Harish Kotra (he/him) for Gaia

Posted on Aug 18 • Originally published at hackmd.io

Building a Document QA System with Supavec and Gaia

#gaia #supavec #rag #ai

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:

Frontend (React)
- File upload interface
- Real-time chat UI
- Document selection
- Response rendering
Backend (Express)
- Request orchestration
- File handling
- API integration
Supavec Integration
- Document processing
- Semantic search
- Context retrieval
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

Clone the repository:

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

Install dependencies:

# Backend
cd backend
npm install

# Frontend
cd ../frontend
npm install

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

Start the development servers:

# Backend
cd backend
npm run dev

# Frontend
cd ../frontend
npm start

Key Features

Document Processing
- PDF and text file support
- Automatic chunking
- Efficient indexing
Search Capabilities
- Semantic search
- Multi-document queries
- Relevance ranking
User Interface
- Real-time chat
- File management
- Response streaming
Development Features
- Hot reloading
- Error handling
- Request validation

Production Considerations

Scaling
- Implement caching
- Add rate limiting
- Configure monitoring
Security
- Input validation
- File type restrictions
- API key management
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.

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