初步重构llmrequest

debug_config.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""
+调试配置加载问题，查看API provider的配置是否正确传递
+"""
+
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+def debug_config_loading():
+    try:
+        # 临时配置API key
+        import toml
+        config_path = "config/model_config.toml"
+        
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config = toml.load(f)
+        
+        original_keys = {}
+        for provider in config['api_providers']:
+            original_keys[provider['name']] = provider['api_key']
+            provider['api_key'] = f"sk-test-key-for-{provider['name'].lower()}-12345"
+        
+        with open(config_path, 'w', encoding='utf-8') as f:
+            toml.dump(config, f)
+        
+        print("✅ 配置了测试API key")
+        
+        try:
+            # 清空缓存
+            modules_to_remove = [
+                'src.config.config',
+                'src.config.api_ada_configs',
+                'src.llm_models.model_manager',
+                'src.llm_models.model_client',
+                'src.llm_models.utils_model'
+            ]
+            for module in modules_to_remove:
+                if module in sys.modules:
+                    del sys.modules[module]
+            
+            # 导入配置
+            from src.config.config import model_config
+            print("\n🔍 调试配置加载:")
+            print(f"model_config类型: {type(model_config)}")
+            
+            # 检查API providers
+            if hasattr(model_config, 'api_providers'):
+                print(f"API providers数量: {len(model_config.api_providers)}")
+                for name, provider in model_config.api_providers.items():
+                    print(f"  - {name}: {provider.base_url}")
+                    print(f"    API key: {provider.api_key[:10]}...{provider.api_key[-5:] if len(provider.api_key) > 15 else provider.api_key}")
+                    print(f"    Client type: {provider.client_type}")
+            
+            # 检查模型配置
+            if hasattr(model_config, 'models'):
+                print(f"模型数量: {len(model_config.models)}")
+                for name, model in model_config.models.items():
+                    print(f"  - {name}: {model.model_identifier} (提供商: {model.api_provider})")
+            
+            # 检查任务配置
+            if hasattr(model_config, 'task_model_arg_map'):
+                print(f"任务配置数量: {len(model_config.task_model_arg_map)}")
+                for task_name, task_config in model_config.task_model_arg_map.items():
+                    print(f"  - {task_name}: {task_config}")
+                    
+            # 尝试初始化ModelManager
+            print("\n🔍 调试ModelManager初始化:")
+            from src.llm_models.model_manager import ModelManager
+            
+            try:
+                model_manager = ModelManager(model_config)
+                print("✅ ModelManager初始化成功")
+                
+                # 检查API客户端映射
+                print(f"API客户端数量: {len(model_manager.api_client_map)}")
+                for name, client in model_manager.api_client_map.items():
+                    print(f"  - {name}: {type(client).__name__}")
+                    if hasattr(client, 'client') and hasattr(client.client, 'api_key'):
+                        api_key = client.client.api_key
+                        print(f"    Client API key: {api_key[:10]}...{api_key[-5:] if len(api_key) > 15 else api_key}")
+                
+                # 尝试获取任务处理器
+                try:
+                    handler = model_manager["llm_normal"]
+                    print("✅ 成功获取llm_normal任务处理器")
+                    print(f"任务处理器类型: {type(handler).__name__}")
+                except Exception as e:
+                    print(f"❌ 获取任务处理器失败: {e}")
+                
+            except Exception as e:
+                print(f"❌ ModelManager初始化失败: {e}")
+                import traceback
+                traceback.print_exc()
+                
+        finally:
+            # 恢复配置
+            for provider in config['api_providers']:
+                provider['api_key'] = original_keys[provider['name']]
+            
+            with open(config_path, 'w', encoding='utf-8') as f:
+                toml.dump(config, f)
+            print("\n✅ 配置已恢复")
+        
+    except Exception as e:
+        print(f"❌ 调试失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    debug_config_loading()

src/chat/maibot_llmreq/__init__.py

@@ -1,19 +0,0 @@
-import loguru
-
-type LoguruLogger = loguru.Logger
-
-_logger: LoguruLogger = loguru.logger
-
-
-def init_logger(
-    logger: LoguruLogger | None = None,
-):
-    """
-    对LLMRequest模块进行配置
-    :param logger: 日志对象
-    """
-    global _logger  # 申明使用全局变量
-    if logger:
-        _logger = logger
-    else:
-        _logger.warning("Warning: No logger provided, using default logger.")

