format: 项目格式化

server/__init__.py

@@ -1,35 +1,38 @@
 """
 包初始化文件
 """
+
+from .api.chat_routes import (chat_endpoint_handler,
+                              delete_conversation_handler,
+                              get_conversation_handler,
+                              get_conversations_handler, get_models_handler,
+                              save_conversation_handler, serve_upload_handler,
+                              stop_generation_handler, upload_file_handler)
 from .models.chat_models import ChatMessage, ChatRequest, ModelInfo
-from .utils.helpers import (
+from .utils.helpers import (extract_delta_content, format_api_response,
-    get_current_timestamp,
+                            generate_unique_id, get_current_timestamp,
-    generate_unique_id,
+                            log_request, log_response)
-    format_api_response,
-    log_request,
-    log_response,
-    extract_delta_content
-)
-from .api.chat_routes import (
-    chat_endpoint_handler,
-    get_models_handler,
-    get_conversations_handler,
-    get_conversation_handler,
-    save_conversation_handler,
-    delete_conversation_handler,
-    upload_file_handler,
-    serve_upload_handler,
-    stop_generation_handler
-)
 
 __all__ = [
     # Models
-    'ChatMessage', 'ChatRequest', 'ModelInfo',
+    "ChatMessage",
+    "ChatRequest",
+    "ModelInfo",
     # Utils
-    'get_current_timestamp', 'generate_unique_id', 'format_api_response',
+    "get_current_timestamp",
-    'log_request', 'log_response', 'extract_delta_content',
+    "generate_unique_id",
+    "format_api_response",
+    "log_request",
+    "log_response",
+    "extract_delta_content",
     # API Handlers
-    'chat_endpoint_handler', 'get_models_handler', 'get_conversations_handler',
+    "chat_endpoint_handler",
-    'get_conversation_handler', 'save_conversation_handler', 'delete_conversation_handler',
+    "get_models_handler",
-    'upload_file_handler', 'serve_upload_handler', 'stop_generation_handler'
+    "get_conversations_handler",
+    "get_conversation_handler",
+    "save_conversation_handler",
+    "delete_conversation_handler",
+    "upload_file_handler",
+    "serve_upload_handler",
+    "stop_generation_handler",
 ]

server/api/chat_routes_glm.py

@@ -2,13 +2,16 @@
 GLM-4.6V 平台路由处理器（zai-sdk）
 所有智谱 GLM 相关逻辑均集中在此文件，main.py 无感知任何平台细节。
 """
+
+import json
 import os
 import sys
-import json
 from pathlib import Path
+
 from fastapi import HTTPException
 from fastapi.responses import JSONResponse, StreamingResponse
-from utils.helpers import get_current_timestamp, generate_unique_id
+
+from utils.helpers import generate_unique_id, get_current_timestamp
 from utils.logger import log_info
 
 
@@ -19,7 +22,9 @@ def init():
     """
     api_key = os.getenv("ZHIPU_API_KEY") or os.getenv("GLM_API_KEY")
     if not api_key:
-        raise ValueError("GLM 模式需要设置环境变量 ZHIPU_API_KEY（在 https://open.bigmodel.cn 申请）")
+        raise ValueError(
+            "GLM 模式需要设置环境变量 ZHIPU_API_KEY（在 https://open.bigmodel.cn 申请）"
+        )
     log_info(f"[GLM] 初始化完成，ZHIPU_API_KEY 已配置")
 
 
@@ -28,47 +33,61 @@ async def chat_handler(body: dict):
     GLM 聊天处理器（对外接口与百炼 chat_endpoint_handler 完全兼容）。
     流式/非流式自动适配，支持图像、文档附件、联网搜索、深度思考。
     """
-    from utils.glm_adapter import glm_stream_generator, glm_chat_sync
+    from utils.glm_adapter import glm_chat_sync, glm_stream_generator
 
     if not isinstance(body, dict):
         raise HTTPException(status_code=400, detail="请求体必须是 JSON 对象")
 
-    messages    = body.get("messages", [])
+    messages = body.get("messages", [])
-    model       = body.get("model", "glm-4.6v")
+    model = body.get("model", "glm-4.6v")
-    stream      = body.get("stream", True)
+    stream = body.get("stream", True)
     temperature = body.get("temperature", 0.7)
-    max_tokens  = body.get("max_tokens", body.get("maxTokens", 2000))
+    max_tokens = body.get("max_tokens", body.get("maxTokens", 2000))
-    web_search  = body.get("webSearch", False) or body.get("deepSearch", False)
+    web_search = body.get("webSearch", False) or body.get("deepSearch", False)
-    deep_think  = body.get("deepThinking", False)
+    deep_think = body.get("deepThinking", False)
-    files       = body.get("files", [])
+    files = body.get("files", [])
 
     # 兼容前端简化格式（非 messages 结构）
     if not messages:
-        msg_text   = body.get("message", "")
+        msg_text = body.get("message", "")
         sys_prompt = body.get("systemPrompt", "你是一个智能助手。")
-        user_content = msg_text if isinstance(msg_text, list) else [{"type": "text", "text": msg_text}]
+        user_content = (
+            msg_text
+            if isinstance(msg_text, list)
+            else [{"type": "text", "text": msg_text}]
+        )
         messages = [
             {"role": "system", "content": sys_prompt},
-            {"role": "user",   "content": user_content},
+            {"role": "user", "content": user_content},
         ]
 
-    log_info(f"[GLM] model={model} stream={stream} web_search={web_search} "
+    log_info(
-             f"thinking={deep_think} files={len(files)} msgs={len(messages)}")
+        f"[GLM] model={model} stream={stream} web_search={web_search} "
+        f"thinking={deep_think} files={len(files)} msgs={len(messages)}"
+    )
 
     if stream:
         return StreamingResponse(
             glm_stream_generator(
-                messages=messages, model=model, temperature=temperature,
+                messages=messages,
-                max_tokens=max_tokens, files=files or None,
+                model=model,
-                web_search=web_search, deep_thinking=deep_think,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                files=files or None,
+                web_search=web_search,
+                deep_thinking=deep_think,
             ),
             media_type="text/event-stream",
         )
 
     result = glm_chat_sync(
-        messages=messages, model=model, temperature=temperature,
+        messages=messages,
-        max_tokens=max_tokens, files=files or None,
+        model=model,
-        web_search=web_search, deep_thinking=deep_think,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        files=files or None,
+        web_search=web_search,
+        deep_thinking=deep_think,
     )
     resp = {
         "id": f"chatcmpl-{generate_unique_id()}",

server/init_logging.py

@@ -2,9 +2,12 @@
 """
 初始化日志系统
 """
+
 import os
+
 from utils.logger import setup_global_logger
 
+
 def init_logging_system():
     """
     初始化日志系统
@@ -26,13 +29,12 @@ def init_logging_system():
 
     # 设置全局日志系统
     logger = setup_global_logger(
-        name="ai-chat-api",
+        name="ai-chat-api", log_level=log_level, log_dir=log_dir
-        log_level=log_level,
-        log_dir=log_dir
     )
 
     return logger
 
+
 if __name__ == "__main__":
     logger = init_logging_system()
     logger.info("Logging system initialized successfully")

server/main.py

@@ -10,6 +10,7 @@ AI Chat API Server — 主入口（纯基础设施层）
   - 百炼 DashScope → api/chat_routes.py
   - 智谱 GLM-4.6V  → api/chat_routes_glm.py  +  utils/glm_adapter.py
 """
+
 import os
 import sys
 from datetime import datetime, timezone
@@ -27,10 +28,10 @@ if _venv_lib.exists():
 
 # ── 第三方导入 ────────────────────────────────────────────────────────
 from dotenv import load_dotenv
-from fastapi import FastAPI, File, UploadFile, Request
+from fastapi import FastAPI, File, Request, UploadFile
 from fastapi.responses import JSONResponse
 
-sys.path.append('/home/mt/project/ai-chat-ui/server')
+sys.path.append("/home/mt/project/ai-chat-ui/server")
 
 # ── 工具/日志（与平台无关）───────────────────────────────────────────
 from utils.helpers import log_response
@@ -55,15 +56,11 @@ else:
 _platform.init()  # 各平台自行完成初始化（API Key 校验等）
 
 # 通用路由处理器（文件上传、会话管理等，与平台无关，统一用百炼路由中的实现）
-from api.chat_routes import (
+from api.chat_routes import (delete_conversation_handler,
-    get_conversations_handler,
+                             get_conversation_handler,
-    get_conversation_handler,
+                             get_conversations_handler,
-    save_conversation_handler,
+                             save_conversation_handler, serve_upload_handler,
-    delete_conversation_handler,
+                             stop_generation_handler, upload_file_handler)
-    upload_file_handler,
-    serve_upload_handler,
-    stop_generation_handler,
-)
 
 # ── FastAPI 应用 ──────────────────────────────────────────────────────
 app = FastAPI(
@@ -80,7 +77,9 @@ async def logging_middleware(request: Request, call_next):
     response = await call_next(request)
     ms = (datetime.now(timezone.utc) - start_time).total_seconds() * 1000
     icon = "✅" if response.status_code < 400 else "❌"
-    logger.info(f"{icon} {request.method} {request.url.path} | 状态: {response.status_code} | 耗时: {ms:.0f}ms")
+    logger.info(
+        f"{icon} {request.method} {request.url.path} | 状态: {response.status_code} | 耗时: {ms:.0f}ms"
+    )
     log_response(response.status_code, ms)
     response.headers["X-Process-Time"] = f"{ms:.2f}ms"
     return response
@@ -88,6 +87,7 @@ async def logging_middleware(request: Request, call_next):
 
 # ── 路由注册 ──────────────────────────────────────────────────────────
 
+
 @app.get("/health")
 async def health_check():
     return {
@@ -115,6 +115,7 @@ async def get_models():
 
 # ── 通用路由（与平台无关）────────────────────────────────────────────
 
+
 @app.get("/api/chat-ui/conversations")
 async def get_conversations():
     return await get_conversations_handler()
@@ -158,6 +159,7 @@ async def stop_generation_by_id(message_id: str):
 # ── 程序入口 ──────────────────────────────────────────────────────────
 if __name__ == "__main__":
     import uvicorn
+
     port = int(os.getenv("PORT", 8000))
     print("=" * 55)
     print(f"  AI Chat Server v3.0  启动中...")

server/models/chat_models.py

@@ -1,14 +1,18 @@
 """
 数据模型定义
 """
+
+from typing import Any, Dict, List, Optional, Union
+
 from pydantic import BaseModel
-from typing import Dict, List, Optional, Any, Union
 
 
 class ChatMessageContentItem(BaseModel):
     type: str  # "text" or "image_url"
     text: Optional[str] = None
-    image_url: Optional[Dict[str, str]] = None  # {"url": "...", "detail": "auto|low|high"}
+    image_url: Optional[Dict[str, str]] = (
+        None  # {"url": "...", "detail": "auto|low|high"}
+    )
 
 
 class ChatMessage(BaseModel):

server/utils/file_cache.py

@@ -1,10 +1,11 @@
 """
 GLM 文件 ID 缓存（基于磁盘的简单 KV，sha256 → file_id，3天有效期）
 """
+
 import hashlib
 import json
-import time
 import threading
+import time
 from pathlib import Path
 
 _CACHE_FILE = Path(__file__).parent.parent / "uploads" / ".glm_file_cache.json"
@@ -24,7 +25,9 @@ def _load() -> dict:
 def _save(data: dict) -> None:
     try:
         _CACHE_FILE.parent.mkdir(parents=True, exist_ok=True)
-        _CACHE_FILE.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+        _CACHE_FILE.write_text(
+            json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8"
+        )
     except Exception as e:
         print(f"[file_cache] 写入失败：{e}")
 

server/utils/glm_adapter.py

@@ -3,10 +3,11 @@ GLM-4.6V 适配层（基于 zai-sdk）
 SDK：pip install zai-sdk
 模型：glm-4.6v（支持文本/图像/文档/深度思考）
 """
+
+import base64
+import json
 import os
 import sys
-import json
-import base64
 import threading
 from pathlib import Path
 from typing import AsyncGenerator
@@ -15,7 +16,9 @@ from typing import AsyncGenerator
 # ── 自动注入 venv site-packages ───────────────────────────────────────
 def _ensure_venv():
     server_dir = Path(__file__).parent.parent
-    for sp in sorted((server_dir / ".venv" / "lib").glob("python*/site-packages"), reverse=True):
+    for sp in sorted(
+        (server_dir / ".venv" / "lib").glob("python*/site-packages"), reverse=True
+    ):
         if sp.exists() and str(sp) not in sys.path:
             sys.path.insert(0, str(sp))
             print(f"[GLM] venv 注入：{sp}")
@@ -34,7 +37,7 @@ def get_client():
             from zai import ZhipuAiClient
         except ImportError:
             raise ImportError("GLM 模式需要安装 zai-sdk：.venv/bin/pip install zai-sdk")
-        api_key = os.getenv("ZHIPU_API_KEY").strip()  or os.getenv("GLM_API_KEY").strip() 
+        api_key = os.getenv("ZHIPU_API_KEY").strip() or os.getenv("GLM_API_KEY").strip()
         if not api_key:
             raise ValueError("GLM 模式需要设置环境变量 ZHIPU_API_KEY")
         _client = ZhipuAiClient(api_key=api_key)
@@ -43,15 +46,15 @@ def get_client():
 
