在Amazon Bedrock上使用AI代理构建实时旅行推荐 |

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Build real-time travel recommendations using AI agents on Amazon Bedrock

解决方案概述

旅行社在实时数据准确性和可扩展性方面面临着日益增长的个性化推荐需求。考虑一家需要提供无障碍度假套餐的旅行社：他们需要将特定的无障碍要求与实时航班和住宿可用性相匹配，但受到传统系统中人工处理时间和过时信息的限制。这种人工智能驱动的解决方案将个性化与实时数据集成相结合，使该机构能够自动将无障碍要求与当前的旅行选择相匹配，在几分钟内而不是几小时内提供准确的建议。该解决方案使用三层架构来帮助旅行社创建个性化的假期推荐：

前端层——提供旅行社输入客户要求和偏好的界面
编排层——处理请求并用客户数据丰富它们
推荐层——结合了两个关键组件：
- 旅行数据存储——维护一个可搜索的旅行套餐存储库
- 实时信息检索–通过API集成获取当前航班详细信息

下图说明了这种架构。

With this layered approach, travel agents can capture customer requirements, enrich them with stored preferences, integrate real-time data, and deliver personalized recommendations that match customer needs. The following diagram illustrates how these components are implemented using AWS services.

The AWS implementation includes:

Amazon API Gateway – Receives requests and routes them to AWS Lambda functions facilitating secure API calls for retrieving recommendations
AWS Lambda – Processes input data, creates the enriched prompt, and executes the recommendation workflow
Amazon DynamoDB – Stores customer preferences and travel history
Amazon Bedrock Knowledge Bases – Helps travel agents build a curated database of destinations, travel packages, and deals, making sure recommendations are based on reliable and up-to-date information
Amazon OpenSearch Serverless – Enables simple, scalable, and high-performing vector search
Amazon Simple Storage Service (Amazon S3) – Stores large datasets such as flight schedules and promotional materials
Amazon Bedrock Agents – Integrates real-time information retrieval, making sure recommended itineraries reflect current availability, pricing, and scheduling through external API integrations

This solution uses a AWS CloudFormation template that automatically provisions and configures the required resources. The template handles the complete setup process, including service configurations and necessary permissions.

For the latest information about service quotas that might affect your deployment, refer to AWS service quotas.

Prerequisites

To deploy and use this solution, you must have the following:

An AWS account with access to Amazon Bedrock
Permissions to create and manage the following services:
- Amazon Bedrock
- Amazon OpenSearch Serverless
- Lambda
- DynamoDB
- Amazon S3
- API Gateway
Access to foundation models in Amazon Bedrock for Amazon Titan Text Embeddings V2 and Anthropic Claude 3 Haiku models

Deploy the CloudFormation stack

You can deploy this solution in your AWS account using AWS CloudFormation. Complete the following steps:

Choose Launch Stack:

You will be redirected to the Create stack wizard on the AWS CloudFormation console with the stack name and the template URL already filled in.

Leave the default settings and complete the stack creation.
Choose View stack events to go to the AWS CloudFormation console to see the deployment details.

The stack takes around 10 minutes to create the resources. Wait until the stack status is CREATE_COMPLETE before continuing to the next steps.

The CloudFormation template automatically creates and configures components for data storage and management, Amazon Bedrock, and the API and interface.

Data storage and management

The template sets up the following data storage and management resources:

An S3 bucket and with a sample dataset (travel_data.json and promotions.csv), prompt template, and the API schema

DynamoDB tables populated with sample user profiles and travel history

An OpenSearch Serverless collection with optimized settings for travel package searches

A vector index with settings compatible with the Amazon Bedrock knowledge base

Amazon Bedrock configuration

For Amazon Bedrock, the CloudFormation template creates the following resources:

A knowledge base with the travel dataset and data sources ingested from Amazon S3 with automatic synchronization

An Amazon Bedrock agent, which is automatically prepared

A new version and alias for the agent

Agent action groups with mock flight data integration

An action group invocation, configured with the FlightPricingLambda Lambda function and the API schema retrieved from the S3 bucket

API and interface setup

To enable API access and the UI, the template configures the following resources:

API Gateway endpoints
Lambda functions with a mock flight API for demonstration purposes
A web interface for travel agents

Verify the setup

After stack creation is complete, you can verify the setup on the Outputs tab of the AWS CloudFormation console, which provides the following information:

WebsiteURL – Access the travel agent interface
ApiEndpoint – Use for programmatic access to the recommendation system

Test the endpoints

The web interface provides an intuitive form where travel agents can input customer requirements, including:

Customer ID (for example, Joe or Will)
Travel budget
Preferred dates
Number of travelers
Travel style

You can call the API directly using the following code:

curl -X POST \
  <ApiEndpoint> \
  -H 'Content-Type: application/json' \
  -d '{
    "userId": "Joe",
    "budget": "3000 GBP",
    "duration": "7 days",
    "travelDate": "2025-07-15",
    "numberOfTravelers": 2
  }'

Test the solution

For demonstration purposes, we create sample user profiles in the UserPreferences and TravelHistory tables in DynamoDB.

