动手实验室 — AIP-C01 AWS Certified Generative AI Developer - Professional

概述

先决条件

步骤

1. 1.选择我们的 Terraform 版本和 AWS 区域

   标准开场。选择 us-east-1 是为了获得最广泛的 Bedrock + Bedrock Agent 功能可用性。提供商版本锁定是不可协商的：aws_bedrockagent_* 资源要求版本 ~> 5.60 或更高。

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   terraform {
     required_version = ">= 1.5"
   
     required_providers {
       aws = {
         source  = "hashicorp/aws"
         version = "~> 5.60"
       }
       archive = {
         source  = "hashicorp/archive"
         version = "~> 2.4"
       }
     }
   }
   
   provider "aws" {
     region = "us-east-1"
   
     default_tags {
       tags = {
         Project   = "certlabpro-aip-c01"
         ManagedBy = "terraform"
       }
     }
   }
   
   data "aws_caller_identity" "current" {}
   data "aws_region" "current" {}

2. 2.为代理提供一个私有 S3 存储桶，用于它可能读取的任何参考数据

   配置服务：

   • Amazon S3

   大多数生产环境的 Bedrock Agent 最终都会从 S3 读取一些内容——知识库源文档、JSON 工具定义、响应模板。我们提前预置一个存储桶，这样就不必在后期再配置权限。加密 + 公共访问阻止的默认设置是 AIP-C01 基础模型集成、数据管理和合规性 领域中的实践。

   AIP-C01 考试会测试围绕此存储桶的一个特定合规模式：当法规要求“AWS 绝不能拥有未加密数据”时，使用应用程序控制的密钥进行客户端加密是正确的答案。对于其他所有情况，SSE-S3（我们在此处采用的方法）就足够了。

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   resource "aws_s3_bucket" "agent_data" {
     bucket_prefix = "certlabpro-aip-c01-"
   }
   
   resource "aws_s3_bucket_public_access_block" "agent_data" {
     bucket = aws_s3_bucket.agent_data.id
   
     block_public_acls       = true
     block_public_policy     = true
     ignore_public_acls      = true
     restrict_public_buckets = true
   }
   
   resource "aws_s3_bucket_server_side_encryption_configuration" "agent_data" {
     bucket = aws_s3_bucket.agent_data.id
   
     rule {
       apply_server_side_encryption_by_default {
         sse_algorithm = "AES256"
       }
     }
   }

3. 3.添加生产级防护栏，包括内容 + PII + 拒绝主题过滤器

   配置服务：

   • Amazon Bedrock Guardrails

   AIP-C01 的 AI 安全、保障和治理 领域（占考试的 20%）比 AIF-C01 版本深入得多。考试会测试多层防护栏的构成：针对有害类别的高强度内容过滤器，针对 PII 的敏感信息策略（使用 BLOCK 而不是 AIF 风格的 ANONYMIZE——生产代理通常需要硬阻止），以及针对特定应用禁用主题的主题策略。

   我们配置所有这三项。策略中被拒绝的主题 "FinancialAdvice" 是非金融聊天机器人常用的生产防护栏——即使是 Claude 3 有时也会偏离主题，提供股票建议，除非您告诉防护栏阻止这种主题漂移。AIP-C01 在场景问题中测试这种精确的组合模式，即提示是良性的，但响应可能会超出策略范围。

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   resource "aws_bedrock_guardrail" "production" {
     name                      = "certlabpro-aip-c01-production"
     description               = "Production-grade safety rail for the AIP-C01 lab agent."
     blocked_input_messaging   = "I can't help with that request."
     blocked_outputs_messaging = "I can't share that response."
   
     content_policy_config {
       filters_config {
         input_strength  = "HIGH"
         output_strength = "HIGH"
         type            = "HATE"
       }
       filters_config {
         input_strength  = "HIGH"
         output_strength = "HIGH"
         type            = "VIOLENCE"
       }
       filters_config {
         input_strength  = "HIGH"
         output_strength = "HIGH"
         type            = "SEXUAL"
       }
       filters_config {
         input_strength  = "HIGH"
         output_strength = "HIGH"
         type            = "INSULTS"
       }
       filters_config {
         input_strength  = "HIGH"
         output_strength = "HIGH"
         type            = "MISCONDUCT"
       }
     }
   
