mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-09-04 23:17:20 +08:00
refactor search AI recommendation flow
This commit is contained in:
jxxghp
@@ -1,265 +0,0 @@
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import asyncio
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import hashlib
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import json
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import re
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from typing import Any, Dict, List, Optional
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from app.agent import agent_manager, prompt_manager
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from app.chain import ChainBase
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from app.core.config import settings
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from app.log import logger
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from app.utils.singleton import Singleton
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from app.utils.string import StringUtils
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class AIRecommendChain(ChainBase, metaclass=Singleton):
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"""
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AI推荐处理链，单例运行
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用于基于搜索结果的AI智能推荐
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使用 agent_manager.run_background_prompt 统一后台任务机制
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"""
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__ai_indices_cache_file = "__ai_recommend_indices__"
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_ai_recommend_running = False
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_ai_recommend_task: Optional[asyncio.Task] = None
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_current_request_hash: Optional[str] = None
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_ai_recommend_result: Optional[List[int]] = None
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_ai_recommend_error: Optional[str] = None
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@staticmethod
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def _calculate_request_hash(
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filtered_indices: Optional[List[int]], search_results_count: int
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) -> str:
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"""
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计算请求的哈希值，用于判断请求是否变化
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"""
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request_data = {
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"filtered_indices": filtered_indices or [],
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"search_results_count": search_results_count,
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}
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return hashlib.md5(
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json.dumps(request_data, sort_keys=True).encode()
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).hexdigest()
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@property
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def is_enabled(self) -> bool:
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"""
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检查AI推荐功能是否已启用。
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"""
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return settings.AI_AGENT_ENABLE and settings.AI_RECOMMEND_ENABLED
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def _build_status(self) -> Dict[str, Any]:
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"""
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构建AI推荐状态字典
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"""
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if not self.is_enabled:
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return {"status": "disabled"}
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if self._ai_recommend_running:
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return {"status": "running"}
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if self._ai_recommend_result is None:
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cached_indices = self.load_cache(self.__ai_indices_cache_file)
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if cached_indices is not None:
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self._ai_recommend_result = cached_indices
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if self._ai_recommend_result is not None:
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return {"status": "completed", "results": self._ai_recommend_result}
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if self._ai_recommend_error is not None:
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return {"status": "error", "error": self._ai_recommend_error}
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return {"status": "idle"}
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def get_current_status_only(self) -> Dict[str, Any]:
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"""
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获取当前状态（不校验hash，用于check_only模式）
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"""
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return self._build_status()
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def get_status(
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self, filtered_indices: Optional[List[int]], search_results_count: int
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) -> Dict[str, Any]:
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"""
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获取AI推荐状态并检查请求是否变化（用于首次请求或force模式）
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如果请求变化（筛选条件变化），返回idle状态
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"""
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request_hash = self._calculate_request_hash(
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filtered_indices, search_results_count
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)
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is_same_request = request_hash == self._current_request_hash
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if not is_same_request:
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return {"status": "idle"} if self.is_enabled else {"status": "disabled"}
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return self._build_status()
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def is_ai_recommend_running(self) -> bool:
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"""
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检查AI推荐是否正在运行
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"""
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return self._ai_recommend_running
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def cancel_ai_recommend(self):
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"""
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取消正在运行的AI推荐任务
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"""
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if self._ai_recommend_task and not self._ai_recommend_task.done():
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self._ai_recommend_task.cancel()
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self._ai_recommend_running = False
