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feat: Agent 服务日志功能和后端更新

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- agent/main.py: 添加日志记录功能
- agent/llm: 更新 anthropic, openai, factory
- agent/core/agent.py: 更新
- server: agent_handler, agent_service, chat_service 更新

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
DESKTOP-72TV0V4\caoxiaozhu
2026-03-11 17:22:40 +08:00
parent f9660a3d7b
commit 25eb277a2a
10 changed files with 260 additions and 25 deletions
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12
agent/app/agent/core/agent.py
View File

@@ -1,6 +1,7 @@
"""
Agent Core - 单智能体核心
"""
import logging
from typing import Optional, List, Dict, Any
from pydantic import BaseModel
from app.agent.memory.manager import MemoryManager
@@ -8,6 +9,8 @@ from app.agent.skills.router import SkillRouter
from app.agent.skills.executor import SkillExecutor
from app.agent.llm.factory import LLMFactory
logger = logging.getLogger("agent.core")
class AgentConfig(BaseModel):
"""智能体配置"""
@@ -16,6 +19,8 @@ class AgentConfig(BaseModel):
role_description: str
model_provider: str = "openai"
model_name: str = "gpt-4"
api_key: Optional[str] = None # API Key（可选，用于覆盖默认配置）
base_url: Optional[str] = None # Base URL（可选，用于覆盖默认配置）
skills: List[int] = [] # 技能 ID 列表
knowledge_base_ids: List[int] = []
timeout: int = 60
@@ -36,7 +41,12 @@ class AgentCore:
def __init__(self, config: AgentConfig):
self.config = config
self.llm = LLMFactory.create(config.model_provider, config.model_name)
# 记录 LLM 初始化信息
api_key_info = f"{config.api_key[:10]}..." if config.api_key else "None"
logger.info(f"初始化 AgentCore: name={config.name}, provider={config.model_provider}, model={config.model_name}, api_key={api_key_info}, base_url={config.base_url}")
self.llm = LLMFactory.create(config.model_provider, config.model_name, config.api_key, config.base_url)
self.memory = MemoryManager(config.id)
self.skill_router = SkillRouter(config.skills)
self.skill_executor = SkillExecutor()

6
agent/app/agent/llm/anthropic.py
View File

@@ -2,17 +2,17 @@
Anthropic LLM 实现
"""
import os
from typing import Dict, Any, List
from typing import Dict, Any, List, Optional
from anthropic import AsyncAnthropic
class AnthropicLLM:
"""Anthropic Claude LLM"""
def __init__(self, model_name: str = "claude-3-sonnet-20240229"):
def __init__(self, model_name: str = "claude-3-sonnet-20240229", api_key: Optional[str] = None):
self.model_name = model_name
self.client = AsyncAnthropic(
api_key=os.getenv("ANTHROPIC_API_KEY", "")
api_key=api_key or os.getenv("ANTHROPIC_API_KEY", "")
)
async def decide(self, prompt: str) -> Dict[str, Any]:

12
agent/app/agent/llm/factory.py
View File

@@ -1,7 +1,7 @@
"""
LLM Factory - LLM 工厂类
"""
from typing import Dict, Any
from typing import Optional
from app.agent.llm.openai import OpenAILLM
from app.agent.llm.anthropic import AnthropicLLM
@@ -10,21 +10,23 @@ class LLMFactory:
"""LLM 工厂类"""
@staticmethod
def create(provider: str, model_name: str):
def create(provider: str, model_name: str, api_key: Optional[str] = None, base_url: Optional[str] = None):
"""
创建 LLM 实例
Args:
provider: 模型提供商 (openai/anthropic)
model_name: 模型名称
api_key: API Key（可选）
base_url: Base URL（可选）
Returns:
LLM 实例
"""
if provider.lower() == "openai":
return OpenAILLM(model_name)
return OpenAILLM(model_name, api_key, base_url)
elif provider.lower() == "anthropic":
return AnthropicLLM(model_name)
return AnthropicLLM(model_name, api_key)
else:
# 默认使用 OpenAI
return OpenAILLM(model_name)
return OpenAILLM(model_name, api_key, base_url)

