Bedrock Integration

Overview

Configure Amazon Bedrock for AI model access including Gemma 3 12B and Titan Embeddings.

Enable Model Access

  1. Navigate to Amazon Bedrock → Model access
  2. Request access to:
    • Amazon Titan Text Express (for assessments)
    • Amazon Titan Embeddings V2 (for RAG)
    • Meta Llama 3 or Anthropic Claude (optional)

AI model

Test Bedrock API

import boto3
import json

bedrock = boto3.client('bedrock-runtime', region_name='ap-southeast-1')

# Test Titan Text
response = bedrock.invoke_model(
    modelId='amazon.titan-text-express-v1',
    body=json.dumps({
        'inputText': 'Hello, how are you?',
        'textGenerationConfig': {
            'maxTokenCount': 100,
            'temperature': 0.7
        }
    })
)

print(json.loads(response['body'].read()))

Titan Embeddings for RAG

# Generate embeddings
response = bedrock.invoke_model(
    modelId='amazon.titan-embed-text-v2:0',
    body=json.dumps({
        'inputText': 'Document chunk text here'
    })
)

embedding = json.loads(response['body'].read())['embedding']
# Store in OpenSearch or use for similarity search

Google Gemini Integration

For smart query generation:

import google.generativeai as genai

genai.configure(api_key=os.environ['GEMINI_API_KEY'])
model = genai.GenerativeModel('gemini-2.5-flash')

response = model.generate_content(
    f"""Analyze this document and generate 10 intelligent questions:
    {document_text}
    """
)

Cost Optimization

  • Use Titan Text Express for assessments (lower cost)
  • Batch embeddings generation where possible
  • Implement caching for repeated queries
  • Use Google Gemini free tier for query generation

Next Steps

Proceed to CI/CD Pipeline.