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self._ai_recommend_task = None
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self._current_request_hash = None
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self._ai_recommend_result = None
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self._ai_recommend_error = None
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self.remove_cache(self.__ai_indices_cache_file)
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def start_recommend_task(
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self,
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filtered_indices: Optional[List[int]],
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search_results_count: int,
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results: List[Any],
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) -> None:
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"""
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启动AI推荐任务
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使用 agent_manager.run_background_prompt 后台Agent机制执行推荐
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:param filtered_indices: 筛选后的索引列表
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:param search_results_count: 搜索结果总数
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:param results: 搜索结果列表
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"""
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if not self.is_enabled:
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logger.warning("AI推荐功能未启用，跳过任务执行")
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return
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new_request_hash = self._calculate_request_hash(
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filtered_indices, search_results_count
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)
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if new_request_hash != self._current_request_hash:
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self.cancel_ai_recommend()
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self._current_request_hash = new_request_hash
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self._ai_recommend_result = None
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self._ai_recommend_error = None
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async def run_recommend():
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current_task = asyncio.current_task()
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try:
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self._ai_recommend_running = True
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items = []
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valid_indices = []
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max_items = settings.AI_RECOMMEND_MAX_ITEMS or 50
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if filtered_indices is not None and len(filtered_indices) > 0:
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results_to_process = [
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results[i]
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for i in filtered_indices
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if 0 <= i < len(results)
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]
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else:
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results_to_process = results
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for i, torrent in enumerate(results_to_process):
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if len(items) >= max_items:
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break
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if not torrent.torrent_info:
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continue
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valid_indices.append(i)
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item_info = {
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"index": i,
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"title": torrent.torrent_info.title or "未知",
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"size": (
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StringUtils.format_size(torrent.torrent_info.size)
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if torrent.torrent_info.size
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else "0 B"
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),
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"seeders": torrent.torrent_info.seeders or 0,
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}
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items.append(json.dumps(item_info, ensure_ascii=False))
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if not items:
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self._ai_recommend_error = "没有可用于AI推荐的资源"
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return
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user_preference = (
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settings.AI_RECOMMEND_USER_PREFERENCE
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or "Prefer high-quality resources with more seeders"
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)
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search_results_text = "User Preference: {preference}\n\nCandidate Resources:\n{items}".format(
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preference=user_preference, items="\n".join(items)
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)
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prompt = prompt_manager.render_system_task_message(
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"search_recommend",
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template_context={"search_results": search_results_text},
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)
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full_output = [""]
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def on_output(text: str):
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full_output[0] = text
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await agent_manager.run_background_prompt(
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message=prompt,
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session_prefix="__agent_search_recommend",
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output_callback=on_output,
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suppress_user_reply=True,
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)
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ai_response = full_output[0]
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if not ai_response:
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self._ai_recommend_error = "AI推荐未返回结果"
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return
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try:
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json_match = re.search(r"\[.*?]", ai_response, re.DOTALL)
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if not json_match:
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raise ValueError(f"无法从响应中提取JSON数组: {ai_response}")
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ai_indices = json.loads(json_match.group())
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if not isinstance(ai_indices, list):
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raise ValueError(f"AI返回格式错误: {ai_response}")