 
 # ── 模型映射 ──────────────────────────────────────────────────────────
-DEFAULT_TEXT_MODEL   = "glm-4.5-Air"   # glm-4.6 文本统一模型
+DEFAULT_TEXT_MODEL = "glm-4.5-Air"  # glm-4.6 文本统一模型
 DEFAULT_VISION_MODEL = "glm-4.5-Air"
 
 MODEL_MAP = {
-    "qwen-max":      "glm-4.5-Air",
+    "qwen-max": "glm-4.5-Air",
-    "qwen-plus":     "glm-4.5-Air",
+    "qwen-plus": "glm-4.5-Air",
-    "qwen-turbo":    "glm-4.5-Air",
+    "qwen-turbo": "glm-4.5-Air",
-    "qwen-vl-max":   "glm-4.5-Air",
+    "qwen-vl-max": "glm-4.5-Air",
-    "qwen-vl-plus":  "glm-4.5-Air",
+    "qwen-vl-plus": "glm-4.5-Air",
 }
 
 
@@ -63,7 +66,9 @@ def resolve_model(model: str, has_vision: bool = False) -> str:
 
 # ── 文件上传（含 file_id 缓存）───────────────────────────────────────
 def upload_file_for_extract(local_path: Path) -> str:
-    from utils.file_cache import sha256_of_file, get as cache_get, set as cache_set
+    from utils.file_cache import get as cache_get
+    from utils.file_cache import set as cache_set
+    from utils.file_cache import sha256_of_file
 
     file_hash = sha256_of_file(local_path)
     cached = cache_get(file_hash)
@@ -73,18 +78,20 @@ def upload_file_for_extract(local_path: Path) -> str:
 
     client = get_client()
     mime_map = {
-        ".pdf":  "application/pdf",
+        ".pdf": "application/pdf",
         ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-        ".doc":  "application/msword",
+        ".doc": "application/msword",
         ".xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-        ".xls":  "application/vnd.ms-excel",
+        ".xls": "application/vnd.ms-excel",
         ".pptx": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
-        ".ppt":  "application/vnd.ms-powerpoint",
+        ".ppt": "application/vnd.ms-powerpoint",
     }
     mime = mime_map.get(local_path.suffix.lower(), "application/octet-stream")
     print(f"[GLM] 上传文件：{local_path.name}（{mime}）")
     with open(local_path, "rb") as f:
-        file_obj = client.files.create(file=(local_path.name, f, mime), purpose="file-extract")
+        file_obj = client.files.create(
+            file=(local_path.name, f, mime), purpose="file-extract"
+        )
     file_id = file_obj.id
     cache_set(file_hash, file_id)
     print(f"[GLM] 上传成功：file_id={file_id}")
@@ -94,7 +101,9 @@ def upload_file_for_extract(local_path: Path) -> str:
 # ── 图像编码 ─────────────────────────────────────────────────────────
 def encode_image(image_source: str) -> dict:
     """将图像来源统一转为 OpenAI image_url 格式"""
-    if image_source.startswith("data:image") or image_source.startswith(("http://", "https://")):
+    if image_source.startswith("data:image") or image_source.startswith(
+        ("http://", "https://")
+    ):
         return {"type": "image_url", "image_url": {"url": image_source}}
     # 本地路径 → base64
     local = Path(image_source.replace("file://", "").lstrip("/"))
@@ -138,7 +147,9 @@ def build_glm_messages(messages: list, files: list | None = None) -> tuple[list,
                 elif t == "image_url":
                     has_vision = True
                     img_val = item.get("image_url", "")
-                    img_src = img_val.get("url", "") if isinstance(img_val, dict) else img_val
+                    img_src = (
+                        img_val.get("url", "") if isinstance(img_val, dict) else img_val
+                    )
                     new_content.append(encode_image(img_src))
                 else:
                     new_content.append({"type": "text", "text": str(item)})
@@ -172,9 +183,13 @@ def build_glm_messages(messages: list, files: list | None = None) -> tuple[list,
                         fid = upload_file_for_extract(local)
                         inserts.append({"type": "file", "file": {"file_id": fid}})
                     except Exception as e:
-                        inserts.append({"type": "text", "text": f"[文件上传失败：{filename}，{e}]"})
+                        inserts.append(
+                            {"type": "text", "text": f"[文件上传失败：{filename}，{e}]"}
+                        )
             else:
-                inserts.append({"type": "text", "text": f"[附件：{filename}，类型：{suffix}]"})
+                inserts.append(
+                    {"type": "text", "text": f"[附件：{filename}，类型：{suffix}]"}
+                )
 
