thomasm291678
feat: 纳入 DAPT AI 引擎 vetnetv2.0 + 测试报告修复(语言约束/must_not_miss兜底/降温度/sdpa) + KB 走 git-lfs
9a71f08 | #!/usr/bin/env python3 | |
| """ | |
| VetNet DAPT 推理服务 — DAPT 微调 Qwen2.5-3B + FAISS RAG + LCPS 2.0 | |
| 启动: | |
| VET_MODEL=./vet-qwen3b-v3-dapt KB_DIR=./knowledge_base_simple python server_gpu.py | |
| API 端点: | |
| GET /health — 健康检查 + 模型状态 | |
| POST /run — LCPS 推理 + SOAP (兼容旧接口) | |
| POST /api/v1/diagnose — LCPS 诊断 (VetCopilot 标准接口) | |
| POST /chat — 兽医自由问答 | |
| """ | |
| import os, sys, time, json, logging | |
| from contextlib import asynccontextmanager | |
| from fastapi import FastAPI, HTTPException | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s') | |
| log = logging.getLogger('vetnet-gpu') | |
| MODEL = None | |
| RAG = None | |
| MODEL_INFO = {} | |
| class RunRequest(BaseModel): | |
| text: str | |
| species: str = "狗" | |
| class ChatRequest(BaseModel): | |
| question: str | |
| species: str = "狗" | |
| class DiagnoseRequest(BaseModel): | |
| """VetCopilot 标准诊断请求""" | |
| dialogue: str | |
| species: str = "狗" | |
| async def lifespan(app: FastAPI): | |
| global MODEL, RAG, MODEL_INFO | |
| model_path = os.getenv("VET_MODEL", "./vet-qwen3b-v3-dapt") | |
| kb_dir = os.getenv("KB_DIR", "./knowledge_base_simple") | |
| log.info("=" * 60) | |
| log.info("VetNet DAPT 推理服务 v3.1 启动") | |
| log.info(f"模型: {model_path}") | |
| log.info(f"知识库: {kb_dir}") | |
| log.info(f"引擎: DAPT Qwen2.5-3B + LCPS 2.0 + FAISS RAG") | |
| log.info("=" * 60) | |
| MODEL_INFO = { | |
| "name": "vet-qwen3b-v3-dapt", | |
| "base_model": "Qwen2.5-3B-Instruct", | |
| "training": "DAPT (Domain-Adaptive Pretraining)", | |
| "engine": "LCPS 2.0", | |
| } | |
| t0 = time.time() | |
| # 加载 RAG | |
| try: | |
| from model.rag_faiss import FAISSRetriever | |
| RAG = FAISSRetriever(kb_dir=kb_dir, device="cuda") | |
| RAG.load() | |
| log.info(f"RAG 加载完成 ({RAG.get_stats()})") | |
| except Exception as e: | |
| log.warning(f"RAG 加载失败 (知识库文件缺失): {e}") | |
| RAG = None | |
| # 加载 DAPT 模型 | |
| from model.qwen_local import QwenLocalClient | |
| MODEL = QwenLocalClient( | |
| model_path=model_path, | |
| temperature=float(os.getenv("VET_TEMPERATURE", "0.1")), # 降低温度减少误诊随机性 | |
| max_tokens=int(os.getenv("VET_MAX_TOKENS", "8192")), | |
| load_in_4bit=False, | |
| device_map="auto", | |
| seed=int(os.getenv("VET_SEED", "42")), # 固定 seed 保证可复现 | |
| ) | |
| MODEL._ensure_loaded() | |
| MODEL_INFO["load_time_ms"] = int((time.time() - t0) * 1000) | |
| log.info(f"模型加载完成 ({MODEL_INFO['load_time_ms']}ms)") | |
| yield | |
| if MODEL: | |
| MODEL.unload() | |
| log.info("服务关闭") | |
| app = FastAPI(title="VetNet GPU v3.0", lifespan=lifespan) | |
| app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]) | |
| def health(): | |
| return { | |
| "status": "ok", | |
| "model_loaded": MODEL is not None and MODEL.ping() if MODEL else False, | |
| "model_info": MODEL_INFO, | |
| "rag": RAG.get_stats() if RAG else {"ready": False, "vectors": 0, "chunks": 0}, | |
| } | |
| def run(req: RunRequest): | |
| """LCPS 推理 + SOAP 生成 (兼容旧接口)""" | |
| return _do_diagnose(req.text, req.species) | |
| def diagnose(req: DiagnoseRequest): | |
| """VetCopilot 标准 LCPS 诊断接口""" | |
| return _do_diagnose(req.dialogue, req.species) | |
| def _do_diagnose(text: str, species: str) -> dict: | |
| """统一诊断逻辑""" | |
| if not MODEL: | |
| raise HTTPException(503, "模型未加载") | |
| t0 = time.time() | |
| try: | |
| from model.lcps import lcps_single_shot, generate_soap_from_lcps | |
| rag = RAG if RAG and RAG._ready else None | |
| result = lcps_single_shot(text, species, MODEL, rag) | |
| soap = generate_soap_from_lcps(result["lcps_chain"], text, MODEL) | |
| elapsed = int((time.time() - t0) * 1000) | |
| return { | |
| "success": True, | |
| "diagnosis": result.get("primary_diagnosis", ""), | |
| "primary_diagnosis": result.get("primary_diagnosis", ""), | |
| "differential_list": result.get("differential_list", []), | |
| "must_not_miss": result.get("must_not_miss", []), | |
| "soap": soap.get("soap", {}), | |
| "lcps_chain": result.get("lcps_chain", {}), | |
| "summary": result.get("prioritisation_rationale", ""), | |
| "kb_used": rag is not None, | |
| "engine": "dapt-lcps-v3.1", | |
| "model": MODEL_INFO.get("name", "vet-qwen3b-v3-dapt"), | |
| "total_ms": elapsed, | |
| "elapsed_ms": elapsed, | |
| "error": "", | |
| } | |
| except Exception as e: | |
| log.exception("推理失败") | |
| return { | |
| "success": False, | |
| "diagnosis": "", | |
| "primary_diagnosis": "", | |
| "differential_list": [], | |
| "must_not_miss": [], | |
| "soap": {}, | |
| "lcps_chain": {}, | |
| "summary": "", | |
| "engine": "dapt-lcps-v3.1", | |
| "model": MODEL_INFO.get("name", ""), | |
| "elapsed_ms": int((time.time() - t0) * 1000), | |
| "error": str(e), | |
| } | |
| def chat(req: ChatRequest): | |
| if not MODEL: | |
| raise HTTPException(503, "模型未加载") | |
| try: | |
| kb_context = RAG.retrieve(f"{req.species} {req.question[:300]}") if RAG else "" | |
| system = "你是一位经验丰富的兽医专家。请用专业但易懂的语言回答以下问题。" | |
| if kb_context: | |
| system += f"\n\n参考知识:\n{kb_context}" | |
| answer = MODEL.chat(system, f"物种: {req.species}\n问题: {req.question}") | |
| return {"answer": answer, "kb_used": bool(kb_context)} | |
| except Exception as e: | |
| log.exception("问答失败") | |
| raise HTTPException(500, str(e)) | |
| if __name__ == "__main__": | |
| import uvicorn | |
| port = int(os.getenv("PORT", "8000")) | |
| uvicorn.run(app, host="0.0.0.0", port=port) | |