feat(search): 添加AI推荐功能并优化相关逻辑

app/agent/__init__.py

@@ -16,6 +16,7 @@ from app.agent.prompt import PromptManager
 from app.agent.tools.factory import MoviePilotToolFactory
 from app.chain import ChainBase
 from app.core.config import settings
+from app.helper.llm import LLMHelper
 from app.helper.message import MessageHelper
 from app.log import logger
 from app.schemas import Notification
@@ -64,57 +65,7 @@ class MoviePilotAgent:
 
     def _initialize_llm(self):
         """初始化LLM模型"""
-        provider = settings.LLM_PROVIDER.lower()
-        api_key = settings.LLM_API_KEY
-
-        if provider == "google":
-            if settings.PROXY_HOST:
-                from langchain_openai import ChatOpenAI
-                return ChatOpenAI(
-                    model=settings.LLM_MODEL,
-                    api_key=api_key,
-                    max_retries=3,
-                    base_url="https://generativelanguage.googleapis.com/v1beta/openai",
-                    temperature=settings.LLM_TEMPERATURE,
-                    streaming=True,
-                    callbacks=[self.callback_handler],
-                    stream_usage=True,
-                    openai_proxy=settings.PROXY_HOST
-                )
-            else:
-                from langchain_google_genai import ChatGoogleGenerativeAI
-                return ChatGoogleGenerativeAI(
-                    model=settings.LLM_MODEL,
-                    google_api_key=api_key,
-                    max_retries=3,
-                    temperature=settings.LLM_TEMPERATURE,
-                    streaming=True,
-                    callbacks=[self.callback_handler]
-                )
-        elif provider == "deepseek":
-            from langchain_deepseek import ChatDeepSeek
-            return ChatDeepSeek(
-                model=settings.LLM_MODEL,
-                api_key=api_key,
-                max_retries=3,
-                temperature=settings.LLM_TEMPERATURE,
-                streaming=True,
-                callbacks=[self.callback_handler],
-                stream_usage=True
-            )
-        else:
-            from langchain_openai import ChatOpenAI
-            return ChatOpenAI(
-                model=settings.LLM_MODEL,
-                api_key=api_key,
-                max_retries=3,
-                base_url=settings.LLM_BASE_URL,
-                temperature=settings.LLM_TEMPERATURE,
-                streaming=True,
-                callbacks=[self.callback_handler],
-                stream_usage=True,
-                openai_proxy=settings.PROXY_HOST
-            )
+        return LLMHelper.get_llm(streaming=True, callbacks=[self.callback_handler])
 
     def _initialize_tools(self) -> List:
         """初始化工具列表"""

app/agent/tools/impl/search_torrents.py

@@ -10,6 +10,7 @@ from app.agent.tools.base import MoviePilotTool
 from app.chain.search import SearchChain
 from app.log import logger
 from app.schemas.types import MediaType
+from app.utils.string import StringUtils
 
 
 class SearchTorrentsInput(BaseModel):
@@ -99,7 +100,7 @@ class SearchTorrentsTool(MoviePilotTool):
                     if t.torrent_info:
                         simplified["torrent_info"] = {
                             "title": t.torrent_info.title,
-                            "size": t.torrent_info.size,
+                            "size": StringUtils.format_size(t.torrent_info.size),
                             "seeders": t.torrent_info.seeders,
                             "peers": t.torrent_info.peers,
                             "site_name": t.torrent_info.site_name,

app/api/endpoints/search.py

@@ -1,14 +1,16 @@
 from typing import List, Any, Optional
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Body
 
 from app import schemas
 from app.chain.media import MediaChain
 from app.chain.search import SearchChain
+from app.chain.ai_recommend import AIRecommendChain
 from app.core.config import settings
 from app.core.event import eventmanager
 from app.core.metainfo import MetaInfo
 from app.core.security import verify_token
+from app.log import logger
 from app.schemas import MediaRecognizeConvertEventData
 from app.schemas.types import MediaType, ChainEventType
 
