Building a Managed RAG Platform with Amazon Bedrock

Article 2: Building a Managed RAG Platform with Amazon Bedrock

Amazon Bedrock provides managed services that simplify the implementation of Retrieval-Augmented Generation systems. Instead of building chunking, embeddings, retrieval, and orchestration from scratch, organizations can use Knowledge Bases for Amazon Bedrock with managed foundation models.

Why Use Bedrock-Managed RAG?

Use Bedrock-managed RAG when you want to build a document question-answering system without managing every RAG component yourself.
It is useful when your team already uses AWS and wants to integrate with S3, IAM, encryption, monitoring, and managed infrastructure.
It reduces the amount of custom code required for ingestion, chunking, embeddings, retrieval, and model orchestration.
It is a good first choice when speed, security, and operational simplicity are more important than full control over every layer.

Key AWS Services

Amazon S3
Amazon Bedrock
Knowledge Bases for Amazon Bedrock
Amazon OpenSearch Serverless, commonly used as the managed vector store
Optional vector stores such as Aurora PostgreSQL Serverless, Amazon S3 Vectors, Neptune Analytics, or a supported existing vector store
Amazon Textract
AWS Lambda
Amazon ECS
Amazon API Gateway
Amazon CloudWatch

Reference Architecture

PDF Upload
    |
    v
Amazon S3
    |
    v
Knowledge Base for Amazon Bedrock
    |
    +--> Chunking
    |
    +--> Embeddings
    |
    +--> Vector Storage
    |
    v
OpenSearch Serverless

User Question
    |
    v
Application API
    |
    v
RetrieveAndGenerate API
    |
    v
Foundation Model
    |
    v
Response with Citations

Document Ingestion Workflow

PDF
 |
 v
S3
 |
 v
Knowledge Base Sync
 |
 v
Text Extraction
 |
 v
Chunk Creation
 |
 v
Embedding Generation
 |
 v
Vector Index Storage

Large PDF Processing

For simple text-based PDFs, Knowledge Bases for Amazon Bedrock can ingest and process the document from S3. For scanned PDFs, table-heavy documents, diagrams, or visually rich content, add a preprocessing step such as Amazon Textract or an appropriate Bedrock parsing option before indexing. The extracted text, metadata, and page references should then be stored and synced into the knowledge base.

PDF
 |
 v
Textract
 |
 v
Structured Text
 |
 v
Knowledge Base
 |
 v
Vector Database

Question Answering Flow

User Question
      |
      v
RetrieveAndGenerate API
      |
      v
Knowledge Base Search
      |
      v
Top Chunks
      |
      v
Claude / Nova / Llama
      |
      v
Generated Response

Retrieve vs RetrieveAndGenerate

Use Retrieve when your application wants only the matching source chunks and will build the prompt itself.
Use RetrieveAndGenerate when you want Amazon Bedrock to retrieve relevant chunks, call the foundation model, and return a generated answer with source references.
Use RetrieveAndGenerateStream when the chat UI should stream the answer back to the user.

Example Retrieval Request Flow

POST /chat

{
  "question": "Explain the disaster recovery strategy."
}

Internal Processing

1. Receive user question
2. Retrieve relevant chunks from the knowledge base
3. Optionally rerank the retrieved chunks
4. Build the grounded prompt
5. Invoke the selected foundation model
6. Return answer with source references

Advantages of Bedrock-Managed RAG

Minimal infrastructure management
Fully managed embeddings
Built-in retrieval workflows
Enterprise-grade security
AWS IAM integration
Simplified scaling
Reduced operational burden

Security Architecture

S3 encryption
KMS-managed keys
IAM access controls
VPC endpoints
CloudTrail auditing
Private networking

Production Best Practices

Store metadata with every chunk.
Include page numbers.
Use guardrails for user input and generated responses, but remember that retrieved source references still need separate data governance controls.
Use document versioning.
Enable citations.
Monitor retrieval quality.
Track latency and token usage.

When Should You Use Bedrock-Managed RAG?

Use Bedrock-managed RAG when you need enterprise document search on AWS.
Use it for policy assistants, internal knowledge bases, and compliance documentation.
Use it when your team wants managed ingestion, embeddings, retrieval, and model access.
Use it when security, IAM integration, encryption, and operational simplicity are important.
Use it for customer support systems and employee self-service assistants that need source-grounded answers.

Continue the RAG series: