format: 项目格式化
This commit is contained in:
parent
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commit
89b02c4c93
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@ -1,35 +1,38 @@
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"""
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包初始化文件
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"""
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from .api.chat_routes import (chat_endpoint_handler,
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delete_conversation_handler,
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get_conversation_handler,
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get_conversations_handler, get_models_handler,
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save_conversation_handler, serve_upload_handler,
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stop_generation_handler, upload_file_handler)
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from .models.chat_models import ChatMessage, ChatRequest, ModelInfo
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from .utils.helpers import (
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get_current_timestamp,
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generate_unique_id,
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format_api_response,
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log_request,
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log_response,
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extract_delta_content
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)
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from .api.chat_routes import (
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chat_endpoint_handler,
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get_models_handler,
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get_conversations_handler,
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get_conversation_handler,
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save_conversation_handler,
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delete_conversation_handler,
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upload_file_handler,
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serve_upload_handler,
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stop_generation_handler
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)
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from .utils.helpers import (extract_delta_content, format_api_response,
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generate_unique_id, get_current_timestamp,
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log_request, log_response)
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__all__ = [
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# Models
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'ChatMessage', 'ChatRequest', 'ModelInfo',
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"ChatMessage",
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"ChatRequest",
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"ModelInfo",
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# Utils
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'get_current_timestamp', 'generate_unique_id', 'format_api_response',
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'log_request', 'log_response', 'extract_delta_content',
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"get_current_timestamp",
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"generate_unique_id",
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"format_api_response",
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"log_request",
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"log_response",
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"extract_delta_content",
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# API Handlers
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'chat_endpoint_handler', 'get_models_handler', 'get_conversations_handler',
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'get_conversation_handler', 'save_conversation_handler', 'delete_conversation_handler',
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'upload_file_handler', 'serve_upload_handler', 'stop_generation_handler'
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]
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"chat_endpoint_handler",
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"get_models_handler",
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"get_conversations_handler",
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"get_conversation_handler",
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"save_conversation_handler",
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"delete_conversation_handler",
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"upload_file_handler",
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"serve_upload_handler",
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"stop_generation_handler",
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]
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@ -1 +1 @@
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# api/__init__.py
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# api/__init__.py
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File diff suppressed because it is too large
Load Diff
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@ -2,13 +2,16 @@
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GLM-4.6V 平台路由处理器（zai-sdk）
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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。
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"""
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import json
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import os
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import sys
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import json
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from pathlib import Path
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from fastapi import HTTPException
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from fastapi.responses import JSONResponse, StreamingResponse
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from utils.helpers import get_current_timestamp, generate_unique_id
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from utils.helpers import generate_unique_id, get_current_timestamp
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from utils.logger import log_info
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@ -19,7 +22,9 @@ def init():
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"""
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api_key = os.getenv("ZHIPU_API_KEY") or os.getenv("GLM_API_KEY")
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if not api_key:
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raise ValueError("GLM 模式需要设置环境变量 ZHIPU_API_KEY（在 https://open.bigmodel.cn 申请）")
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raise ValueError(
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"GLM 模式需要设置环境变量 ZHIPU_API_KEY（在 https://open.bigmodel.cn 申请）"
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)
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log_info(f"[GLM] 初始化完成，ZHIPU_API_KEY 已配置")
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@ -28,47 +33,61 @@ async def chat_handler(body: dict):
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GLM 聊天处理器（对外接口与百炼 chat_endpoint_handler 完全兼容）。
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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。
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"""
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from utils.glm_adapter import glm_stream_generator, glm_chat_sync
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from utils.glm_adapter import glm_chat_sync, glm_stream_generator
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if not isinstance(body, dict):
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raise HTTPException(status_code=400, detail="请求体必须是 JSON 对象")
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messages = body.get("messages", [])
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model = body.get("model", "glm-4.6v")
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stream = body.get("stream", True)
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messages = body.get("messages", [])
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model = body.get("model", "glm-4.6v")
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stream = body.get("stream", True)
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temperature = body.get("temperature", 0.7)
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max_tokens = body.get("max_tokens", body.get("maxTokens", 2000))
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web_search = body.get("webSearch", False) or body.get("deepSearch", False)
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deep_think = body.get("deepThinking", False)
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files = body.get("files", [])
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max_tokens = body.get("max_tokens", body.get("maxTokens", 2000))
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web_search = body.get("webSearch", False) or body.get("deepSearch", False)
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deep_think = body.get("deepThinking", False)
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files = body.get("files", [])
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# 兼容前端简化格式（非 messages 结构）
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if not messages:
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msg_text = body.get("message", "")
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msg_text = body.get("message", "")
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sys_prompt = body.get("systemPrompt", "你是一个智能助手。")
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user_content = msg_text if isinstance(msg_text, list) else [{"type": "text", "text": msg_text}]
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user_content = (
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msg_text
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if isinstance(msg_text, list)
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else [{"type": "text", "text": msg_text}]
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)
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messages = [
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{"role": "system", "content": sys_prompt},
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{"role": "user", "content": user_content},
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{"role": "user", "content": user_content},
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]
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log_info(f"[GLM] model={model} stream={stream} web_search={web_search} "
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f"thinking={deep_think} files={len(files)} msgs={len(messages)}")
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log_info(
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f"[GLM] model={model} stream={stream} web_search={web_search} "
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f"thinking={deep_think} files={len(files)} msgs={len(messages)}"
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)
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if stream:
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return StreamingResponse(
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glm_stream_generator(
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messages=messages, model=model, temperature=temperature,
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max_tokens=max_tokens, files=files or None,
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web_search=web_search, deep_thinking=deep_think,
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messages=messages,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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files=files or None,
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web_search=web_search,
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deep_thinking=deep_think,
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),
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media_type="text/event-stream",
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)
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result = glm_chat_sync(
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messages=messages, model=model, temperature=temperature,
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max_tokens=max_tokens, files=files or None,
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web_search=web_search, deep_thinking=deep_think,
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messages=messages,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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files=files or None,
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web_search=web_search,
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deep_thinking=deep_think,
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)
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resp = {
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"id": f"chatcmpl-{generate_unique_id()}",
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@ -2,9 +2,12 @@
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"""
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初始化日志系统
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"""
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import os
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from utils.logger import setup_global_logger
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def init_logging_system():
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"""
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初始化日志系统
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@ -26,13 +29,12 @@ def init_logging_system():
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# 设置全局日志系统
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logger = setup_global_logger(
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name="ai-chat-api",
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log_level=log_level,
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log_dir=log_dir
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name="ai-chat-api", log_level=log_level, log_dir=log_dir
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)
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return logger
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if __name__ == "__main__":
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logger = init_logging_system()
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logger.info("Logging system initialized successfully")
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logger.info("Logging system initialized successfully")
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@ -10,6 +10,7 @@ AI Chat API Server — 主入口（纯基础设施层）
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- 百炼 DashScope → api/chat_routes.py
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- 智谱 GLM-4.6V → api/chat_routes_glm.py + utils/glm_adapter.py
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"""
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import os
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import sys
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from datetime import datetime, timezone
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@ -27,10 +28,10 @@ if _venv_lib.exists():
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# ── 第三方导入 ────────────────────────────────────────────────────────
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from dotenv import load_dotenv
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from fastapi import FastAPI, File, UploadFile, Request
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from fastapi import FastAPI, File, Request, UploadFile
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from fastapi.responses import JSONResponse
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sys.path.append('/home/mt/project/ai-chat-ui/server')
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sys.path.append("/home/mt/project/ai-chat-ui/server")
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# ── 工具/日志（与平台无关）───────────────────────────────────────────
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from utils.helpers import log_response
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@ -55,15 +56,11 @@ else:
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_platform.init() # 各平台自行完成初始化（API Key 校验等）
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# 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）
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from api.chat_routes import (
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get_conversations_handler,
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get_conversation_handler,
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save_conversation_handler,
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delete_conversation_handler,
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upload_file_handler,
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serve_upload_handler,
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stop_generation_handler,
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)
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from api.chat_routes import (delete_conversation_handler,
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get_conversation_handler,
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get_conversations_handler,
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save_conversation_handler, serve_upload_handler,
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stop_generation_handler, upload_file_handler)
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# ── FastAPI 应用 ──────────────────────────────────────────────────────
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app = FastAPI(
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@ -80,7 +77,9 @@ async def logging_middleware(request: Request, call_next):
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response = await call_next(request)
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ms = (datetime.now(timezone.utc) - start_time).total_seconds() * 1000
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icon = "✅" if response.status_code < 400 else "❌"
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logger.info(f"{icon} {request.method} {request.url.path} | 状态: {response.status_code} | 耗时: {ms:.0f}ms")
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logger.info(
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f"{icon} {request.method} {request.url.path} | 状态: {response.status_code} | 耗时: {ms:.0f}ms"
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)
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log_response(response.status_code, ms)
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response.headers["X-Process-Time"] = f"{ms:.2f}ms"
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return response
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@ -88,6 +87,7 @@ async def logging_middleware(request: Request, call_next):
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# ── 路由注册 ──────────────────────────────────────────────────────────
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@app.get("/health")
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async def health_check():
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return {
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@ -115,6 +115,7 @@ async def get_models():
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# ── 通用路由（与平台无关）────────────────────────────────────────────
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@app.get("/api/chat-ui/conversations")
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async def get_conversations():
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return await get_conversations_handler()
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@ -158,6 +159,7 @@ async def stop_generation_by_id(message_id: str):
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# ── 程序入口 ──────────────────────────────────────────────────────────
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if __name__ == "__main__":
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import uvicorn
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port = int(os.getenv("PORT", 8000))
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print("=" * 55)
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print(f" AI Chat Server v3.0 启动中...")
