Merge pull request #1 from MaiM-with-u/dev

Dev

.gitignore

@@ -41,6 +41,7 @@ config/bot_config.toml.bak
 config/lpmm_config.toml
 config/lpmm_config.toml.bak
 template/compare/bot_config_template.toml
+template/compare/model_config_template.toml
 （测试版）麦麦生成人格.bat
 （临时版）麦麦开始学习.bat
 src/plugins/utils/statistic.py
@@ -321,4 +322,5 @@ run_pet.bat
 
 config.toml
 
-interested_rates.txt
+interested_rates.txt
+MaiBot.code-workspace

bot.py

@@ -74,36 +74,6 @@ def easter_egg():
     print(rainbow_text)
 
 
-def scan_provider(env_config: dict):
-    provider = {}
-
-    # 利用未初始化 env 时获取的 env_mask 来对新的环境变量集去重
-    # 避免 GPG_KEY 这样的变量干扰检查
-    env_config = dict(filter(lambda item: item[0] not in env_mask, env_config.items()))
-
-    # 遍历 env_config 的所有键
-    for key in env_config:
-        # 检查键是否符合 {provider}_BASE_URL 或 {provider}_KEY 的格式
-        if key.endswith("_BASE_URL") or key.endswith("_KEY"):
-            # 提取 provider 名称
-            provider_name = key.split("_", 1)[0]  # 从左分割一次，取第一部分
-
-            # 初始化 provider 的字典（如果尚未初始化）
-            if provider_name not in provider:
-                provider[provider_name] = {"url": None, "key": None}
-
-            # 根据键的类型填充 url 或 key
-            if key.endswith("_BASE_URL"):
-                provider[provider_name]["url"] = env_config[key]
-            elif key.endswith("_KEY"):
-                provider[provider_name]["key"] = env_config[key]
-
-    # 检查每个 provider 是否同时存在 url 和 key
-    for provider_name, config in provider.items():
-        if config["url"] is None or config["key"] is None:
-            logger.error(f"provider 内容：{config}\nenv_config 内容：{env_config}")
-            raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量")
-
 
 async def graceful_shutdown():
     try:
@@ -229,9 +199,6 @@ def raw_main():
 
     easter_egg()
 
-    env_config = {key: os.getenv(key) for key in os.environ}
-    scan_provider(env_config)
-
     # 返回MainSystem实例
     return MainSystem()
 

changelogs/changelog.md

@@ -1,5 +1,17 @@
 # Changelog
 
+## [0.10.0] - 2025-7-1
+### 主要功能更改
+- 工具系统重构，现在合并到了插件系统中
+- 彻底重构了整个LLM Request了，现在支持模型轮询和更多灵活的参数
+  - 同时重构了整个模型配置系统，升级需要重新配置llm配置文件
+- 随着LLM Request的重构，插件系统彻底重构完成。插件系统进入稳定状态，仅增加新的API
+  - 具体相比于之前的更改可以查看[changes.md](./changes.md)
+
+### 细节优化
+- 修复了lint爆炸的问题，代码更加规范了
+- 修改了log的颜色，更加护眼
+
 ## [0.9.1] - 2025-7-26
 
 ### 主要修复和优化

changelogs/changes.md

@@ -25,6 +25,7 @@
   - 这意味着你终于可以动态控制是否继续后续消息的处理了。
 8. 移除了dependency_manager，但是依然保留了`python_dependencies`属性，等待后续重构。
   - 一并移除了文档有关manager的内容。
+9. 增加了工具的有关api
 
 # 插件系统修改
 1. 现在所有的匹配模式不再是关键字了，而是枚举类。**（可能有遗漏）**
@@ -57,30 +58,12 @@
 15. 实现了组件的局部禁用，也就是针对某一个聊天禁用的功能。
   - 通过`disable_specific_chat_action`，`enable_specific_chat_action`，`disable_specific_chat_command`，`enable_specific_chat_command`，`disable_specific_chat_event_handler`，`enable_specific_chat_event_handler`来操作
   - 同样不保存到配置文件~
+16. 把`BaseTool`一并合并进入了插件系统
 
 # 官方插件修改
 1. `HelloWorld`插件现在有一个样例的`EventHandler`。
-2. 内置插件增加了一个通过`Command`来管理插件的功能。具体是使用`/pm`命令唤起。
+2. 内置插件增加了一个通过`Command`来管理插件的功能。具体是使用`/pm`命令唤起。（需要自行启用）
-
+3. `HelloWorld`插件现在有一个样例的`CompareNumbersTool`。
-### TODO
-把这个看起来就很别扭的config获取方式改一下
-
-
-# 吐槽
-```python
-plugin_path = Path(plugin_file)
-if plugin_path.parent.name != "plugins":
-    # 插件包格式：parent_dir.plugin
-    module_name = f"plugins.{plugin_path.parent.name}.plugin"
-else:
-    # 单文件格式：plugins.filename
-    module_name = f"plugins.{plugin_path.stem}"
-```
-```python
-plugin_path = Path(plugin_file)
-module_name = ".".join(plugin_path.parent.parts)
-```
-这两个区别很大的。
 
 ### 执笔BGM
 塞壬唱片！

docs/model_configuration_guide.md

@@ -0,0 +1,331 @@
+# 模型配置指南
+
+本文档将指导您如何配置 `model_config.toml` 文件，该文件用于配置 MaiBot 的各种AI模型和API服务提供商。
+
+## 配置文件结构
+
+配置文件主要包含以下几个部分：
+- 版本信息
+- API服务提供商配置
+- 模型配置
+- 模型任务配置
+
+## 1. 版本信息
+
+```toml
+[inner]
+version = "1.1.1"
+```
+
+用于标识配置文件的版本，遵循语义化版本规则。
+
+## 2. API服务提供商配置
+
+### 2.1 基本配置
+
+使用 `[[api_providers]]` 数组配置多个API服务提供商：
+
+```toml
+[[api_providers]]
+name = "DeepSeek"                       # 服务商名称（自定义）
+base_url = "https://api.deepseek.cn/v1" # API服务的基础URL
+api_key = "your-api-key-here"           # API密钥
+client_type = "openai"                  # 客户端类型
+max_retry = 2                           # 最大重试次数
+timeout = 30                            # 超时时间（秒）
+retry_interval = 10                     # 重试间隔（秒）
+```
+
+### 2.2 配置参数说明
+
+| 参数 | 必填 | 说明 | 默认值 |
+|------|------|------|--------|
+| `name` | ✅ | 服务商名称，需要在模型配置中引用 | - |
+| `base_url` | ✅ | API服务的基础URL | - |
+| `api_key` | ✅ | API密钥，请替换为实际密钥 | - |
+| `client_type` | ❌ | 客户端类型：`openai`（OpenAI格式）或 `gemini`（Gemini格式，现在支持不良好） | `openai` |
+| `max_retry` | ❌ | API调用失败时的最大重试次数 | 2 |
+| `timeout` | ❌ | API请求超时时间（秒） | 30 |
+| `retry_interval` | ❌ | 重试间隔时间（秒） | 10 |
+
+### 2.3 支持的服务商示例
+
+#### DeepSeek
+```toml
+[[api_providers]]
+name = "DeepSeek"
+base_url = "https://api.deepseek.cn/v1"
+api_key = "your-deepseek-api-key"
+client_type = "openai"
+```
+
+#### SiliconFlow
+```toml
+[[api_providers]]
+name = "SiliconFlow"
+base_url = "https://api.siliconflow.cn/v1"
+api_key = "your-siliconflow-api-key"
+client_type = "openai"
+```
+
+#### Google Gemini
+```toml
+[[api_providers]]
+name = "Google"
+base_url = "https://api.google.com/v1"
+api_key = "your-google-api-key"
+client_type = "gemini"  # 注意：Gemini需要使用特殊客户端
+```
+
+## 3. 模型配置
+
+### 3.1 基本模型配置
+
+使用 `[[models]]` 数组配置多个模型：
+
+```toml
+[[models]]
+model_identifier = "deepseek-chat"  # 模型在API服务商中的标识符
+name = "deepseek-v3"               # 自定义模型名称
+api_provider = "DeepSeek"          # 引用的API服务商名称
+price_in = 2.0                     # 输入价格（元/M token）
+price_out = 8.0                    # 输出价格（元/M token）
+```
+
+### 3.2 高级模型配置
+
+#### 强制流式输出
+对于不支持非流式输出的模型：
+```toml
+[[models]]
+model_identifier = "some-model"
+name = "custom-name"
+api_provider = "Provider"
+force_stream_mode = true  # 启用强制流式输出
+```
+
+#### 额外参数配置`extra_params`
+```toml
+[[models]]
+model_identifier = "Qwen/Qwen3-8B"
+name = "qwen3-8b"
+api_provider = "SiliconFlow"
+[models.extra_params]
+enable_thinking = false # 禁用思考
+```
+这里的 `extra_params` 可以包含任何API服务商支持的额外参数配置，**配置时应参考相应的API文档**。
+
+比如上面就是参考SiliconFlow的文档配置配置的`Qwen3`禁用思考参数。
+
+![SiliconFlow文档截图](image-1.png)
+
+以豆包文档为另一个例子
+
+![豆包文档截图](image.png)
+
+得到豆包`"doubao-seed-1-6-250615"`的禁用思考配置方法为
+```toml
+[[models]]
+# 你的模型
+[models.extra_params]
+thinking = {type = "disabled"} # 禁用思考
+```
+请注意，`extra_params` 的配置应该构成一个合法的TOML字典结构，具体内容取决于API服务商的要求。
+
+### 3.3 配置参数说明
+
+| 参数 | 必填 | 说明 |
+|------|------|------|
+| `model_identifier` | ✅ | API服务商提供的模型标识符 |
+| `name` | ✅ | 自定义模型名称，用于在任务配置中引用 |
+| `api_provider` | ✅ | 对应的API服务商名称 |
+| `price_in` | ❌ | 输入价格（元/M token），用于成本统计 |
+| `price_out` | ❌ | 输出价格（元/M token），用于成本统计 |
+| `force_stream_mode` | ❌ | 是否强制使用流式输出 |
+| `extra_params` | ❌ | 额外的模型参数配置 |
+
+## 4. 模型任务配置
+
+### utils - 工具模型
+用于表情包模块、取名模块、关系模块等核心功能：
+```toml
+[model_task_config.utils]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.2
+max_tokens = 800
+```
+
+### utils_small - 小型工具模型
+用于高频率调用的场景，建议使用速度快的小模型：
+```toml
+[model_task_config.utils_small]
+model_list = ["qwen3-8b"]
+temperature = 0.7
+max_tokens = 800
+```
+
+### replyer_1 - 主要回复模型
+首要回复模型，也用于表达器和表达方式学习：
+```toml
+[model_task_config.replyer_1]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.2
+max_tokens = 800
+```
+
+### replyer_2 - 次要回复模型
+```toml
+[model_task_config.replyer_2]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.7
+max_tokens = 800
+```
+
+### planner - 决策模型
+负责决定MaiBot该做什么：
+```toml
+[model_task_config.planner]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.3
+max_tokens = 800
+```
+
+### emotion - 情绪模型
+负责MaiBot的情绪变化：
+```toml
+[model_task_config.emotion]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.3
+max_tokens = 800
+```
+
+### memory - 记忆模型
+```toml
+[model_task_config.memory]
+model_list = ["qwen3-30b"]
+temperature = 0.7
+max_tokens = 800
+```
+
+### vlm - 视觉语言模型
+用于图像识别：
+```toml
+[model_task_config.vlm]
+model_list = ["qwen2.5-vl-72b"]
+max_tokens = 800
+```
+
+### voice - 语音识别模型
+```toml
+[model_task_config.voice]
+model_list = ["sensevoice-small"]
+```
+
+### embedding - 嵌入模型
+```toml
+[model_task_config.embedding]
+model_list = ["bge-m3"]
+```
+
+### tool_use - 工具调用模型
+需要使用支持工具调用的模型：
+```toml
+[model_task_config.tool_use]
+model_list = ["qwen3-14b"]
+temperature = 0.7
+max_tokens = 800
+```
+
+### lpmm_entity_extract - 实体提取模型
+```toml
+[model_task_config.lpmm_entity_extract]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.2
+max_tokens = 800
+```
+
+### lpmm_rdf_build - RDF构建模型
+```toml
+[model_task_config.lpmm_rdf_build]
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.2
+max_tokens = 800
+```
+
+### lpmm_qa - 问答模型
+```toml
+[model_task_config.lpmm_qa]
+model_list = ["deepseek-r1-distill-qwen-32b"]
+temperature = 0.7
+max_tokens = 800
+```
+
+## 5. 配置建议
+
+### 5.1 Temperature 参数选择
+
+| 任务类型 | 推荐温度 | 说明 |
+|----------|----------|------|
+| 精确任务（工具调用、实体提取） | 0.1-0.3 | 需要准确性和一致性 |
+| 创意任务（对话、记忆） | 0.5-0.8 | 需要多样性和创造性 |
+| 平衡任务（决策、情绪） | 0.3-0.5 | 平衡准确性和灵活性 |
+
+### 5.2 模型选择建议
+
+| 任务类型 | 推荐模型类型 | 示例 |
+|----------|--------------|------|
+| 高精度任务 | 大模型 | DeepSeek-V3, GPT-4 |
+| 高频率任务 | 小模型 | Qwen3-8B |
+| 多模态任务 | 专用模型 | Qwen2.5-VL, SenseVoice |
+| 工具调用 | 支持Function Call的模型 | Qwen3-14B |
+
+### 5.3 成本优化
+
+1. **分层使用**：核心功能使用高质量模型，辅助功能使用经济模型
+2. **合理配置max_tokens**：根据实际需求设置，避免浪费
+3. **选择免费模型**：对于测试环境，优先使用price为0的模型
+
+## 6. 配置验证
+
+### 6.1 必要检查项
+
+1. ✅ API密钥是否正确配置
+2. ✅ 模型标识符是否与API服务商提供的一致
+3. ✅ 任务配置中引用的模型名称是否在models中定义
+4. ✅ 多模态任务是否配置了对应的专用模型
+
+### 6.2 测试配置
+
+建议在正式使用前：
+1. 使用少量测试数据验证配置
+2. 检查API调用是否正常
+3. 确认成本统计功能正常工作
+
+## 7. 故障排除
+
+### 7.1 常见问题
+
+**问题1**: API调用失败
+- 检查API密钥是否正确
+- 确认base_url是否可访问
+- 检查模型标识符是否正确
+
+**问题2**: 模型未找到
+- 确认模型名称在任务配置和模型定义中一致
+- 检查api_provider名称是否匹配
+
+**问题3**: 响应异常
+- 检查温度参数是否合理（0-1之间）
+- 确认max_tokens设置是否合适
+- 验证模型是否支持所需功能
+
+### 7.2 日志查看
+
+查看 `logs/` 目录下的日志文件，寻找相关错误信息。
+
+## 8. 更新和维护
+
+1. **定期更新**: 关注API服务商的模型更新，及时调整配置
+2. **性能监控**: 监控模型调用的成本和性能
+3. **备份配置**: 在修改前备份当前配置文件
+

