Agent skill

AWS AI Services Expert

Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment

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SKILL.md

AWS AI Services Expert

You are an expert in Amazon Web Services AI and ML services, specializing in Amazon Bedrock for foundation models, SageMaker for custom ML, and the broader AWS AI ecosystem.

AWS Bedrock

Overview

Amazon Bedrock is a fully managed service providing foundation models from leading AI companies through a single API.

Available Models

Provider Models Best For
Anthropic Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku Complex reasoning, coding, analysis
Meta Llama 3.1 (8B/70B/405B) Open weights, customization
Mistral Mistral Large, Mixtral 8x7B European data residency, efficiency
Cohere Command R+, Command R, Embed Enterprise RAG, search
Amazon Titan Text, Titan Embeddings AWS-native integration
AI21 Jamba 1.5 Long context, efficiency
Stability Stable Diffusion Image generation

Basic Usage

python
import boto3
import json

# Initialize Bedrock client
bedrock = boto3.client(
    service_name='bedrock-runtime',
    region_name='us-east-1'
)

# Claude 3.5 Sonnet
def invoke_claude(prompt: str) -> str:
    response = bedrock.invoke_model(
        modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
        body=json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 1024,
            "messages": [
                {"role": "user", "content": prompt}
            ]
        })
    )
    result = json.loads(response['body'].read())
    return result['content'][0]['text']

# Llama 3.1
def invoke_llama(prompt: str) -> str:
    response = bedrock.invoke_model(
        modelId='meta.llama3-1-70b-instruct-v1:0',
        body=json.dumps({
            "prompt": f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>",
            "max_gen_len": 1024,
            "temperature": 0.7
        })
    )
    result = json.loads(response['body'].read())
    return result['generation']

Streaming Responses

python
def stream_claude(prompt: str):
    response = bedrock.invoke_model_with_response_stream(
        modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
        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']['text']

Embeddings with Titan

python
def get_embeddings(text: str) -> list:
    response = bedrock.invoke_model(
        modelId='amazon.titan-embed-text-v2:0',
        body=json.dumps({
            "inputText": text,
            "dimensions": 1024,
            "normalize": True
        })
    )
    result = json.loads(response['body'].read())
    return result['embedding']

Bedrock Knowledge Bases (RAG)

python
import boto3

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

def query_knowledge_base(query: str, kb_id: str) -> str:
    response = bedrock_agent.retrieve_and_generate(
        input={'text': query},
        retrieveAndGenerateConfiguration={
            'type': 'KNOWLEDGE_BASE',
            'knowledgeBaseConfiguration': {
                'knowledgeBaseId': kb_id,
                'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-5-sonnet-20241022-v2:0',
                'retrievalConfiguration': {
                    'vectorSearchConfiguration': {
                        'numberOfResults': 5
                    }
                }
            }
        }
    )
    return response['output']['text']

Bedrock Agents

python
# Creating an agent programmatically
import boto3

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

# Create agent
response = bedrock_agent_client.create_agent(
    agentName='customer-support-agent',
    foundationModel='anthropic.claude-3-5-sonnet-20241022-v2:0',
    instruction='''You are a customer support agent.
    Use the available tools to help customers with their orders.''',
    idleSessionTTLInSeconds=600
)

agent_id = response['agent']['agentId']

# Add action group (tools)
bedrock_agent_client.create_agent_action_group(
    agentId=agent_id,
    agentVersion='DRAFT',
    actionGroupName='order-management',
    actionGroupExecutor={
        'lambda': 'arn:aws:lambda:us-east-1:123456789:function:order-handler'
    },
    apiSchema={
        's3': {
            's3BucketName': 'my-schemas-bucket',
            's3ObjectKey': 'order-api-schema.json'
        }
    }
)

Amazon SageMaker

SageMaker for Custom Models

python
import sagemaker
from sagemaker.huggingface import HuggingFace

# Training job
huggingface_estimator = HuggingFace(
    entry_point='train.py',
    source_dir='./scripts',
    instance_type='ml.p4d.24xlarge',
    instance_count=1,
    role=sagemaker.get_execution_role(),
    transformers_version='4.36',
    pytorch_version='2.1',
    py_version='py310',
    hyperparameters={
        'model_name': 'meta-llama/Llama-3.1-8B',
        'epochs': 3,
        'learning_rate': 2e-5,
    }
)

huggingface_estimator.fit({'train': 's3://bucket/train-data/'})

SageMaker Endpoints

python
from sagemaker.huggingface import HuggingFaceModel

# Deploy model
hub_model = HuggingFaceModel(
    model_data='s3://bucket/model.tar.gz',
    role=sagemaker.get_execution_role(),
    transformers_version='4.36',
    pytorch_version='2.1',
    py_version='py310',
)

predictor = hub_model.deploy(
    initial_instance_count=1,
    instance_type='ml.g5.2xlarge',
    endpoint_name='my-llm-endpoint'
)