@@ -36,6 +38,9 @@ async def search_by_id(mediaid: str,
     """
     根据TMDBID/豆瓣ID精确搜索站点资源 tmdb:/douban:/bangumi:
     """
+    # 取消正在运行的AI推荐（会清除数据库缓存）
+    AIRecommendChain().cancel_ai_recommend()
+    
     if mtype:
         media_type = MediaType(mtype)
     else:
@@ -159,6 +164,9 @@ async def search_by_title(keyword: Optional[str] = None,
     """
     根据名称模糊搜索站点资源，支持分页，关键词为空是返回首页资源
     """
+    # 取消正在运行的AI推荐并清除数据库缓存
+    AIRecommendChain().cancel_ai_recommend()
+    
     torrents = await SearchChain().async_search_by_title(
         title=keyword, page=page,
         sites=[int(site) for site in sites.split(",") if site] if sites else None,
@@ -167,3 +175,82 @@ async def search_by_title(keyword: Optional[str] = None,
     if not torrents:
         return schemas.Response(success=False, message="未搜索到任何资源")
     return schemas.Response(success=True, data=[torrent.to_dict() for torrent in torrents])
+
+
+@router.post("/recommend", summary="AI推荐资源", response_model=schemas.Response)
+async def recommend_search_results(
+        filtered_indices: Optional[List[int]] = Body(None, embed=True, description="筛选后的索引列表"),
+        check_only: bool = Body(False, embed=True, description="仅检查状态，不启动新任务"),
+        force: bool = Body(False, embed=True, description="强制重新推荐，清除旧结果"),
+        _: schemas.TokenPayload = Depends(verify_token)) -> Any:
+    """
+    AI推荐资源 - 轮询接口
+    前端轮询此接口，发送筛选后的索引（如果有筛选）
+    后端根据请求变化自动取消旧任务并启动新任务
+    
+    参数：
+    - filtered_indices: 筛选后的索引列表（可选，为空或不提供时使用所有结果）
+    - check_only: 仅检查状态（首次打开页面时使用，避免触发不必要的重新推理）
+    - force: 强制重新推荐（清除旧结果并重新启动）
+    
+    返回数据结构：
+    {
+        "success": bool,
+        "message": string,   // 错误信息（仅在错误时存在）
+        "data": {
+            "status": string,    // 状态: disabled | idle | running | completed | error
+            "results": array     // 推荐结果（仅status=completed时存在）
+        }
+    }
+    """
+    # 从缓存获取上次搜索结果
+    results = await SearchChain().async_last_search_results() or []
+    if not results:
+        return schemas.Response(success=False, message="没有可用的搜索结果", data={
+            "status": "error"
+        })
+    
+    recommend_chain = AIRecommendChain()
+    
+    # 如果是强制模式，先取消并清除旧结果，然后直接启动新任务
+    if force:
+        logger.info("收到新推荐请求，清除旧结果并启动新任务")
+        recommend_chain.cancel_ai_recommend()
+        recommend_chain.start_recommend_task(filtered_indices, len(results), results)
+        # 直接返回运行中状态
+        return schemas.Response(success=True, data={
+            "status": "running"
+        })
+    
+    # 如果是仅检查模式，不传递 filtered_indices（避免触发请求变化检测）
+    if check_only:
+        # 返回当前运行状态，不做任何任务启动或取消操作
+        current_status = recommend_chain.get_current_status_only()
+        # 如果有错误，将错误信息放到message中
+        if current_status.get("status") == "error":
+            error_msg = current_status.pop("error", "未知错误")
+            return schemas.Response(success=False, message=error_msg, data=current_status)
+        return schemas.Response(success=True, data=current_status)
+    
+    # 获取当前状态（会检测请求是否变化）
+    status_data = recommend_chain.get_status(filtered_indices, len(results))
+    
+    # 如果功能未启用，直接返回禁用状态
+    if status_data.get("status") == "disabled":
+        return schemas.Response(success=True, data=status_data)
+    
+    # 如果是空闲状态，启动新任务
+    if status_data["status"] == "idle":
+        recommend_chain.start_recommend_task(filtered_indices, len(results), results)
+        # 立即返回运行中状态
+        return schemas.Response(success=True, data={
+            "status": "running"
+        })
+    
+    # 如果有错误，将错误信息放到message中
+    if status_data.get("status") == "error":
+        error_msg = status_data.pop("error", "未知错误")
+        return schemas.Response(success=False, message=error_msg, data=status_data)
+    
+    # 返回当前状态
+    return schemas.Response(success=True, data=status_data)

app/api/endpoints/system.py

@@ -130,18 +130,40 @@ async def cache_img(
 def get_global_setting(token: str):
     """
     查询非敏感系统设置（默认鉴权）
+    仅包含登录前UI初始化必需的字段
     """
     if token != "moviepilot":
         raise HTTPException(status_code=403, detail="Forbidden")
 