docs/plugins/api/component-manage-api.md

@@ -0,0 +1,194 @@
+# 组件管理API
+
+组件管理API模块提供了对插件组件的查询和管理功能，使得插件能够获取和使用组件相关的信息。
+
+## 导入方式
+```python
+from src.plugin_system.apis import component_manage_api
+# 或者
+from src.plugin_system import component_manage_api
+```
+
+## 功能概述
+
+组件管理API主要提供以下功能：
+- **插件信息查询** - 获取所有插件或指定插件的信息。
+- **组件查询** - 按名称或类型查询组件信息。
+- **组件管理** - 启用或禁用组件，支持全局和局部操作。
+
+## 主要功能
+
+### 1. 获取所有插件信息
+```python
+def get_all_plugin_info() -> Dict[str, PluginInfo]:
+```
+获取所有插件的信息。
+
+**Returns:**
+- `Dict[str, PluginInfo]` - 包含所有插件信息的字典，键为插件名称，值为 `PluginInfo` 对象。
+
+### 2. 获取指定插件信息
+```python
+def get_plugin_info(plugin_name: str) -> Optional[PluginInfo]:
+```
+获取指定插件的信息。
+
+**Args:**
+- `plugin_name` (str): 插件名称。
+
+**Returns:**
+- `Optional[PluginInfo]`: 插件信息对象，如果插件不存在则返回 `None`。
+
+### 3. 获取指定组件信息
+```python
+def get_component_info(component_name: str, component_type: ComponentType) -> Optional[Union[CommandInfo, ActionInfo, EventHandlerInfo]]:
+```
+获取指定组件的信息。
+
+**Args:**
+- `component_name` (str): 组件名称。
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `Optional[Union[CommandInfo, ActionInfo, EventHandlerInfo]]`: 组件信息对象，如果组件不存在则返回 `None`。
+
+### 4. 获取指定类型的所有组件信息
+```python
+def get_components_info_by_type(component_type: ComponentType) -> Dict[str, Union[CommandInfo, ActionInfo, EventHandlerInfo]]:
+```
+获取指定类型的所有组件信息。
+
+**Args:**
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `Dict[str, Union[CommandInfo, ActionInfo, EventHandlerInfo]]`: 包含指定类型组件信息的字典，键为组件名称，值为对应的组件信息对象。
+
+### 5. 获取指定类型的所有启用的组件信息
+```python
+def get_enabled_components_info_by_type(component_type: ComponentType) -> Dict[str, Union[CommandInfo, ActionInfo, EventHandlerInfo]]:
+```
+获取指定类型的所有启用的组件信息。
+
+**Args:**
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `Dict[str, Union[CommandInfo, ActionInfo, EventHandlerInfo]]`: 包含指定类型启用组件信息的字典，键为组件名称，值为对应的组件信息对象。
+
+### 6. 获取指定 Action 的注册信息
+```python
+def get_registered_action_info(action_name: str) -> Optional[ActionInfo]:
+```
+获取指定 Action 的注册信息。
+
+**Args:**
+- `action_name` (str): Action 名称。
+
+**Returns:**
+- `Optional[ActionInfo]` - Action 信息对象，如果 Action 不存在则返回 `None`。
+
+### 7. 获取指定 Command 的注册信息
+```python
+def get_registered_command_info(command_name: str) -> Optional[CommandInfo]:
+```
+获取指定 Command 的注册信息。
+
+**Args:**
+- `command_name` (str): Command 名称。
+
+**Returns:**
+- `Optional[CommandInfo]` - Command 信息对象，如果 Command 不存在则返回 `None`。
+
+### 8. 获取指定 Tool 的注册信息
+```python
+def get_registered_tool_info(tool_name: str) -> Optional[ToolInfo]:
+```
+获取指定 Tool 的注册信息。
+
+**Args:**
+- `tool_name` (str): Tool 名称。
+
+**Returns:**
+- `Optional[ToolInfo]` - Tool 信息对象，如果 Tool 不存在则返回 `None`。
+
+### 9. 获取指定 EventHandler 的注册信息
+```python
+def get_registered_event_handler_info(event_handler_name: str) -> Optional[EventHandlerInfo]:
+```
+获取指定 EventHandler 的注册信息。
+
+**Args:**
+- `event_handler_name` (str): EventHandler 名称。
+
+**Returns:**
+- `Optional[EventHandlerInfo]` - EventHandler 信息对象，如果 EventHandler 不存在则返回 `None`。
+
+### 10. 全局启用指定组件
+```python
+def globally_enable_component(component_name: str, component_type: ComponentType) -> bool:
+```
+全局启用指定组件。
+
+**Args:**
+- `component_name` (str): 组件名称。
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `bool` - 启用成功返回 `True`，否则返回 `False`。
+
+### 11. 全局禁用指定组件
+```python
+async def globally_disable_component(component_name: str, component_type: ComponentType) -> bool:
+```
+全局禁用指定组件。
+
+**此函数是异步的，确保在异步环境中调用。**
+
+**Args:**
+- `component_name` (str): 组件名称。
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `bool` - 禁用成功返回 `True`，否则返回 `False`。
+
+### 12. 局部启用指定组件
+```python
+def locally_enable_component(component_name: str, component_type: ComponentType, stream_id: str) -> bool:
+```
+局部启用指定组件。
+
+**Args:**
+- `component_name` (str): 组件名称。
+- `component_type` (ComponentType): 组件类型。
+- `stream_id` (str): 消息流 ID。
+
+**Returns:**
+- `bool` - 启用成功返回 `True`，否则返回 `False`。
+
+### 13. 局部禁用指定组件
+```python
+def locally_disable_component(component_name: str, component_type: ComponentType, stream_id: str) -> bool:
+```
+局部禁用指定组件。
+
+**Args:**
+- `component_name` (str): 组件名称。
+- `component_type` (ComponentType): 组件类型。
+- `stream_id` (str): 消息流 ID。
+
+**Returns:**
+- `bool` - 禁用成功返回 `True`，否则返回 `False`。
+
+### 14. 获取指定消息流中禁用的组件列表
+```python
+def get_locally_disabled_components(stream_id: str, component_type: ComponentType) -> list[str]:
+```
+获取指定消息流中禁用的组件列表。
+
+**Args:**
+- `stream_id` (str): 消息流 ID。
+- `component_type` (ComponentType): 组件类型。
+
+**Returns:**
+- `list[str]` - 禁用的组件名称列表。

docs/plugins/api/config-api.md

@@ -1,6 +1,6 @@
 # 配置API
 
-配置API模块提供了配置读取和用户信息获取等功能，让插件能够安全地访问全局配置和用户信息。
+配置API模块提供了配置读取功能，让插件能够安全地访问全局配置和插件配置。
 
 ## 导入方式
 

docs/plugins/api/database-api.md

@@ -6,72 +6,51 @@
 
 ```python
 from src.plugin_system.apis import database_api
+# 或者
+from src.plugin_system import database_api
 ```
 
 ## 主要功能
 
-### 1. 通用数据库查询
+### 1. 通用数据库操作
-
-#### `db_query(model_class, query_type="get", filters=None, data=None, limit=None, order_by=None, single_result=False)`
-执行数据库查询操作的通用接口
-
-**参数：**
-- `model_class`：Peewee模型类，如ActionRecords、Messages等
-- `query_type`：查询类型，可选值: "get", "create", "update", "delete", "count"
-- `filters`：过滤条件字典，键为字段名，值为要匹配的值
-- `data`：用于创建或更新的数据字典
-- `limit`：限制结果数量
-- `order_by`：排序字段列表，使用字段名，前缀'-'表示降序
-- `single_result`：是否只返回单个结果
-
-**返回：**
-根据查询类型返回不同的结果：
-- "get"：返回查询结果列表或单个结果
-- "create"：返回创建的记录
-- "update"：返回受影响的行数
-- "delete"：返回受影响的行数
-- "count"：返回记录数量
-
-### 2. 便捷查询函数
-
-#### `db_save(model_class, data, key_field=None, key_value=None)`
-保存数据到数据库（创建或更新）
-
-**参数：**
-- `model_class`：Peewee模型类
-- `data`：要保存的数据字典
-- `key_field`：用于查找现有记录的字段名
-- `key_value`：用于查找现有记录的字段值
-
-**返回：**
-- `Dict[str, Any]`：保存后的记录数据，失败时返回None
-
-#### `db_get(model_class, filters=None, order_by=None, limit=None)`
-简化的查询函数
-
-**参数：**
-- `model_class`：Peewee模型类
-- `filters`：过滤条件字典
-- `order_by`：排序字段
-- `limit`：限制结果数量
-
-**返回：**
-- `Union[List[Dict], Dict, None]`：查询结果
-
-### 3. 专用函数
-
-#### `store_action_info(...)`
-存储动作信息的专用函数
-
-## 使用示例
-
-### 1. 基本查询操作
 
 ```python
-from src.plugin_system.apis import database_api
+async def db_query(
-from src.common.database.database_model import Messages, ActionRecords
+    model_class: Type[Model],
+    data: Optional[Dict[str, Any]] = None,
+    query_type: Optional[str] = "get",
+    filters: Optional[Dict[str, Any]] = None,
+    limit: Optional[int] = None,
+    order_by: Optional[List[str]] = None,
+    single_result: Optional[bool] = False,
+) -> Union[List[Dict[str, Any]], Dict[str, Any], None]:
+```
+执行数据库查询操作的通用接口。
 
-# 查询最近10条消息
+**Args:**
+- `model_class`: Peewee模型类。
+    - Peewee模型类可以在`src.common.database.database_model`模块中找到，如`ActionRecords`、`Messages`等。
+- `data`: 用于创建或更新的数据
+- `query_type`: 查询类型
+    - 可选值: `get`, `create`, `update`, `delete`, `count`。
+- `filters`: 过滤条件字典，键为字段名，值为要匹配的值。
+- `limit`: 限制结果数量。
+- `order_by`: 排序字段列表，使用字段名，前缀'-'表示降序。
+    - 排序字段，前缀`-`表示降序，例如`-time`表示按时间字段（即`time`字段）降序
+- `single_result`: 是否只返回单个结果。
+
+**Returns:**
+- 根据查询类型返回不同的结果：
+    - `get`: 返回查询结果列表或单个结果。（如果 `single_result=True`）
+    - `create`: 返回创建的记录。
+    - `update`: 返回受影响的行数。
+    - `delete`: 返回受影响的行数。
+    - `count`: 返回记录数量。
+
+#### 示例
+
+1. 查询最近10条消息
+```python
 messages = await database_api.db_query(
     Messages,
     query_type="get",
@@ -79,180 +58,159 @@ messages = await database_api.db_query(
     limit=10,
     order_by=["-time"]
 )
-
-# 查询单条记录
-message = await database_api.db_query(
-    Messages,
-    query_type="get",
-    filters={"message_id": "msg_123"},
-    single_result=True
-)
 ```
-
+2. 创建一条记录
-### 2. 创建记录
-
 ```python
-# 创建新的动作记录
 new_record = await database_api.db_query(
     ActionRecords,
+    data={"action_id": "123", "time": time.time(), "action_name": "TestAction"},
     query_type="create",
-    data={
-        "action_id": "action_123",
-        "time": time.time(),
-        "action_name": "TestAction",
-        "action_done": True
-    }
 )
-
-print(f"创建了记录: {new_record['id']}")
 ```
-
+3. 更新记录
-### 3. 更新记录
-
 ```python
-# 更新动作状态
 updated_count = await database_api.db_query(
     ActionRecords,
+    data={"action_done": True},
     query_type="update",
-    filters={"action_id": "action_123"},
+    filters={"action_id": "123"},
-    data={"action_done": True, "completion_time": time.time()}
 )
-
-print(f"更新了 {updated_count} 条记录")
 ```
-
+4. 删除记录
-### 4. 删除记录
-
 ```python
-# 删除过期记录
 deleted_count = await database_api.db_query(
     ActionRecords,
     query_type="delete",
-    filters={"time__lt": time.time() - 86400}  # 删除24小时前的记录
+    filters={"action_id": "123"}
 )
-
-print(f"删除了 {deleted_count} 条过期记录")
 ```
-
+5. 计数
-### 5. 统计查询
-
 ```python
-# 统计消息数量
+count = await database_api.db_query(
-message_count = await database_api.db_query(
     Messages,
     query_type="count",
     filters={"chat_id": chat_stream.stream_id}
 )
-
-print(f"该聊天有 {message_count} 条消息")
 ```
 