src/chat/maibot_llmreq/config/parser.py

@@ -1,267 +0,0 @@
-import os
-from typing import Any, Dict, List
-
-import tomli
-from packaging import version
-from packaging.specifiers import SpecifierSet
-from packaging.version import Version, InvalidVersion
-
-from .. import _logger as logger
-
-from .config import (
-    ModelUsageArgConfigItem,
-    ModelUsageArgConfig,
-    APIProvider,
-    ModelInfo,
-    NEWEST_VER,
-    ModuleConfig,
-)
-
-
-def _get_config_version(toml: Dict) -> Version:
-    """提取配置文件的 SpecifierSet 版本数据
-    Args:
-        toml[dict]: 输入的配置文件字典
-    Returns:
-        Version
-    """
-
-    if "inner" in toml and "version" in toml["inner"]:
-        config_version: str = toml["inner"]["version"]
-    else:
-        config_version = "0.0.0"  # 默认版本
-
-    try:
-        ver = version.parse(config_version)
-    except InvalidVersion as e:
-        logger.error(
-            "配置文件中 inner段 的 version 键是错误的版本描述\n"
-            f"请检查配置文件，当前 version 键: {config_version}\n"
-            f"错误信息: {e}"
-        )
-        raise InvalidVersion(
-            "配置文件中 inner段 的 version 键是错误的版本描述\n"
-        ) from e
-
-    return ver
-
-
-def _request_conf(parent: Dict, config: ModuleConfig):
-    request_conf_config = parent.get("request_conf")
-    config.req_conf.max_retry = request_conf_config.get(
-        "max_retry", config.req_conf.max_retry
-    )
-    config.req_conf.timeout = request_conf_config.get(
-        "timeout", config.req_conf.timeout
-    )
-    config.req_conf.retry_interval = request_conf_config.get(
-        "retry_interval", config.req_conf.retry_interval
-    )
-    config.req_conf.default_temperature = request_conf_config.get(
-        "default_temperature", config.req_conf.default_temperature
-    )
-    config.req_conf.default_max_tokens = request_conf_config.get(
-        "default_max_tokens", config.req_conf.default_max_tokens
-    )
-
-
-def _api_providers(parent: Dict, config: ModuleConfig):
-    api_providers_config = parent.get("api_providers")
-    for provider in api_providers_config:
-        name = provider.get("name", None)
-        base_url = provider.get("base_url", None)
-        api_key = provider.get("api_key", None)
-        client_type = provider.get("client_type", "openai")
-
-        if name in config.api_providers:  # 查重
-            logger.error(f"重复的API提供商名称: {name}，请检查配置文件。")
-            raise KeyError(f"重复的API提供商名称: {name}，请检查配置文件。")
-
-        if name and base_url:
-            config.api_providers[name] = APIProvider(
-                name=name,
-                base_url=base_url,
-                api_key=api_key,
-                client_type=client_type,
-            )
-        else:
-            logger.error(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
-            raise ValueError(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
-
-
-def _models(parent: Dict, config: ModuleConfig):
-    models_config = parent.get("models")
-    for model in models_config:
-        model_identifier = model.get("model_identifier", None)
-        name = model.get("name", model_identifier)
-        api_provider = model.get("api_provider", None)
-        price_in = model.get("price_in", 0.0)
-        price_out = model.get("price_out", 0.0)
-        force_stream_mode = model.get("force_stream_mode", False)
-
-        if name in config.models:  # 查重
-            logger.error(f"重复的模型名称: {name}，请检查配置文件。")
-            raise KeyError(f"重复的模型名称: {name}，请检查配置文件。")
-
-        if model_identifier and api_provider:
-            # 检查API提供商是否存在
-            if api_provider not in config.api_providers:
-                logger.error(f"未声明的API提供商 '{api_provider}' ，请检查配置文件。")
-                raise ValueError(
-                    f"未声明的API提供商 '{api_provider}' ，请检查配置文件。"
-                )
-            config.models[name] = ModelInfo(
-                name=name,
-                model_identifier=model_identifier,
-                api_provider=api_provider,
-                price_in=price_in,
-                price_out=price_out,
-                force_stream_mode=force_stream_mode,
-            )
-        else:
-            logger.error(f"模型 '{name}' 的配置不完整，请检查配置文件。")
-            raise ValueError(f"模型 '{name}' 的配置不完整，请检查配置文件。")
-
-
-def _task_model_usage(parent: Dict, config: ModuleConfig):
-    model_usage_configs = parent.get("task_model_usage")
-    config.task_model_arg_map = {}
-    for task_name, item in model_usage_configs.items():
-        if task_name in config.task_model_arg_map:
-            logger.error(f"子任务 {task_name} 已存在，请检查配置文件。")
-            raise KeyError(f"子任务 {task_name} 已存在，请检查配置文件。")
-
-        usage = []
-        if isinstance(item, Dict):
-            if "model" in item:
-                usage.append(
-                    ModelUsageArgConfigItem(
-                        name=item["model"],
-                        temperature=item.get("temperature", None),
