Building RAG Pipelines on AWS: A Practical Guide to Bedrock + Pinecone

Building RAG Pipelines on AWS: A Practical Guide to Bedrock + Pinecone

Most teams spend weeks wrestling with RAG infrastructure before they can answer their first question. I built this open-source AWS RAG application to cut that timeline from weeks to hours—giving you a solid foundation that demonstrates enterprise-grade patterns and scales from proof-of-concept to production deployment.

The RAG Infrastructure Problem

Here's what I see repeatedly: engineering teams get excited about Retrieval-Augmented Generation, spin up a quick prototype with OpenAI embeddings and a local vector database, then hit a wall when they need to ship something that handles real traffic, integrates with existing AWS infrastructure, and meets enterprise security requirements.

The gap between "RAG demo" and "RAG in production" is massive. You need:

  • Managed embedding models that don't require ML infrastructure
  • Enterprise vector databases with proper access controls and monitoring
  • Scalable ingestion pipelines that handle thousands of documents
  • Production APIs with error handling, logging, and health checks
  • Cost-efficient architecture that doesn't break the budget during experimentation

That's exactly what this AWS RAG application delivers.

How This Open-Source RAG Pipeline Works

I designed this implementation around two core principles: use managed services wherever possible and optimize for developer velocity. The result is a RAG pipeline that leverages AWS Bedrock and Pinecone to eliminate infrastructure complexity while demonstrating enterprise-grade patterns.

The Technical Stack

AWS Bedrock Integration:

  • Titan Text Embeddings V2 for 1024-dimensional vectors with superior retrieval performance
  • Claude Sonnet 4 for response generation with built-in safety guardrails
  • Native AWS IAM integration for enterprise security and compliance
  • Pay-per-use pricing that scales from $10/month POCs to enterprise workloads

Pinecone Vector Database:

  • Serverless vector search with sub-second query latency
  • Metadata filtering for source attribution and access control
  • Automatic scaling that handles traffic spikes without configuration
  • Free starter tier perfect for development and testing

FastAPI Application with Enterprise Patterns:

@app.post("/query", response_model=QueryResponse)
async def query_documents(request: QueryRequest, service: RAGService = Depends(get_rag_service)):
    """Process RAG queries with comprehensive error handling and monitoring."""
    start_time = time.time()
    
    # Generate query embedding using Bedrock Titan
    query_embedding = await service.generate_embedding(request.query)
    
    # Search similar chunks in Pinecone
    matches = await service.search_similar_chunks(
        query_embedding, 
        request.max_chunks or settings.top_k,
        request.similarity_threshold or settings.similarity_threshold
    )
    
    # Generate response using Claude Sonnet 4
    response = await service.generate_response(request.query, matches)
    
    processing_time = (time.time() - start_time) * 1000
    return QueryResponse(
        answer=response,
        query=request.query,
        sources=matches,
        processing_time_ms=processing_time
    )

What You Get Out of the Box

Complete Document Ingestion Pipeline:

  • Support for local files and S3 buckets
  • Intelligent text chunking with LangChain text splitters
  • Batch processing for efficient embedding generation
  • Comprehensive error handling and retry logic

FastAPI Server with Best Practices:

  • Async request handling for high concurrency
  • Pydantic validation for type safety
  • Structured logging with contextual information
  • Health checks for all external dependencies
  • CORS middleware for web application integration

Developer Experience Tools:

  • Interactive setup wizard that validates your environment
  • Comprehensive test suite for API validation
  • Docker containerization for consistent deployments
  • Sample documents for immediate experimentation

Enterprise-Grade Patterns:

  • IAM-based access control through AWS Bedrock
  • Secrets management best practices
  • Cost monitoring and optimization guidance
  • Scalable deployment options (Lambda, ECS, Kubernetes)

Performance Characteristics

I optimized this implementation for real-world usage patterns:

  • Query latency: Sub-2-second response times for most queries
  • Concurrent users: Handles 50+ simultaneous requests
  • Document processing: 1000+ documents per hour ingestion rate
  • Cost efficiency: Linear scaling with predictable pricing

The vector database uses cosine similarity with 1024-dimensional Titan embeddings, providing superior retrieval accuracy compared to smaller embedding models. Claude Sonnet 4 generates responses with built-in citation capabilities, ensuring transparency in source attribution.

Getting Started: From Zero to RAG in 30 Minutes

The fastest path to a working RAG pipeline:

# Clone and setup
git clone https://github.com/ColeMurray/aws-rag-application.git
cd aws-rag-application
pip install -r requirements.txt
 
# Interactive configuration
python scripts/quickstart.py
 
# Ingest sample documents
python src/ingest.py --source-type local --path ./data
 
# Start the API server
python src/app.py

The quickstart script handles environment validation, AWS credential verification, and service connectivity testing. Within minutes, you'll have a working RAG API that can answer questions about your documents.

For production deployment, the included Docker configuration supports both local development and cloud deployment:

# Local development with hot reload
docker-compose up --build
 
# Production deployment
docker build -t rag-pipeline .
docker run -p 8000:8000 rag-pipeline

Why This Matters for Your Team

This isn't just another RAG tutorial;it's a demonstration of how we approach RAG implementations in our consulting practice. The application showcases modern best practices that we apply when building production systems for clients:

  • Type-safe configuration with Pydantic prevents runtime errors
  • Structured logging provides observability for production debugging
  • Comprehensive error handling ensures graceful degradation
  • Security-first design follows AWS IAM best practices
  • Cost optimization leverages managed services to minimize operational overhead

Whether you're building internal knowledge bases, customer support automation, or document analysis tools, this RAG pipeline demonstrates the architectural patterns and best practices that scale with your requirements.

The complete implementation, documentation, and deployment guides are available on GitHub. Start with the sample documents, then point the ingestion pipeline at your own data sources—S3 buckets, document repositories, or any text-based content.

Ready to build your RAG pipeline?

The code demonstrates proven patterns, the documentation is comprehensive, and the architecture showcases how to scale from prototype to enterprise. Clone the repository and start experimenting with your first intelligent document system this week.