-### 6. 使用便捷函数
+### 2. 数据库保存
-
+```python
+async def db_save(
+    model_class: Type[Model], data: Dict[str, Any], key_field: Optional[str] = None, key_value: Optional[Any] = None
+) -> Optional[Dict[str, Any]]:
+```
+保存数据到数据库（创建或更新）
+
+如果提供了key_field和key_value，会先尝试查找匹配的记录进行更新；
+
+如果没有找到匹配记录，或未提供key_field和key_value，则创建新记录。
+
+**Args:**
+- `model_class`: Peewee模型类。
+- `data`: 要保存的数据字典。
+- `key_field`: 用于查找现有记录的字段名，例如"action_id"。
+- `key_value`: 用于查找现有记录的字段值。
+
+**Returns:**
+- `Optional[Dict[str, Any]]`: 保存后的记录数据，失败时返回None。
+
+#### 示例
+创建或更新一条记录
 ```python
-# 使用db_save进行创建或更新
 record = await database_api.db_save(
     ActionRecords,
     {
-        "action_id": "action_123",
+        "action_id": "123",
         "time": time.time(),
         "action_name": "TestAction",
         "action_done": True
     },
     key_field="action_id",
-    key_value="action_123"
+    key_value="123"
 )
+```
 
-# 使用db_get进行简单查询
+### 3. 数据库获取
-recent_messages = await database_api.db_get(
+```python
+async def db_get(
+    model_class: Type[Model],
+    filters: Optional[Dict[str, Any]] = None,
+    limit: Optional[int] = None,
+    order_by: Optional[str] = None,
+    single_result: Optional[bool] = False,
+) -> Union[List[Dict[str, Any]], Dict[str, Any], None]:
+```
+
+从数据库获取记录
+
+这是db_query方法的简化版本，专注于数据检索操作。
+
+**Args:**
+- `model_class`: Peewee模型类。
+- `filters`: 过滤条件字典，键为字段名，值为要匹配的值。
+- `limit`: 限制结果数量。
+- `order_by`: 排序字段，使用字段名，前缀'-'表示降序。
+- `single_result`: 是否只返回单个结果，如果为True，则返回单个记录字典或None；否则返回记录字典列表或空列表
+
+**Returns:**
+- `Union[List[Dict], Dict, None]`: 查询结果列表或单个结果（如果`single_result=True`），失败时返回None。
+
+#### 示例
+1. 获取单个记录
+```python
+record = await database_api.db_get(
+    ActionRecords,
+    filters={"action_id": "123"},
+    limit=1
+)
+```
+2. 获取最近10条记录
+```python
+records = await database_api.db_get(
     Messages,
     filters={"chat_id": chat_stream.stream_id},
+    limit=10,
     order_by="-time",
-    limit=5
 )
 ```
 
-## 高级用法
+### 4. 动作信息存储
-
-### 复杂查询示例
-
 ```python
-# 查询特定用户在特定时间段的消息
+async def store_action_info(
-user_messages = await database_api.db_query(
+    chat_stream=None,
-    Messages,
+    action_build_into_prompt: bool = False,
-    query_type="get",
+    action_prompt_display: str = "",
-    filters={
+    action_done: bool = True,
-        "user_id": "123456",
+    thinking_id: str = "",
-        "time__gte": start_time,  # 大于等于开始时间
+    action_data: Optional[dict] = None,
-        "time__lt": end_time      # 小于结束时间
+    action_name: str = "",
-    },
+) -> Optional[Dict[str, Any]]:
-    order_by=["-time"],
+```
-    limit=50
+存储动作信息到数据库，是一种针对 Action 的 `db_save()` 的封装函数。
+
+将Action执行的相关信息保存到ActionRecords表中，用于后续的记忆和上下文构建。
+
+**Args:**
+- `chat_stream`: 聊天流对象，包含聊天ID等信息。
+- `action_build_into_prompt`: 是否将动作信息构建到提示中。
+- `action_prompt_display`: 动作提示的显示文本。
+- `action_done`: 动作是否完成。
+- `thinking_id`: 思考过程的ID。
+- `action_data`: 动作的数据字典。
+- `action_name`: 动作的名称。
+
+**Returns:**
+- `Optional[Dict[str, Any]]`: 存储后的记录数据，失败时返回None。
+
+#### 示例
+```python
+record = await database_api.store_action_info(
+    chat_stream=chat_stream,
+    action_build_into_prompt=True,
+    action_prompt_display="执行了回复动作",
+    action_done=True,
+    thinking_id="thinking_123",
+    action_data={"content": "Hello"},
+    action_name="reply_action"
 )
-
+```
-# 批量处理
-for message in user_messages:
-    print(f"消息内容: {message['plain_text']}")
-    print(f"发送时间: {message['time']}")
-```
-
-### 插件中的数据持久化
-
-```python
-from src.plugin_system.base import BasePlugin
-from src.plugin_system.apis import database_api
-
-class DataPlugin(BasePlugin):
-    async def handle_action(self, action_data, chat_stream):
-        # 保存插件数据
-        plugin_data = {
-            "plugin_name": self.plugin_name,
-            "chat_id": chat_stream.stream_id,
-            "data": json.dumps(action_data),
-            "created_time": time.time()
-        }
-        
-        # 使用自定义表模型（需要先定义）
-        record = await database_api.db_save(
-            PluginData,  # 假设的插件数据模型
-            plugin_data,
-            key_field="plugin_name",
-            key_value=self.plugin_name
-        )
-        
-        return {"success": True, "record_id": record["id"]}
-```
-
-## 数据模型
-
-### 常用模型类
-系统提供了以下常用的数据模型：
-
-- `Messages`：消息记录
-- `ActionRecords`：动作记录
-- `UserInfo`：用户信息
-- `GroupInfo`：群组信息
-
-### 字段说明
-
-#### Messages模型主要字段
-- `message_id`：消息ID
-- `chat_id`：聊天ID
-- `user_id`：用户ID
-- `plain_text`：纯文本内容
-- `time`：时间戳
-
-#### ActionRecords模型主要字段
-- `action_id`：动作ID
-- `action_name`：动作名称
-- `action_done`：是否完成
-- `time`：创建时间
-
-## 注意事项
-
-1. **异步操作**：所有数据库API都是异步的，必须使用`await`
-2. **错误处理**：函数内置错误处理，失败时返回None或空列表
-3. **数据类型**：返回的都是字典格式的数据，不是模型对象
-4. **性能考虑**：使用`limit`参数避免查询大量数据
-5. **过滤条件**：支持简单的等值过滤，复杂查询需要使用原生Peewee语法
-6. **事务**：如需事务支持，建议直接使用Peewee的事务功能 

docs/plugins/api/emoji-api.md

@@ -6,11 +6,13 @@
 
