here's the chat interface that brings it all together:

export function ChatInterface({ selectedFiles }) { const [messages, setMessages] = useState([]); const handleQuestion = async (question) => { try { // Get relevant context from documents const searchResponse = await searchEmbeddings(question, selectedFiles); const context = searchResponse.documents .map(doc => doc.content) .join('\n\n'); // Generate response using context const answer = await askQuestion(question, context); setMessages(prev => [...prev, { role: 'user', content: question }, { role: 'assistant', content: answer } ]); } catch (error) { console.error('Error processing question:', error); } }; return ( <div className="chat-container"> <MessageList messages={messages} />  <QuestionInput onSubmit={handleQuestion} />  </div>  ); } 

Why This Approach Works Better

1. Intelligent Context Retrieval

Instead of simple keyword matching, Supavec uses semantic search to find relevant document sections:

// Semantic search implementation const getRelevantContext = async (question, fileIds) => { const response = await fetch('https://api.supavec.com/embeddings', { method: 'POST', headers: { 'Content-Type': 'application/json', authorization: apiKey }, body: JSON.stringify({ query: question, file_ids: fileIds, k: 3 // Number of relevant chunks to retrieve }) }); return response.json(); }; 

2. Natural Response Generation

Gaia doesn't just stitch together document chunks - it understands and synthesizes information:

// Example response generation const generateResponse = async (question, context) => { const response = await fetch('https://llama3b.gaia.domains/v1/chat/completions', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ messages: [ { role: 'system', content: 'You are a helpful assistant that answers questions based on provided context.' }, { role: 'user', content: `Context: ${context}\n\nQuestion: ${question}` } ] }) }); return response.json(); }; 

Getting Started

1. Clone the repository:

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

1. Install dependencies:

# Backend cd backend npm install # Frontend cd ../frontend npm install 

1. Configure environment variables:

# backend/.env SUPAVEC_API_KEY=your_supavec_key GAIA_API=https://llama3b.gaia.domains/v1/chat/completions FRONTEND_URL=http://localhost:3000 # frontend/.env REACT_APP_API_URL=http://localhost:3001 

1. Start the development servers:

# Backend cd backend npm run dev # Frontend cd ../frontend npm start 

Key Features

1. Document Processing

   • PDF and text file support
   • Automatic chunking
   • Efficient indexing

2. Search Capabilities

   • Semantic search
   • Multi-document queries
   • Relevance ranking

3. User Interface

   • Real-time chat
   • File management
   • Response streaming

4. Development Features

   • Hot reloading
   • Error handling
   • Request validation

Production Considerations

1. Scaling

   • Implement caching
   • Add rate limiting
   • Configure monitoring

2. Security

   • Input validation
   • File type restrictions
   • API key management

3. Performance

   • Response streaming
   • Lazy loading
   • Request batching

Future Improvements

Enhanced Features

• [ ] Conversation memory
• [ ] More file formats
• [ ] Batch processing

User Experience

• [ ] Progress indicators
• [ ] Error recovery
• [ ] Mobile optimization

Developer Experience

• [ ] Better documentation
• [ ] Testing utilities
• [ ] Deployment guides

Building a document QA system doesn't have to be complicated. By leveraging Supavec for document processing and Gaia for language understanding, we can create powerful, user-friendly systems without getting lost in implementation details. The complete code is available on GitHub, and I encourage you to try it out.

Resources

• GitHub Repository
• Supavec Documentation
• Gaia API Reference

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