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+"""
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+搜索评估工具：搜索帖子并评估是否与账号人设匹配，提取关键词并匹配选题点。
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+
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+处理流程：
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+1. 使用 xhs（失败或空则用 zhihu）搜索帖子
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+2. 并发对每篇帖子调用 LLM 判断人设匹配 & 提取关键词
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+3. 对匹配人设的帖子，调用 match_derivation_to_post_points 匹配选题点
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+4. 返回完整评估结果列表
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+"""
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+
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+import asyncio
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+import json
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+import logging
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+import re
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+import sys
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+from pathlib import Path
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+from typing import Any, Dict, List, Optional
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+
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+logger = logging.getLogger(__name__)
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+
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+import httpx
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+
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+# 保证直接运行或作为包加载时都能解析 utils/tools（IDE 可跳转）
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+_root = Path(__file__).resolve().parent.parent
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+if str(_root) not in sys.path:
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+ sys.path.insert(0, str(_root))
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+
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+from tools.point_match import match_derivation_to_post_points
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+
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+try:
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+ from agent.tools import tool, ToolResult, ToolContext
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+ from agent.llm.openrouter import openrouter_llm_call
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+except ImportError:
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+ def tool(*args, **kwargs):
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+ return lambda f: f
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+ ToolResult = None
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+ ToolContext = None
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+ openrouter_llm_call = None
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+
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+_BASE_INPUT = Path(__file__).resolve().parent.parent / "input"
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+_TOOLS_DIR = Path(__file__).resolve().parent
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+
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+BASE_URL = "http://aigc-channel.aiddit.com/aigc/channel"
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+DEFAULT_TIMEOUT = 60.0
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+
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+# 支持多模态（视觉+文本）的 LLM 模型
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+EVAL_LLM_MODEL = "google/gemini-3-flash-preview"
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+
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+# 每篇帖子最多传入的图片数量（避免 token 过多）
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+MAX_IMAGES_PER_POST = 20
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+
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+
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+def _load_match_and_extract_prompt() -> str:
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+ """读取帖子人设匹配 & 关键词提取的 system prompt 模板"""
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+ prompt_file = _TOOLS_DIR / "match_and_extract_prompt.md"
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+ with open(prompt_file, "r", encoding="utf-8") as f:
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+ return f.read()
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+
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+
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+def _load_persona_text(account_name: str) -> str:
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+ """读取账号人设摘要，返回可读字符串；文件不存在时返回空人设提示"""
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+ persona_file = _BASE_INPUT / account_name / "persona_data" / "persona_summary.json"
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+ if not persona_file.is_file():
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+ logger.warning("_load_persona_text: persona file not found: %s", persona_file)
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+ return f"账号：{account_name}（暂无人设数据）"
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+ with open(persona_file, "r", encoding="utf-8") as f:
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+ data = json.load(f)
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+ logger.debug("_load_persona_text: loaded persona for account=%s", account_name)
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+ return json.dumps(data, ensure_ascii=False, indent=2)
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+
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+
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+async def _do_search(query: str, channel: str) -> Optional[List[dict]]:
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+ """执行单次搜索，返回帖子列表；失败或空列表返回 None"""
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+ logger.debug("_do_search: channel=%s, query=%s", channel, query)
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+ payload = {
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+ "type": channel,
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+ "keyword": query,
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+ "cursor": "0",
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+ "max_count": 5,
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+ "content_type": "图文",
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+ }
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+ try:
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+ async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as client:
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+ resp = await client.post(
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+ f"{BASE_URL}/data",
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+ json=payload,
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+ headers={"Content-Type": "application/json"},
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+ )
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+ resp.raise_for_status()
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+ data = resp.json()
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+ posts = data.get("data") or []
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+ count = len(posts) if posts else 0
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+ logger.info("_do_search: channel=%s, query=%s -> %d posts", channel, query, count)
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+ return posts if posts else None
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+ except Exception as e:
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+ logger.warning("_do_search: channel=%s, query=%s failed: %s", channel, query, e)
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+ return None
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+
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+
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+async def _search_posts(query: str) -> List[dict]:
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+ """优先用 xhs 搜索，失败或空则用 zhihu，返回帖子列表"""
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+ posts = await _do_search(query, "xhs")
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+ if posts:
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+ logger.info("_search_posts: using xhs, %d posts for query=%s", len(posts), query)
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+ return posts
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+ posts = await _do_search(query, "zhihu")
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+ if posts:
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+ logger.info("_search_posts: xhs empty/failed, using zhihu, %d posts for query=%s", len(posts), query)
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+ else:
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+ logger.warning("_search_posts: no posts from xhs or zhihu for query=%s", query)
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+ return posts or []
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+
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+
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+def _build_user_message_content(post: dict) -> List[dict]:
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+ """
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+ 将帖子数据构建为 OpenAI 多模态 user message content。