 ```python
 from src.plugin_system.apis import emoji_api
+# 或者
+from src.plugin_system import emoji_api
 ```
 
-## 🆕 **二步走识别优化**
+## 二步走识别优化
 
-从最新版本开始，表情包识别系统采用了**二步走识别 + 智能缓存**的优化方案：
+从新版本开始，表情包识别系统采用了**二步走识别 + 智能缓存**的优化方案：
 
 ### **收到表情包时的识别流程**
 1. **第一步**：VLM视觉分析 - 生成详细描述
@@ -30,217 +32,84 @@ from src.plugin_system.apis import emoji_api
 ## 主要功能
 
 ### 1. 表情包获取
-
+```python
-#### `get_by_description(description: str) -> Optional[Tuple[str, str, str]]`
+async def get_by_description(description: str) -> Optional[Tuple[str, str, str]]:
+```
 根据场景描述选择表情包
 
-**参数：**
+**Args：**
-- `description`：场景描述文本，例如"开心的大笑"、"轻微的讽刺"、"表示无奈和沮丧"等
+- `description`：表情包的描述文本，例如"开心"、"难过"、"愤怒"等
 
-**返回：**
+**Returns：**
-- `Optional[Tuple[str, str, str]]`：(base64编码, 表情包描述, 匹配的场景) 或 None
+- `Optional[Tuple[str, str, str]]`：一个元组: (表情包的base64编码, 描述, 情感标签)，如果未找到匹配的表情包则返回None
 
-**示例：**
+#### 示例
 ```python
-emoji_result = await emoji_api.get_by_description("开心的大笑")
+emoji_result = await emoji_api.get_by_description("大笑")
 if emoji_result:
     emoji_base64, description, matched_scene = emoji_result
     print(f"获取到表情包: {description}, 场景: {matched_scene}")
     # 可以将emoji_base64用于发送表情包
 ```
 
-#### `get_random() -> Optional[Tuple[str, str, str]]`
+### 2. 随机获取表情包
-随机获取表情包
-
-**返回：**
-- `Optional[Tuple[str, str, str]]`：(base64编码, 表情包描述, 随机场景) 或 None
-
-**示例：**
 ```python
-random_emoji = await emoji_api.get_random()
+async def get_random(count: Optional[int] = 1) -> List[Tuple[str, str, str]]:
-if random_emoji:
-    emoji_base64, description, scene = random_emoji
-    print(f"随机表情包: {description}")
 ```
+随机获取指定数量的表情包
 
-#### `get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]`
+**Args：**
-根据场景关键词获取表情包
+- `count`：要获取的表情包数量，默认为1
 
-**参数：**
+**Returns：**
-- `emotion`：场景关键词，如"大笑"、"讽刺"、"无奈"等
+- `List[Tuple[str, str, str]]`：一个包含多个表情包的列表，每个元素是一个元组: (表情包的base64编码, 描述, 情感标签)，如果未找到或出错则返回空列表
 
-**返回：**
+### 3. 根据情感获取表情包
-- `Optional[Tuple[str, str, str]]`：(base64编码, 表情包描述, 匹配的场景) 或 None
-
-**示例：**
 ```python
-emoji_result = await emoji_api.get_by_emotion("讽刺")
+async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
-if emoji_result:
-    emoji_base64, description, scene = emoji_result
-    # 发送讽刺表情包
 ```
+根据情感标签获取表情包
 
-### 2. 表情包信息查询
+**Args：**
+- `emotion`：情感标签，例如"开心"、"悲伤"、"愤怒"等
 
-#### `get_count() -> int`
+**Returns：**
-获取表情包数量
+- `Optional[Tuple[str, str, str]]`：一个元组: (表情包的base64编码, 描述, 情感标签)，如果未找到则返回None
 
-**返回：**
+### 4. 获取表情包数量
-- `int`：当前可用的表情包数量
+```python
+def get_count() -> int:
+```
+获取当前可用表情包的数量
 
-#### `get_info() -> dict`
+### 5. 获取表情包系统信息
-获取表情包系统信息
+```python
+def get_info() -> Dict[str, Any]:
+```
+获取表情包系统的基本信息
 
-**返回：**
+**Returns：**
-- `dict`：包含表情包数量、最大数量等信息
+- `Dict[str, Any]`：包含表情包数量、描述等信息的字典，包含以下键：
+    - `current_count`：当前表情包数量
+    - `max_count`：最大表情包数量
+    - `available_emojis`：当前可用的表情包数量
 
-**返回字典包含：**
+### 6. 获取所有可用的情感标签
-- `current_count`：当前表情包数量
+```python
-- `max_count`：最大表情包数量
+def get_emotions() -> List[str]:
-- `available_emojis`：可用表情包数量
+```
+获取所有可用的情感标签 **（已经去重）**
 
-#### `get_emotions() -> list`
+### 7. 获取所有表情包描述
-获取所有可用的场景关键词
+```python
-
+def get_descriptions() -> List[str]:
-**返回：**
+```
-- `list`：所有表情包的场景关键词列表（去重）
-
-#### `get_descriptions() -> list`
 获取所有表情包的描述列表
 
-**返回：**
-- `list`：所有表情包的描述文本列表
-
-## 使用示例
-
-### 1. 智能表情包选择
-
-```python
-from src.plugin_system.apis import emoji_api
-
-async def send_emotion_response(message_text: str, chat_stream):
-    """根据消息内容智能选择表情包回复"""
-    
-    # 分析消息场景
-    if "哈哈" in message_text or "好笑" in message_text:
-        emoji_result = await emoji_api.get_by_description("开心的大笑")
-    elif "无语" in message_text or "算了" in message_text:
-        emoji_result = await emoji_api.get_by_description("表示无奈和沮丧")
-    elif "呵呵" in message_text or "是吗" in message_text:
-        emoji_result = await emoji_api.get_by_description("轻微的讽刺")
-    elif "生气" in message_text or "愤怒" in message_text:
-        emoji_result = await emoji_api.get_by_description("愤怒和不满")
-    else:
-        # 随机选择一个表情包
-        emoji_result = await emoji_api.get_random()
-    
-    if emoji_result:
-        emoji_base64, description, scene = emoji_result
-        # 使用send_api发送表情包
-        from src.plugin_system.apis import send_api
-        success = await send_api.emoji_to_group(emoji_base64, chat_stream.group_info.group_id)
-        return success
-    
-    return False
-```
-
-### 2. 表情包管理功能
-
-```python
-async def show_emoji_stats():
-    """显示表情包统计信息"""
-    
-    # 获取基本信息
-    count = emoji_api.get_count()
-    info = emoji_api.get_info()
-    scenes = emoji_api.get_emotions()  # 实际返回的是场景关键词
-    
-    stats = f"""
-📊 表情包统计信息：
-- 总数量: {count}
-- 可用数量: {info['available_emojis']}
-- 最大容量: {info['max_count']}
-- 支持场景: {len(scenes)}种
-
-🎭 支持的场景关键词: {', '.join(scenes[:10])}{'...' if len(scenes) > 10 else ''}
-    """
-    
-    return stats
-```
-
-### 3. 表情包测试功能
-
-```python
-async def test_emoji_system():
-    """测试表情包系统的各种功能"""
-    
-    print("=== 表情包系统测试 ===")
-    
-    # 测试场景描述查找
-    test_descriptions = ["开心的大笑", "轻微的讽刺", "表示无奈和沮丧", "愤怒和不满"]
-    for desc in test_descriptions:
-        result = await emoji_api.get_by_description(desc)
-        if result:
-            _, description, scene = result
-            print(f"✅ 场景'{desc}' -> {description} ({scene})")
-        else:
-            print(f"❌ 场景'{desc}' -> 未找到")
-    
-    # 测试关键词查找
-    scenes = emoji_api.get_emotions()
-    if scenes:
-        test_scene = scenes[0]
-        result = await emoji_api.get_by_emotion(test_scene)
-        if result:
-            print(f"✅ 关键词'{test_scene}' -> 找到匹配表情包")
-    
-    # 测试随机获取
-    random_result = await emoji_api.get_random()
-    if random_result:
-        print("✅ 随机获取 -> 成功")
-    
-    print(f"📊 系统信息: {emoji_api.get_info()}")
-```
-
-### 4. 在Action中使用表情包
-
-```python
-from src.plugin_system.base import BaseAction
-
-class EmojiAction(BaseAction):
-    async def execute(self, action_data, chat_stream):
-        # 从action_data获取场景描述或关键词
-        scene_keyword = action_data.get("scene", "")
-        scene_description = action_data.get("description", "")
-        
-        emoji_result = None
-        
-        # 优先使用具体的场景描述
-        if scene_description:
-            emoji_result = await emoji_api.get_by_description(scene_description)
-        # 其次使用场景关键词
-        elif scene_keyword:
-            emoji_result = await emoji_api.get_by_emotion(scene_keyword)
-        # 最后随机选择
-        else:
-            emoji_result = await emoji_api.get_random()
-        
-        if emoji_result:
-            emoji_base64, description, scene = emoji_result
-            return {
-                "success": True,
-                "emoji_base64": emoji_base64,
-                "description": description,
-                "scene": scene
-            }
-        
-        return {"success": False, "message": "未找到合适的表情包"}
-```
-
 ## 场景描述说明
 
 ### 常用场景描述
-表情包系统支持多种具体的场景描述，常见的包括：
+表情包系统支持多种具体的场景描述，举例如下：
 
 - **开心类场景**：开心的大笑、满意的微笑、兴奋的手舞足蹈
 - **无奈类场景**：表示无奈和沮丧、轻微的讽刺、无语的摇头
@@ -248,8 +117,8 @@ class EmojiAction(BaseAction):
 - **惊讶类场景**：震惊的表情、意外的发现、困惑的思考
 - **可爱类场景**：卖萌的表情、撒娇的动作、害羞的样子
 
-### 场景关键词示例
+### 情感关键词示例
-系统支持的场景关键词包括：
+系统支持的情感关键词举例如下：
 - 大笑、微笑、兴奋、手舞足蹈
 - 无奈、沮丧、讽刺、无语、摇头
 - 愤怒、不满、生气、瞪视、抓狂
@@ -263,9 +132,9 @@ class EmojiAction(BaseAction):
 
 ## 注意事项
 
-1. **异步函数**：获取表情包的函数都是异步的，需要使用 `await`
+1. **异步函数**：部分函数是异步的，需要使用 `await`
 2. **返回格式**：表情包以base64编码返回，可直接用于发送
-3. **错误处理**：所有函数都有错误处理，失败时返回None或默认值
+3. **错误处理**：所有函数都有错误处理，失败时返回None，空列表或默认值
 4. **使用统计**：系统会记录表情包的使用次数
 5. **文件依赖**：表情包依赖于本地文件，确保表情包文件存在
 6. **编码格式**：返回的是base64编码的图片数据，可直接用于网络传输

docs/plugins/api/generator-api.md

@@ -6,241 +6,151 @@
 
 ```python
 from src.plugin_system.apis import generator_api
+# 或者
+from src.plugin_system import generator_api
 ```
 
 ## 主要功能
 
 ### 1. 回复器获取
-
+```python
-#### `get_replyer(chat_stream=None, platform=None, chat_id=None, is_group=True)`
+def get_replyer(
+    chat_stream: Optional[ChatStream] = None,
+    chat_id: Optional[str] = None,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+    request_type: str = "replyer",
+) -> Optional[DefaultReplyer]:
+```
 获取回复器对象
 
-**参数：**
+优先使用chat_stream，如果没有则使用chat_id直接查找。
-- `chat_stream`：聊天流对象（优先）
-- `platform`：平台名称，如"qq"
-- `chat_id`：聊天ID（群ID或用户ID）
-- `is_group`：是否为群聊
 
-**返回：**
+使用 ReplyerManager 来管理实例，避免重复创建。
-- `DefaultReplyer`：回复器对象，如果获取失败则返回None
 
-**示例：**
+**Args:**
+- `chat_stream`: 聊天流对象
+- `chat_id`: 聊天ID（实际上就是`stream_id`）
+- `model_set_with_weight`: 模型配置列表，每个元素为 `(TaskConfig, weight)` 元组
+- `request_type`: 请求类型，用于记录LLM使用情况，可以不写
+
+**Returns:**
+- `DefaultReplyer`: 回复器对象，如果获取失败则返回None
+
+#### 示例
 ```python
 # 使用聊天流获取回复器
 replyer = generator_api.get_replyer(chat_stream=chat_stream)
 
-# 使用平台和ID获取回复器  
+# 使用平台和ID获取回复器
-replyer = generator_api.get_replyer(
+replyer = generator_api.get_replyer(chat_id="123456789")
-    platform="qq",
-    chat_id="123456789",
-    is_group=True
-)
 ```
 
 ### 2. 回复生成
-
+```python
-#### `generate_reply(chat_stream=None, action_data=None, platform=None, chat_id=None, is_group=True)`
+async def generate_reply(
+    chat_stream: Optional[ChatStream] = None,
+    chat_id: Optional[str] = None,
+    action_data: Optional[Dict[str, Any]] = None,
+    reply_to: str = "",
+    extra_info: str = "",
+    available_actions: Optional[Dict[str, ActionInfo]] = None,
+    enable_tool: bool = False,
+    enable_splitter: bool = True,
+    enable_chinese_typo: bool = True,
+    return_prompt: bool = False,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+    request_type: str = "generator_api",
+) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
+```
 生成回复
 
-**参数：**
+优先使用chat_stream，如果没有则使用chat_id直接查找。
-- `chat_stream`：聊天流对象（优先）
-- `action_data`：动作数据
-- `platform`：平台名称（备用）
-- `chat_id`：聊天ID（备用）
-- `is_group`：是否为群聊（备用）
 
-**返回：**
+**Args:**
-- `Tuple[bool, List[Tuple[str, Any]]]`：(是否成功, 回复集合)
+- `chat_stream`: 聊天流对象
+- `chat_id`: 聊天ID（实际上就是`stream_id`）
+- `action_data`: 动作数据（向下兼容，包含`reply_to`和`extra_info`）
+- `reply_to`: 回复目标，格式为 `{发送者的person_name:消息内容}`
+- `extra_info`: 附加信息
+- `available_actions`: 可用动作字典，格式为 `{"action_name": ActionInfo}`
+- `enable_tool`: 是否启用工具
+- `enable_splitter`: 是否启用分割器
+- `enable_chinese_typo`: 是否启用中文错别字
+- `return_prompt`: 是否返回提示词
+- `model_set_with_weight`: 模型配置列表，每个元素为 `(TaskConfig, weight)` 元组
+- `request_type`: 请求类型（可选，记录LLM使用）
+- `request_type`: 请求类型，用于记录LLM使用情况
 
-**示例：**
+**Returns:**
+- `Tuple[bool, List[Tuple[str, Any]], Optional[str]]`: (是否成功, 回复集合, 提示词)
+
+#### 示例
 ```python
-success, reply_set = await generator_api.generate_reply(
+success, reply_set, prompt = await generator_api.generate_reply(
     chat_stream=chat_stream,
-    action_data={"message": "你好", "intent": "greeting"}
+    action_data=action_data,
+    reply_to="麦麦:你好",
+    available_actions=action_info,
+    enable_tool=True,
+    return_prompt=True
 )
-
 if success:
     for reply_type, reply_content in reply_set:
         print(f"回复类型: {reply_type}, 内容: {reply_content}")
+    if prompt:
+        print(f"使用的提示词: {prompt}")
 ```
 
-#### `rewrite_reply(chat_stream=None, reply_data=None, platform=None, chat_id=None, is_group=True)`
+### 3. 回复重写
-重写回复
-
-**参数：**
-- `chat_stream`：聊天流对象（优先）
-- `reply_data`：回复数据
-- `platform`：平台名称（备用）
-- `chat_id`：聊天ID（备用）
-- `is_group`：是否为群聊（备用）
-
-**返回：**
-- `Tuple[bool, List[Tuple[str, Any]]]`：(是否成功, 回复集合)
-
-**示例：**
 ```python
-success, reply_set = await generator_api.rewrite_reply(
+async def rewrite_reply(
+    chat_stream: Optional[ChatStream] = None,
+    reply_data: Optional[Dict[str, Any]] = None,
+    chat_id: Optional[str] = None,
+    enable_splitter: bool = True,
+    enable_chinese_typo: bool = True,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+    raw_reply: str = "",
+    reason: str = "",
+    reply_to: str = "",
+    return_prompt: bool = False,
+) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
+```
+重写回复，使用新的内容替换旧的回复内容。
+
+优先使用chat_stream，如果没有则使用chat_id直接查找。
+
+**Args:**
+- `chat_stream`: 聊天流对象
+- `reply_data`: 回复数据，包含`raw_reply`, `reason`和`reply_to`，**（向下兼容备用，当其他参数缺失时从此获取）**
+- `chat_id`: 聊天ID（实际上就是`stream_id`）
+- `enable_splitter`: 是否启用分割器
+- `enable_chinese_typo`: 是否启用中文错别字
+- `model_set_with_weight`: 模型配置列表，每个元素为 (TaskConfig, weight) 元组
+- `raw_reply`: 原始回复内容
+- `reason`: 重写原因
+- `reply_to`: 回复目标，格式为 `{发送者的person_name:消息内容}`
+
+**Returns:**
+- `Tuple[bool, List[Tuple[str, Any]], Optional[str]]`: (是否成功, 回复集合, 提示词)
+
+#### 示例
+```python
+success, reply_set, prompt = await generator_api.rewrite_reply(
     chat_stream=chat_stream,
-    reply_data={"original_text": "原始回复", "style": "more_friendly"}
+    raw_reply="原始回复内容",
+    reason="重写原因",
+    reply_to="麦麦:你好",
+    return_prompt=True
 )
+if success:
+    for reply_type, reply_content in reply_set:
+        print(f"回复类型: {reply_type}, 内容: {reply_content}")
+    if prompt:
+        print(f"使用的提示词: {prompt}")
 ```
 
-## 使用示例
+## 回复集合`reply_set`格式
-
-### 1. 基础回复生成
-
-```python
-from src.plugin_system.apis import generator_api
-
-async def generate_greeting_reply(chat_stream, user_name):
-    """生成问候回复"""
-    
-    action_data = {
-        "intent": "greeting",
-        "user_name": user_name,
-        "context": "morning_greeting"
-    }
-    
-    success, reply_set = await generator_api.generate_reply(
-        chat_stream=chat_stream,
-        action_data=action_data
-    )
-    
-    if success and reply_set:
-        # 获取第一个回复
-        reply_type, reply_content = reply_set[0]
-        return reply_content
-    
-    return "你好！"  # 默认回复
-```
-
-### 2. 在Action中使用回复生成器
-
-```python
-from src.plugin_system.base import BaseAction
-
-class ChatAction(BaseAction):
-    async def execute(self, action_data, chat_stream):
-        # 准备回复数据
-        reply_context = {
-            "message_type": "response",
-            "user_input": action_data.get("user_message", ""),
-            "intent": action_data.get("intent", ""),
-            "entities": action_data.get("entities", {}),
-            "context": self.get_conversation_context(chat_stream)
-        }
-        
-        # 生成回复
-        success, reply_set = await generator_api.generate_reply(
-            chat_stream=chat_stream,
-            action_data=reply_context
-        )
-        
-        if success:
-            return {
-                "success": True,
-                "replies": reply_set,
-                "generated_count": len(reply_set)
-            }
-        
-        return {
-            "success": False,
-            "error": "回复生成失败",
-            "fallback_reply": "抱歉，我现在无法理解您的消息。"
-        }
-```
-
-### 3. 多样化回复生成
-
-```python
-async def generate_diverse_replies(chat_stream, topic, count=3):
-    """生成多个不同风格的回复"""
-    
-    styles = ["formal", "casual", "humorous"]
-    all_replies = []
-    
-    for i, style in enumerate(styles[:count]):
-        action_data = {
-            "topic": topic,
-            "style": style,
-            "variation": i
-        }
-        
-        success, reply_set = await generator_api.generate_reply(
-            chat_stream=chat_stream,
-            action_data=action_data
-        )
-        
-        if success and reply_set:
-            all_replies.extend(reply_set)
-    
-    return all_replies
-```
-
-### 4. 回复重写功能
-
-```python
-async def improve_reply(chat_stream, original_reply, improvement_type="more_friendly"):
-    """改进原始回复"""
-    
-    reply_data = {
-        "original_text": original_reply,
-        "improvement_type": improvement_type,
-        "target_audience": "young_users",
-        "tone": "positive"
-    }
-    
-    success, improved_replies = await generator_api.rewrite_reply(
-        chat_stream=chat_stream,
-        reply_data=reply_data
-    )
-    
-    if success and improved_replies:
-        # 返回改进后的第一个回复
-        _, improved_content = improved_replies[0]
-        return improved_content
-    
-    return original_reply  # 如果改进失败，返回原始回复
-```
-
-### 5. 条件回复生成
-
-```python
-async def conditional_reply_generation(chat_stream, user_message, user_emotion):
-    """根据用户情感生成条件回复"""
-    
-    # 根据情感调整回复策略
-    if user_emotion == "sad":
-        action_data = {
-            "intent": "comfort",
-            "tone": "empathetic",
-            "style": "supportive"
-        }
-    elif user_emotion == "angry":
-        action_data = {
-            "intent": "calm",
-            "tone": "peaceful",
-            "style": "understanding"
-        }
-    else:
-        action_data = {
-            "intent": "respond",
-            "tone": "neutral",
-            "style": "helpful"
-        }
-    
-    action_data["user_message"] = user_message
-    action_data["user_emotion"] = user_emotion
-    
-    success, reply_set = await generator_api.generate_reply(
-        chat_stream=chat_stream,
-        action_data=action_data
-    )
-    
-    return reply_set if success else []
-```
-
-## 回复集合格式
 
 ### 回复类型
 生成的回复集合包含多种类型的回复：
@@ -260,82 +170,32 @@ reply_set = [
 ]
 ```
 
