Building an Advanced AI Assistant for QAPilot: Combining RAG and LLM Capabilities

Introduction

This comprehensive guide details the implementation of a sophisticated AI assistant for QAPilot, combining Retrieval-Augmented Generation (RAG) with OpenAI's GPT models. The system features a hybrid architecture that leverages both pre-existing knowledge and AI capabilities to provide accurate, context-aware responses about QAPilot's mobile testing platform.

Key Features

• Multi-tier retrieval system with MongoDB and JSON fallback

• Context-aware AI responses using RAG

• Source attribution for responses

• Scalable architecture using Node.js and Express

• Intelligent query processing and relevance ranking

• Error handling with graceful fallbacks

Technical Architecture

1. Data Layer

javascriptCopy// MongoDB for primary storage
const db = client.db("QApilotCoreData");
const collection = db.collection("Phrases");

// JSON fallback for basic responses
const jsonData = require('./data.json');

2. Retrieval System

The RAG implementation uses a three-stage retrieval process:

javascriptCopyasync function findSimilarDocuments(query, collection) {
  // 1. Query Processing
  const keywords = query.toLowerCase().split(/\s+/);
  const regexPatterns = keywords.map(keyword => 
    new RegExp(keyword.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'), 'i')
  );

  // 2. Document Retrieval
  const documents = await collection.find({
    $or: [
      { query: { $in: regexPatterns } },
      { response: { $in: regexPatterns } }
    ]
  }).limit(3).toArray();

  // 3. Relevance Ranking
  return documents.sort((a, b) => {
    const aMatches = keywords.filter(keyword => 
      new RegExp(keyword, 'i').test(a.query) || 
      new RegExp(keyword, 'i').test(a.response)
    ).length;
    return bMatches - aMatches;
  });
}

3. Context-Aware AI Response Generation

The system uses OpenAI's GPT model with custom context injection:

javascriptCopyasync function getAIResponseWithContext(query, context) {
  const systemPrompt = `You are a helpful QA assistant for QAPilot, a mobile automation testing tool. 
  Use the following context to answer the question:
  Context: ${context}

  If the context doesn't contain relevant information, provide a general response 
  based on common mobile automation testing knowledge.`;

  const response = await axios.post(
    'https://api.openai.com/v1/chat/completions',
    {
      model: 'gpt-3.5-turbo-0125',
      messages: [
        { role: 'system', content: systemPrompt },
        { role: 'user', content: query }
      ],
      temperature: 0.7
    }
  );
  return response.data.choices[0].message.content;
}

4. Intelligent Query Processing

The system implements a fallback mechanism to ensure reliable responses:

javascriptCopyapp.post('/search', async (req, res) => {
  const query = req.body.query?.toLowerCase() || '';

  try {
    // Primary: MongoDB RAG
    const similarDocs = await findSimilarDocuments(query, collection);

    if (similarDocs.length > 0) {
      const context = similarDocs.map(doc => 
        `Question: ${doc.query}\nAnswer: ${doc.response}`
      ).join('\n\n');

      response = await getAIResponseWithContext(query, context);
      sourceMessage = 'Response generated from database context';
    } else {
      // Secondary: JSON Fallback
      const jsonResponse = await getResponseFromJSON(query);
      if (jsonResponse) {
        response = jsonResponse;
        sourceMessage = 'Response from JSON knowledge base';
      } else {
        // Tertiary: Pure LLM
        response = await getAIResponseWithContext(query, '');
        sourceMessage = 'AI-generated response';
      }
    }

    res.send({ response, source: sourceMessage });
  } catch (error) {
    console.error('Error:', error);
    res.status(500).send('An error occurred while processing your request.');
  }
});

Future Enhancements

1. Vector Embeddings Integration

javascriptCopyasync function getEmbeddings(text) {
  const response = await axios.post(
    'https://api.openai.com/v1/embeddings',
    {
      model: 'text-embedding-ada-002',
      input: text
    }
  );
  return response.data.embeddings[0];
}

2. Semantic Search Implementation

javascriptCopyasync function semanticSearch(query, collection) {
  const queryEmbedding = await getEmbeddings(query);
  return await collection.aggregate([
    {
      $search: {
        knnBeta: {
          vector: queryEmbedding,
          path: "embedding",
          k: 5
        }
      }
    }
  ]).toArray();
}

Best Practices for Deployment

1. Knowledge Base Management

• Regularly update the MongoDB collection with new QAPilot features

• Maintain consistent document structure

• Include metadata for better context matching

2. Performance Optimization

• Implement caching for frequent queries

• Use connection pooling for MongoDB

• Optimize regex patterns for better matching

3. Monitoring and Logging

• Track response sources

• Monitor response times

• Log unsuccessful queries for knowledge base improvement

Security Considerations

1. Data Protection

• Secure MongoDB connection string

• Implement rate limiting

• Validate user input

2. API Security

• Use environment variables for sensitive data

• Implement request validation

• Add authentication if needed

3. Error Handling

• Graceful fallbacks for each layer

• Proper error messages

• Request timeout handling

Conclusion

This implementation provides a robust foundation for an AI-powered QA assistant that combines the benefits of RAG with the capabilities of modern language models. The system's ability to provide context-aware responses while maintaining fallback options ensures reliable and accurate information delivery about QAPilot's features and capabilities.