-    # 白名单模式，仅包含前端业务逻辑必需的字段
+    # 白名单模式，仅包含登录前UI初始化必需的字段
     info = settings.model_dump(
         include={
             "TMDB_IMAGE_DOMAIN",
             "GLOBAL_IMAGE_CACHE",
             "ADVANCED_MODE",
+        }
+    )
+    # 追加版本信息（用于版本检查）
+    info.update({
+        "FRONTEND_VERSION": SystemChain.get_frontend_version(),
+        "BACKEND_VERSION": APP_VERSION
+    })
+    return schemas.Response(success=True,
+                            data=info)
+
+
+@router.get("/global/user", summary="查询用户相关系统设置", response_model=schemas.Response)
+async def get_user_global_setting(_: User = Depends(get_current_active_user_async)):
+    """
+    查询用户相关系统设置（登录后获取）
+    包含业务功能相关的配置和用户权限信息
+    """
+    # 业务功能相关的配置字段
+    info = settings.model_dump(
+        include={
             "RECOGNIZE_SOURCE",
-            "SEARCH_SOURCE"
+            "SEARCH_SOURCE",
+            "AI_RECOMMEND_ENABLED"
         }
     )
     # 追加用户唯一ID和订阅分享管理权限
@@ -150,8 +172,6 @@ def get_global_setting(token: str):
         "USER_UNIQUE_ID": SubscribeHelper().get_user_uuid(),
         "SUBSCRIBE_SHARE_MANAGE": share_admin,
         "WORKFLOW_SHARE_MANAGE": share_admin,
-        "FRONTEND_VERSION": SystemChain.get_frontend_version(),
-        "BACKEND_VERSION": APP_VERSION
     })
     return schemas.Response(success=True,
                             data=info)