# Invoke
response = predictor.predict({
    "inputs": "What is machine learning?"
})

SageMaker JumpStart

python
from sagemaker.jumpstart.model import JumpStartModel

# Deploy foundation model from JumpStart
model = JumpStartModel(model_id="meta-textgeneration-llama-3-1-70b-instruct")
predictor = model.deploy()

# Use the model
response = predictor.predict({
    "inputs": "Explain quantum computing",
    "parameters": {"max_new_tokens": 256}
})

AWS AI Service Integration

Amazon Kendra (Enterprise Search)

python
import boto3

kendra = boto3.client('kendra')

# Query
response = kendra.query(
    IndexId='your-index-id',
    QueryText='What is our refund policy?',
    AttributeFilter={
        'EqualsTo': {
            'Key': 'Department',
            'Value': {'StringValue': 'Customer Service'}
        }
    }
)

for result in response['ResultItems']:
    print(f"Score: {result['ScoreAttributes']['ScoreConfidence']}")
    print(f"Answer: {result['DocumentExcerpt']['Text']}")

Amazon Comprehend (NLP)

python
comprehend = boto3.client('comprehend')

# Sentiment analysis
response = comprehend.detect_sentiment(
    Text="I love this product! It's amazing.",
    LanguageCode='en'
)
# {'Sentiment': 'POSITIVE', 'SentimentScore': {...}}

# Entity extraction
entities = comprehend.detect_entities(
    Text="Amazon was founded by Jeff Bezos in Seattle",
    LanguageCode='en'
)

Amazon Textract (Document AI)

python
textract = boto3.client('textract')

# Analyze document
response = textract.analyze_document(
    Document={'S3Object': {'Bucket': 'bucket', 'Name': 'invoice.pdf'}},
    FeatureTypes=['FORMS', 'TABLES']
)

# Extract key-value pairs
for block in response['Blocks']:
    if block['BlockType'] == 'KEY_VALUE_SET':
        # Process form fields
        pass

Architecture Patterns

Serverless AI Pipeline

┌─────────────────────────────────────────────────────────────────┐
│                  SERVERLESS AI ARCHITECTURE                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  API Gateway ──▶ Lambda ──▶ Bedrock                             │
│       │              │                                           │
│       │              ▼                                           │
│       │         DynamoDB (conversation history)                  │
│       │              │                                           │
│       │              ▼                                           │
│       │         S3 (documents)                                   │
│       │              │                                           │
│       │              ▼                                           │
│       │         OpenSearch (vector store)                        │
│       │                                                          │
│       └────────▶ CloudWatch (monitoring)                        │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Enterprise RAG on AWS

python
# terraform/main.tf for RAG infrastructure
"""
module "bedrock_kb" {
  source = "./modules/bedrock-knowledge-base"

  name = "enterprise-kb"
  embedding_model = "amazon.titan-embed-text-v2:0"

  data_sources = [
    {
      type = "S3"
      bucket = aws_s3_bucket.documents.id
      prefix = "knowledge/"
    }
  ]

  vector_store = {
    type = "OPENSEARCH_SERVERLESS"
    collection_arn = aws_opensearchserverless_collection.vectors.arn
  }
}
"""

Pricing Optimization

Bedrock Pricing (per 1K tokens)

Model Input Output
Claude 3.5 Sonnet $0.003 $0.015
Claude 3 Haiku $0.00025 $0.00125
Llama 3.1 70B $0.00265 $0.0035
Titan Text Express $0.0002 $0.0006

Cost Optimization Strategies

python
class BedrockCostOptimizer:
    MODEL_COSTS = {
        "claude-3-5-sonnet": {"input": 0.003, "output": 0.015},
        "claude-3-haiku": {"input": 0.00025, "output": 0.00125},
        "llama-3-70b": {"input": 0.00265, "output": 0.0035},
    }

    def select_model(self, task_complexity: str, max_cost: float = None):
        """Select most cost-effective model for task"""
        if task_complexity == "simple":
            return "claude-3-haiku"  # Cheapest
        elif task_complexity == "moderate":
            return "llama-3-70b"  # Good balance
        else:
            return "claude-3-5-sonnet"  # Best capability

    def estimate_cost(self, model: str, input_tokens: int, output_tokens: int):
        costs = self.MODEL_COSTS[model]
        return (input_tokens * costs["input"] + output_tokens * costs["output"]) / 1000

Security Best Practices

IAM Policies

json
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": [
        "arn:aws:bedrock:*::foundation-model/anthropic.claude-3-5-sonnet*",
        "arn:aws:bedrock:*::foundation-model/meta.llama3*"
      ],
      "Condition": {
        "StringEquals": {
          "aws:RequestedRegion": ["us-east-1", "us-west-2"]
        }
      }
    }
  ]
}

VPC Endpoints

hcl
resource "aws_vpc_endpoint" "bedrock" {
  vpc_id            = aws_vpc.main.id
  service_name      = "com.amazonaws.us-east-1.bedrock-runtime"
  vpc_endpoint_type = "Interface"
  subnet_ids        = aws_subnet.private[*].id
  security_group_ids = [aws_security_group.bedrock.id]
  private_dns_enabled = true
}

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