Fully Managed Cloud RAG with Bedrock

Building a production RAG system involves managing ingestion jobs, vector databases, and LLM APIs. AWS Bedrock Knowledge Bases automates this entire lifecycle.

How Knowledge Bases Work

1. Data Source: An S3 bucket where you upload your documents.
2. Embedding Model: Choose Titan or Cohere (Managed by Bedrock).
3. Vector Store: Bedrock automatically manages an OpenSearch Serverless, Pinecone, or Aurora index for you.
4. Retrieve and Generate API: A single API call that takes the user query and returns a fully cited answer.

Implementation with Boto3

import boto3

client = boto3.client('bedrock-agent-runtime')

response = client.retrieve_and_generate(
    input={
        'text': 'What is the company policy on remote work?'
    },
    retrieveAndGenerateConfiguration={
        'type': 'KNOWLEDGE_BASE',
        'knowledgeBaseConfiguration': {
            'knowledgeBaseId': 'YOUR_KB_ID',
            'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0'
        }
    }
)

print(response['output']['text'])

Scaling and Management

Because it's "Serverless," it scales automatically.

• Auto-Sync: When you add a file to S3, you can trigger a "Sync" job to update your vector index.
• Monitoring: Integration with CloudWatch for search latency and token usage.

When to Use Bedrock Knowledge Bases

• When you are already on AWS.
• When you want to minimize "DevOps" and management overhead.
• When you need a system that can be deployed in minutes.

Pricing Considerations

You pay for:

1. Model usage (Inference).
2. OpenSearch Serverless (Storage/Compute).
3. Bedrock service fees.

Exercises

1. Create a Knowledge Base in the AWS Console.
2. Upload 3 files to S3 and trigger a sync.
3. How long does the "Ingestion" take for 100 pages of text?