app/chain/ai_recommend.py

@@ -0,0 +1,310 @@
+import re
+from typing import List, Optional, Dict, Any
+import asyncio
+import hashlib
+import json
+
+from app.chain import ChainBase
+from app.core.config import settings
+from app.log import logger
+from app.utils.common import log_execution_time
+from app.utils.singleton import Singleton
+from app.utils.string import StringUtils
+
+
+class AIRecommendChain(ChainBase, metaclass=Singleton):
+    """
+    AI推荐处理链，单例运行
+    用于基于搜索结果的AI智能推荐
+    """
+
+    # 缓存文件名
+    __ai_indices_cache_file = "__ai_recommend_indices__"
+
+    # AI推荐状态
+    _ai_recommend_running = False
+    _ai_recommend_task: Optional[asyncio.Task] = None
+    _current_request_hash: Optional[str] = None  # 当前请求的哈希值
+    _ai_recommend_result: Optional[List[int]] = None  # AI推荐索引缓存（索引列表）
+    _ai_recommend_error: Optional[str] = None  # AI推荐错误信息
+
+    @staticmethod
+    def _calculate_request_hash(
+        filtered_indices: Optional[List[int]], search_results_count: int
+    ) -> str:
+        """
+        计算请求的哈希值，用于判断请求是否变化
+        """
+        request_data = {
+            "filtered_indices": filtered_indices or [],
+            "search_results_count": search_results_count,
+        }
+        return hashlib.md5(
+            json.dumps(request_data, sort_keys=True).encode()
+        ).hexdigest()
+
+    def _build_status(self) -> Dict[str, Any]:
+        """
+        构建AI推荐状态字典
+        :return: 状态字典
+        """
+        if not settings.AI_RECOMMEND_ENABLED:
+            return {"status": "disabled"}
+
+        if self._ai_recommend_running:
+            return {"status": "running"}
+
+        # 尝试从数据库加载缓存
+        if self._ai_recommend_result is None:
+            cached_indices = self.load_cache(self.__ai_indices_cache_file)
+            if cached_indices is not None:
+                self._ai_recommend_result = cached_indices
+
+        # 只要有结果，始终返回completed状态和数据
+        if self._ai_recommend_result is not None:
+            return {"status": "completed", "results": self._ai_recommend_result}
+
+        if self._ai_recommend_error is not None:
+            return {"status": "error", "error": self._ai_recommend_error}
+
+        return {"status": "idle"}
+
+    def get_current_status_only(self) -> Dict[str, Any]:
+        """
+        获取当前状态（不校验hash，用于check_only模式）
+        """
+        return self._build_status()
+
+    def get_status(
+        self, filtered_indices: Optional[List[int]], search_results_count: int
+    ) -> Dict[str, Any]:
+        """
+        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）
+        如果请求变化（筛选条件变化），返回idle状态
+        """
+        # 计算当前请求的hash
+        request_hash = self._calculate_request_hash(
+            filtered_indices, search_results_count
+        )
+
+        # 检查请求是否变化
+        is_same_request = request_hash == self._current_request_hash
+
+        # 如果请求变化了（筛选条件改变），返回idle状态
+        if not is_same_request:
+            return (
+                {"status": "idle"}
+                if settings.AI_RECOMMEND_ENABLED
+                else {"status": "disabled"}
+            )
+
+        # 请求未变化，返回当前实际状态
+        return self._build_status()
+
+    @log_execution_time(logger=logger)
+    async def async_ai_recommend(self, items: List[str], preference: str = None) -> str:
+        """
+        AI推荐
+        :param items: 候选资源列表(JSON字符串格式)
+        :param preference: 用户偏好(可选)
+        :return: AI返回的推荐结果
+        """
+        # 设置运行状态
+        self._ai_recommend_running = True
+        try:
+            # 导入LLMHelper
+            from app.helper.llm import LLMHelper
+
+            # 获取LLM实例
+            llm = LLMHelper.get_llm()
+
+            # 构建提示词
+            user_preference = (
+                preference
+                or settings.AI_RECOMMEND_USER_PREFERENCE
+                or "Prefer high-quality resources with more seeders"
+            )
+
+            # 添加指令
+            instruction = """
+Task: Select the best matching items from the list based on user preferences.
+
+Each item contains:
+- index: Item number
+- title: Full torrent title
+- size: File size
+- seeders: Number of seeders
+
+Output Format: Return ONLY a JSON array of "index" numbers (e.g., [0, 3, 1]). Do NOT include any explanations or other text.
+"""
+            message = (
+                f"User Preference: {user_preference}\n{instruction}\nCandidate Resources:\n"
+                + "\n".join(items)
+            )
+
+            # 调用LLM
+            response = await llm.ainvoke(message)
+            return response.content
+
+        except ValueError as e:
+            logger.error(f"AI推荐配置错误: {e}")
+            raise
+        except Exception as e:
+            raise
+        finally:
+            # 清除运行状态
+            self._ai_recommend_running = False
+            self._ai_recommend_task = None
+
+    def is_ai_recommend_running(self) -> bool:
+        """
+        检查AI推荐是否正在运行
+        """
+        return self._ai_recommend_running
+
+    def cancel_ai_recommend(self):
+        """
+        取消正在运行的AI推荐任务
+        """
+        if self._ai_recommend_task and not self._ai_recommend_task.done():
+            self._ai_recommend_task.cancel()
+        self._ai_recommend_running = False
+        self._ai_recommend_task = None
+        self._current_request_hash = None
+        self._ai_recommend_result = None
+        self._ai_recommend_error = None
+        self.remove_cache(self.__ai_indices_cache_file)
+
+    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
+        :param filtered_indices: 筛选后的索引列表
+        :param search_results_count: 搜索结果总数
+        :param results: 搜索结果列表
+        """
+        # 计算新请求的哈希值
+        new_request_hash = self._calculate_request_hash(
+            filtered_indices, search_results_count
+        )
+
+        # 如果请求变化了，取消旧任务
+        if new_request_hash != self._current_request_hash:
+            self.cancel_ai_recommend()
+
+            # 更新请求哈希值
+            self._current_request_hash = new_request_hash
+
+            # 重置状态
+            self._ai_recommend_result = None
+            self._ai_recommend_error = None
+
+            # 启动新任务
+            async def run_recommend():
+                # 获取当前任务对象，用于在finally中比对
+                current_task = asyncio.current_task()
+                try:
+                    self._ai_recommend_running = True
+
+                    # 准备数据
+                    items = []
+                    valid_indices = []
+                    max_items = settings.AI_RECOMMEND_MAX_ITEMS or 50
+
+                    # 如果提供了筛选索引，先筛选结果；否则使用所有结果
+                    if filtered_indices is not None and len(filtered_indices) > 0:
+                        results_to_process = [
+                            results[i]
+                            for i in filtered_indices
+                            if 0 <= i < len(results)
+                        ]
+                    else:
+                        results_to_process = results
+
+                    for i, torrent in enumerate(results_to_process):
+                        if len(items) >= max_items:
+                            break
+
+                        if not torrent.torrent_info:
+                            continue
+
+                        valid_indices.append(i)
+
+                        item_info = {
+                            "index": i,
+                            "title": torrent.torrent_info.title or "未知",
+                            "size": (
+                                StringUtils.format_size(torrent.torrent_info.size)
+                                if torrent.torrent_info.size
+                                else "0 B"
+                            ),
+                            "seeders": torrent.torrent_info.seeders or 0,
+                        }
+
+                        items.append(json.dumps(item_info, ensure_ascii=False))
+
+                    if not items:
+                        self._ai_recommend_error = "没有可用于AI推荐的资源"
+                        return
+
+                    # 调用AI推荐
+                    ai_response = await self.async_ai_recommend(items)
+
+                    # 解析AI返回的索引
+                    try:
+                        # 使用正则提取JSON数组（非贪婪模式，避免匹配多个数组）
+                        json_match = re.search(r'\[.*?\]', ai_response, re.DOTALL)
+                        if not json_match:
+                            raise ValueError(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}")
+
+                        # 映射回原始索引
+                        if filtered_indices:
+                            original_indices = [
+                                filtered_indices[valid_indices[i]]
+                                for i in ai_indices
+                                if i < len(valid_indices)
+                                and 0 <= filtered_indices[valid_indices[i]] < len(results)
+                            ]
+                        else:
+                            original_indices = [
+                                valid_indices[i]
+                                for i in ai_indices
+                                if i < len(valid_indices)
+                                and 0 <= valid_indices[i] < len(results)
+                            ]
+
+                        # 只返回索引列表，不返回完整数据
+                        self._ai_recommend_result = original_indices
+
+                        # 保存到数据库
+                        self.save_cache(original_indices, self.__ai_indices_cache_file)
+                        logger.info(f"AI推荐完成: {len(original_indices)}项")
+
+                    except Exception as e:
+                        logger.error(
+                            f"解析AI返回结果失败: {e}, 原始响应: {ai_response}"
+                        )
+                        self._ai_recommend_error = str(e)
+
+                except asyncio.CancelledError:
+                    logger.info("AI推荐任务被取消")
+                except Exception as e:
+                    logger.error(f"AI推荐任务失败: {e}")
+                    self._ai_recommend_error = str(e)
+                finally:
+                    # 只有当 self._ai_recommend_task 仍然是当前任务时，才清理状态
+                    # 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
+                    if self._ai_recommend_task == current_task:
+                        self._ai_recommend_running = False
+                        self._ai_recommend_task = None
+
+            # 创建并启动任务
+            self._ai_recommend_task = asyncio.create_task(run_recommend())