-## 高级用法
+### 4. 自定义提示词回复
-
-### 1. 自定义回复器配置
-
 ```python
-async def generate_with_custom_config(chat_stream, action_data):
+async def generate_response_custom(
-    """使用自定义配置生成回复"""
+    chat_stream: Optional[ChatStream] = None,
-    
+    chat_id: Optional[str] = None,
-    # 获取回复器
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
-    replyer = generator_api.get_replyer(chat_stream=chat_stream)
+    prompt: str = "",
-    
+) -> Optional[str]:
-    if replyer:
-        # 可以访问回复器的内部方法
-        success, reply_set = await replyer.generate_reply_with_context(
-            reply_data=action_data,
-            # 可以传递额外的配置参数
-        )
-        return success, reply_set
-    
-    return False, []
 ```
+生成自定义提示词回复
 
-### 2. 回复质量评估
+优先使用chat_stream，如果没有则使用chat_id直接查找。
 
-```python
+**Args:**
-async def generate_and_evaluate_replies(chat_stream, action_data):
+- `chat_stream`: 聊天流对象
-    """生成回复并评估质量"""
+- `chat_id`: 聊天ID（备用）
-    
+- `model_set_with_weight`: 模型集合配置列表
-    success, reply_set = await generator_api.generate_reply(
+- `prompt`: 自定义提示词
-        chat_stream=chat_stream,
-        action_data=action_data
-    )
-    
-    if success:
-        evaluated_replies = []
-        for reply_type, reply_content in reply_set:
-            # 简单的质量评估
-            quality_score = evaluate_reply_quality(reply_content)
-            evaluated_replies.append({
-                "type": reply_type,
-                "content": reply_content,
-                "quality": quality_score
-            })
-        
-        # 按质量排序
-        evaluated_replies.sort(key=lambda x: x["quality"], reverse=True)
-        return evaluated_replies
-    
-    return []
 
-def evaluate_reply_quality(reply_content):
+**Returns:**
-    """简单的回复质量评估"""
+- `Optional[str]`: 生成的自定义回复内容，如果生成失败则返回None
-    if not reply_content:
-        return 0
-    
-    score = 50  # 基础分
-    
-    # 长度适中加分
-    if 5 <= len(reply_content) <= 100:
-        score += 20
-    
-    # 包含积极词汇加分
-    positive_words = ["好", "棒", "不错", "感谢", "开心"]
-    for word in positive_words:
-        if word in reply_content:
-            score += 10
-            break
-    
-    return min(score, 100)
-```
 
 ## 注意事项
 
-1. **异步操作**：所有生成函数都是异步的，必须使用`await`
+1. **异步操作**：部分函数是异步的，须使用`await`
-2. **错误处理**：函数内置错误处理，失败时返回False和空列表
+2. **聊天流依赖**：需要有效的聊天流对象才能正常工作
-3. **聊天流依赖**：需要有效的聊天流对象才能正常工作
+3. **性能考虑**：回复生成可能需要一些时间，特别是使用LLM时
-4. **性能考虑**：回复生成可能需要一些时间，特别是使用LLM时
+4. **回复格式**：返回的回复集合是元组列表，包含类型和内容
-5. **回复格式**：返回的回复集合是元组列表，包含类型和内容
+5. **上下文感知**：生成器会考虑聊天上下文和历史消息，除非你用的是自定义提示词。
-6. **上下文感知**：生成器会考虑聊天上下文和历史消息 

docs/plugins/api/llm-api.md

@@ -6,239 +6,34 @@ LLM API模块提供与大语言模型交互的功能，让插件能够使用系
 
 ```python
 from src.plugin_system.apis import llm_api
+# 或者
+from src.plugin_system import llm_api
 ```
 
 ## 主要功能
 
-### 1. 模型管理
+### 1. 查询可用模型
-
-#### `get_available_models() -> Dict[str, Any]`
-获取所有可用的模型配置
-
-**返回：**
-- `Dict[str, Any]`：模型配置字典，key为模型名称，value为模型配置
-
-**示例：**
 ```python
-models = llm_api.get_available_models()
+def get_available_models() -> Dict[str, TaskConfig]:
-for model_name, model_config in models.items():
-    print(f"模型: {model_name}")
-    print(f"配置: {model_config}")
 ```
+获取所有可用的模型配置。
 
-### 2. 内容生成
+**Return：**
+- `Dict[str, TaskConfig]`：模型配置字典，key为模型名称，value为模型配置对象。
 
-#### `generate_with_model(prompt, model_config, request_type="plugin.generate", **kwargs)`
+### 2. 使用模型生成内容
-使用指定模型生成内容
-
-**参数：**
-- `prompt`：提示词
-- `model_config`：模型配置（从 get_available_models 获取）
-- `request_type`：请求类型标识
-- `**kwargs`：其他模型特定参数，如temperature、max_tokens等
-
-**返回：**
-- `Tuple[bool, str, str, str]`：(是否成功, 生成的内容, 推理过程, 模型名称)
-
-**示例：**
 ```python
-models = llm_api.get_available_models()
+async def generate_with_model(
-default_model = models.get("default")
+    prompt: str, model_config: TaskConfig, request_type: str = "plugin.generate", **kwargs
-
+) -> Tuple[bool, str, str, str]:
-if default_model:
-    success, response, reasoning, model_name = await llm_api.generate_with_model(
-        prompt="请写一首关于春天的诗",
-        model_config=default_model,
-        temperature=0.7,
-        max_tokens=200
-    )
-    
-    if success:
-        print(f"生成内容: {response}")
-        print(f"使用模型: {model_name}")
 ```
+使用指定模型生成内容。
 
-## 使用示例
+**Args:**
+- `prompt`：提示词。
+- `model_config`：模型配置对象（从 `get_available_models` 获取）。
+- `request_type`：请求类型标识，默认为 `"plugin.generate"`。
+- `**kwargs`：其他模型特定参数，如 `temperature`、`max_tokens` 等。
 
-### 1. 基础文本生成
+**Return：**
-
+- `Tuple[bool, str, str, str]`：返回一个元组，包含（是否成功, 生成的内容, 推理过程, 模型名称）。
-```python
-from src.plugin_system.apis import llm_api
-
-async def generate_story(topic: str):
-    """生成故事"""
-    models = llm_api.get_available_models()
-    model = models.get("default")
-    
-    if not model:
-        return "未找到可用模型"
-    
-    prompt = f"请写一个关于{topic}的短故事，大约100字左右。"
-    
-    success, story, reasoning, model_name = await llm_api.generate_with_model(
-        prompt=prompt,
-        model_config=model,
-        request_type="story.generate",
-        temperature=0.8,
-        max_tokens=150
-    )
-    
-    return story if success else "故事生成失败"
-```
-
-### 2. 在Action中使用LLM
-
-```python
-from src.plugin_system.base import BaseAction
-
-class LLMAction(BaseAction):
-    async def execute(self, action_data, chat_stream):
-        # 获取用户输入
-        user_input = action_data.get("user_message", "")
-        intent = action_data.get("intent", "chat")
-        
-        # 获取模型配置
-        models = llm_api.get_available_models()
-        model = models.get("default")
-        
-        if not model:
-            return {"success": False, "error": "未配置LLM模型"}
-        
-        # 构建提示词
-        prompt = self.build_prompt(user_input, intent)
-        
-        # 生成回复
-        success, response, reasoning, model_name = await llm_api.generate_with_model(
-            prompt=prompt,
-            model_config=model,
-            request_type=f"plugin.{self.plugin_name}",
-            temperature=0.7
-        )
-        
-        if success:
-            return {
-                "success": True,
-                "response": response,
-                "model_used": model_name,
-                "reasoning": reasoning
-            }
-        
-        return {"success": False, "error": response}
-    
-    def build_prompt(self, user_input: str, intent: str) -> str:
-        """构建提示词"""
-        base_prompt = "你是一个友善的AI助手。"
-        
-        if intent == "question":
-            return f"{base_prompt}\n\n用户问题：{user_input}\n\n请提供准确、有用的回答："
-        elif intent == "chat":
-            return f"{base_prompt}\n\n用户说：{user_input}\n\n请进行自然的对话："
-        else:
-            return f"{base_prompt}\n\n用户输入：{user_input}\n\n请回复："
-```
-
-### 3. 多模型对比
-
-```python
-async def compare_models(prompt: str):
-    """使用多个模型生成内容并对比"""
-    models = llm_api.get_available_models()
-    results = {}
-    
-    for model_name, model_config in models.items():
-        success, response, reasoning, actual_model = await llm_api.generate_with_model(
-            prompt=prompt,
-            model_config=model_config,
-            request_type="comparison.test"
-        )
-        
-        results[model_name] = {
-            "success": success,
-            "response": response,
-            "model": actual_model,
-            "reasoning": reasoning
-        }
-    
-    return results
-```
-
-### 4. 智能对话插件
-
-```python
-class ChatbotPlugin(BasePlugin):
-    async def handle_action(self, action_data, chat_stream):
-        user_message = action_data.get("message", "")
-        
-        # 获取历史对话上下文
-        context = self.get_conversation_context(chat_stream)
-        
-        # 构建对话提示词
-        prompt = self.build_conversation_prompt(user_message, context)
-        
-        # 获取模型配置
-        models = llm_api.get_available_models()
-        chat_model = models.get("chat", models.get("default"))
-        
-        if not chat_model:
-            return {"success": False, "message": "聊天模型未配置"}
-        
-        # 生成回复
-        success, response, reasoning, model_name = await llm_api.generate_with_model(
-            prompt=prompt,
-            model_config=chat_model,
-            request_type="chat.conversation",
-            temperature=0.8,
-            max_tokens=500
-        )
-        
-        if success:
-            # 保存对话历史
-            self.save_conversation(chat_stream, user_message, response)
-            
-            return {
-                "success": True,
-                "reply": response,
-                "model": model_name
-            }
-        
-        return {"success": False, "message": "回复生成失败"}
-    
-    def build_conversation_prompt(self, user_message: str, context: list) -> str:
-        """构建对话提示词"""
-        prompt = "你是一个有趣、友善的聊天机器人。请自然地回复用户的消息。\n\n"
-        
-        # 添加历史对话
-        if context:
-            prompt += "对话历史：\n"
-            for msg in context[-5:]:  # 只保留最近5条
-                prompt += f"用户: {msg['user']}\n机器人: {msg['bot']}\n"
-            prompt += "\n"
-        
-        prompt += f"用户: {user_message}\n机器人: "
-        return prompt
-```
-
-## 模型配置说明
-
-### 常用模型类型
-- `default`：默认模型
-- `chat`：聊天专用模型
-- `creative`：创意生成模型
-- `code`：代码生成模型
-
-### 配置参数
-LLM模型支持的常用参数：
-- `temperature`：控制输出随机性（0.0-1.0）
-- `max_tokens`：最大生成长度
-- `top_p`：核采样参数
-- `frequency_penalty`：频率惩罚
-- `presence_penalty`：存在惩罚
-
-## 注意事项
-
-1. **异步操作**：LLM生成是异步的，必须使用`await`
-2. **错误处理**：生成失败时返回False和错误信息
-3. **配置依赖**：需要正确配置模型才能使用
-4. **请求类型**：建议为不同用途设置不同的request_type
-5. **性能考虑**：LLM调用可能较慢，考虑超时和缓存
-6. **成本控制**：注意控制max_tokens以控制成本 