-                        max_tokens=item.get("max_tokens", None),
-                        max_retry=item.get("max_retry", None),
-                    )
-                )
-            else:
-                logger.error(f"子任务 {task_name} 的模型配置不合法，请检查配置文件。")
-                raise ValueError(
-                    f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
-                )
-        elif isinstance(item, List):
-            for model in item:
-                if isinstance(model, Dict):
-                    usage.append(
-                        ModelUsageArgConfigItem(
-                            name=model["model"],
-                            temperature=model.get("temperature", None),
-                            max_tokens=model.get("max_tokens", None),
-                            max_retry=model.get("max_retry", None),
-                        )
-                    )
-                elif isinstance(model, str):
-                    usage.append(
-                        ModelUsageArgConfigItem(
-                            name=model,
-                            temperature=None,
-                            max_tokens=None,
-                            max_retry=None,
-                        )
-                    )
-                else:
-                    logger.error(
-                        f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
-                    )
-                    raise ValueError(
-                        f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
-                    )
-        elif isinstance(item, str):
-            usage.append(
-                ModelUsageArgConfigItem(
-                    name=item,
-                    temperature=None,
-                    max_tokens=None,
-                    max_retry=None,
-                )
-            )
-
-        config.task_model_arg_map[task_name] = ModelUsageArgConfig(
-            name=task_name,
-            usage=usage,
-        )
-
-
-def load_config(config_path: str) -> ModuleConfig:
-    """从TOML配置文件加载配置"""
-    config = ModuleConfig()
-
-    include_configs: Dict[str, Dict[str, Any]] = {
-        "request_conf": {
-            "func": _request_conf,
-            "support": ">=0.0.0",
-            "necessary": False,
-        },
-        "api_providers": {"func": _api_providers, "support": ">=0.0.0"},
-        "models": {"func": _models, "support": ">=0.0.0"},
-        "task_model_usage": {"func": _task_model_usage, "support": ">=0.0.0"},
-    }
-
-    if os.path.exists(config_path):
-        with open(config_path, "rb") as f:
-            try:
-                toml_dict = tomli.load(f)
-            except tomli.TOMLDecodeError as e:
-                logger.critical(
-                    f"配置文件model_list.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}"
-                )
-                exit(1)
-
-        # 获取配置文件版本
-        config.INNER_VERSION = _get_config_version(toml_dict)
-
-        # 检查版本
-        if config.INNER_VERSION > Version(NEWEST_VER):
-            logger.warning(
-                f"当前配置文件版本 {config.INNER_VERSION} 高于支持的最新版本 {NEWEST_VER}，可能导致异常，建议更新依赖。"
-            )
-
-        # 解析配置文件
-        # 如果在配置中找到了需要的项，调用对应项的闭包函数处理
-        for key in include_configs:
-            if key in toml_dict:
-                group_specifier_set: SpecifierSet = SpecifierSet(
-                    include_configs[key]["support"]
-                )
-
-                # 检查配置文件版本是否在支持范围内
-                if config.INNER_VERSION in group_specifier_set:
-                    # 如果版本在支持范围内，检查是否存在通知
-                    if "notice" in include_configs[key]:
-                        logger.warning(include_configs[key]["notice"])
-                    # 调用闭包函数处理配置
-                    (include_configs[key]["func"])(toml_dict, config)
-                else:
-                    # 如果版本不在支持范围内，崩溃并提示用户
-                    logger.error(
-                        f"配置文件中的 '{key}' 字段的版本 ({config.INNER_VERSION}) 不在支持范围内。\n"
-                        f"当前程序仅支持以下版本范围: {group_specifier_set}"
-                    )
-                    raise InvalidVersion(
-                        f"当前程序仅支持以下版本范围: {group_specifier_set}"
-                    )
-
-            # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理
-            elif (
-                "necessary" in include_configs[key]
-                and include_configs[key].get("necessary") is False
-            ):
-                # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理
-                if key == "keywords_reaction":
-                    pass
-            else:
-                # 如果用户根本没有需要的配置项，提示缺少配置
-                logger.error(f"配置文件中缺少必需的字段: '{key}'")
-                raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
-
-        logger.success(f"成功加载配置文件: {config_path}")
-
-    return config

src/chat/maibot_llmreq/tests/test_config_load.py

@@ -1,84 +0,0 @@
-import pytest
-from packaging.version import InvalidVersion
-
-from src import maibot_llmreq
-from src.maibot_llmreq.config.parser import _get_config_version, load_config
-
-
-class TestConfigLoad:
-    def test_loads_valid_version_from_toml(self):
-        maibot_llmreq.init_logger()
-
-        toml_data = {"inner": {"version": "1.2.3"}}
-        version = _get_config_version(toml_data)
-        assert str(version) == "1.2.3"
-
-    def test_handles_missing_version_key(self):
-        maibot_llmreq.init_logger()
-
-        toml_data = {}
-        version = _get_config_version(toml_data)
-        assert str(version) == "0.0.0"
-
-    def test_raises_error_for_invalid_version(self):
-        maibot_llmreq.init_logger()
-
-        toml_data = {"inner": {"version": "invalid_version"}}
-        with pytest.raises(InvalidVersion):
-            _get_config_version(toml_data)
-
-    def test_loads_complete_config_successfully(self, tmp_path):
-        maibot_llmreq.init_logger()
-
-        config_path = tmp_path / "config.toml"
-        config_path.write_text("""
-        [inner]
-        version = "0.1.0"
-    
-        [request_conf]
-        max_retry = 5
-        timeout = 10
-
-        [[api_providers]]
-        name = "provider1"
-        base_url = "https://api.example.com"
-        api_key = "key123"
-        
-        [[api_providers]]
-        name = "provider2"
-        base_url = "https://api.example2.com"
-        api_key = "key456"
-    
-        [[models]]
-        model_identifier = "model1"
-        api_provider = "provider1"
-        
-        [[models]]
-        model_identifier = "model2"
-        api_provider = "provider2"
-    
-        [task_model_usage]
-        task1 = { model = "model1" }
-        task2 = "model1"
-        task3 = [
-            "model1",
-            { model = "model2", temperature = 0.5 }
-        ]
-        """)
-        config = load_config(str(config_path))
-        assert config.req_conf.max_retry == 5
-        assert config.req_conf.timeout == 10
-        assert "provider1" in config.api_providers
-        assert "model1" in config.models
-        assert "task1" in config.task_model_arg_map
-
-    def test_raises_error_for_missing_required_field(self, tmp_path):
-        maibot_llmreq.init_logger()
-
-        config_path = tmp_path / "config.toml"
-        config_path.write_text("""
-        [inner]
-        version = "1.0.0"
-        """)
-        with pytest.raises(KeyError):
-            load_config(str(config_path))

