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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.
// 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 ECS container orchestration for running Docker containers. Use when deploying containerized applications, configuring task definitions, setting up services, managing clusters, or troubleshooting container issues.
AWS EC2 virtual machine management — instances, security groups, key pairs, AMIs, EBS volumes, Auto Scaling Groups, Spot Instances, Session Manager, placement groups, and instance lifecycle automation. Trigger on ANY of these, even when EC2 isn't named explicitly: - Launching or provisioning: "spin up a server", "create a VM", "new instance", "run-instances", mention of instance types (t3, m5, c5, r6, g5, p4d, t4g, c7g, etc.) - SSH / connectivity problems: "connection refused", "connection timed out", "permission denied publickey", "can't connect to my instance", "SSH not working" - Instance management: resize, stop, start, terminate, reboot, change instance type - Cost optimization: stop dev instances overnight, save money on EC2, spot vs on-demand, reserved instances - Auto Scaling: ASG, launch template, mixed instances policy, scale to zero, scheduled scaling - Spot Instances: spot fleet, spot interruption, capacity-optimized, price-capacity-optimized - AMIs and backups: create image, custom AMI, EBS snaps
AWS API Gateway for REST and HTTP API management. Use when creating APIs, configuring integrations, setting up authorization, managing stages, implementing rate limiting, or troubleshooting API issues.
AWS CloudFormation infrastructure as code for stack management. Use when writing templates, deploying stacks, managing drift, troubleshooting deployments, or organizing infrastructure with nested stacks.
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
AWS Cognito user authentication and authorization service. Use when setting up user pools, configuring identity pools, implementing OAuth flows, managing user attributes, or integrating with social identity providers.
| name | 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. |
| last_updated | 2026-01-07 |
| doc_source | https://docs.aws.amazon.com/bedrock/latest/userguide/ |
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.
Pre-trained models available through Bedrock:
Models must be enabled in your account before use:
| 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 |
AWS CLI:
# Invoke Claude
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 | jq -r '.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)
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)
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)}')
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 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'
# Get model details
aws bedrock get-foundation-model \
--model-identifier anthropic.claude-3-sonnet-20240229-v1:0
# List model access status
aws bedrock list-foundation-model-agreement-offers \
--model-id anthropic.claude-3-sonnet-20240229-v1:0
| 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 |
aws bedrock list-provisioned-model-throughputs | List provisioned capacity |
| 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 |
| 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 |
{
"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"
]
}
]
}
Causes:
bedrock:InvokeModelDebug:
# Check model access status
aws bedrock list-foundation-models \
--query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'
# Test IAM permissions
aws iam simulate-principal-policy \
--policy-source-arn arn:aws:iam::123456789012:role/my-role \
--action-names bedrock:InvokeModel \
--resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0"
Cause: Model is still being provisioned or temporarily unavailable.
Solution: Implement 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'] == 'ModelNotReadyException':
time.sleep(2 ** attempt)
else:
raise
raise Exception('Max retries exceeded')
Causes:
Solutions:
Common issues:
Debug:
# Check model-specific requirements
aws bedrock get-foundation-model \
--model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \
--query 'modelDetails.inferenceTypesSupported'