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@ -165,4 +167,4 @@ if __name__ == "__main__":
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print(f" 监听端口 : {port}")
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print(f" 切换平台 : 修改 .env 中 LLM_BACKEND=glm|dashscope，重启")
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print("=" * 55)
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uvicorn.run(app, host="0.0.0.0", port=port)
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uvicorn.run(app, host="0.0.0.0", port=port)
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@ -1 +1 @@
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# models/__init__.py
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# models/__init__.py
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@ -1,14 +1,18 @@
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"""
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数据模型定义
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"""
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from typing import Any, Dict, List, Optional, Union
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from pydantic import BaseModel
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from typing import Dict, List, Optional, Any, Union
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class ChatMessageContentItem(BaseModel):
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type: str # "text" or "image_url"
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text: Optional[str] = None
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image_url: Optional[Dict[str, str]] = None # {"url": "...", "detail": "auto|low|high"}
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image_url: Optional[Dict[str, str]] = (
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None # {"url": "...", "detail": "auto|low|high"}
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)
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class ChatMessage(BaseModel):
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@ -35,4 +39,4 @@ class ModelInfo(BaseModel):
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name: str
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description: str
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maxTokens: int
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provider: str
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provider: str
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@ -1 +1 @@
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# utils/__init__.py
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# utils/__init__.py
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@ -1,10 +1,11 @@
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"""
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GLM 文件 ID 缓存（基于磁盘的简单 KV，sha256 → file_id，3天有效期）
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"""
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import hashlib
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import json
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import time
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import threading
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import time
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from pathlib import Path
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_CACHE_FILE = Path(__file__).parent.parent / "uploads" / ".glm_file_cache.json"
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@ -24,7 +25,9 @@ def _load() -> dict:
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def _save(data: dict) -> None:
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try:
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_CACHE_FILE.parent.mkdir(parents=True, exist_ok=True)
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_CACHE_FILE.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
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_CACHE_FILE.write_text(
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json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8"
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)
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except Exception as e:
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print(f"[file_cache] 写入失败：{e}")
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@ -3,10 +3,11 @@ GLM-4.6V 适配层（基于 zai-sdk）
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SDK：pip install zai-sdk
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模型：glm-4.6v（支持文本/图像/文档/深度思考）
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"""
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import base64
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import json
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import os
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import sys
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import json
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import base64
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import threading
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from pathlib import Path
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from typing import AsyncGenerator
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@ -15,7 +16,9 @@ from typing import AsyncGenerator
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# ── 自动注入 venv site-packages ───────────────────────────────────────
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def _ensure_venv():
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server_dir = Path(__file__).parent.parent
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for sp in sorted((server_dir / ".venv" / "lib").glob("python*/site-packages"), reverse=True):
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for sp in sorted(