src/config/config.py

@@ -13,7 +13,6 @@ from packaging.version import Version, InvalidVersion
 from typing import Any, Dict, List
 
 from src.common.logger import get_logger
-from src.common.message import api
 from src.config.config_base import ConfigBase
 from src.config.official_configs import (
     BotConfig,
@@ -314,7 +313,7 @@ def api_ada_load_config(config_path: str) -> ModuleConfig:
                 logger.error(f"配置文件中缺少必需的字段: '{key}'")
                 raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
 
-        logger.success(f"成功加载配置文件: {config_path}")
+        logger.info(f"成功加载配置文件: {config_path}")
 
     return config
 
@@ -653,4 +652,4 @@ update_model_config()
 logger.info("正在品鉴配置文件...")
 global_config = load_config(config_path=os.path.join(CONFIG_DIR, "bot_config.toml"))
 model_config = api_ada_load_config(config_path=os.path.join(CONFIG_DIR, "model_config.toml"))
-logger.info("非常的新鲜，非常的美味！")
+logger.info("非常的新鲜，非常的美味！")

src/llm_models/model_client/__init__.py

@@ -5,8 +5,7 @@ from openai import AsyncStream
 from openai.types.chat import ChatCompletionChunk, ChatCompletion
 
 from .base_client import BaseClient, APIResponse
-from .. import _logger as logger
-from ..config.config import (
+from src.config.api_ada_configs import (
     ModelInfo,
     ModelUsageArgConfigItem,
     RequestConfig,
@@ -22,6 +21,9 @@ from ..payload_content.message import Message
 from ..payload_content.resp_format import RespFormat
 from ..payload_content.tool_option import ToolOption
 from ..utils import compress_messages
+from src.common.logger import get_logger
+
+logger = get_logger("模型客户端")
 
 
 def _check_retry(
@@ -288,7 +290,7 @@ class ModelRequestHandler:
                         interrupt_flag=interrupt_flag,
                     )
                 except Exception as e:
-                    logger.trace(e)
+                    logger.debug(e)
                     remain_try -= 1  # 剩余尝试次数减1
 
                     # 处理异常
@@ -340,7 +342,7 @@ class ModelRequestHandler:
                         embedding_input=embedding_input,
                     )
                 except Exception as e:
-                    logger.trace(e)
+                    logger.debug(e)
                     remain_try -= 1  # 剩余尝试次数减1
 
                     # 处理异常

src/llm_models/model_client/base_client.py

@@ -5,7 +5,7 @@ from typing import Callable, Any
 from openai import AsyncStream
 from openai.types.chat import ChatCompletionChunk, ChatCompletion
 
-from ..config.config import ModelInfo, APIProvider
+from src.config.api_ada_configs import ModelInfo, APIProvider
 from ..payload_content.message import Message
 from ..payload_content.resp_format import RespFormat
 from ..payload_content.tool_option import ToolOption, ToolCall

src/llm_models/model_client/gemini_client.py

@@ -15,7 +15,7 @@ from google.genai.errors import (
 )
 
 from .base_client import APIResponse, UsageRecord
-from ..config.config import ModelInfo, APIProvider
+from src.config.api_ada_configs import ModelInfo, APIProvider
 from . import BaseClient
 
 from ..exceptions import (

src/llm_models/model_client/openai_client.py

@@ -21,7 +21,7 @@ from openai.types.chat import (
 from openai.types.chat.chat_completion_chunk import ChoiceDelta
 
 from .base_client import APIResponse, UsageRecord
-from ..config.config import ModelInfo, APIProvider
+from src.config.api_ada_configs import ModelInfo, APIProvider
 from . import BaseClient
 
 from ..exceptions import (

src/llm_models/model_manager.py

@@ -1,15 +1,13 @@
 import importlib
 from typing import Dict
 
+from src.config.config import model_config
+from src.config.api_ada_configs import ModuleConfig, ModelUsageArgConfig
+from src.common.logger import get_logger
 