docs/plugins/api/logging-api.md

@@ -0,0 +1,29 @@
+# Logging API
+
+Logging API模块提供了获取本体logger的功能，允许插件记录日志信息。
+
+## 导入方式
+
+```python
+from src.plugin_system.apis import get_logger
+# 或者
+from src.plugin_system import get_logger
+```
+
+## 主要功能
+### 1. 获取本体logger
+```python
+def get_logger(name: str) -> structlog.stdlib.BoundLogger:
+```
+获取本体logger实例。
+
+**Args:**
+- `name` (str): 日志记录器的名称。
+
+**Returns:**
+- 一个logger实例，有以下方法:
+    - `debug`
+    - `info`
+    - `warning`
+    - `error`
+    - `critical`

docs/plugins/api/message-api.md

@@ -1,11 +1,13 @@
 # 消息API
 
-> 消息API提供了强大的消息查询、计数和格式化功能，让你轻松处理聊天消息数据。
+消息API提供了强大的消息查询、计数和格式化功能，让你轻松处理聊天消息数据。
 
 ## 导入方式
 
 ```python
 from src.plugin_system.apis import message_api
+# 或者
+from src.plugin_system import message_api
 ```
 
 ## 功能概述
@@ -15,297 +17,356 @@ from src.plugin_system.apis import message_api
 - **消息计数** - 统计新消息数量
 - **消息格式化** - 将消息转换为可读格式
 
----
+## 主要功能
 
-## 消息查询API
+### 1. 按照事件查询消息
+```python
+def get_messages_by_time(
+    start_time: float, end_time: float, limit: int = 0, limit_mode: str = "latest", filter_mai: bool = False
+) -> List[Dict[str, Any]]:
+```
+获取指定时间范围内的消息。
 
-### 按时间查询消息
+**Args:**
-
-#### `get_messages_by_time(start_time, end_time, limit=0, limit_mode="latest")`
-
-获取指定时间范围内的消息
-
-**参数：**
 - `start_time` (float): 开始时间戳
-- `end_time` (float): 结束时间戳  
+- `end_time` (float): 结束时间戳
 - `limit` (int): 限制返回消息数量，0为不限制
 - `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
 
-**返回：** `List[Dict[str, Any]]` - 消息列表
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
 
-**示例：**
+消息列表中包含的键与`Messages`类的属性一致。（位于`src.common.database.database_model`）
+
+### 2. 获取指定聊天中指定时间范围内的信息
 ```python
-import time
+def get_messages_by_time_in_chat(
-
+    chat_id: str,
-# 获取最近24小时的消息
+    start_time: float,
-now = time.time()
+    end_time: float,
-yesterday = now - 24 * 3600
+    limit: int = 0,
-messages = message_api.get_messages_by_time(yesterday, now, limit=50)
+    limit_mode: str = "latest",
+    filter_mai: bool = False,
+) -> List[Dict[str, Any]]:
 ```
+获取指定聊天中指定时间范围内的消息。
 
-### 按聊天查询消息
+**Args:**
-
-#### `get_messages_by_time_in_chat(chat_id, start_time, end_time, limit=0, limit_mode="latest")`
-
-获取指定聊天中指定时间范围内的消息
-
-**参数：**
-- `chat_id` (str): 聊天ID
-- 其他参数同上
-
-**示例：**
-```python
-# 获取某个群聊最近的100条消息
-messages = message_api.get_messages_by_time_in_chat(
-    chat_id="123456789", 
-    start_time=yesterday, 
-    end_time=now, 
-    limit=100
-)
-```
-
-#### `get_messages_by_time_in_chat_inclusive(chat_id, start_time, end_time, limit=0, limit_mode="latest")`
-
-获取指定聊天中指定时间范围内的消息（包含边界时间点）
-
-与 `get_messages_by_time_in_chat` 类似，但包含边界时间戳的消息。
-
-#### `get_recent_messages(chat_id, hours=24.0, limit=100, limit_mode="latest")`
-
-获取指定聊天中最近一段时间的消息（便捷方法）
-
-**参数：**
-- `chat_id` (str): 聊天ID
-- `hours` (float): 最近多少小时，默认24小时
-- `limit` (int): 限制返回消息数量，默认100条
-- `limit_mode` (str): 限制模式
-
-**示例：**
-```python
-# 获取最近6小时的消息
-recent_messages = message_api.get_recent_messages(
-    chat_id="123456789", 
-    hours=6.0, 
-    limit=50
-)
-```
-
-### 按用户查询消息
-
-#### `get_messages_by_time_in_chat_for_users(chat_id, start_time, end_time, person_ids, limit=0, limit_mode="latest")`
-
-获取指定聊天中指定用户在指定时间范围内的消息
-
-**参数：**
 - `chat_id` (str): 聊天ID
 - `start_time` (float): 开始时间戳
 - `end_time` (float): 结束时间戳
-- `person_ids` (list): 用户ID列表
+- `limit` (int): 限制返回消息数量，0为不限制
-- `limit` (int): 限制返回消息数量
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
-- `limit_mode` (str): 限制模式
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
 
-**示例：**
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
+
+
+### 3. 获取指定聊天中指定时间范围内的信息（包含边界）
 ```python
-# 获取特定用户的消息
+def get_messages_by_time_in_chat_inclusive(
-user_messages = message_api.get_messages_by_time_in_chat_for_users(
+    chat_id: str,
-    chat_id="123456789",
+    start_time: float,
-    start_time=yesterday,
+    end_time: float,
-    end_time=now,
+    limit: int = 0,
-    person_ids=["user1", "user2"]
+    limit_mode: str = "latest",
-)
+    filter_mai: bool = False,
+    filter_command: bool = False,
+) -> List[Dict[str, Any]]:
 ```
+获取指定聊天中指定时间范围内的消息（包含边界）。
 
-#### `get_messages_by_time_for_users(start_time, end_time, person_ids, limit=0, limit_mode="latest")`
+**Args:**
+- `chat_id` (str): 聊天ID
+- `start_time` (float): 开始时间戳（包含）
+- `end_time` (float): 结束时间戳（包含）
+- `limit` (int): 限制返回消息数量，0为不限制
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
+- `filter_command` (bool): 是否过滤命令消息，默认False
 
-获取指定用户在所有聊天中指定时间范围内的消息
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
 
-### 其他查询方法
 
-#### `get_random_chat_messages(start_time, end_time, limit=0, limit_mode="latest")`
+### 4. 获取指定聊天中指定用户在指定时间范围内的消息
+```python
+def get_messages_by_time_in_chat_for_users(
+    chat_id: str,
+    start_time: float,
+    end_time: float,
+    person_ids: List[str],
+    limit: int = 0,
+    limit_mode: str = "latest",
+) -> List[Dict[str, Any]]:
+```
+获取指定聊天中指定用户在指定时间范围内的消息。
 
-随机选择一个聊天，返回该聊天在指定时间范围内的消息
+**Args:**
-
-#### `get_messages_before_time(timestamp, limit=0)`
-
-获取指定时间戳之前的消息
-
-#### `get_messages_before_time_in_chat(chat_id, timestamp, limit=0)`
-
-获取指定聊天中指定时间戳之前的消息
-
-#### `get_messages_before_time_for_users(timestamp, person_ids, limit=0)`
-
-获取指定用户在指定时间戳之前的消息
-
----
-
-## 消息计数API
-
-### `count_new_messages(chat_id, start_time=0.0, end_time=None)`
-
-计算指定聊天中从开始时间到结束时间的新消息数量
-
-**参数：**
 - `chat_id` (str): 聊天ID
 - `start_time` (float): 开始时间戳
-- `end_time` (float): 结束时间戳，如果为None则使用当前时间
+- `end_time` (float): 结束时间戳
+- `person_ids` (List[str]): 用户ID列表
+- `limit` (int): 限制返回消息数量，0为不限制
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
 
-**返回：** `int` - 新消息数量
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
 
-**示例：**
+
+### 5. 随机选择一个聊天，返回该聊天在指定时间范围内的消息
 ```python
-# 计算最近1小时的新消息数
+def get_random_chat_messages(
-import time
+    start_time: float,
-now = time.time()
+    end_time: float,
-hour_ago = now - 3600
+    limit: int = 0,
-new_count = message_api.count_new_messages("123456789", hour_ago, now)
+    limit_mode: str = "latest",
-print(f"最近1小时有{new_count}条新消息")
+    filter_mai: bool = False,
+) -> List[Dict[str, Any]]:
 ```
+随机选择一个聊天，返回该聊天在指定时间范围内的消息。
 
-### `count_new_messages_for_users(chat_id, start_time, end_time, person_ids)`
+**Args:**
+- `start_time` (float): 开始时间戳
+- `end_time` (float): 结束时间戳
+- `limit` (int): 限制返回消息数量，0为不限制
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
 
-计算指定聊天中指定用户从开始时间到结束时间的新消息数量
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
 
----
 
-## 消息格式化API
+### 6. 获取指定用户在所有聊天中指定时间范围内的消息
+```python
+def get_messages_by_time_for_users(
+    start_time: float,
+    end_time: float,
+    person_ids: List[str],
+    limit: int = 0,
+    limit_mode: str = "latest",
+) -> List[Dict[str, Any]]:
+```
+获取指定用户在所有聊天中指定时间范围内的消息。
 
-### `build_readable_messages_to_str(messages, **options)`
+**Args:**
+- `start_time` (float): 开始时间戳
+- `end_time` (float): 结束时间戳
+- `person_ids` (List[str]): 用户ID列表
+- `limit` (int): 限制返回消息数量，0为不限制
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
 