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if filtered_indices:
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original_indices = [
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filtered_indices[valid_indices[i]]
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for i in ai_indices
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if i < len(valid_indices)
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and 0
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<= filtered_indices[valid_indices[i]]
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< len(results)
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]
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else:
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original_indices = [
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valid_indices[i]
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for i in ai_indices
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if i < len(valid_indices)
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and 0 <= valid_indices[i] < len(results)
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]
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self._ai_recommend_result = original_indices
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self.save_cache(original_indices, self.__ai_indices_cache_file)
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logger.info(f"AI推荐完成: {len(original_indices)}项")
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except Exception as e:
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logger.error(
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f"解析AI返回结果失败: {e}, 原始响应: {ai_response}"
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)
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self._ai_recommend_error = str(e)
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except asyncio.CancelledError:
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logger.info("AI推荐任务被取消")
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except Exception as e:
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logger.error(f"AI推荐任务失败: {e}")
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self._ai_recommend_error = str(e)
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finally:
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if self._ai_recommend_task == current_task:
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self._ai_recommend_running = False
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self._ai_recommend_task = None
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self._ai_recommend_task = asyncio.create_task(run_recommend())
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+301
-13
@@ -1,5 +1,8 @@
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import asyncio
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import hashlib
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import json
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import random
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import re
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime
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@@ -21,6 +24,7 @@ from app.helper.torrent import TorrentHelper
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from app.log import logger
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from app.schemas import NotExistMediaInfo
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from app.schemas.types import MediaType, ProgressKey, SystemConfigKey, EventType
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from app.utils.string import StringUtils
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class SearchChain(ChainBase):
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@@ -29,7 +33,291 @@ class SearchChain(ChainBase):
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"""
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__result_temp_file = "__search_result__"
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__ai_result_temp_file = "__ai_search_result__"
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__ai_indices_cache_file = "__ai_recommend_indices__"
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_ai_recommend_running = False
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_ai_recommend_task: Optional[asyncio.Task] = None
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_current_recommend_request_hash: Optional[str] = None
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_ai_recommend_result: Optional[List[int]] = None
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_ai_recommend_error: Optional[str] = None
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@property
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def is_ai_recommend_enabled(self) -> bool:
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"""
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检查AI推荐功能是否已启用。
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"""
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return settings.AI_AGENT_ENABLE and settings.AI_RECOMMEND_ENABLED
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@staticmethod
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def _calculate_recommend_request_hash(
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filtered_indices: Optional[List[int]], search_results_count: int
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) -> str:
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"""
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计算当前推荐请求哈希，用于识别筛选条件是否变化。
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"""
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request_data = {
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"filtered_indices": filtered_indices or [],
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"search_results_count": search_results_count,
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}
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return hashlib.md5(
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json.dumps(request_data, sort_keys=True).encode()
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).hexdigest()
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def _build_ai_recommend_status(self) -> Dict[str, Any]:
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"""
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构建AI推荐状态字典。
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"""
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state = type(self)
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if not self.is_ai_recommend_enabled:
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return {"status": "disabled"}
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if state._ai_recommend_running:
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return {"status": "running"}
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if state._ai_recommend_result is None:
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cached_indices = self.load_cache(self.__ai_indices_cache_file)
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if cached_indices is not None:
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state._ai_recommend_result = cached_indices
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if state._ai_recommend_result is not None:
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return {"status": "completed", "results": state._ai_recommend_result}
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if state._ai_recommend_error is not None:
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return {"status": "error", "error": state._ai_recommend_error}
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return {"status": "idle"}
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def get_current_recommend_status_only(self) -> Dict[str, Any]:
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"""
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获取当前推荐状态，不校验请求是否变化。
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"""
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return self._build_ai_recommend_status()
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def get_recommend_status(
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self, filtered_indices: Optional[List[int]], search_results_count: int
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) -> Dict[str, Any]:
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"""
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获取AI推荐状态，并在筛选条件变化时返回 idle。
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"""
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state = type(self)
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request_hash = self._calculate_recommend_request_hash(
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filtered_indices, search_results_count
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)
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if request_hash != state._current_recommend_request_hash:
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return {"status": "idle"} if self.is_ai_recommend_enabled else {"status": "disabled"}
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return self._build_ai_recommend_status()
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def cancel_ai_recommend(self):
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"""
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取消当前AI推荐任务并清空缓存状态。
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"""
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state = type(self)
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if state._ai_recommend_task and not state._ai_recommend_task.done():
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state._ai_recommend_task.cancel()
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state._ai_recommend_running = False
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state._ai_recommend_task = None
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state._current_recommend_request_hash = None
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state._ai_recommend_result = None
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state._ai_recommend_error = None
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self.remove_cache(self.__ai_indices_cache_file)
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@staticmethod
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def _normalize_ai_indices(ai_indices: List[Any]) -> List[int]:
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"""
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过滤模型返回的非法或重复索引，保留原顺序。
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"""
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normalized = []
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seen = set()
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for index in ai_indices:
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try:
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value = int(index)
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except (TypeError, ValueError):
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continue
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if value in seen:
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continue
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seen.add(value)
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normalized.append(value)
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return normalized
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@staticmethod
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def _extract_recommend_items(
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filtered_indices: Optional[List[int]], results: List[Any]
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) -> tuple[List[str], List[int]]:
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"""
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构建发送给模型的候选列表和索引映射。
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"""
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items: List[str] = []
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valid_indices: List[int] = []
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max_items = settings.AI_RECOMMEND_MAX_ITEMS or 50
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if filtered_indices:
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results_to_process = [
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results[index] for index in filtered_indices if 0 <= index < len(results)
|
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]
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else:
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results_to_process = results
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|
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for index, torrent in enumerate(results_to_process):
|
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if len(items) >= max_items:
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break
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if not torrent.torrent_info:
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continue
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|
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valid_indices.append(index)
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item_info = {
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"index": index,
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"title": torrent.torrent_info.title or "未知",
|
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"size": (
|
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StringUtils.format_size(torrent.torrent_info.size)
|
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if torrent.torrent_info.size
|
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else "0 B"
|
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),
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"seeders": torrent.torrent_info.seeders or 0,
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}
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items.append(json.dumps(item_info, ensure_ascii=False))
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return items, valid_indices
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@staticmethod
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def _restore_original_indices(
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ai_indices: List[int],
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filtered_indices: Optional[List[int]],
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valid_indices: List[int],
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results_count: int,
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) -> List[int]:
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"""
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将模型输出的局部索引映射回原始搜索结果索引。
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"""
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original_indices = []