app/chain/search.py

@@ -29,6 +29,7 @@ class SearchChain(ChainBase):
     """
 
     __result_temp_file = "__search_result__"
+    __ai_result_temp_file = "__ai_search_result__"
 
     def search_by_id(self, tmdbid: Optional[int] = None, doubanid: Optional[str] = None,
                      mtype: MediaType = None, area: Optional[str] = "title", season: Optional[int] = None,
@@ -98,6 +99,18 @@ 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]:

app/chain/site.py

@@ -489,20 +489,18 @@ class SiteChain(ChainBase):
             logger.warn(f"站点 {domain} 索引器不存在！")
             return
         # 查询站点图标
-        site_icon = siteoper.get_icon_by_domain(domain)
-        if not site_icon or not site_icon.base64:
-            logger.info(f"开始缓存站点 {indexer.get('name')} 图标 ...")
-            icon_url, icon_base64 = self.__parse_favicon(url=indexer.get("domain"),
-                                                         cookie=cookie,
-                                                         ua=settings.USER_AGENT)
-            if icon_url:
-                siteoper.update_icon(name=indexer.get("name"),
-                                     domain=domain,
-                                     icon_url=icon_url,
-                                     icon_base64=icon_base64)
-                logger.info(f"缓存站点 {indexer.get('name')} 图标成功")
-            else:
-                logger.warn(f"缓存站点 {indexer.get('name')} 图标失败")
+        logger.info(f"开始缓存站点 {indexer.get('name')} 图标 ...")
+        icon_url, icon_base64 = self.__parse_favicon(url=indexer.get("domain"),
+                                                     cookie=cookie,
+                                                     ua=settings.USER_AGENT)
+        if icon_url:
+            siteoper.update_icon(name=indexer.get("name"),
+                                 domain=domain,
+                                 icon_url=icon_url,
+                                 icon_base64=icon_base64)
+            logger.info(f"缓存站点 {indexer.get('name')} 图标成功")
+        else:
+            logger.warn(f"缓存站点 {indexer.get('name')} 图标失败")
 