19
agent/app/agent/llm/openai.py
View File

@@ -2,18 +2,31 @@
OpenAI LLM 实现
"""
import os
import logging
from typing import Dict, Any, List, Optional
from openai import AsyncOpenAI
logger = logging.getLogger("llm.openai")
class OpenAILLM:
"""OpenAI LLM"""
def __init__(self, model_name: str = "gpt-4"):
def __init__(self, model_name: str = "gpt-4", api_key: Optional[str] = None, base_url: Optional[str] = None):
self.model_name = model_name
# 优先使用传入的参数，否则使用环境变量
self.api_key = api_key or os.getenv("OPENAI_API_KEY", "")
self.base_url = base_url or os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
api_key_info = f"{self.api_key[:10]}..." if self.api_key else "None"
logger.info(f"初始化 OpenAI LLM: model={model_name}, api_key={api_key_info}, base_url={self.base_url}")
if not self.api_key:
logger.warning("⚠️ WARNING: No API key provided!")
self.client = AsyncOpenAI(
api_key=os.getenv("OPENAI_API_KEY", ""),
base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
api_key=self.api_key,
base_url=self.base_url
)
async def decide(self, prompt: str) -> Dict[str, Any]:

132
agent/app/main.py
View File

@@ -2,7 +2,10 @@
FastAPI Agent Engine Server
"""
import os
import sys
import time
import logging
from datetime import datetime
from typing import Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
@@ -12,6 +15,90 @@ from app.agent.core import AgentCore, Supervisor, AgentConfig
from app.agent.llm import LLMFactory
# 日志目录 - 放在 server/logs 下
LOG_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "server", "logs", datetime.now().strftime("%Y-%m-%d"))
os.makedirs(LOG_DIR, exist_ok=True)
# 成功/失败日志文件
today = datetime.now().strftime("%Y-%m-%d")
success_log_file = os.path.join(LOG_DIR, f"python_success.log")
failure_log_file = os.path.join(LOG_DIR, f"python_failure.log")
def setup_logging():
"""配置日志系统"""
log_level = os.getenv("LOG_LEVEL", "INFO")
# 创建格式化器
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
# 控制台处理器
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(formatter)
# 成功日志文件处理器
success_handler = logging.FileHandler(success_log_file, encoding='utf-8')
success_handler.setFormatter(formatter)
success_handler.setLevel(logging.INFO)
# 失败日志文件处理器
failure_handler = logging.FileHandler(failure_log_file, encoding='utf-8')
failure_handler.setFormatter(formatter)
failure_handler.setLevel(logging.WARNING)
# 根日志配置
root_logger = logging.getLogger()
root_logger.setLevel(getattr(logging, log_level))
root_logger.addHandler(console_handler)
root_logger.addHandler(success_handler)
root_logger.addHandler(failure_handler)
return root_logger
# 成功日志记录器（只记录 INFO 级别到成功日志）
class SuccessLogger:
"""成功日志记录器"""
@staticmethod
def log(message: str):
"""记录成功日志"""
logger = logging.getLogger("success")
logger.setLevel(logging.INFO)
handler = logging.FileHandler(success_log_file, encoding='utf-8')
handler.setFormatter(logging.Formatter('%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(handler)
logger.info(message)
# 同时输出到控制台
print(f"✅ {message}")
# 失败日志记录器
class FailureLogger:
"""失败日志记录器"""
@staticmethod
def log(message: str, error: str = ""):
"""记录失败日志"""
logger = logging.getLogger("failure")
logger.setLevel(logging.WARNING)
handler = logging.FileHandler(failure_log_file, encoding='utf-8')
handler.setFormatter(logging.Formatter('%(asctime)s - %(message)s - %(error)s', datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(handler)
