Discover how to leverage AWS services to create a Retrieval-Augmented Generation (RAG)-enabled generative AI application. This solution integrates LangChain and Amazon Aurora PostgreSQL-Compatible as a vector store, enabling near real-time document ingestion.
Architecture Overview
This solution consists of several key components. Amazon S3 is used for file uploads, while AWS Lambda handles data processing. For model integration, the architecture leverages Amazon Bedrock.
Workflow and architecture components
Technical Highlights
The implementation includes Amazon Titan Text Embeddings v2 for efficient text representation and Anthropic Claude 3 Sonnet for advanced generative AI capabilities. Infrastructure deployment is fully automated using Terraform.
Explore the detailed workflow and best practices for implementation.
Learn More
📖 AWS Documentation: Deploy a RAG use case on AWS using Terraform and Amazon Bedrock
💻 GitHub Repository: Terraform RAG Template Using Amazon Bedrock