# -*- coding: utf-8 -*-
"""搜索 + 评估 · 任意 query → 多渠道搜索去重 → LLM 逐帖评估 → search_process/search_tools 表
(方向由 --mode-type 决定:工序 → search_process,工具 → search_tools)
================================================================================
引擎函数全部只读复用 search_and_evaluate.py(搜索/去重/转写/评估/英平台翻译)。

用法(一般由 server.py 起子进程调):
  python pipeline/search_eval.py --query-id q0004 --query "AI 人像 图片 生成 怎么做"
  python pipeline/search_eval.py --query-id q0005 --query "GPT image2 评测" \
      --synonyms "GPT image2 测评,GPT image2 实测" --platforms xhs,gzh --max-count 10
"""
import argparse
import asyncio
import json
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parents[3]   # …/Agent
sys.path.insert(0, str(PROJECT_ROOT))

from dotenv import load_dotenv
load_dotenv()

from examples.process_pipeline.script.search_eval.search_and_evaluate import (
    search_all, evaluate_posts, transcribe_video_posts, build_query_overrides,
)
from examples.process_pipeline.script.llm_evaluate_sources import (
    build_eval_llm_call, EVAL_MODELS, DEFAULT_EVAL_MODEL,
)

HERE = Path(__file__).resolve().parent
MW = HERE.parent
sys.path.insert(0, str(MW))
import db


async def run(args):
    phrasings = [args.query] + [s.strip() for s in (args.synonyms or "").split(",") if s.strip()]
    # 去重保序
    seen, uniq = set(), []
    for q in phrasings:
        if q not in seen:
            seen.add(q); uniq.append(q)
    phrasings = uniq
    platforms = [p.strip() for p in args.platforms.split(",") if p.strip()]

    eval_llm, eval_model_id = build_eval_llm_call(args.eval_model)
    print(f"▶ {args.query_id}  query={args.query!r}  措辞={phrasings}  渠道={platforms}")

    overrides = await build_query_overrides(platforms, phrasings, eval_llm, eval_model_id)
    sources = await search_all(platforms, phrasings, args.max_count,
                               args.max_concurrent, query_overrides=overrides)
    print(f"🔎 去重后 {len(sources)} 帖")
    if not sources:
        print("❌ 搜索无结果"); return 1

    try:
        from examples.process_pipeline.script.extract_sources import _convert_timestamps
        _convert_timestamps(sources)
    except Exception:
        pass

    if not args.no_transcribe:
        n = await transcribe_video_posts(sources, concurrency=args.max_concurrent)
        if n:
            print(f"🎙️  视频转写 {n} 条")

    cost = 0.0
    if not args.no_eval:
        sources, cost = await evaluate_posts(
            sources, "", eval_llm, eval_model_id, args.max_concurrent,
            include_images=not args.no_images, max_images=args.max_images,
            image_mode=args.image_mode, query=args.query,
        )
    for s in sources:
        s.pop("_image_data_urls", None)

    table = "search_tools" if args.mode_type == "工具" else "search_process"
    n = db.upsert_search_posts(args.query_id, args.query, sources, table=table)
    print(f"🗄️  {table} 入库 {n} 行 · 方向 {args.mode_type or '工序'} · 评估成本 ${cost:.4f}")

    out_dir = MW / "runs" / table
    out_dir.mkdir(parents=True, exist_ok=True)
    (out_dir / f"{args.query_id}.json").write_text(json.dumps({
        "query_id": args.query_id, "query": args.query, "phrasings": phrasings,
        "platforms": platforms, "total": len(sources), "results": sources,
    }, ensure_ascii=False, indent=2), encoding="utf-8")
    return 0


def main():
    p = argparse.ArgumentParser(description="搜索+评估 → search_process/search_tools")
    p.add_argument("--query-id", required=True, help="如 q0004(server 自动分配)")
    p.add_argument("--query", required=True, help="基准 query(评估锚点)")
    p.add_argument("--synonyms", default="", help="逗号分隔的同义措辞(可选)")
    p.add_argument("--mode-type", default="", choices=["", "工序", "工具"],
                   help="解构方向,决定写哪张表(工具 → search_tools;其余 → search_process)")
    p.add_argument("--platforms", default="xhs,gzh")
    p.add_argument("--max-count", type=int, default=10)
    p.add_argument("--eval-model", default=DEFAULT_EVAL_MODEL, choices=list(EVAL_MODELS))
    p.add_argument("--max-concurrent", type=int, default=3)
    p.add_argument("--max-images", type=int, default=4)
    p.add_argument("--image-mode", choices=["url", "base64"], default="url")
    p.add_argument("--no-transcribe", action="store_true")
    p.add_argument("--no-eval", action="store_true")
    p.add_argument("--no-images", action="store_true")
    args = p.parse_args()
    raise SystemExit(asyncio.run(run(args)))


if __name__ == "__main__":
    main()