     @eventmanager.register(EventType.SiteUpdated)
     def clear_site_data(self, event: Event):

app/core/config.py

@@ -439,6 +439,12 @@ class ConfigModel(BaseModel):
     LLM_MEMORY_RETENTION_DAYS: int = 1
     # Redis记忆保留天数（如果使用Redis）
     LLM_REDIS_MEMORY_RETENTION_DAYS: int = 7
+    # 是否启用AI推荐
+    AI_RECOMMEND_ENABLED: bool = False
+    # AI推荐用户偏好
+    AI_RECOMMEND_USER_PREFERENCE: str = ""
+    # AI推荐条目数量限制
+    AI_RECOMMEND_MAX_ITEMS: int = 50
 
 
 class Settings(BaseSettings, ConfigModel, LogConfigModel):

app/helper/llm.py

@@ -1,12 +1,76 @@
 """LLM模型相关辅助功能"""
-from typing import List
+from typing import List, Optional
 
+from app.core.config import settings
 from app.log import logger
 
 
 class LLMHelper:
     """LLM模型相关辅助功能"""
 
+    @staticmethod
+    def get_llm(streaming: bool = False, callbacks: Optional[list] = None):
+        """
+        获取LLM实例
+        :param streaming: 是否启用流式输出
+        :param callbacks: 回调处理器列表
+        :return: LLM实例
+        """
+        provider = settings.LLM_PROVIDER.lower()
+        api_key = settings.LLM_API_KEY
+
+        if not api_key:
+            raise ValueError("未配置LLM API Key")
+
+        if provider == "google":
+            if settings.PROXY_HOST:
+                from langchain_openai import ChatOpenAI
+                return ChatOpenAI(
+                    model=settings.LLM_MODEL,
+                    api_key=api_key,
+                    max_retries=3,
+                    base_url="https://generativelanguage.googleapis.com/v1beta/openai",
+                    temperature=settings.LLM_TEMPERATURE,
+                    streaming=streaming,
+                    callbacks=callbacks,
+                    stream_usage=True,
+                    openai_proxy=settings.PROXY_HOST
+                )
+            else:
+                from langchain_google_genai import ChatGoogleGenerativeAI
+                return ChatGoogleGenerativeAI(
+                    model=settings.LLM_MODEL,
+                    google_api_key=api_key,
+                    max_retries=3,
+                    temperature=settings.LLM_TEMPERATURE,
+                    streaming=streaming,
+                    callbacks=callbacks
+                )
+        elif provider == "deepseek":
+            from langchain_deepseek import ChatDeepSeek
+            return ChatDeepSeek(
+                model=settings.LLM_MODEL,
+                api_key=api_key,
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                callbacks=callbacks,
+                stream_usage=True
+            )
+        else:
+            from langchain_openai import ChatOpenAI
+            return ChatOpenAI(
+                model=settings.LLM_MODEL,
+                api_key=api_key,
+                max_retries=3,
+                base_url=settings.LLM_BASE_URL,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                callbacks=callbacks,
+                stream_usage=True,
+                openai_proxy=settings.PROXY_HOST
+            )
+
     def get_models(self, provider: str, api_key: str, base_url: str = None) -> List[str]:
         """获取模型列表"""
         logger.info(f"获取 {provider} 模型列表...")

app/utils/string.py

@@ -242,6 +242,27 @@ class StringUtils:
         else:
             return size + "B"
 
+    @staticmethod
+    def format_size(size_bytes: int) -> str:
+        """
+        将字节转换为人类可读格式
+        """
+        if not size_bytes or size_bytes == 0:
+            return "0 B"
+
+        units = ["B", "KB", "MB", "GB", "TB", "PB"]
+        size = float(size_bytes)
+        unit_index = 0
+
+        while size >= 1024 and unit_index < len(units) - 1:
+            size /= 1024
+            unit_index += 1
+
+        # 保留两位小数
+        if unit_index == 0:
+            return f"{int(size)} {units[unit_index]}"
+        return f"{size:.2f} {units[unit_index]}"
+
     @staticmethod
     def url_equal(url1: str, url2: str) -> bool:
         """