         if inserts:
             for i in range(len(glm_messages) - 1, -1, -1):
@@ -195,6 +210,7 @@ def build_glm_messages(messages: list, files: list | None = None) -> tuple[list,
 # ── 哨兵对象 ─────────────────────────────────────────────────────────
 _SENTINEL = object()
 
+
 # ── 流式调用 ────────────────────────────────────────────────────────
 async def glm_stream_generator(
     messages: list,
@@ -213,7 +229,7 @@ async def glm_stream_generator(
     import asyncio
     import queue
 
-    from utils.helpers import get_current_timestamp, generate_unique_id
+    from utils.helpers import generate_unique_id, get_current_timestamp
 
     glm_msgs, has_vision = build_glm_messages(messages, files)
     actual_model = resolve_model(model, has_vision)
@@ -221,13 +237,18 @@ async def glm_stream_generator(
     extra_kwargs: dict = {}
     if web_search:
         extra_kwargs["tools"] = [
-            {"type": "web_search", "web_search": {"enable":True,"search_result": True}}
+            {
+                "type": "web_search",
+                "web_search": {"enable": True, "search_result": True},
+            }
         ]
     if not deep_thinking:
         # 智普默认开启思考模式，所以要用非门（不知道“非门”描述是否准确。前端选择开启思考模式，这里不做变动。前端选择关闭思考模式，这里关闭。）
         extra_kwargs["thinking"] = {"type": "disabled"}
-    print(f"[GLM] 流式请求：model={actual_model} vision={has_vision} "
+    print(
-          f"web_search={web_search} thinking={deep_thinking}")
+        f"[GLM] 流式请求：model={actual_model} vision={has_vision} "
+        f"web_search={web_search} thinking={deep_thinking}"
+    )
 
     chunk_queue: queue.Queue = queue.Queue(maxsize=128)
 
@@ -254,8 +275,8 @@ async def glm_stream_generator(
 
     loop = asyncio.get_running_loop()
 
-    full_reasoning = ""   # 累计思考内容（用于判断是否首次）
+    full_reasoning = ""  # 累计思考内容（用于判断是否首次）
-    full_content   = ""   # 累计正式回答（用于判断是否首次）
+    full_content = ""  # 累计正式回答（用于判断是否首次）
 
     while True:
         item = await loop.run_in_executor(None, chunk_queue.get)
@@ -271,7 +292,7 @@ async def glm_stream_generator(
         try:
             delta = item.choices[0].delta
             reasoning = getattr(delta, "reasoning_content", "") or ""
-            text      = getattr(delta, "content", "")      or ""
+            text = getattr(delta, "content", "") or ""
 
             delta_str = ""
 
@@ -300,7 +321,9 @@ async def glm_stream_generator(
                 "object": "chat.completion.chunk",
                 "created": get_current_timestamp(),
                 "model": actual_model,
-                "choices": [{"index": 0, "delta": {"content": delta_str}, "finish_reason": None}],
+                "choices": [
+                    {"index": 0, "delta": {"content": delta_str}, "finish_reason": None}
+                ],
             }
             yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
 
@@ -318,7 +341,6 @@ async def glm_stream_generator(
     yield "data: [DONE]\n\n"
 
 
-
 # ── 非流式调用 ────────────────────────────────────────────────────────
 def glm_chat_sync(
     messages: list,
@@ -334,13 +356,12 @@ def glm_chat_sync(
 
     extra_kwargs: dict = {}
     if web_search:
-        extra_kwargs["tools"] = [{
+        extra_kwargs["tools"] = [
-    "type": "web_search",
+            {
-    "web_search": {
+                "type": "web_search",
-        "enable": True,
+                "web_search": {"enable": True, "search_result": True},
-        "search_result": True
+            }
-    }
+        ]
-}]
     if deep_thinking:
         extra_kwargs["thinking"] = {"type": "enabled"}
 
