This project implements a sophisticated Retrieval-Augmented Generation (RAG) pipeline using AWS services, enabling multimodal document understanding and intelligent question answering. The system processes various document types and provides context-aware responses using advanced AI capabilities.
Built with AWS CDK, the infrastructure is fully automated and reproducible, demonstrating modern approaches to building AI-powered applications in the cloud.
What it is
An advanced RAG pipeline that combines:
- Multimodal Processing: Handles text, images, and other document formats for comprehensive understanding.
- Vector Search: Efficient similarity search using embeddings for relevant context retrieval.
- Agentic AI: Uses intelligent agents to orchestrate complex document processing workflows.
- AWS Integration: Leverages AWS services for scalable and serverless AI operations.
Key Technical Details
- AWS CDK: Infrastructure defined as Python code for reproducible deployments.
- RAG Architecture: Implements retrieval-augmented generation for accurate, context-aware responses.
- Vector Database: Stores and queries document embeddings for semantic search.
- AWS Strands: Utilizes AWS Strands for building the agentic workflow.
- Serverless Design: Fully serverless architecture for cost-effective scaling.
- Document Processing: Automated pipeline for ingesting, processing, and indexing various document types.
What I Learned
- RAG Patterns: Understanding and implementing retrieval-augmented generation architectures.
- Multimodal AI: Working with AI models that process multiple types of input data.
- Vector Embeddings: How to use embeddings for semantic search and similarity matching.
- Agentic Systems: Building AI systems that use agents to perform complex reasoning tasks.
- AWS AI Services: Integrating various AWS AI and ML services into a cohesive application.