travel-agent
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@travel-agentWhat's the weather in Tokyo tomorrow?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI 旅游助手
基于 Qwen2.5-1.5B + LoRA 微调与 ChromaDB + BGE 向量检索的智能旅游 Agent。 通过 MCP 协议 暴露工具服务,可被 Claude Code 等 AI 客户端直接调用。
功能一览
功能 | 说明 |
🌤️ 天气查询 | 30+ 城市实时/预报天气,支持"明天""后天"等中文日期 |
🚄 高铁查票 | 84 条内置线路,起止城市双向查询 |
✈️ 航班查询 | 89 条内置航线,含航司、时刻、参考票价 |
🗺️ 行程规划 | 智能解析预算/人数/天数/偏好,自动生成完整方案 |
📋 RAG 攻略 | 19 城旅游知识库,多轮对话记忆,追问自动补城市名 |
🔀 复合查询 | 多工具同时调用(如 "天气 + 高铁") |
⚡ 流式响应 | SSE 逐 token 推送,前端逐字渲染 |
Related MCP server: Travel Planner MCP Server
快速开始
环境要求
Python 3.10+
CUDA 11.8+(显存 ≥ 4GB)
已下载 Qwen2.5-1.5B-Instruct 模型到本地缓存
启动
pip install -r requirements.txt
# 方式一:一键启动
python launch_api.py
# 方式二:直接运行
python api/main.py
# 访问 http://127.0.0.1:8000测试
python test_api.py "深圳明天天气怎么样"
python test_api.py "北京故宫有什么特色"项目结构
├── api/ # Web API
│ ├── main.py # FastAPI 主入口(REST + SSE 端点)
│ ├── session_store.py # SQLite 会话持久化
│ └── static/index.html # 卡片式聊天前端
├── agent/ # Agent 核心
│ ├── dispatcher.py # 工具调度器
│ ├── agent_loop.py # 复合意图编排器
│ ├── planner.py # 行程规划器
│ ├── mcp_server.py # MCP 协议服务器
│ └── tools/ # 工具实现
│ ├── weather.py # 天气工具
│ ├── train.py # 高铁工具
│ └── flight.py # 航班工具
├── finetune/ # LoRA 微调
│ ├── train_lora.py # 训练脚本
│ └── rebuild_chroma_v3.py # 重建向量库
├── docs/ # 知识文档
│ └── travel_knowledge # 19 城攻略原始数据
├── launch_api.py # 一键启动脚本
└── requirements.txt # 依赖清单技术栈
模型与推理: Qwen2.5-1.5B · LoRA (PEFT) · BitsAndBytes 4-bit · Transformers
RAG 检索: BAAI/bge-small-zh-v1.5 · ChromaDB · LangChain
Web 框架: FastAPI · Uvicorn · SSE 流式 · Pydantic
前端: 原生 HTML/CSS/JS · 卡片式 UI · 响应式布局
协议: MCP (Model Context Protocol) — stdio + SSE 双模式
持久化: SQLite · WAL 模式 · 线程安全
工具: wttr.in API · 内置 84 条高铁线路 · 89 条航线
License
MIT
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