     sensitive_information_policy_config {
       pii_entities_config {
         action = "BLOCK"
         type   = "EMAIL"
       }
       pii_entities_config {
         action = "BLOCK"
         type   = "PHONE"
       }
       pii_entities_config {
         action = "BLOCK"
         type   = "US_SOCIAL_SECURITY_NUMBER"
       }
       pii_entities_config {
         action = "BLOCK"
         type   = "CREDIT_DEBIT_CARD_NUMBER"
       }
     }
   
     topic_policy_config {
       topics_config {
         name       = "FinancialAdvice"
         definition = "Specific recommendations to buy, sell, or hold financial instruments."
         examples   = ["Should I buy NVDA?", "Is now a good time to sell my Bitcoin?"]
         type       = "DENY"
       }
     }
   }

4. 4.创建具有范围 IAM 角色的 Bedrock Agent

   配置服务：

   • Amazon Bedrock
   • AWS IAM

   代理将基础模型、系统提示以及（在步骤 5 中）一个或多个扩展其功能的 Action Group 连接起来。我们附加的 IAM 角色是代理的 执行角色——它是代理用于调用模型、从知识库检索以及调用 Action Group Lambda 的角色。AIP-C01 的 实施和集成 领域测试这种精确的角色/信任形状：bedrock.amazonaws.com 作为主体，对特定模型 ARN 上的 bedrock:InvokeModel 和特定函数 ARN 上的 lambda:InvokeFunction 具有范围权限。

   代理的 instruction 字段是系统提示——简短、面向行动，并指明代理的用途。AIP-C01 关于代理级别提示工程的问题正是测试这一点：指令是持久的“个性”；用户消息则每轮都会改变。

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   resource "aws_iam_role" "agent" {
     name = "certlabpro-aip-c01-agent"
   
     assume_role_policy = jsonencode({
       Version = "2012-10-17"
       Statement = [{
         Effect    = "Allow"
         Principal = { Service = "bedrock.amazonaws.com" }
         Action    = "sts:AssumeRole"
         Condition = {
           StringEquals = {
             "aws:SourceAccount" = data.aws_caller_identity.current.account_id
           }
         }
       }]
     })
   }
   
   resource "aws_iam_role_policy" "agent_invoke_model" {
     name = "invoke-foundation-model"
     role = aws_iam_role.agent.id
   
     policy = jsonencode({
       Version = "2012-10-17"
       Statement = [{
         Effect = "Allow"
         Action = ["bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream"]
         Resource = "arn:aws:bedrock:${data.aws_region.current.name}::foundation-model/anthropic.claude-3-haiku-20240307-v1:0"
       }]
     })
   }
   
   resource "aws_bedrockagent_agent" "main" {
     agent_name                  = "certlabpro-aip-c01-agent"
     agent_resource_role_arn     = aws_iam_role.agent.arn
     foundation_model            = "anthropic.claude-3-haiku-20240307-v1:0"
     instruction                 = "You are a helpful assistant for the certlabpro AIP-C01 lab. Answer concisely. When the user asks about the current time, use the get_current_time tool."
     idle_session_ttl_in_seconds = 600
   
     # Attach the guardrail from Step 3. Bedrock evaluates inputs and outputs
     # against the guardrail policy on every model call.
     guardrail_configuration {
       guardrail_identifier = aws_bedrock_guardrail.production.guardrail_id
       guardrail_version    = aws_bedrock_guardrail.production.version
     }
   }

5. 5.添加一个 Action Group，让代理能够调用 Lambda 工具

   配置服务：

   • Amazon Bedrock
   • AWS Lambda
   • AWS IAM

   Action Groups 是 Bedrock Agent 调用您的代码的方式。其模式是：定义 API 模式（工具接受和返回什么），指向一个实现这些操作的 Lambda 函数，Bedrock 则处理其余部分——当模型决定需要该工具时，它会使用结构化参数调用您的 Lambda，并将响应反馈到对话中。

   我们使用 Bedrock 所期望的 OpenAPI 3.0 模式定义了一个最小的 get_current_time 操作。Lambda 实现故意非常小巧——考试测试的是集成的 形式（函数调用 Lambda + OpenAPI 模式 + Bedrock 调用权限），而不是函数内部逻辑。

   aws_lambda_permission 资源是 AIP-C01 的一个典型陷阱：如果没有它，即使其他所有部分都正确连接，代理调用 Lambda 也会收到 AccessDenied。Bedrock Agent → Lambda 是一种外部服务调用模式；函数的资源策略需要明确允许 bedrock.amazonaws.com。

