Content
# The AI Travel Agents
The AI Travel Agents is a robust **enterprise application** that leverages multiple **AI agents** to enhance travel agency operations. The application demonstrates how **six AI agents** collaborate to assist employees in handling customer queries, providing destination recommendations, and planning itineraries.
## Overview of AI Agents
| Agent Name | Purpose |
| -------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| **Customer Query Understanding** | Extracts key **preferences** from customer inquiries. |
| **Destination Recommendation** | Suggests destinations based on customer preferences. |
| **Itinerary Planning** | Creates a detailed itinerary and travel plan. |
| **Code Evaluation** | Executes custom logic and scripts if needed. |
| **Model Inference** | Runs a custom **LLM** using **ONNX** and **vLLM** on **Azure Container Apps' serverless GPU** for high-performance inference. |
| **Echo Ping** | Echoes back any received input (used as an MCP server example). |
| **Web Search** | Uses Grounding with Bing Search to fetch live travel data. |
## High-Level Architecture
The architecture of the AI Travel Agents application is designed to be modular and scalable:
- All components are containerized using **Docker** so that they can be easily deployed and managed by **[Azure Container Apps](https://learn.microsoft.com/azure/container-apps/)**.
- All agents tools are available as MCP ([Model Context Protocol](https://github.com/modelcontextprotocol)) servers and are called by the MCP clients.
- MCP servers are implemented independently using variant technologies, such as **Python**, **Node.js**, **Java**, and **.NET**.
- The Agent Workflow Service orchestrates the interaction between the agents and MCP clients, allowing them to work together seamlessly.
- The Aspire Dashboard is used to monitor the application, providing insights into the performance and behavior of the agents (through the [OpenTelemetry integration](https://opentelemetry.io/ecosystem/integrations/)).
## Project Structure
```
ai-travel-agents/
│── src/
│ ├── tools/
│ │ ├── customer-query/
│ │ ├── destination-recommendation/
│ │ ├── itinerary-planning/
│ │ ├── code-evaluation/
│ │ ├── model-inference/
│ │ ├── web-search/
│ │ └── echo-ping/
│ │
│ ├── api/ # API Gateway for backend services
│ └── ui/ # Frontend application
│
│── infra/ # azd-related files
│── docs/ # Documentation files
│
│── README.md # Project documentation
```
## Prerequisites
Ensure you have the following installed before running the application:
- **[Docker](https://www.docker.com/)**
## Environment Variables setup for containerized services
The application uses environment variables to configure the services. You can set them in a `.env` file in the root directory or directly in your terminal. We recommend the following approach:
1. Create a `.env.dev` file for each containerized service under `src/`, and optionally a `.env.docker` file for Docker-specific configurations:
- `src/ui/.env.dev`
- `src/ui/.env.docker`
- `src/api/.env.dev`
- `src/api/.env.docker`
- `src/tools/customer-query/.env.dev`
- `src/tools/customer-query/.env.docker`
- `src/tools/destination-recommendation/.env.dev`
- `src/tools/destination-recommendation/.env.docker`
- `src/tools/itinerary-planning/.env.dev`
- `src/tools/itinerary-planning/.env.docker`
- `src/tools/code-evaluation/.env.dev`
- `src/tools/code-evaluation/.env.docker`
- `src/tools/model-inference/.env.dev`
- `src/tools/model-inference/.env.docker`
- `src/tools/web-search/.env.dev`
- `src/tools/web-search/.env.docker`
- `src/tools/echo-ping/.env.dev`
- `src/tools/echo-ping/.env.docker`
2. `.env.docker` files are used to set environment variables for Docker containers. These files should contain the same variables as `.env.dev` files, but with values specific to the Docker environment. For example:
```bash
# src/api/.env.dev
TOOL_CUSTOMER_QUERY_URL=http://localhost:8080
# src/api/.env.docker
TOOL_CUSTOMER_QUERY_URL=http://tool-customer-query:8080
```
3. Load the environment variable files in `docker-compose.yml` using the `env_file` directive, in the following order:
```yml
web-api:
container_name: web-api
# ...
env_file:
- "./api/.env.dev"
- "./api/.env.docker" # override .env with .env.docker
```
> [!Note]
> adding the `- environment:` directive to the `docker-compose.yml` file will override the environment variables set in the `.env.*` files.
## Run the Entire Application
To run the entire application, use the scripts in the root directory. The scripts will build and run all the services defined in the `src/docker-compose.yml` file.
```sh
./run.sh
```
> On Windows, you may need to use `run.ps1` instead of `run.sh`.
Alternatively, if you're in VS Code you can use the **Run Task** command (Ctrl+Shift+P) and select the `Run AI Travel Agents` task.
This command will build and start all the services defined in the `docker-compose.yml` file.
Once all services are up and running, you can view the messages (currently logging messages) via the [Aspire Dashboard](https://aspiredashboard.com/) at http://localhost:18888. On `Structured` tab you'll see the logging messages from the **tool-echo-ping** and **api** services. The `Traces` tab will show the traces across the services, such as the call from **api** to **echo-agent**.