-from .config.config import (
-    ModelUsageArgConfig,
-    ModuleConfig,
-)
-
-from . import _logger as logger
 from .model_client import ModelRequestHandler, BaseClient
 
+logger = get_logger("模型管理器")
 
 class ModelManager:
     # TODO: 添加读写锁，防止异步刷新配置时发生数据竞争
@@ -77,3 +75,5 @@ class ModelManager:
         :return: 是否在模型列表中
         """
         return task_name in self.config.task_model_arg_map
+
+

src/llm_models/temp.py

@@ -0,0 +1,8 @@
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+from src.config.config import model_config
+print(f"当前模型配置: {model_config}")
+print(model_config.req_conf.default_max_tokens)

src/llm_models/usage_statistic.py

@@ -2,10 +2,11 @@ from datetime import datetime
 from enum import Enum
 from typing import Tuple
 
-from pymongo.synchronous.database import Database
+from src.common.logger import get_logger
+from src.config.api_ada_configs import ModelInfo
+from src.common.database.database_model import LLMUsage
 
-from . import _logger as logger
-from .config.config import ModelInfo
+logger = get_logger("模型使用统计")
 
 
 class ReqType(Enum):
@@ -29,33 +30,21 @@ class UsageCallStatus(Enum):
 
 
 class ModelUsageStatistic:
-    db: Database | None = None
+    """
+    模型使用统计类 - 使用SQLite+Peewee
+    """
 
-    def __init__(self, db: Database):
-        if db is None:
-            logger.warning(
-                "Warning: No database provided, ModelUsageStatistic will not work."
-            )
-            return
-        if self._init_database(db):
-            # 成功初始化
-            self.db = db
-
-    @staticmethod
-    def _init_database(db: Database):
+    def __init__(self):
         """
-        初始化数据库相关索引
+        初始化统计类
+        由于使用Peewee ORM，不需要传入数据库实例
         """
+        # 确保表已经创建
         try:
-            db.llm_usage.create_index([("timestamp", 1)])
-            db.llm_usage.create_index([("model_name", 1)])
-            db.llm_usage.create_index([("task_name", 1)])
-            db.llm_usage.create_index([("request_type", 1)])
-            db.llm_usage.create_index([("status", 1)])
-            return True
+            from src.common.database.database import db
+            db.create_tables([LLMUsage], safe=True)
         except Exception as e:
-            logger.error(f"创建数据库索引失败: {e}")
-            return False
+            logger.error(f"创建LLMUsage表失败: {e}")
 
     @staticmethod
     def _calculate_cost(
@@ -67,6 +56,7 @@ class ModelUsageStatistic:
         Args:
             prompt_tokens: 输入token数量
             completion_tokens: 输出token数量
+            model_info: 模型信息
 
         Returns:
             float: 总成本（元）
@@ -81,46 +71,50 @@ class ModelUsageStatistic:
         model_name: str,
         task_name: str = "N/A",
         request_type: ReqType = ReqType.CHAT,
-    ) -> str | None:
+        user_id: str = "system",
+        endpoint: str = "/chat/completions",
+    ) -> int | None:
         """
         创建模型使用情况记录
-        :param model_name: 模型名
-        :param task_name: 任务名称
-        :param request_type: 请求类型，默认为Chat
-        :return:
-        """
-        if self.db is None:
-            return None  # 如果没有数据库连接，则不记录使用情况
 