@@ -358,8 +379,8 @@ def glm_chat_sync(
     usage = None
     if hasattr(resp, "usage") and resp.usage:
         usage = {
-            "promptTokens":     resp.usage.prompt_tokens,
+            "promptTokens": resp.usage.prompt_tokens,
             "completionTokens": resp.usage.completion_tokens,
-            "totalTokens":      resp.usage.total_tokens,
+            "totalTokens": resp.usage.total_tokens,
         }
     return {"content": content, "model": actual_model, "usage": usage}

server/utils/helpers.py

@@ -1,13 +1,15 @@
 """
 通用工具函数
 """
-import os
+
 import json
+import os
 import uuid
 from datetime import datetime
 from typing import Dict
 
-from .logger import log_request_info, log_response_info, log_error_detail, log_chat_interaction
+from .logger import (log_chat_interaction, log_error_detail, log_request_info,
+                     log_response_info)
 
 
 def get_current_timestamp():
@@ -20,14 +22,16 @@ def generate_unique_id():
     return str(uuid.uuid4())
 
 
-def format_api_response(content: str, conversation_id: str = None, model: str = "qwen-plus"):
+def format_api_response(
+    content: str, conversation_id: str = None, model: str = "qwen-plus"
+):
     """格式化API响应"""
     return {
         "id": generate_unique_id(),
         "conversationId": conversation_id or generate_unique_id(),
         "content": content,
         "model": model,
-        "createdAt": get_current_timestamp()
+        "createdAt": get_current_timestamp(),
     }
 
 
@@ -44,5 +48,5 @@ def log_response(status_code: int, process_time: float):
 def extract_delta_content(full_content: str, previous_content: str) -> str:
     """提取增量内容"""
     if len(full_content) > len(previous_content):
-        return full_content[len(previous_content):]
+        return full_content[len(previous_content) :]
     return ""

server/utils/logger.py

@@ -2,20 +2,27 @@
 统一日志管理系统
 提供结构化日志记录功能，支持不同日志级别、文件输出、轮转等
 """
+
+import json
 import logging
 import os
 import sys
 from datetime import datetime
-from pathlib import Path
 from logging.handlers import RotatingFileHandler
-import json
+from pathlib import Path
 
 
 class LoggerSetup:
     """日志系统配置类"""
 
-    def __init__(self, name: str = "ai-chat-server", log_level: str = "INFO",
+    def __init__(
-                 log_dir: str = "logs", max_bytes: int = 10 * 1024 * 1024, backup_count: int = 5):
+        self,
+        name: str = "ai-chat-server",
+        log_level: str = "INFO",
+        log_dir: str = "logs",
+        max_bytes: int = 10 * 1024 * 1024,
+        backup_count: int = 5,
+    ):
         """
         初始化日志系统
 
@@ -37,7 +44,7 @@ class LoggerSetup:
 
         # 设置日志格式（去掉 funcName:lineno，保持人类可读性）
         self.formatter = logging.Formatter(
-            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
         )
 
         # 创建logger实例
@@ -66,7 +73,7 @@ class LoggerSetup:
             str(log_file),
             maxBytes=self.max_bytes,
             backupCount=self.backup_count,
-            encoding='utf-8'
+            encoding="utf-8",
         )
         file_handler.setLevel(self.log_level)
         file_handler.setFormatter(self.formatter)
@@ -83,9 +90,13 @@ class LoggerSetup:
 _logger_instance = None
 
 
-def setup_global_logger(name: str = "ai-chat-server", log_level: str = "INFO",
+def setup_global_logger(
-                       log_dir: str = "logs", max_bytes: int = 10 * 1024 * 1024,
+    name: str = "ai-chat-server",
-                       backup_count: int = 5):
+    log_level: str = "INFO",
+    log_dir: str = "logs",
+    max_bytes: int = 10 * 1024 * 1024,
+    backup_count: int = 5,
+):
     """
     设置全局日志系统
 
@@ -174,8 +185,13 @@ def log_structured(level: str, message: str, **details):
     getattr(logger, level.lower())(formatted_msg)
 
