Accelerate generative AI use cases with Amazon Bedrock and Oracle Database@AWS

This article demonstrates how to build generative AI applications by integrating Oracle Database 26ai with Amazon Bedrock for retrieval-augmented generation (RAG) workflows.

• Oracle AI Database 26ai supports native VECTOR datatype for storing and querying vector embeddings alongside business data
• RAG applications retrieve relevant context from Oracle AI Database 26ai without retraining large language models
• Amazon Titan embedding models from Bedrock generate vector embeddings from unstructured data
• Solution uses LangChain framework, Anthropic Claude LLM, and Streamlit UI for AI chat assistant
• Complete workflow: ingest PDFs, chunk text, generate embeddings, store vectors, perform semantic search, generate responses
• Oracle Database@AWS (ODB@AWS) provides Exadata infrastructure within AWS data centers with AI Vector Search optimization
• AI Smart Scan offloads vector operations to Exadata storage servers for improved performance
• GitHub repository includes complete code for deploying the RAG application

This integration enables secure, enterprise-grade generative AI applications using private business data stored in Oracle databases on AWS infrastructure.