+        Args:
+            model_name: 模型名
+            task_name: 任务名称
+            request_type: 请求类型，默认为Chat
+            user_id: 用户ID，默认为system
+            endpoint: API端点
+
+        Returns:
+            int | None: 返回记录ID，失败返回None
+        """
         try:
-            usage_data = {
-                "model_name": model_name,
-                "task_name": task_name,
-                "request_type": request_type.value,
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "total_tokens": 0,
-                "cost": 0.0,
-                "status": "processing",
-                "timestamp": datetime.now(),
-                "ext_msg": None,
-            }
-            result = self.db.llm_usage.insert_one(usage_data)
+            usage_record = LLMUsage.create(
+                model_name=model_name,
+                user_id=user_id,
+                request_type=request_type.value,
+                endpoint=endpoint,
+                prompt_tokens=0,
+                completion_tokens=0,
+                total_tokens=0,
+                cost=0.0,
+                status=UsageCallStatus.PROCESSING.value,
+                timestamp=datetime.now(),
+            )
 
             logger.trace(
                 f"创建了一条模型使用情况记录 - 模型: {model_name}, "
-                f"子任务: {task_name}, 类型: {request_type}"
-                f"记录ID: {str(result.inserted_id)}"
+                f"子任务: {task_name}, 类型: {request_type.value}, "
+                f"用户: {user_id}, 记录ID: {usage_record.id}"
             )
 
-            return str(result.inserted_id)
+            return usage_record.id
         except Exception as e:
             logger.error(f"创建模型使用情况记录失败: {str(e)}")
             return None
 
     def update_usage(
         self,
-        record_id: str | None,
+        record_id: int | None,
         model_info: ModelInfo,
         usage_data: Tuple[int, int, int] | None = None,
         stat: UsageCallStatus = UsageCallStatus.SUCCESS,
@@ -136,9 +130,6 @@ class ModelUsageStatistic:
             stat: 任务调用状态
             ext_msg: 额外信息
         """
-        if self.db is None:
-            return  # 如果没有数据库连接，则不记录使用情况
-
         if not record_id:
             logger.error("更新模型使用情况失败: record_id不能为空")
             return
@@ -153,28 +144,27 @@ class ModelUsageStatistic:
         total_tokens = usage_data[2] if usage_data else 0
 
         try:
-            self.db.llm_usage.update_one(
-                {"_id": record_id},
-                {
-                    "$set": {
-                        "status": stat.value,
-                        "ext_msg": ext_msg,
-                        "prompt_tokens": prompt_tokens,
-                        "completion_tokens": completion_tokens,
-                        "total_tokens": total_tokens,
-                        "cost": self._calculate_cost(
-                            prompt_tokens, completion_tokens, model_info
-                        )
-                        if usage_data
-                        else 0.0,
-                    }
-                },
-            )
+            # 使用Peewee更新记录
+            update_query = LLMUsage.update(
+                status=stat.value,
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=total_tokens,
+                cost=self._calculate_cost(
+                    prompt_tokens, completion_tokens, model_info
+                ) if usage_data else 0.0,
+            ).where(LLMUsage.id == record_id)
+            
+            updated_count = update_query.execute()
+            
+            if updated_count == 0:
+                logger.warning(f"记录ID {record_id} 不存在，无法更新")
+                return
 
-            logger.trace(
+            logger.debug(
                 f"Token使用情况 - 模型: {model_info.name}, "
-                f"记录ID： {record_id}, "
-                f"任务状态: {stat.value}, 额外信息： {ext_msg if ext_msg else 'N/A'}, "
+                f"记录ID: {record_id}, "
+                f"任务状态: {stat.value}, 额外信息: {ext_msg or 'N/A'}, "
                 f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
                 f"总计: {total_tokens}"
             )

src/llm_models/utils.py

@@ -3,9 +3,11 @@ import io
 
 from PIL import Image
 
-from . import _logger as logger
+from src.common.logger import get_logger
 from .payload_content.message import Message, MessageBuilder
 