The UserPreferences table stores user-specific travel preferences. For instance, Joe represents a luxury traveler with wheelchair accessibility requirements.

Will represents a budget traveler with elderly-friendly needs. These profiles help showcase how the system handles different customer requirements and preferences.

The TravelHistory table stores past trips taken by users. The following tables show the past trips taken by the user Joe, showing destinations, trip durations, ratings, and travel dates.

Let’s walk through a typical use case to demonstrate how a travel agent can use this solution to create personalized holiday recommendations.Consider a scenario where a travel agent is helping Joe, a customer who requires wheelchair accessibility, plan a luxury vacation. The travel agent enters the following information:

Customer ID: Joe
Budget: 4,000 GBP
Duration: 5 days
Travel dates: July 15, 2025
Number of travelers: 2
Travel style: Luxury

When a travel agent submits a request, the system orchestrates a series of actions through the PersonalisedHolidayFunction Lambda function, which will query the knowledge base, check real-time flight information using the mock API, and return personalized recommendations that match the customer’s specific needs and preferences. The recommendation layer uses the following prompt template:

Based on the profile and requirements:

User Preferences:
- Travel Preferences: {travelStyle}
- Interests: {interests}
- Dietary Restrictions: {dietaryRestrictions}
- Accessibility Needs: {accessibility}

Current Request:
- Budget: {budget}
- Duration: {duration}
- Travel Date: {travelDate}
- Number of Travelers: {numberOfTravelers}

Previous Destinations: {previousDestinations}

Instructions:
1. Match the user's budget, travel style and interests
2. Consider dietary restrictions and accessibility needs
3. Avoid previously visited destinations
4. Include:
   - Recommended destinations
   - Suitable accommodations
   - Relevant activities and experiences
   - Transportation options
   - Estimated cost breakdown
   - Travel tips

Please follow the <Instructions> and provide a personalized holiday recommendation in the below format:
Destination: [Primary recommended destination]

[Detailed recommendation]

The system retrieves Joe’s preferences from the user profile, including:

{
    "userPreferences": {
        "preferences": "Prefer warm climate and cultural experiences",
        "budget": 3000,
        "duration": "5 days",
        "travelDate": "2025-03-04",
        "interests": [
            "photography",
            "food",
            "beach"
        ],
        "travelStyle": "Luxury",
        "numberOfTravelers": 2,
        "dietaryRestrictions": [
            "plant based",
            "vegetarian"
        ],
        "accessibility": [
            "wheelchair-accessible"
        ],
        "previousDestinations": [
            "Maldives",
            "Bali"
        ]
    }
}

The system then generates personalized recommendations that consider the following:

Destinations with proven wheelchair accessibility
Available luxury accommodations
Flight details for the recommended destination

Each recommendation includes the following details:

Detailed accessibility information
Real-time flight pricing and availability
Accommodation details with accessibility features
Available activities and experiences
Total package cost breakdown

Clean up

To avoid incurring future charges, delete the CloudFormation stack. For more information, see Delete a stack from the CloudFormation console.

The template includes proper deletion policies, making sure the resources you created, including S3 buckets, DynamoDB tables, and OpenSearch collections, are properly removed.

Next steps

To further enhance this solution, consider the following:

Explore multi-agent capabilities:
- Create specialized agents for different travel aspects (hotels, activities, local transport)
- Enable agent-to-agent communication for complex itinerary planning
- Implement an orchestrator agent to coordinate responses and resolve conflicts
Implement multi-language support using multi-language foundation models in Amazon Bedrock
Integrate with customer relationship management (CRM) systems

结论

在这篇文章中，您学习了如何使用Amazon Bedrock构建一个基于人工智能的假期推荐系统，帮助旅行社提供个性化体验。我们的实施展示了如何将亚马逊基岩知识库与亚马逊基岩代理相结合，有效地将历史旅行信息与实时数据需求联系起来，同时使用无服务器架构和矢量搜索，将客户偏好与旅行套餐有效匹配。该解决方案展示了旅行推荐系统如何平衡全面的旅行知识、实时数据准确性和大规模的个性化。这种方法对于需要集成实时定价数据、处理特定的可访问性要求或扩展其个性化建议的旅行组织来说尤其有价值。该解决方案提供了一个实用的起点，根据特定的业务需求，从现代化旅行计划系统到处理复杂的客户需求，都有明确的增强路径。