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Bedrock
/SKILLModèles de base AWS Bedrock pour l'IA générative. À utiliser pour appeler des modèles de base, développer des applications d'IA, créer des représentations vectorielles, configurer l'accès aux modèles
// contenu du skill
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/
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
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 Model (Text Generation)
AWS CLI:
bash
# 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:
python
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
python
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
python
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)}')Conversation with History
python
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// source originale publique
itsmostafa/aws-agent-skills/skills/bedrock/SKILL.md
Licence : MIT License
Projet indépendant, non affilié à Anthropic. Ce skill reste la propriété de son auteur original.