   有了 Action Groups，完整的代理形态就完成了：模型 + 系统提示 + 安全防护栏 + 工具调用扩展性，所有这些都在一个有范围的 IAM 身份下。每个额外的 AIP-C01 模式（知识库检索、多动作代理、通过主管代理进行代理协作）都以此基础进行扩展。

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   # ── Lambda tool ───────────────────────────────────────────────
   data "archive_file" "tool" {
     type        = "zip"
     output_path = "${path.module}/build/tool.zip"
   
     source {
       filename = "index.py"
       content  = <<-EOT
         import json
         from datetime import datetime, timezone
         def handler(event, context):
           # Bedrock Agent passes the parsed args under `requestBody` / `parameters`.
           # For a simple no-arg tool, just return the current time.
           now_iso = datetime.now(timezone.utc).isoformat()
           return {
             "messageVersion": "1.0",
             "response": {
               "actionGroup": event.get("actionGroup", ""),
               "apiPath": event.get("apiPath", "/"),
               "httpMethod": event.get("httpMethod", "GET"),
               "httpStatusCode": 200,
               "responseBody": {"application/json": {"body": json.dumps({"current_time_utc": now_iso})}},
             },
           }
       EOT
     }
   }
   
   resource "aws_iam_role" "tool_lambda" {
     name = "certlabpro-aip-c01-tool"
   
     assume_role_policy = jsonencode({
       Version = "2012-10-17"
       Statement = [{
         Effect    = "Allow"
         Principal = { Service = "lambda.amazonaws.com" }
         Action    = "sts:AssumeRole"
       }]
     })
   }
   
   resource "aws_iam_role_policy_attachment" "tool_logs" {
     role       = aws_iam_role.tool_lambda.name
     policy_arn = "arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
   }
   
   resource "aws_lambda_function" "tool" {
     function_name    = "certlabpro-aip-c01-tool"
     role             = aws_iam_role.tool_lambda.arn
     runtime          = "python3.12"
     handler          = "index.handler"
     filename         = data.archive_file.tool.output_path
     source_code_hash = data.archive_file.tool.output_base64sha256
     timeout          = 10
   }
   
   resource "aws_lambda_permission" "agent_invoke_tool" {
     statement_id  = "AllowBedrockAgentInvoke"
     action        = "lambda:InvokeFunction"
     function_name = aws_lambda_function.tool.function_name
     principal     = "bedrock.amazonaws.com"
     source_arn    = aws_bedrockagent_agent.main.agent_arn
   }
   
   # Also let the agent invoke the lambda from its execution role.
   resource "aws_iam_role_policy" "agent_invoke_lambda" {
     name = "invoke-tool"
     role = aws_iam_role.agent.id
   
     policy = jsonencode({
       Version = "2012-10-17"
       Statement = [{
         Effect   = "Allow"
         Action   = "lambda:InvokeFunction"
         Resource = aws_lambda_function.tool.arn
       }]
     })
   }
   
   # ── Action Group ──────────────────────────────────────────────
   resource "aws_bedrockagent_agent_action_group" "time" {
     action_group_name          = "time-tools"
     agent_id                   = aws_bedrockagent_agent.main.agent_id
     agent_version              = "DRAFT"
     description                = "Time-related tool calls for the agent."
     skip_resource_in_use_check = false
   
     action_group_executor {
       lambda = aws_lambda_function.tool.arn
     }
   
     api_schema {
       payload = jsonencode({
         openapi = "3.0.0"
         info    = { title = "Time tools", version = "1.0.0" }
         paths = {
           "/get_current_time" = {
             get = {
               operationId = "get_current_time"
               description = "Returns the current UTC time as an ISO-8601 string."
               responses = {
                 "200" = {
                   description = "Current UTC time"
                   content = {
                     "application/json" = {
                       schema = {
                         type = "object"
                         properties = {
                           current_time_utc = { type = "string", format = "date-time" }
                         }
                       }
                     }
                   }
                 }
               }
             }
           }
         }
       })
     }
   }

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