siemens_ragas/webapp/server.py

"""FastAPI application factory for the RAGAS evaluation console.

The app mounts three JSON API routers and serves the single-page static
frontend. It imports rag_eval only lazily (inside the task manager worker), so
the server starts even when the evaluation dependencies are not yet installed.
"""

from __future__ import annotations

import logging
import time
from pathlib import Path

from fastapi import FastAPI, Request
from fastapi.encoders import jsonable_encoder
from fastapi.exceptions import RequestValidationError
from fastapi.responses import FileResponse, JSONResponse
from fastapi.staticfiles import StaticFiles

from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score

STATIC_DIR = Path(__file__).resolve().parent / "static"
logger = logging.getLogger("webapp.server")
access_logger = logging.getLogger("webapp.access")

# OpenAPI tag metadata — controls the grouping and descriptions in /docs.
OPENAPI_TAGS = [
    {
        "name": "pipeline",
        "description": (
            "**全链路评估 Pipeline API**\n\n"
            "一次调用完成「解析文档 → 生成题库 → RAGAS 评估 → 输出报告」全流程。\n\n"
            "**使用流程**\n"
            "1. `POST /api/pipeline/jobs` 提交任务，立即拿到 `job_id`。\n"
            "2. `GET /api/pipeline/jobs/{job_id}` 轮询 `status` / `phase` / `logs`。\n"
            "3. 当 `status=completed` 时，`result` 字段包含所有产物路径。\n\n"
            "**Pipeline 阶段**\n"
            "| phase | 说明 |\n"
            "|-------|------|\n"
            "| `parsing_documents` | 调用阿里云 DocMind 解析每份 PDF |\n"
            "| `generating_questions` | LLM 从文档片段生成草稿题库 |\n"
            "| `evaluating` | RAGAS 在线评测打分 |\n"
            "| `done` | 所有产物写入磁盘，任务完成 |"
        ),
    },
    {
        "name": "evaluations",
        "description": (
            "**单场景评估 API**\n\n"
            "基于已有 YAML 场景文件触发评估任务，并查询任务状态与日志。"
        ),
    },
    {
        "name": "llm-profiles",
        "description": (
            "**LLM 配置管理 API**\n\n"
            "增删改查已保存的 LLM 连接配置（模型名称、Base URL、API Key）；"
            "支持连通性测试；可将配置一键写入场景 YAML 文件。"
        ),
    },
    {
        "name": "runs",
        "description": "**评估运行列表 API**\n\n查询历史评估运行记录及详细报告数据。",
    },
    {
        "name": "scenarios",
        "description": "**场景文件 API**\n\n扫描并列出 `scenarios/` 目录下所有可用的 YAML 场景文件。",
    },
    {
        "name": "score",
        "description": (
            "**实时评分 API（Dify 外部 Tool）**\n\n"
            "接受单条问答记录 `(question, answer, contexts, ground_truth)`，\n"
            "同步运行 RAGAS 指标打分，返回各指标得分和加权综合得分。\n\n"
            "适用场景：Dify Agent 在回答后即时调用，用于质量监控或自我改进。\n\n"
            "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
            "`Authorization: Bearer <token>` 请求头。"
        ),
    },
    {
        "name": "meta",
        "description": "**系统 API**\n\n健康检查等基础接口。",
    },
]


def create_app() -> FastAPI:
    """Build and configure the FastAPI application instance."""
    app = FastAPI(
        title="RAGAS 评估系统",
        description=(
            "西门子医疗影像 RAG 评估平台 API 文档。\n\n"
            "提供以下能力：\n"
            "- **Pipeline API** — 一键完成「解析文档 → 生成题库 → RAGAS 评估」全链路\n"
            "- **实时评分 API** — 供 Dify 外部 Tool 调用的单题 RAGAS 评分接口\n"
            "- **评估 API** — 基于 YAML 场景文件触发单次评估\n"
            "- **LLM 配置 API** — 管理多个 LLM 连接配置，支持连通性测试\n"
            "- **报告 API** — 查询历史运行记录与评估报告\n\n"
            "> **快速开始**：调用 `POST /api/pipeline/jobs` 传入 PDF 文件夹路径即可启动完整评估流程。"
        ),
        version="0.3.0",
        openapi_tags=OPENAPI_TAGS,
    )

    app.include_router(runs.router)
    app.include_router(scenarios.router)
    app.include_router(evaluations.router)
    app.include_router(llm_profiles.router)
    app.include_router(pipeline.router)
    app.include_router(score.router)

    @app.middleware("http")
    async def access_log_middleware(request: Request, call_next):
        """Log every API request with method, path, status code and latency.

        Static file requests are logged at DEBUG level to keep the console clean.
        """
        t0 = time.monotonic()
        response = await call_next(request)
        latency_ms = int((time.monotonic() - t0) * 1000)
        path = request.url.path
        is_static = path.startswith("/static/") or path in ("/", "/favicon.ico")
        msg = "%s %s → %d  (%dms)", request.method, path, response.status_code, latency_ms
        if is_static:
            access_logger.debug(*msg)
        else:
            access_logger.info(*msg)
        return response

    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
        """Log full validation error detail to help diagnose 422 responses."""
        errors = jsonable_encoder(exc.errors())
        logger.warning(
            "[422] validation error  url=%s  content_type=%s  errors=%s",
            request.url.path,
            request.headers.get("content-type", ""),
            errors,
        )
        return JSONResponse(
            status_code=422,
            content={"detail": errors},
        )

    @app.get("/api/health", tags=["meta"])
    def health() -> dict[str, str]:
        """Report basic liveness so the UI can confirm the server is reachable."""
        return {"status": "ok"}

    @app.get("/", include_in_schema=False)
    def index() -> FileResponse:
        """Serve the single-page console entry document."""
        return FileResponse(STATIC_DIR / "index.html")

    # Serve CSS/JS assets under /static while keeping API routes at /api.
    app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")

    return app


app = create_app()