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+ 包含帖子文本描述 + 前 MAX_IMAGES_PER_POST 张图片。
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+ """
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+ parts: List[dict] = []
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+
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+ # 文本部分：将帖子的关键字段序列化给 LLM
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+ post_text = json.dumps(
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+ {
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+ "channel_content_id": post.get("channel_content_id", ""),
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+ "title": post.get("title", ""),
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+ "body_text": post.get("body_text", ""),
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+ },
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+ ensure_ascii=False,
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+ )
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+ parts.append({"type": "text", "text": post_text})
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+
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+ # 图片部分
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+ images = post.get("images") or []
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+ for img_url in images[:MAX_IMAGES_PER_POST]:
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+ if img_url:
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+ parts.append({"type": "image_url", "image_url": {"url": img_url}})
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+
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+ return parts
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+
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+
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+def _extract_json_object(content: str) -> dict:
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+ """从 LLM 回复中解析第一个 JSON 对象（允许被 ```json ... ``` 包裹）"""
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+ content = content.strip()
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+ m = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", content)
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+ if m:
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+ content = m.group(1).strip()
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+ # 找到最外层 { ... }
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+ start = content.find("{")
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+ end = content.rfind("}")
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+ if start != -1 and end != -1:
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+ content = content[start : end + 1]
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+ return json.loads(content)
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+
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+
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+async def _eval_single_post(
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+ post: dict,
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+ system_prompt: str,
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+ account_name: str,
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+ post_id: str,
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+) -> dict:
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+ """
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+ 评估单篇帖子：
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+ 1. 调用 LLM 判断人设匹配并提取关键词
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+ 2. 若匹配，调用 match_derivation_to_post_points 匹配选题点
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+ 返回完整评估结果字典。
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+ """
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+ post_cid = post.get("channel_content_id", "")
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+ result: dict = {
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+ "channel_content_id": post_cid,
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+ "title": post.get("title", ""),
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+ "body_text": post.get("body_text", ""),
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+ "images": post.get("images") or [],
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+ "persona_match_result": False,
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+ "persona_match_reason": "",
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+ "post_keywords": [],
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+ "point_match_results": [],
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+ }
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+
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+ try:
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+ logger.debug("_eval_single_post: evaluating post_id=%s, title=%s", post_cid, (result["title"] or "")[:40])
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+ user_content = _build_user_message_content(post)
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+ messages = [
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+ {"role": "system", "content": system_prompt},
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+ {"role": "user", "content": user_content},
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+ ]
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+
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+ llm_result = await openrouter_llm_call(messages, model=EVAL_LLM_MODEL)
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+ content = llm_result.get("content", "")
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+ if not content:
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+ result["error"] = "LLM 未返回内容"
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+ logger.warning("_eval_single_post: post_id=%s LLM returned empty content", post_cid)
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+ return result
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+
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+ parsed = _extract_json_object(content)
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+ result["persona_match_result"] = bool(parsed.get("persona_match_result", False))
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+ result["persona_match_reason"] = parsed.get("persona_match_reason", "")
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+ result["post_keywords"] = parsed.get("post_keywords") or []
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+ logger.info(
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+ "_eval_single_post: post_id=%s persona_match=%s keywords=%s",
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+ post_cid,
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+ result["persona_match_result"],
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+ result["post_keywords"],
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+ )
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+
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+ # 仅对与人设匹配的帖子做选题点匹配
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+ if result["persona_match_result"] and result["post_keywords"]:
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+ matched = await match_derivation_to_post_points(
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+ result["post_keywords"], account_name, post_id
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+ )
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+ result["point_match_results"] = matched
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+ logger.info(
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+ "_eval_single_post: post_id=%s point_match count=%d",
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+ post_cid,
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+ len(matched),
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+ )
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+
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+ except Exception as e:
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+ logger.exception("_eval_single_post: post_id=%s error: %s", post_cid, e)
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+ result["error"] = str(e)
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+
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+ return result
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+
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+
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+@tool(
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+ description=(
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+ "搜索帖子并评估是否与账号人设匹配，提取帖子关键词并与帖子选题点进行匹配。"
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+ "参数：account_name 账号名称；post_id 帖子ID；query 搜索词。"
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+ )
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+)
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+async def search_and_eval(
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+ account_name: str,
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+ post_id: str,
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+ query: str,
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+ context: Optional[ToolContext] = None,
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+) -> ToolResult:
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+ """
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+ 搜索帖子并评估是否与账号人设匹配，提取关键词并匹配选题点。
