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https://github.com/jxxghp/MoviePilot.git
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feat(search): 添加AI推荐功能并优化相关逻辑
This commit is contained in:
PKC278
310
app/chain/ai_recommend.py
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310
app/chain/ai_recommend.py
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import re
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from typing import List, Optional, Dict, Any
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import asyncio
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import hashlib
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import json
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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.common import log_execution_time
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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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"""
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# 缓存文件名
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__ai_indices_cache_file = "__ai_recommend_indices__"
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# AI推荐状态
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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 # 当前请求的哈希值
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_ai_recommend_result: Optional[List[int]] = None # AI推荐索引缓存（索引列表）
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_ai_recommend_error: Optional[str] = None # AI推荐错误信息
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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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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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:return: 状态字典
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"""
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if not settings.AI_RECOMMEND_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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# 尝试从数据库加载缓存
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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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# 只要有结果，始终返回completed状态和数据
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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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# 计算当前请求的hash
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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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# 检查请求是否变化
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is_same_request = request_hash == self._current_request_hash
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# 如果请求变化了（筛选条件改变），返回idle状态
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if not is_same_request:
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return (
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{"status": "idle"}
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if settings.AI_RECOMMEND_ENABLED
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else {"status": "disabled"}
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)
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# 请求未变化，返回当前实际状态
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return self._build_status()
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@log_execution_time(logger=logger)
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async def async_ai_recommend(self, items: List[str], preference: str = None) -> str:
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"""
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AI推荐
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:param items: 候选资源列表(JSON字符串格式)
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:param preference: 用户偏好(可选)
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:return: AI返回的推荐结果
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"""
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# 设置运行状态
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self._ai_recommend_running = True
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try:
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# 导入LLMHelper
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from app.helper.llm import LLMHelper
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# 获取LLM实例
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llm = LLMHelper.get_llm()
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# 构建提示词
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user_preference = (
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preference
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or 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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# 添加指令
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instruction = """
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Task: Select the best matching items from the list based on user preferences.
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Each item contains:
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- index: Item number
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- title: Full torrent title
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- size: File size
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- seeders: Number of seeders
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Output Format: Return ONLY a JSON array of "index" numbers (e.g., [0, 3, 1]). Do NOT include any explanations or other text.
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"""
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message = (
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f"User Preference: {user_preference}\n{instruction}\nCandidate Resources:\n"
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+ "\n".join(items)
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)
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# 调用LLM
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response = await llm.ainvoke(message)
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return response.content
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except ValueError as e:
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logger.error(f"AI推荐配置错误: {e}")
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raise
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except Exception as e:
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raise
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finally:
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# 清除运行状态
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self._ai_recommend_running = False
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self._ai_recommend_task = None
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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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: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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# 计算新请求的哈希值
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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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# 如果请求变化了，取消旧任务
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if new_request_hash != self._current_request_hash:
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self.cancel_ai_recommend()
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# 更新请求哈希值
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self._current_request_hash = new_request_hash
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# 重置状态
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self._ai_recommend_result = None
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self._ai_recommend_error = None
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# 启动新任务
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async def run_recommend():
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# 获取当前任务对象，用于在finally中比对
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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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# 准备数据
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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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# 如果提供了筛选索引，先筛选结果；否则使用所有结果
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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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# 调用AI推荐
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ai_response = await self.async_ai_recommend(items)
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# 解析AI返回的索引
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try:
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# 使用正则提取JSON数组（非贪婪模式，避免匹配多个数组）
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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(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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# 映射回原始索引
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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 <= filtered_indices[valid_indices[i]] < 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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# 只返回索引列表，不返回完整数据
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self._ai_recommend_result = original_indices
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# 保存到数据库
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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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# 只有当 self._ai_recommend_task 仍然是当前任务时，才清理状态
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# 如果任务被取消并启动了新任务，self._ai_recommend_task 已经指向新任务，不应重置
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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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# 创建并启动任务
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self._ai_recommend_task = asyncio.create_task(run_recommend())
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@@ -29,6 +29,7 @@ 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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def search_by_id(self, tmdbid: Optional[int] = None, doubanid: Optional[str] = None,
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mtype: MediaType = None, area: Optional[str] = "title", season: Optional[int] = None,
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@@ -98,6 +99,18 @@ class SearchChain(ChainBase):
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"""
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return await self.async_load_cache(self.__result_temp_file)
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async def async_last_ai_results(self) -> Optional[List[Context]]:
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"""
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异步获取上次AI推荐结果
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"""
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return await self.async_load_cache(self.__ai_result_temp_file)
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async def async_save_ai_results(self, results: List[Context]):
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"""
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异步保存AI推荐结果
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"""
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await self.async_save_cache(results, self.__ai_result_temp_file)
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async def async_search_by_id(self, tmdbid: Optional[int] = None, doubanid: Optional[str] = None,
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mtype: MediaType = None, area: Optional[str] = "title", season: Optional[int] = None,
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sites: List[int] = None, cache_local: bool = False) -> List[Context]:
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@@ -489,20 +489,18 @@ class SiteChain(ChainBase):
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logger.warn(f"站点 {domain} 索引器不存在！")
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return
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# 查询站点图标
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site_icon = siteoper.get_icon_by_domain(domain)
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if not site_icon or not site_icon.base64:
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logger.info(f"开始缓存站点 {indexer.get('name')} 图标 ...")
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icon_url, icon_base64 = self.__parse_favicon(url=indexer.get("domain"),
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cookie=cookie,
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ua=settings.USER_AGENT)
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if icon_url:
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siteoper.update_icon(name=indexer.get("name"),
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domain=domain,
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icon_url=icon_url,
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icon_base64=icon_base64)
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logger.info(f"缓存站点 {indexer.get('name')} 图标成功")
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else:
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logger.warn(f"缓存站点 {indexer.get('name')} 图标失败")
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logger.info(f"开始缓存站点 {indexer.get('name')} 图标 ...")
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icon_url, icon_base64 = self.__parse_favicon(url=indexer.get("domain"),
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cookie=cookie,
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ua=settings.USER_AGENT)
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if icon_url:
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siteoper.update_icon(name=indexer.get("name"),
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domain=domain,
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icon_url=icon_url,
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icon_base64=icon_base64)
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logger.info(f"缓存站点 {indexer.get('name')} 图标成功")
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else:
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logger.warn(f"缓存站点 {indexer.get('name')} 图标失败")
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@eventmanager.register(EventType.SiteUpdated)
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def clear_site_data(self, event: Event):
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