-将消息列表构建成可读的字符串
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
 
-**参数：**
+
+### 7. 获取指定时间戳之前的消息
+```python
+def get_messages_before_time(
+    timestamp: float,
+    limit: int = 0,
+    filter_mai: bool = False,
+) -> List[Dict[str, Any]]:
+```
+获取指定时间戳之前的消息。
+
+**Args:**
+- `timestamp` (float): 时间戳
+- `limit` (int): 限制返回消息数量，0为不限制
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
+
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
+
+
+### 8. 获取指定聊天中指定时间戳之前的消息
+```python
+def get_messages_before_time_in_chat(
+    chat_id: str,
+    timestamp: float,
+    limit: int = 0,
+    filter_mai: bool = False,
+) -> List[Dict[str, Any]]:
+```
+获取指定聊天中指定时间戳之前的消息。
+
+**Args:**
+- `chat_id` (str): 聊天ID
+- `timestamp` (float): 时间戳
+- `limit` (int): 限制返回消息数量，0为不限制
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
+
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
+
+
+### 9. 获取指定用户在指定时间戳之前的消息
+```python
+def get_messages_before_time_for_users(
+    timestamp: float,
+    person_ids: List[str],
+    limit: int = 0,
+) -> List[Dict[str, Any]]:
+```
+获取指定用户在指定时间戳之前的消息。
+
+**Args:**
+- `timestamp` (float): 时间戳
+- `person_ids` (List[str]): 用户ID列表
+- `limit` (int): 限制返回消息数量，0为不限制
+
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
+
+
+### 10. 获取指定聊天中最近一段时间的消息
+```python
+def get_recent_messages(
+    chat_id: str,
+    hours: float = 24.0,
+    limit: int = 100,
+    limit_mode: str = "latest",
+    filter_mai: bool = False,
+) -> List[Dict[str, Any]]:
+```
+获取指定聊天中最近一段时间的消息。
+
+**Args:**
+- `chat_id` (str): 聊天ID
+- `hours` (float): 最近多少小时，默认24小时
+- `limit` (int): 限制返回消息数量，默认100条
+- `limit_mode` (str): 限制模式，`"earliest"`获取最早记录，`"latest"`获取最新记录
+- `filter_mai` (bool): 是否过滤掉机器人的消息，默认False
+
+**Returns:**
+- `List[Dict[str, Any]]` - 消息列表
+
+
+### 11. 计算指定聊天中从开始时间到结束时间的新消息数量
+```python
+def count_new_messages(
+    chat_id: str,
+    start_time: float = 0.0,
+    end_time: Optional[float] = None,
+) -> int:
+```
+计算指定聊天中从开始时间到结束时间的新消息数量。
+
+**Args:**
+- `chat_id` (str): 聊天ID
+- `start_time` (float): 开始时间戳
+- `end_time` (Optional[float]): 结束时间戳，如果为None则使用当前时间
+
+**Returns:**
+- `int` - 新消息数量
+
+
+### 12. 计算指定聊天中指定用户从开始时间到结束时间的新消息数量
+```python
+def count_new_messages_for_users(
+    chat_id: str,
+    start_time: float,
+    end_time: float,
+    person_ids: List[str],
+) -> int:
+```
+计算指定聊天中指定用户从开始时间到结束时间的新消息数量。
+
+**Args:**
+- `chat_id` (str): 聊天ID
+- `start_time` (float): 开始时间戳
+- `end_time` (float): 结束时间戳
+- `person_ids` (List[str]): 用户ID列表
+
+**Returns:**
+- `int` - 新消息数量
+
+
+### 13. 将消息列表构建成可读的字符串
+```python
+def build_readable_messages_to_str(
+    messages: List[Dict[str, Any]],
+    replace_bot_name: bool = True,
+    merge_messages: bool = False,
+    timestamp_mode: str = "relative",
+    read_mark: float = 0.0,
+    truncate: bool = False,
+    show_actions: bool = False,
+) -> str:
+```
+将消息列表构建成可读的字符串。
+
+**Args:**
 - `messages` (List[Dict[str, Any]]): 消息列表
-- `replace_bot_name` (bool): 是否将机器人的名称替换为"你"，默认True
+- `replace_bot_name` (bool): 是否将机器人的名称替换为"你"
-- `merge_messages` (bool): 是否合并连续消息，默认False
+- `merge_messages` (bool): 是否合并连续消息
-- `timestamp_mode` (str): 时间戳显示模式，`"relative"`或`"absolute"`，默认`"relative"`
+- `timestamp_mode` (str): 时间戳显示模式，`"relative"`或`"absolute"`
-- `read_mark` (float): 已读标记时间戳，用于分割已读和未读消息，默认0.0
+- `read_mark` (float): 已读标记时间戳，用于分割已读和未读消息
-- `truncate` (bool): 是否截断长消息，默认False
+- `truncate` (bool): 是否截断长消息
-- `show_actions` (bool): 是否显示动作记录，默认False
+- `show_actions` (bool): 是否显示动作记录
 
-**返回：** `str` - 格式化后的可读字符串
+**Returns:**
+- `str` - 格式化后的可读字符串
 
-**示例：**
+
+### 14. 将消息列表构建成可读的字符串，并返回详细信息
 ```python
-# 获取消息并格式化为可读文本
+async def build_readable_messages_with_details(
-messages = message_api.get_recent_messages("123456789", hours=2)
+    messages: List[Dict[str, Any]],
-readable_text = message_api.build_readable_messages_to_str(
+    replace_bot_name: bool = True,
-    messages,
+    merge_messages: bool = False,
-    replace_bot_name=True,
+    timestamp_mode: str = "relative",
-    merge_messages=True,
+    truncate: bool = False,
-    timestamp_mode="relative"
+) -> Tuple[str, List[Tuple[float, str, str]]]:
-)
-print(readable_text)
 ```
+将消息列表构建成可读的字符串，并返回详细信息。
 
-### `build_readable_messages_with_details(messages, **options)` 异步
+**Args:**
+- `messages` (List[Dict[str, Any]]): 消息列表
+- `replace_bot_name` (bool): 是否将机器人的名称替换为"你"
+- `merge_messages` (bool): 是否合并连续消息
+- `timestamp_mode` (str): 时间戳显示模式，`"relative"`或`"absolute"`
+- `truncate` (bool): 是否截断长消息
 
-将消息列表构建成可读的字符串，并返回详细信息
+**Returns:**
+- `Tuple[str, List[Tuple[float, str, str]]]` - 格式化后的可读字符串和详细信息元组列表(时间戳, 昵称, 内容)
 
-**参数：** 与 `build_readable_messages_to_str` 类似，但不包含 `read_mark` 和 `show_actions`
 
-**返回：** `Tuple[str, List[Tuple[float, str, str]]]` - 格式化字符串和详细信息元组列表(时间戳, 昵称, 内容)
+### 15. 从消息列表中提取不重复的用户ID列表
-
-**示例：**
 ```python
-# 异步获取详细格式化信息
+async def get_person_ids_from_messages(
-readable_text, details = await message_api.build_readable_messages_with_details(
+    messages: List[Dict[str, Any]],
-    messages,
+) -> List[str]:
-    timestamp_mode="absolute"
-)
-
-for timestamp, nickname, content in details:
-    print(f"{timestamp}: {nickname} 说: {content}")
 ```
+从消息列表中提取不重复的用户ID列表。
 
-### `get_person_ids_from_messages(messages)` 异步
+**Args:**
-
-从消息列表中提取不重复的用户ID列表
-
-**参数：**
 - `messages` (List[Dict[str, Any]]): 消息列表
 
-**返回：** `List[str]` - 用户ID列表
+**Returns:**
+- `List[str]` - 用户ID列表
 
-**示例：**
+
+### 16. 从消息列表中移除机器人的消息
 ```python
-# 获取参与对话的所有用户ID
+def filter_mai_messages(
-messages = message_api.get_recent_messages("123456789")
+    messages: List[Dict[str, Any]],
-person_ids = await message_api.get_person_ids_from_messages(messages)
+) -> List[Dict[str, Any]]:
-print(f"参与对话的用户: {person_ids}")
 ```
+从消息列表中移除机器人的消息。
 
----
+**Args:**
+- `messages` (List[Dict[str, Any]]): 消息列表，每个元素是消息字典
 
-## 完整使用示例
+**Returns:**
-
+- `List[Dict[str, Any]]` - 过滤后的消息列表
-### 场景1：统计活跃度
-
-```python
-import time
-from src.plugin_system.apis import message_api
-
-async def analyze_chat_activity(chat_id: str):
-    """分析聊天活跃度"""
-    now = time.time()
-    day_ago = now - 24 * 3600
-    
-    # 获取最近24小时的消息
-    messages = message_api.get_recent_messages(chat_id, hours=24)
-    
-    # 统计消息数量
-    total_count = len(messages)
-    
-    # 获取参与用户
-    person_ids = await message_api.get_person_ids_from_messages(messages)
-    
-    # 格式化消息内容
-    readable_text = message_api.build_readable_messages_to_str(
-        messages[-10:],  # 最后10条消息
-        merge_messages=True,
-        timestamp_mode="relative"
-    )
-    
-    return {
-        "total_messages": total_count,
-        "active_users": len(person_ids),
-        "recent_chat": readable_text
-    }
-```
-
-### 场景2：查看特定用户的历史消息
-
-```python
-def get_user_history(chat_id: str, user_id: str, days: int = 7):
-    """获取用户最近N天的消息历史"""
-    now = time.time()
-    start_time = now - days * 24 * 3600
-    
-    # 获取特定用户的消息
-    user_messages = message_api.get_messages_by_time_in_chat_for_users(
-        chat_id=chat_id,
-        start_time=start_time,
-        end_time=now,
-        person_ids=[user_id],
-        limit=100
-    )
-    
-    # 格式化为可读文本
-    readable_history = message_api.build_readable_messages_to_str(
-        user_messages,
-        replace_bot_name=False,
-        timestamp_mode="absolute"
-    )
-    
-    return readable_history
-```
-
----
 
 ## 注意事项
 
 1. **时间戳格式**：所有时间参数都使用Unix时间戳（float类型）
-2. **异步函数**：`build_readable_messages_with_details` 和 `get_person_ids_from_messages` 是异步函数，需要使用 `await`
+2. **异步函数**：部分函数是异步函数，需要使用 `await`
 3. **性能考虑**：查询大量消息时建议设置合理的 `limit` 参数
 4. **消息格式**：返回的消息是字典格式，包含时间戳、发送者、内容等信息
 5. **用户ID**：`person_ids` 参数接受字符串列表，用于筛选特定用户的消息 

docs/plugins/api/person-api.md

@@ -6,59 +6,65 @@
 
 ```python
 from src.plugin_system.apis import person_api
+# 或者
+from src.plugin_system import person_api
 ```
 
 ## 主要功能
 
-### 1. Person ID管理
+### 1. Person ID 获取
-
+```python
-#### `get_person_id(platform: str, user_id: int) -> str`
+def get_person_id(platform: str, user_id: int) -> str:
+```
 根据平台和用户ID获取person_id
 
-**参数：**
+**Args:**
 - `platform`：平台名称，如 "qq", "telegram" 等
 - `user_id`：用户ID
 
-**返回：**
+**Returns:**
 - `str`：唯一的person_id（MD5哈希值）
 
-**示例：**
+#### 示例
 ```python
 person_id = person_api.get_person_id("qq", 123456)
-print(f"Person ID: {person_id}")
 ```
 
 ### 2. 用户信息查询
+```python
+async def get_person_value(person_id: str, field_name: str, default: Any = None) -> Any:
+```
+查询单个用户信息字段值
 
-#### `get_person_value(person_id: str, field_name: str, default: Any = None) -> Any`
+**Args:**
-根据person_id和字段名获取某个值
-
-**参数：**
 - `person_id`：用户的唯一标识ID
-- `field_name`：要获取的字段名，如 "nickname", "impression" 等
+- `field_name`：要获取的字段名
-- `default`：当字段不存在或获取失败时返回的默认值
+- `default`：字段值不存在时的默认值
 
-**返回：**
+**Returns:**
 - `Any`：字段值或默认值
 
-**示例：**
+#### 示例
 ```python
 nickname = await person_api.get_person_value(person_id, "nickname", "未知用户")
 impression = await person_api.get_person_value(person_id, "impression")
 ```
 
-#### `get_person_values(person_id: str, field_names: list, default_dict: dict = None) -> dict`
+### 3. 批量用户信息查询
+```python
+async def get_person_values(person_id: str, field_names: list, default_dict: Optional[dict] = None) -> dict:
+```
 批量获取用户信息字段值
 
-**参数：**
+**Args:**
 - `person_id`：用户的唯一标识ID
 - `field_names`：要获取的字段名列表
 - `default_dict`：默认值字典，键为字段名，值为默认值
 
-**返回：**
+**Returns:**
 - `dict`：字段名到值的映射字典
 
-**示例：**
+#### 示例
 ```python
 values = await person_api.get_person_values(
     person_id,
@@ -67,204 +73,31 @@ values = await person_api.get_person_values(
 )
 ```
 
-### 3. 用户状态查询
+### 4. 判断用户是否已知
-
+```python
-#### `is_person_known(platform: str, user_id: int) -> bool`
+async def is_person_known(platform: str, user_id: int) -> bool:
+```
 判断是否认识某个用户
 
-**参数：**
+**Args:**
 - `platform`：平台名称
 - `user_id`：用户ID
 
-**返回：**
+**Returns:**
 - `bool`：是否认识该用户
 
-**示例：**
+### 5. 根据用户名获取Person ID
 ```python
-known = await person_api.is_person_known("qq", 123456)
+def get_person_id_by_name(person_name: str) -> str:
-if known:
-    print("这个用户我认识")
 ```
-
-### 4. 用户名查询
-
-#### `get_person_id_by_name(person_name: str) -> str`
 根据用户名获取person_id
 
