Building a secure end-to-end generative AI application in the cloud
summary
Reference architecture shows an end‑to‑end RAG pipeline that keeps data off the public internet by routing Bedrock calls over AWS PrivateLink. Documents land in S3, a Lambda uses LangChain in Python to chunk files, create embeddings with Amazon Titan, and index vectors into Amazon OpenSearch Serverless. The app runs on EC2/Streamlit, queries produce embeddings, semantic search returns context, and Bedrock generates responses — the pipeline runs entirely over PrivateLink and produces contextually accurate answers.
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chapters (7)
- 0:00Introduction and overview
- 1:55Generative AI and Bedrock
- 4:40Private network connectivity
- 7:24Retrieval Augmented Generation
- 9:40Data ingestion and workflow
- 14:15Solution architecture
- 18:48Conclusion and resources
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