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(server_dir / ".venv" / "lib").glob("python*/site-packages"), reverse=True
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):
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if sp.exists() and str(sp) not in sys.path:
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sys.path.insert(0, str(sp))
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print(f"[GLM] venv 注入：{sp}")
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@ -34,7 +37,7 @@ def get_client():
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from zai import ZhipuAiClient
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except ImportError:
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raise ImportError("GLM 模式需要安装 zai-sdk：.venv/bin/pip install zai-sdk")
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api_key = os.getenv("ZHIPU_API_KEY").strip() or os.getenv("GLM_API_KEY").strip()
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api_key = os.getenv("ZHIPU_API_KEY").strip() or os.getenv("GLM_API_KEY").strip()
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if not api_key:
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raise ValueError("GLM 模式需要设置环境变量 ZHIPU_API_KEY")
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_client = ZhipuAiClient(api_key=api_key)
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@ -43,15 +46,15 @@ def get_client():
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# ── 模型映射 ──────────────────────────────────────────────────────────
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DEFAULT_TEXT_MODEL = "glm-4.5-Air" # glm-4.6 文本统一模型
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DEFAULT_TEXT_MODEL = "glm-4.5-Air" # glm-4.6 文本统一模型
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DEFAULT_VISION_MODEL = "glm-4.5-Air"
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MODEL_MAP = {
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"qwen-max": "glm-4.5-Air",
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"qwen-plus": "glm-4.5-Air",
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"qwen-turbo": "glm-4.5-Air",
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"qwen-vl-max": "glm-4.5-Air",
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"qwen-vl-plus": "glm-4.5-Air",
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"qwen-max": "glm-4.5-Air",
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"qwen-plus": "glm-4.5-Air",
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"qwen-turbo": "glm-4.5-Air",
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"qwen-vl-max": "glm-4.5-Air",
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"qwen-vl-plus": "glm-4.5-Air",
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}
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@ -63,7 +66,9 @@ def resolve_model(model: str, has_vision: bool = False) -> str:
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# ── 文件上传（含 file_id 缓存）───────────────────────────────────────
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def upload_file_for_extract(local_path: Path) -> str:
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from utils.file_cache import sha256_of_file, get as cache_get, set as cache_set
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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} "
|
||||
f"web_search={web_search} thinking={deep_thinking}")
|
||||
print(
|
||||
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_content = "" # 累计正式回答（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）
|
||||
|
||||
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"] = [{
|
||||
"type": "web_search",
|
||||
"web_search": {
|
||||
"enable": True,
|
||||
"search_result": True
|
||||
}
|
||||
}]
|
||||
extra_kwargs["tools"] = [
|
||||
{
|
||||
"type": "web_search",
|
||||
"web_search": {"enable": 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}
|
||||
|
|
|
|||
|
|
@ -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 ""
|
||||
return full_content[len(previous_content) :]
|
||||
return ""
|
||||
|
|
|
|||
|
|
@ -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",
|
||||
log_dir: str = "logs", max_bytes: int = 10 * 1024 * 1024, backup_count: int = 5):
|
||||
def __init__(
|
||||
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",
|
||||
log_dir: str = "logs", max_bytes: int = 10 * 1024 * 1024,
|
||||
backup_count: int = 5):
|
||||
def setup_global_logger(
|
||||
name: str = "ai-chat-server",
|
||||
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
|
||||
|
||||
|
|
@ -170,12 +181,17 @@ def log_structured(level: str, message: str, **details):
|
|||
# 转换为更易读的格式
|
||||
detail_str = ", ".join(f"{k}={v}" for k, v in details.items() if v)
|
||||
formatted_msg = f"[{message}] {detail_str}"
|
||||
|
||||
|
||||
getattr(logger, level.lower())(formatted_msg)
|
||||
|
||||
|
||||
def log_request_info(method: str, path: str, client_ip: str = "unknown",
|
||||
user_agent: str = "", referer: str = ""):
|
||||
def log_request_info(
|
||||
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 = "",
|
||||
method: str = "", client_ip: str = ""):
|
||||
def log_response_info(
|
||||
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 = "",
|
||||
context: dict = None):
|
||||
def log_error_detail(
|
||||
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 = "",
|
||||
conversation_id: str = "", tokens_used: dict = None):
|
||||
def log_chat_interaction(
|
||||
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
|
||||
ai_response=ai_response[:100] + "..." if len(ai_response) > 100 else ai_response,
|
||||
user_input=(
|
||||
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,
|
||||
memory_usage: float = 0, disk_usage: float = 0):
|
||||
def log_system_status(
|
||||
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,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
Loading…
Reference in New Issue