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+
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+ Args:
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+ account_name: 账号名称，用于读取人设数据和选题点文件
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+ post_id: 帖子ID，用于定位选题点匹配文件
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+ query: 搜索词
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+
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+ Returns:
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+ ToolResult，output 为 JSON 格式的帖子评估结果列表，每项包含：
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+ - channel_content_id: 帖子ID
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+ - title: 标题
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+ - body_text: 正文
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+ - images: 图集URL列表
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+ - persona_match_result: 是否与账号人设匹配（bool）
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+ - post_keywords: 提取的帖子关键词列表
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+ - point_match_results: 关键词与帖子选题点的匹配结果列表，
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+ 每项含「推导选题点」「帖子选题点」「匹配分数」
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+ """
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+ logger.info(
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+ "search_and_eval: account_name=%s post_id=%s query=%s",
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+ account_name,
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+ post_id,
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+ query,
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+ )
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+ try:
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+ # 1. 搜索帖子
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+ posts = await _search_posts(query)
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+ if not posts:
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+ logger.warning("search_and_eval: no posts found for query=%s", query)
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+ return ToolResult(
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+ title=f"搜索评估: {query}",
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+ output="[]",
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+ long_term_memory=f"search_and_eval: query='{query}', no posts found",
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+ )
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+
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+ logger.info("search_and_eval: got %d posts, loading prompt and persona", len(posts))
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+ # 2. 构建 system prompt（替换账号人设）
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+ prompt_template = _load_match_and_extract_prompt()
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+ persona_text = _load_persona_text(account_name)
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+ system_prompt = prompt_template.replace("{persona}", persona_text)
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+
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+ # 3. 并发评估所有帖子
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+ tasks = [
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+ _eval_single_post(post, system_prompt, account_name, post_id)
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+ for post in posts
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+ ]
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+ results: List[dict] = await asyncio.gather(*tasks)
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+
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+ matched_count = sum(1 for r in results if r.get("persona_match_result"))
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+ error_count = sum(1 for r in results if r.get("error"))
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+ logger.info(
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+ "search_and_eval: done. total=%d persona_matched=%d errors=%d",
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+ len(results),
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+ matched_count,
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+ error_count,
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+ )
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+ output = json.dumps(results, ensure_ascii=False, indent=2)
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+ logger.info("search_and_eval: output=%s", output)
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+
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+ return ToolResult(
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+ title=(
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+ f"搜索评估: {query} "
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+ f"（共 {len(results)} 条，{matched_count} 条匹配人设）"
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+ ),
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+ output=output,
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+ long_term_memory=(
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+ f"search_and_eval: query='{query}', "
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+ f"found {len(results)} posts, {matched_count} matched persona"
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+ ),
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+ metadata={"items": results},
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+ )
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+
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+ except Exception as e:
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+ logger.exception("search_and_eval: failed: %s", e)
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+ return ToolResult(
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+ title="搜索评估失败",
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+ output="",
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+ error=str(e),
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+ )
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+
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+
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+def main() -> None:
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+ """本地测试：用家有大志账号测试搜索评估"""
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+ import asyncio
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+
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+ logging.basicConfig(
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+ level=logging.DEBUG,
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+ format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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+ datefmt="%H:%M:%S",
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+ )
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+ account_name = "家有大志"
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+ post_id = "68fb6a5c000000000302e5de"
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+ query = "柴犬 鞋子"
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+
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+ async def run():
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+ if ToolResult is None:
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+ print("agent 依赖未安装，无法直接运行 tool 版本")
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+ return
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+ result = await search_and_eval(
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+ account_name=account_name,
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+ post_id=post_id,
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+ query=query,
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+ )
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+ if result.error:
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+ print(f"Error: {result.error}")
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+ else:
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+ print(result.title)
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+ data = json.loads(result.output)
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+ for item in data:
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+ print(
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+ f" [{item.get('persona_match_result')}] {item.get('title', '')[:30]}"
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+ f" | keywords: {item.get('post_keywords')}"
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+ f" | matches: {len(item.get('point_match_results', []))}"
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+ )
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+
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+ asyncio.run(run())
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+
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+
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+if __name__ == "__main__":
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+ _project_root = str(Path(__file__).resolve().parent.parent.parent.parent)
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+ if _project_root not in sys.path:
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+ sys.path.insert(0, _project_root)
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+ main()
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