Retain

Project Overview

Retain is an AI-powered educational platform that transforms complex academic materials into personalized audio lectures. The system uses advanced language models and RAG to process documents, extract key concepts, and generate clear, accessible explanations tailored to each student's needs.

Key Features

• Document Processing: Secure upload and intelligent analysis of academic materials
• AI-Powered Analysis: RAG implementation with GPT-4 for context-aware content generation
• Audio Generation: Text-to-speech conversion of processed content
• User Management: Secure authentication and personalized content storage

Technical Implementation

Microservices Architecture

I designed a scalable microservices architecture to handle complex data flows:

// Document Processing Service
class DocumentProcessor {
    async processDocument(file) {
        const s3Client = new S3Client({
            credentials: {
                accessKeyId: process.env.AWS_ACCESS_KEY_ID,
                secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY
            },
            region: process.env.AWS_REGION
        });

        // Upload to S3 with metadata
        const upload = await s3Client.send(new PutObjectCommand({
            Bucket: 'retain-documents',
            Key: `lectures/${Date.now()}_${file.originalname}`,
            Body: file.buffer,
            ContentType: file.mimetype,
            Metadata: {
                userId: req.user.id,
                documentType: file.documentType
            }
        }));

        return upload.Location;
    }
}

RAG Implementation

I built a RAG system using OpenAI and Pinecone:

def perform_rag(pdf_url, question):
    # Configure vector store
    index = pc.Index(
        name='semantic-search-fast',
        dimension=1536,
        metric='cosine'
    )
    
    # Process document and generate embeddings
    loader = PyPDFLoader(pdf_url)
    documents = loader.load()
    text_splitter = CharacterTextSplitter(
        chunk_size=2000, 
        chunk_overlap=0
    )
    docs = text_splitter.split_documents(documents)
    
    # Generate and store embeddings
    embeddings = OpenAIEmbeddings()
    for i, doc in enumerate(docs):
        vector = embeddings.embed_query(doc.page_content)
        index.upsert(
            vectors=[(str(i), vector, {"text": doc.page_content})]
        )

User Authentication

I implemented secure user management with JWT:

const userAuth = {
    async authenticateUser(email, password) {
        const user = await pool.query(
            'SELECT * FROM users WHERE email = $1',
            [email]
        );

        if (user && await bcrypt.compare(password, user.password)) {
            const token = jwt.sign(
                { userId: user.id },
                process.env.JWT_SECRET,
                { expiresIn: '24h' }
            );
            return { token, user };
        }
    }
};

Technical Architecture

The application follows a microservices-based architecture:

1. Frontend Layer

   • React-based UI components
   • State management with Context API
   • Real-time audio playback interface
   • Document upload handling

2. Backend Services

   • Node.js/Express API server
   • Flask RAG service
   • PostgreSQL database management
   • AWS S3 integration

3. AI Processing Pipeline

   • Document preprocessing
   • OpenAI GPT-4 integration
   • Vector embeddings with Pinecone
   • Text-to-speech conversion

Infrastructure

I designed a robust cloud infrastructure:

version: '3.8'
services:
  api:
    build: ./backend
    environment:
      - NODE_ENV=production
      - DB_HOST=${DB_HOST}
      - DB_USER=${DB_USER}
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
    ports:
      - "3000:3000"
    depends_on:
      - db
      
  rag:
    build: ./rag_service
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - PINECONE_API_KEY=${PINECONE_API_KEY}
    ports:
      - "5000:5000"

  db:
    image: postgres:14
    volumes:
      - pgdata:/var/lib/postgresql/data
    environment:
      - POSTGRES_DB=${DB_NAME}
      - POSTGRES_USER=${DB_USER}
      - POSTGRES_PASSWORD=${DB_PASSWORD}

Security Implementation

I implemented comprehensive security measures:

• JWT-based authentication flow
• Secure document handling in AWS S3
• Environment-based configuration
• Input validation and sanitization
• Rate limiting and request throttling

Technical Challenges

• Implementing efficient vector similarity search
• Managing concurrent document processing
• Optimizing audio generation pipeline
• Ensuring data consistency across microservices

Lessons Learned

• Importance of proper service isolation
• Benefits of vector databases for semantic search
• Challenges of maintaining microservices architecture
• Value of comprehensive error handling

Future Development

• Enhanced document preprocessing
• Improved audio synthesis quality
• Advanced content personalization
• Expanded file format support
• Real-time collaboration features