aws-bedrock

---

title: "Aws Bedrock" sidebar_label: "Aws Bedrock" description: "AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns."

Aws Bedrock

AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.

Details

Property	Value
Skill Directory	.github/skills/aws-bedrock/
Phase	General
User Invocable	✅ Yes
Usage	/aws-bedrock Model name, capability, or pattern to look up (e.g. 'Claude text generation', 'Titan embeddings', 'streaming response', 'RAG with knowledge base')

Documentation

AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

Table of Contents

• Core Concepts
• Common Patterns
• CLI Reference
• Best Practices
• Troubleshooting
• References

Core Concepts

Foundation Models

Pre-trained models available through Bedrock:

• Claude (Anthropic): Text generation, analysis, coding
• Titan (Amazon): Text, embeddings, image generation
• Llama (Meta): Open-weight text generation
• Mistral: Efficient text generation
• Stable Diffusion (Stability AI): Image generation

Model Access

Models must be enabled in your account before use:

• Request access in Bedrock console
• Some models require acceptance of EULAs
• Access is region-specific

Inference Types

Type	Use Case	Pricing
On-Demand	Variable workloads	Per token
Provisioned Throughput	Consistent high-volume	Hourly commitment
Batch Inference	Async large-scale	Discounted per token

Common Patterns

Invoke Claude (Text Generation)

AWS CLI:

aws bedrock-runtime invoke-model \
  --model-id anthropic.claude-3-sonnet-20240229-v1:0 \
  --content-type application/json \
  --accept application/json \
  --body '{
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Explain AWS Lambda in 3 sentences."}
    ]
  }' \
  response.json

cat response.json | python3 -c "import sys,json; print(json.load(sys.stdin)['content'][0]['text'])"

boto3:

import boto3
import json

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

def invoke_claude(prompt, max_tokens=1024):
    response = bedrock.invoke_model(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': max_tokens,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )

    result = json.loads(response['body'].read())
    return result['content'][0]['text']

# Usage
response = invoke_claude('What is Amazon S3?')
print(response)

Streaming Response

import boto3
import json

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

def stream_claude(prompt):
    response = bedrock.invoke_model_with_response_stream(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 1024,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )

    for event in response['body']:
        chunk = json.loads(event['chunk']['bytes'])
        if chunk['type'] == 'content_block_delta':
            yield chunk['delta'].get('text', '')

# Usage
for text in stream_claude('Write a haiku about cloud computing.'):
    print(text, end='', flush=True)

Generate Embeddings

import boto3
import json

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

def get_embedding(text):
    response = bedrock.invoke_model(
        modelId='amazon.titan-embed-text-v2:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'inputText': text,
            'dimensions': 1024,
            'normalize': True
        })
    )

    result = json.loads(response['body'].read())
    return result['embedding']

# Usage
embedding = get_embedding('AWS Lambda is a serverless compute service.')
print(f'Embedding dimension: {len(embedding)}')

Multi-Turn Conversation

import boto3
import json

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

class Conversation:
    def __init__(self, system_prompt=None):
        self.messages = []
        self.system = system_prompt

    def chat(self, user_message):
        self.messages.append({
            'role': 'user',
            'content': user_message
        })

        body = {
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 1024,
            'messages': self.messages
        }

        if self.system:
            body['system'] = self.system

        response = bedrock.invoke_model(
            modelId='anthropic.claude-3-sonnet-20240229-v1:0',
            contentType='application/json',
            accept='application/json',
            body=json.dumps(body)
        )

        result = json.loads(response['body'].read())
        assistant_message = result['content'][0]['text']

        self.messages.append({
            'role': 'assistant',
            'content': assistant_message
        })

        return assistant_message

# Usage
conv = Conversation(system_prompt='You are an AWS solutions architect.')
print(conv.chat('What database should I use for a chat application?'))
print(conv.chat('What about for time-series data?'))

List and Check Available Models

# List all foundation models
aws bedrock list-foundation-models \
  --query 'modelSummaries[*].[modelId,modelName,providerName]' \
  --output table

# Filter by provider
aws bedrock list-foundation-models \
  --by-provider anthropic \
  --query 'modelSummaries[*].modelId'

CLI Reference

Bedrock (Control Plane)

Command	Description
aws bedrock list-foundation-models	List available models
aws bedrock get-foundation-model	Get model details
aws bedrock list-custom-models	List fine-tuned models
aws bedrock create-model-customization-job	Start fine-tuning

Bedrock Runtime (Data Plane)

Command	Description
aws bedrock-runtime invoke-model	Invoke model synchronously
aws bedrock-runtime invoke-model-with-response-stream	Invoke with streaming
aws bedrock-runtime converse	Multi-turn conversation API
aws bedrock-runtime converse-stream	Streaming conversation

Bedrock Agent Runtime

Command	Description
aws bedrock-agent-runtime invoke-agent	Invoke a Bedrock agent
aws bedrock-agent-runtime retrieve	Query knowledge base
aws bedrock-agent-runtime retrieve-and-generate	RAG query

Best Practices

Cost Optimization

• Use appropriate models: Smaller models for simple tasks
• Set max_tokens: Limit output length when possible
• Cache responses: For repeated identical queries
• Batch when possible: Use batch inference for bulk processing
• Monitor usage: Set up CloudWatch alarms for cost

Performance

• Use streaming: For better user experience with long outputs
• Connection pooling: Reuse boto3 clients
• Regional deployment: Use closest region to reduce latency
• Provisioned throughput: For consistent high-volume workloads

Security

• Least privilege IAM: Only grant needed model access
• VPC endpoints: Keep traffic private
• Guardrails: Implement content filtering
• Audit with CloudTrail: Track model invocations

IAM Permissions

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": [
        "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0",
        "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0"
      ]
    }
  ]
}

Troubleshooting

AccessDeniedException

Causes:

• Model access not enabled in console
• IAM policy missing bedrock:InvokeModel
• Wrong model ID or region

Debug:

# Check model access status
aws bedrock list-foundation-models \
  --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

ThrottlingException

Causes:

• Exceeded on-demand quota
• Too many concurrent requests

Solutions:

• Request quota increase
• Implement exponential backoff
• Consider provisioned throughput

ValidationException

Common issues:

• Invalid model ID
• Malformed request body
• max_tokens exceeds model limit

Retry with Exponential Backoff

import time
from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3):
    for attempt in range(max_retries):
        try:
            return bedrock.invoke_model(
                modelId='anthropic.claude-3-sonnet-20240229-v1:0',
                body=json.dumps(body)
            )
        except ClientError as e:
            if e.response['Error']['Code'] in ('ThrottlingException', 'ModelNotReadyException'):
                time.sleep(2 ** attempt)
            else:
                raise
    raise Exception('Max retries exceeded')

References

• Bedrock User Guide
• Bedrock API Reference
• Bedrock Runtime API
• Model Parameters
• Bedrock Pricing