+logger = get_logger("消息压缩工具")
+
 
 def compress_messages(
     messages: list[Message], img_target_size: int = 1 * 1024 * 1024

template/compare/model_config_template.toml

@@ -0,0 +1,77 @@
+[inner]
+version = "0.1.0"
+
+# 配置文件版本号迭代规则同bot_config.toml
+
+[request_conf] # 请求配置（此配置项数值均为默认值，如想修改，请取消对应条目的注释）
+#max_retry = 2 # 最大重试次数（单个模型API调用失败，最多重试的次数）
+#timeout = 10 # API调用的超时时长（超过这个时长，本次请求将被视为“请求超时”，单位：秒）
+#retry_interval = 10 # 重试间隔（如果API调用失败，重试的间隔时间，单位：秒）
+#default_temperature = 0.7 # 默认的温度（如果bot_config.toml中没有设置temperature参数，默认使用这个值）
+#default_max_tokens = 1024 # 默认的最大输出token数（如果bot_config.toml中没有设置max_tokens参数，默认使用这个值）
+
+
+[[api_providers]] # API服务提供商（可以配置多个）
+name = "DeepSeek"                       # API服务商名称（可随意命名，在models的api-provider中需使用这个命名）
+base_url = "https://api.deepseek.cn"    # API服务商的BaseURL
+key = "******"                          # API Key （可选，默认为None）
+client_type = "openai"                  # 请求客户端（可选，默认值为"openai"，使用gimini等Google系模型时请配置为"google"）
+
+#[[api_providers]] # 特殊：Google的Gimini使用特殊API，与OpenAI格式不兼容，需要配置client为"google"
+#name = "Google"
+#base_url = "https://api.google.com"
+#key = "******"
+#client_type = "google"
+#
+#[[api_providers]]
+#name = "SiliconFlow"
+#base_url = "https://api.siliconflow.cn"
+#key = "******"
+#
+#[[api_providers]]
+#name = "LocalHost"
+#base_url = "https://localhost:8888"
+#key = "lm-studio"
+
+
+[[models]] # 模型（可以配置多个）
+# 模型标识符（API服务商提供的模型标识符）
+model_identifier = "deepseek-chat"
+# 模型名称（可随意命名，在bot_config.toml中需使用这个命名）
+#（可选，若无该字段，则将自动使用model_identifier填充）
+name = "deepseek-v3"
+# API服务商名称（对应在api_providers中配置的服务商名称）
+api_provider = "DeepSeek"
+# 输入价格（用于API调用统计，单位：元/兆token）（可选，若无该字段，默认值为0）
+price_in = 2.0
+# 输出价格（用于API调用统计，单位：元/兆token）（可选，若无该字段，默认值为0）
+price_out = 8.0
+# 强制流式输出模式（若模型不支持非流式输出，请取消该注释，启用强制流式输出）
+#（可选，若无该字段，默认值为false）
+#force_stream_mode = true
+
+#[[models]]
+#model_identifier = "deepseek-reasoner"
+#name = "deepseek-r1"
+#api_provider = "DeepSeek"
+#model_flags = ["text", "tool_calling", "reasoning"]
+#price_in = 4.0
+#price_out = 16.0
+#
+#[[models]]
+#model_identifier = "BAAI/bge-m3"
+#name = "siliconflow-bge-m3"
+#api_provider = "SiliconFlow"
+#model_flags = ["text", "embedding"]
+#price_in = 0
+#price_out = 0
+
+
+[task_model_usage]
+#llm_reasoning = {model="deepseek-r1", temperature=0.8, max_tokens=1024, max_retry=0}
+#llm_normal = {model="deepseek-r1", max_tokens=1024, max_retry=0}
+#embedding = "siliconflow-bge-m3"
+#schedule = [
+#    "deepseek-v3",
+#    "deepseek-r1",
+#]