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b6c412f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | VetCopilot-DAPT v3.1 部署
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包含目录:
vetcopilot-app/ — 前端 (React/TypeScript + Vite + PWA)
vetcopilot-backend/ — 后端 (FastAPI + SQLAlchemy + LCPS Client)
vetnetv2.0/ — DAPT AI 引擎 (Qwen2.5-3B + LCPS 2.0 + FAISS RAG)
deploy_dapt.sh — 一键部署脚本
快速启动:
chmod +x deploy_dapt.sh && ./deploy_dapt.sh
手动启动:
1. 启动 AI 引擎:
cd vetnetv2.0
VET_MODEL=./vet-qwen3b-v3-dapt PORT=8000 python server_gpu.py
2. 启动后端:
cd vetcopilot-backend
PORT=8080 python -m uvicorn app.main:app --host 0.0.0.0
3. 启动前端 (开发):
cd vetcopilot-app
npm install && npm run dev
API 端点:
GET :8000/health — AI 引擎健康检查
POST :8000/api/v1/diagnose — LCPS 诊断 (VetCopilot 标准接口)
POST :8000/run — LCPS 诊断 (兼容旧接口)
POST :8000/chat — 兽医自由问答
GET :8080/api/health — 应用健康检查
POST :8080/api/consultations/start — 开始问诊
模型下载:
# 从 ModelScope 下载 DAPT 模型权重
pip install modelscope
python -c "from modelscope.hub.snapshot_download import snapshot_download; snapshot_download('WWSCA123/vet-qwen3b-v3-dapt-inference', local_dir='./vet-qwen3b-v3-dapt')"
# 下载 tokenizer (从 HF mirror)
python -c "from huggingface_hub import snapshot_download; import shutil, os; d=snapshot_download('Qwen/Qwen2.5-3B-Instruct', allow_patterns=['tokenizer*','vocab*','merges*','generation_config*']); [shutil.copy(os.path.join(d,f), f'./vet-qwen3b-v3-dapt/{f}') for f in os.listdir(d)]"
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