Building a Serverless, Event-Driven RAG Ingestion Pipeline with Bedrock and S3
Learn how to automate vector embedding generation using Amazon S3, SQS, AWS Lambda, Amazon Bedrock (Titan Text Embed v2), and OpenSearch Serverless with zero idle server costs.
Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in proprietary domain knowledge, drastically reducing hallucinations. However, most tutorials rely on synchronous API calls running on single compute instances, leading to timeouts and rate limit bottlenecks during bulk document uploads.
In this tutorial, we will build an automated, fully serverless document indexing architecture that triggers whenever an engineer or automated workflow uploads technical documentation to Amazon S3.
1. Solution Architecture
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[User / CI/CD]
│
▼ (Upload PDF/Markdown)
[ Amazon S3 ] ──(ObjectCreated Event)──▶ [ Amazon SQS ]
│
▼
[ AWS Lambda (Worker) ]
├── Extracts & Chunks Text
├── Calls Amazon Bedrock
│ │
│ ▼
│ [ Amazon Bedrock ]
│ (Titan Text Embeddings v2)
▼
[ Amazon OpenSearch Serverless ]
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