 
-def log_request_info(method: str, path: str, client_ip: str = "unknown",
+def log_request_info(
-                     user_agent: str = "", referer: str = ""):
+    method: str,
+    path: str,
+    client_ip: str = "unknown",
+    user_agent: str = "",
+    referer: str = "",
+):
     """记录请求信息日志"""
     log_structured(
         "info",
@@ -184,12 +200,17 @@ def log_request_info(method: str, path: str, client_ip: str = "unknown",
         path=path,
         client_ip=client_ip,
         user_agent=user_agent,
-        referer=referer
+        referer=referer,
     )
 
 
-def log_response_info(status_code: int, process_time: float, path: str = "",
+def log_response_info(
-                      method: str = "", client_ip: str = ""):
+    status_code: int,
+    process_time: float,
+    path: str = "",
+    method: str = "",
+    client_ip: str = "",
+):
     """记录响应信息日志"""
     log_structured(
         "info",
@@ -198,37 +219,52 @@ def log_response_info(status_code: int, process_time: float, path: str = "",
         process_time_ms=process_time,
         path=path,
         method=method,
-        client_ip=client_ip
+        client_ip=client_ip,
     )
 
 
-def log_error_detail(error_type: str, error_message: str, traceback_info: str = "",
+def log_error_detail(
-                     context: dict = None):
+    error_type: str, error_message: str, traceback_info: str = "", context: dict = None
+):
     """记录详细的错误信息"""
     log_structured(
         "error",
         f"{error_type}: {error_message}",
         traceback=traceback_info,
-        context=context or {}
+        context=context or {},
     )
 
 
-def log_chat_interaction(user_input: str, ai_response: str, model: str = "",
+def log_chat_interaction(
-                         conversation_id: str = "", tokens_used: dict = None):
+    user_input: str,
+    ai_response: str,
+    model: str = "",
+    conversation_id: str = "",
+    tokens_used: dict = None,
+):
     """记录聊天交互日志"""
     log_structured(
         "info",
         "Chat Interaction",
-        user_input=user_input[:100] + "..." if len(user_input) > 100 else user_input,  # 截断长输入
+        user_input=(
-        ai_response=ai_response[:100] + "..." if len(ai_response) > 100 else ai_response,
+            user_input[:100] + "..." if len(user_input) > 100 else user_input
+        ),  # 截断长输入
+        ai_response=(
+            ai_response[:100] + "..." if len(ai_response) > 100 else ai_response
+        ),
         model=model,
         conversation_id=conversation_id,
-        tokens_used=tokens_used
+        tokens_used=tokens_used,
     )
 
 
-def log_system_status(status: str, uptime: float = 0, cpu_usage: float = 0,
+def log_system_status(
-                      memory_usage: float = 0, disk_usage: float = 0):
+    status: str,
+    uptime: float = 0,
+    cpu_usage: float = 0,
+    memory_usage: float = 0,
+    disk_usage: float = 0,
+):
     """记录系统状态日志"""
     log_structured(
         "info",
@@ -237,5 +273,5 @@ def log_system_status(status: str, uptime: float = 0, cpu_usage: float = 0,
         uptime_seconds=uptime,
         cpu_percent=cpu_usage,
         memory_percent=memory_usage,
-        disk_percent=disk_usage
+        disk_percent=disk_usage,
     )

server/utils/test_glm_search.py

@@ -1,30 +1,36 @@
+import asyncio
 import os
 import sys
-import asyncio
 from pathlib import Path
 
 # Add project root to sys.path
 root_dir = Path(__file__).parent
 sys.path.insert(0, str(root_dir))
 
-from utils.glm_adapter import glm_stream_generator, _ensure_venv, glm_chat_sync
-
 # Set API key from .env if needed
 from dotenv import load_dotenv
+
+from utils.glm_adapter import _ensure_venv, glm_chat_sync, glm_stream_generator
+
 load_dotenv()
 
+
 async def test_stream():
     msgs = [{"role": "user", "content": "今天北京天气怎样？"}]
     print("Testing stream...")
-    async for chunk in glm_stream_generator(msgs, "glm-4.5-air", 0.7, 1024, web_search=True):
+    async for chunk in glm_stream_generator(
+        msgs, "glm-4.5-air", 0.7, 1024, web_search=True
+    ):
         print(chunk, end="")
 
+
 def test_sync():
     msgs = [{"role": "user", "content": "今天几号？武汉天气怎样？"}]
     print("Testing sync...")
     res = glm_chat_sync(msgs, "glm-4.5-air", 0.7, 1024, web_search=True)
     print(res)
 
+
 if __name__ == "__main__":
     _ensure_venv()
     # test_sync()