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seen = set()
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for index in ai_indices:
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if not 0 <= index < len(valid_indices):
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continue
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original_index = (
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filtered_indices[valid_indices[index]]
|
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if filtered_indices
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else valid_indices[index]
|
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)
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if not 0 <= original_index < results_count or original_index in seen:
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continue
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seen.add(original_index)
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original_indices.append(original_index)
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return original_indices
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|
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async def _invoke_recommend_llm(self, search_results_text: str) -> str:
|
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"""
|
||||
通过统一后台提示词机制执行资源推荐。
|
||||
"""
|
||||
from app.agent import agent_manager
|
||||
from app.agent.prompt import prompt_manager
|
||||
|
||||
prompt = prompt_manager.render_system_task_message(
|
||||
"search_recommend",
|
||||
template_context={"search_results": search_results_text},
|
||||
)
|
||||
full_output = [""]
|
||||
|
||||
def on_output(text: str):
|
||||
full_output[0] = text
|
||||
|
||||
await agent_manager.run_background_prompt(
|
||||
message=prompt,
|
||||
session_prefix="__agent_search_recommend",
|
||||
output_callback=on_output,
|
||||
suppress_user_reply=True,
|
||||
)
|
||||
return full_output[0].strip()
|
||||
|
||||
def start_recommend_task(
|
||||
self,
|
||||
filtered_indices: Optional[List[int]],
|
||||
search_results_count: int,
|
||||
results: List[Any],
|
||||
) -> None:
|
||||
"""
|
||||
启动AI推荐任务。
|
||||
"""
|
||||
if not self.is_ai_recommend_enabled:
|
||||
logger.warning("AI推荐功能未启用，跳过任务执行")
|
||||
return
|
||||
|
||||
state = type(self)
|
||||
request_hash = self._calculate_recommend_request_hash(
|
||||
filtered_indices, search_results_count
|
||||
)
|
||||
if request_hash == state._current_recommend_request_hash:
|
||||
return
|
||||
|
||||
self.cancel_ai_recommend()
|
||||
state._current_recommend_request_hash = request_hash
|
||||
|
||||
async def run_recommend():
|
||||
current_task = asyncio.current_task()
|
||||
|
||||
def is_current_request() -> bool:
|
||||
return state._current_recommend_request_hash == request_hash
|
||||
|
||||
try:
|
||||
state._ai_recommend_running = True
|
||||
|
||||
items, valid_indices = self._extract_recommend_items(
|
||||
filtered_indices=filtered_indices,
|
||||
results=results,
|
||||
)
|
||||
if not items:
|
||||
if is_current_request():
|
||||
state._ai_recommend_error = "没有可用于AI推荐的资源"
|
||||
return
|
||||
|
||||
user_preference = (
|
||||
settings.AI_RECOMMEND_USER_PREFERENCE
|
||||
or "Prefer high-quality resources with more seeders"
|
||||
)
|
||||
search_results_text = (
|
||||
f"User Preference: {user_preference}\n\n"
|
||||
f"Candidate Resources:\n{chr(10).join(items)}"
|
||||
)
|
||||
ai_response = await self._invoke_recommend_llm(search_results_text)
|
||||
if not ai_response:
|
||||
if is_current_request():
|
||||
state._ai_recommend_error = "AI推荐未返回结果"
|
||||
return
|
||||
|
||||
json_match = re.search(r"\[.*?]", ai_response, re.DOTALL)
|
||||
if not json_match:
|
||||
raise ValueError(f"无法从响应中提取JSON数组: {ai_response}")
|
||||
|
||||
ai_indices = json.loads(json_match.group())
|
||||
if not isinstance(ai_indices, list):
|
||||
raise ValueError(f"AI返回格式错误: {ai_response}")
|
||||
|
||||
original_indices = self._restore_original_indices(
|
||||
ai_indices=self._normalize_ai_indices(ai_indices),
|
||||
filtered_indices=filtered_indices,
|
||||
valid_indices=valid_indices,
|
||||
results_count=len(results),
|
||||
)
|
||||
if not is_current_request():
|
||||
logger.info("AI推荐结果已过期，丢弃旧结果")
|
||||
return
|
||||
|
||||
state._ai_recommend_result = original_indices
|
||||
self.save_cache(original_indices, self.__ai_indices_cache_file)
|
||||
logger.info(f"AI推荐完成: {len(original_indices)}项")
|
||||
except asyncio.CancelledError:
|
||||
logger.info("AI推荐任务被取消")
|
||||
except Exception as err:
|
||||
logger.error(f"AI推荐任务失败: {err}")
|
||||
if is_current_request():
|
||||
state._ai_recommend_error = str(err)
|
||||
finally:
|
||||
if state._ai_recommend_task == current_task:
|
||||
state._ai_recommend_running = False
|
||||
state._ai_recommend_task = None
|
||||
|
||||
state._ai_recommend_task = asyncio.create_task(run_recommend())
|
||||
|
||||
def search_by_id(self, tmdbid: Optional[int] = None, doubanid: Optional[str] = None,
|
||||
mtype: MediaType = None, area: Optional[str] = "title", season: Optional[int] = None,
|
||||
@@ -44,6 +332,8 @@ class SearchChain(ChainBase):
|
||||
:param sites: 站点ID列表
|
||||
:param cache_local: 是否缓存到本地
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
mediainfo = self.recognize_media(tmdbid=tmdbid, doubanid=doubanid, mtype=mtype)
|
||||
if not mediainfo:
|
||||
logger.error(f'{tmdbid} 媒体信息识别失败！')
|
||||
@@ -70,6 +360,8 @@ class SearchChain(ChainBase):
|
||||
:param sites: 站点ID列表
|
||||
:param cache_local: 是否缓存到本地
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
if title:
|
||||
logger.info(f'开始搜索资源，关键词：{title} ...')
|
||||
else:
|
||||
@@ -99,18 +391,6 @@ class SearchChain(ChainBase):
|
||||
"""
|
||||
return await self.async_load_cache(self.__result_temp_file)
|
||||
|
||||
async def async_last_ai_results(self) -> Optional[List[Context]]:
|
||||
"""
|
||||
异步获取上次AI推荐结果
|
||||
"""
|
||||
return await self.async_load_cache(self.__ai_result_temp_file)
|
||||
|
||||
async def async_save_ai_results(self, results: List[Context]):
|
||||
"""
|
||||
异步保存AI推荐结果
|
||||
"""
|
||||
await self.async_save_cache(results, self.__ai_result_temp_file)
|
||||
|
||||
async def async_search_by_id(self, tmdbid: Optional[int] = None, doubanid: Optional[str] = None,
|
||||
mtype: MediaType = None, area: Optional[str] = "title", season: Optional[int] = None,
|
||||
sites: List[int] = None, cache_local: bool = False) -> List[Context]:
|
||||
@@ -124,6 +404,8 @@ class SearchChain(ChainBase):
|
||||
:param sites: 站点ID列表
|
||||
:param cache_local: 是否缓存到本地
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
mediainfo = await self.async_recognize_media(tmdbid=tmdbid, doubanid=doubanid, mtype=mtype)
|
||||
if not mediainfo:
|
||||
logger.error(f'{tmdbid} 媒体信息识别失败！')
|
||||
@@ -150,6 +432,8 @@ class SearchChain(ChainBase):
|
||||
:param sites: 站点ID列表
|
||||
:param cache_local: 是否缓存到本地
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
if title:
|
||||
logger.info(f'开始搜索资源，关键词：{title} ...')
|
||||
else:
|
||||
@@ -173,6 +457,8 @@ class SearchChain(ChainBase):
|
||||
"""
|
||||
根据标题渐进式搜索资源，不识别不过滤，按站点完成顺序返回结果
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
if title:
|
||||
logger.info(f'开始渐进式搜索资源，关键词：{title} ...')
|
||||
else:
|
||||
@@ -214,6 +500,8 @@ class SearchChain(ChainBase):
|
||||
"""
|
||||
根据TMDBID/豆瓣ID渐进式搜索资源，先返回站点原始候选，再返回过滤匹配后的最终结果
|
||||
"""
|
||||
if cache_local:
|
||||
self.cancel_ai_recommend()
|
||||
mediainfo = await self.async_recognize_media(tmdbid=tmdbid, doubanid=doubanid, mtype=mtype)
|
||||
if not mediainfo:
|
||||
logger.error(f'{tmdbid} 媒体信息识别失败！')
|
||||
|
||||
Reference in New Issue
Block a user