full_message = f"{message} - Error: {error}" if error else message
logger.warning(full_message)
# 同时输出到控制台
print(f"❌ {full_message}")
logger = setup_logging()
app = FastAPI(title="X-Agents Python Engine", version="1.0.0")
# CORS
@@ -32,6 +119,12 @@ class ChatRequest(BaseModel):
message: str
user_id: int = 1
session_id: Optional[str] = None
# 模型参数（可选，如果传了就使用，否则用智能体配置的默认模型）
model_id: Optional[str] = None
model_name: Optional[str] = None
model_provider: Optional[str] = None
api_key: Optional[str] = None
base_url: Optional[str] = None
class TeamChatRequest(BaseModel):
@@ -76,7 +169,7 @@ _mock_agents = {
}
def get_agent_config(agent_id: int) -> AgentConfig:
def get_agent_config(agent_id: int, api_key: str = None, base_url: str = None) -> AgentConfig:
"""获取智能体配置"""
agent_data = _mock_agents.get(agent_id)
if not agent_data:
@@ -88,6 +181,8 @@ def get_agent_config(agent_id: int) -> AgentConfig:
role_description=agent_data["role_description"],
model_provider=agent_data["model_provider"],
model_name=agent_data["model_name"],
api_key=api_key,
base_url=base_url,
skills=agent_data.get("skills", [])
)
@@ -109,16 +204,42 @@ async def chat(request: ChatRequest):
"""
单智能体对话
"""
chat_logger = logging.getLogger("agent.chat")
# 打印请求参数（隐藏 api_key 敏感信息）
api_key_preview = f"{request.api_key[:10]}..." if request.api_key else "None"
chat_logger.info(f"========== 收到聊天请求 ==========")
chat_logger.info(f"agent_id: {request.agent_id}")
chat_logger.info(f"model_id: {request.model_id}")
chat_logger.info(f"model_provider: {request.model_provider}")
chat_logger.info(f"model_name: {request.model_name}")
chat_logger.info(f"api_key: {api_key_preview}")
chat_logger.info(f"base_url: {request.base_url}")
chat_logger.info(f"message: {request.message[:50]}...")
start_time = time.time()
# 获取智能体配置
try:
config = get_agent_config(request.agent_id)
except HTTPException:
config = get_agent_config(request.agent_id, request.api_key, request.base_url)
chat_logger.info(f"Agent config loaded: provider={config.model_provider}, model={config.model_name}")
except HTTPException as e:
FailureLogger.log(f"Agent not found: agent_id={request.agent_id}", str(e))
chat_logger.error(f"Agent not found: {e}")
raise
except Exception as e:
FailureLogger.log(f"Error loading config: agent_id={request.agent_id}", str(e))
chat_logger.error(f"Error loading config: {e}")
raise HTTPException(status_code=400, detail=str(e))
# 如果请求中指定了模型，覆盖智能体的默认配置
if request.model_provider:
config.model_provider = request.model_provider
if request.model_name:
config.model_name = request.model_name
chat_logger.info(f"Final LLM config: provider={config.model_provider}, model={config.model_name}, api_key={config.api_key[:10] if config.api_key else 'None'}..., base_url={config.base_url}")
# 创建智能体实例
agent = AgentCore(config)
@@ -129,10 +250,15 @@ async def chat(request: ChatRequest):
try:
result = await agent.run(request.message, request.user_id, session_id)
except Exception as e:
FailureLogger.log(f"Agent execution failed: agent_id={request.agent_id}, message={request.message[:30]}", str(e))
chat_logger.error(f"Agent execution error: {e}")
raise HTTPException(status_code=500, detail=str(e))
duration_ms = int((time.time() - start_time) * 1000)
# 记录成功日志
SuccessLogger.log(f"Chat success: agent_id={request.agent_id}, duration={duration_ms}ms, message={request.message[:30]}...")
return ChatResponse(
agent_id=request.agent_id,
response=result.content,