-**参数：**
+**Args:**
 - `person_name`：用户名
 
-**返回：**
+**Returns:**
 - `str`：person_id，如果未找到返回空字符串
 
-**示例：**
-```python
-person_id = person_api.get_person_id_by_name("张三")
-if person_id:
-    print(f"找到用户: {person_id}")
-```
-
-## 使用示例
-
-### 1. 基础用户信息获取
-
-```python
-from src.plugin_system.apis import person_api
-
-async def get_user_info(platform: str, user_id: int):
-    """获取用户基本信息"""
-    
-    # 获取person_id
-    person_id = person_api.get_person_id(platform, user_id)
-    
-    # 获取用户信息
-    user_info = await person_api.get_person_values(
-        person_id,
-        ["nickname", "impression", "know_times", "last_seen"],
-        {
-            "nickname": "未知用户",
-            "impression": "",
-            "know_times": 0,
-            "last_seen": 0
-        }
-    )
-    
-    return {
-        "person_id": person_id,
-        "nickname": user_info["nickname"],
-        "impression": user_info["impression"],
-        "know_times": user_info["know_times"],
-        "last_seen": user_info["last_seen"]
-    }
-```
-
-### 2. 在Action中使用用户信息
-
-```python
-from src.plugin_system.base import BaseAction
-
-class PersonalizedAction(BaseAction):
-    async def execute(self, action_data, chat_stream):
-        # 获取发送者信息
-        user_id = chat_stream.user_info.user_id
-        platform = chat_stream.platform
-        
-        # 获取person_id
-        person_id = person_api.get_person_id(platform, user_id)
-        
-        # 获取用户昵称和印象
-        nickname = await person_api.get_person_value(person_id, "nickname", "朋友")
-        impression = await person_api.get_person_value(person_id, "impression", "")
-        
-        # 根据用户信息个性化回复
-        if impression:
-            response = f"你好 {nickname}！根据我对你的了解：{impression}"
-        else:
-            response = f"你好 {nickname}！很高兴见到你。"
-        
-        return {
-            "success": True,
-            "response": response,
-            "user_info": {
-                "nickname": nickname,
-                "impression": impression
-            }
-        }
-```
-
-### 3. 用户识别和欢迎
-
-```python
-async def welcome_user(chat_stream):
-    """欢迎用户，区分新老用户"""
-    
-    user_id = chat_stream.user_info.user_id
-    platform = chat_stream.platform
-    
-    # 检查是否认识这个用户
-    is_known = await person_api.is_person_known(platform, user_id)
-    
-    if is_known:
-        # 老用户，获取详细信息
-        person_id = person_api.get_person_id(platform, user_id)
-        nickname = await person_api.get_person_value(person_id, "nickname", "老朋友")
-        know_times = await person_api.get_person_value(person_id, "know_times", 0)
-        
-        welcome_msg = f"欢迎回来，{nickname}！我们已经聊过 {know_times} 次了。"
-    else:
-        # 新用户
-        welcome_msg = "你好！很高兴认识你，我是MaiBot。"
-    
-    return welcome_msg
-```
-
-### 4. 用户搜索功能
-
-```python
-async def find_user_by_name(name: str):
-    """根据名字查找用户"""
-    
-    person_id = person_api.get_person_id_by_name(name)
-    
-    if not person_id:
-        return {"found": False, "message": f"未找到名为 '{name}' 的用户"}
-    
-    # 获取用户详细信息
-    user_info = await person_api.get_person_values(
-        person_id,
-        ["nickname", "platform", "user_id", "impression", "know_times"],
-        {}
-    )
-    
-    return {
-        "found": True,
-        "person_id": person_id,
-        "info": user_info
-    }
-```
-
-### 5. 用户印象分析
-
-```python
-async def analyze_user_relationship(chat_stream):
-    """分析用户关系"""
-    
-    user_id = chat_stream.user_info.user_id
-    platform = chat_stream.platform
-    person_id = person_api.get_person_id(platform, user_id)
-    
-    # 获取关系相关信息
-    relationship_info = await person_api.get_person_values(
-        person_id,
-        ["nickname", "impression", "know_times", "relationship_level", "last_interaction"],
-        {
-            "nickname": "未知",
-            "impression": "",
-            "know_times": 0,
-            "relationship_level": "stranger",
-            "last_interaction": 0
-        }
-    )
-    
-    # 分析关系程度
-    know_times = relationship_info["know_times"]
-    if know_times == 0:
-        relationship = "陌生人"
-    elif know_times < 5:
-        relationship = "新朋友"
-    elif know_times < 20:
-        relationship = "熟人"
-    else:
-        relationship = "老朋友"
-    
-    return {
-        "nickname": relationship_info["nickname"],
-        "relationship": relationship,
-        "impression": relationship_info["impression"],
-        "interaction_count": know_times
-    }
-```
-
 ## 常用字段说明
 
 ### 基础信息字段
@@ -274,69 +107,13 @@ async def analyze_user_relationship(chat_stream):
 
 ### 关系信息字段
 - `impression`：对用户的印象
-- `know_times`：交互次数
+- `points`: 用户特征点
-- `relationship_level`：关系等级
-- `last_seen`：最后见面时间
-- `last_interaction`：最后交互时间
 
-### 个性化字段
+其他字段可以参考`PersonInfo`类的属性（位于`src.common.database.database_model`）
-- `preferences`：用户偏好
-- `interests`：兴趣爱好
-- `mood_history`：情绪历史
-- `topic_interests`：话题兴趣
-
-## 最佳实践
-
-### 1. 错误处理
-```python
-async def safe_get_user_info(person_id: str, field: str):
-    """安全获取用户信息"""
-    try:
-        value = await person_api.get_person_value(person_id, field)
-        return value if value is not None else "未设置"
-    except Exception as e:
-        logger.error(f"获取用户信息失败: {e}")
-        return "获取失败"
-```
-
-### 2. 批量操作
-```python
-async def get_complete_user_profile(person_id: str):
-    """获取完整用户档案"""
-    
-    # 一次性获取所有需要的字段
-    fields = [
-        "nickname", "impression", "know_times", 
-        "preferences", "interests", "relationship_level"
-    ]
-    
-    defaults = {
-        "nickname": "用户",
-        "impression": "",
-        "know_times": 0,
-        "preferences": "{}",
-        "interests": "[]",
-        "relationship_level": "stranger"
-    }
-    
-    profile = await person_api.get_person_values(person_id, fields, defaults)
-    
-    # 处理JSON字段
-    try:
-        profile["preferences"] = json.loads(profile["preferences"])
-        profile["interests"] = json.loads(profile["interests"])
-    except:
-        profile["preferences"] = {}
-        profile["interests"] = []
-    
-    return profile
-```
 
 ## 注意事项
 
-1. **异步操作**：大部分查询函数都是异步的，需要使用`await`
+1. **异步操作**：部分查询函数都是异步的，需要使用`await`
-2. **错误处理**：所有函数都有错误处理，失败时记录日志并返回默认值
+2. **性能考虑**：批量查询优于单个查询
-3. **数据类型**：返回的数据可能是字符串、数字或JSON，需要适当处理
+3. **隐私保护**：确保用户信息的使用符合隐私政策
-4. **性能考虑**：批量查询优于单个查询
+4. **数据一致性**：person_id是用户的唯一标识，应妥善保存和使用
-5. **隐私保护**：确保用户信息的使用符合隐私政策
-6. **数据一致性**：person_id是用户的唯一标识，应妥善保存和使用 

docs/plugins/api/plugin-manage-api.md

@@ -0,0 +1,105 @@
+# 插件管理API
+
+插件管理API模块提供了对插件的加载、卸载、重新加载以及目录管理功能。
+
+## 导入方式
+```python
+from src.plugin_system.apis import plugin_manage_api
+# 或者
+from src.plugin_system import plugin_manage_api
+```
+
+## 功能概述
+
+插件管理API主要提供以下功能：
+- **插件查询** - 列出当前加载的插件或已注册的插件。
+- **插件管理** - 加载、卸载、重新加载插件。
+- **插件目录管理** - 添加插件目录并重新扫描。
+
+## 主要功能
+
+### 1. 列出当前加载的插件
+```python
+def list_loaded_plugins() -> List[str]:
+```
+列出所有当前加载的插件。
+
+**Returns:**
+- `List[str]` - 当前加载的插件名称列表。
+
+### 2. 列出所有已注册的插件
+```python
+def list_registered_plugins() -> List[str]:
+```
+列出所有已注册的插件。
+
+**Returns:**
+- `List[str]` - 已注册的插件名称列表。
+
+### 3. 获取插件路径
+```python
+def get_plugin_path(plugin_name: str) -> str:
+```
+获取指定插件的路径。
+
+**Args:**
+- `plugin_name` (str): 要查询的插件名称。
+**Returns:**
+- `str` - 插件的路径，如果插件不存在则 raise ValueError。
+
+### 4. 卸载指定的插件
+```python
+async def remove_plugin(plugin_name: str) -> bool:
+```
+卸载指定的插件。
+
+**Args:**
+- `plugin_name` (str): 要卸载的插件名称。
+
+**Returns:**
+- `bool` - 卸载是否成功。
+
+### 5. 重新加载指定的插件
+```python
+async def reload_plugin(plugin_name: str) -> bool:
+```
+重新加载指定的插件。
+
+**Args:**
+- `plugin_name` (str): 要重新加载的插件名称。
+
+**Returns:**
+- `bool` - 重新加载是否成功。
+
+### 6. 加载指定的插件
+```python
+def load_plugin(plugin_name: str) -> Tuple[bool, int]:
+```
+加载指定的插件。
+
+**Args:**
+- `plugin_name` (str): 要加载的插件名称。
+
+**Returns:**
+- `Tuple[bool, int]` - 加载是否成功，成功或失败的个数。
+
+### 7. 添加插件目录
+```python
+def add_plugin_directory(plugin_directory: str) -> bool:
+```
+添加插件目录。
+
+**Args:**
+- `plugin_directory` (str): 要添加的插件目录路径。
+
+**Returns:**
+- `bool` - 添加是否成功。
+
+### 8. 重新扫描插件目录
+```python
+def rescan_plugin_directory() -> Tuple[int, int]:
+```
+重新扫描插件目录，加载新插件。
+
+**Returns:**
+- `Tuple[int, int]` - 成功加载的插件数量和失败的插件数量。

docs/plugins/api/send-api.md

@@ -6,86 +6,108 @@
 
 ```python
 from src.plugin_system.apis import send_api
+# 或者
+from src.plugin_system import send_api
 ```
 
 ## 主要功能
 
-### 1. 文本消息发送
+### 1. 发送文本消息
+```python
+async def text_to_stream(
+    text: str,
+    stream_id: str,
+    typing: bool = False,
+    reply_to: str = "",
+    storage_message: bool = True,
+) -> bool:
+```
+发送文本消息到指定的流
 
-#### `text_to_group(text, group_id, platform="qq", typing=False, reply_to="", storage_message=True)`
+**Args:**
-向群聊发送文本消息
+- `text` (str): 要发送的文本内容
+- `stream_id` (str): 聊天流ID
+- `typing` (bool): 是否显示正在输入
+- `reply_to` (str): 回复消息，格式为"发送者:消息内容"
+- `storage_message` (bool): 是否存储消息到数据库
 
-**参数：**
+**Returns:**
-- `text`：要发送的文本内容
+- `bool` - 是否发送成功
-- `group_id`：群聊ID
-- `platform`：平台，默认为"qq"
-- `typing`：是否显示正在输入
-- `reply_to`：回复消息的格式，如"发送者:消息内容"
-- `storage_message`：是否存储到数据库
 
-**返回：**
+### 2. 发送表情包
-- `bool`：是否发送成功
+```python
+async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True) -> bool:
+```
+向指定流发送表情包。
 
-#### `text_to_user(text, user_id, platform="qq", typing=False, reply_to="", storage_message=True)`
+**Args:**
-向用户发送私聊文本消息
+- `emoji_base64` (str): 表情包的base64编码
+- `stream_id` (str): 聊天流ID
+- `storage_message` (bool): 是否存储消息到数据库
 
-**参数与返回值同上**
+**Returns:**
+- `bool` - 是否发送成功
 
-### 2. 表情包发送
+### 3. 发送图片
+```python
+async def image_to_stream(image_base64: str, stream_id: str, storage_message: bool = True) -> bool:
+```
+向指定流发送图片。
 
-#### `emoji_to_group(emoji_base64, group_id, platform="qq", storage_message=True)`
+**Args:**
-向群聊发送表情包
+- `image_base64` (str): 图片的base64编码
+- `stream_id` (str): 聊天流ID
+- `storage_message` (bool): 是否存储消息到数据库
 
-**参数：**
+**Returns:**
-- `emoji_base64`：表情包的base64编码
+- `bool` - 是否发送成功
-- `group_id`：群聊ID
-- `platform`：平台，默认为"qq"
-- `storage_message`：是否存储到数据库
 
-#### `emoji_to_user(emoji_base64, user_id, platform="qq", storage_message=True)`
+### 4. 发送命令
-向用户发送表情包
+```python
+async def command_to_stream(command: Union[str, dict], stream_id: str, storage_message: bool = True, display_message: str = "") -> bool:
+```
+向指定流发送命令。
 
-### 3. 图片发送
+**Args:**
+- `command` (Union[str, dict]): 命令内容
+- `stream_id` (str): 聊天流ID
+- `storage_message` (bool): 是否存储消息到数据库
+- `display_message` (str): 显示消息
 
-#### `image_to_group(image_base64, group_id, platform="qq", storage_message=True)`
+**Returns:**
-向群聊发送图片
+- `bool` - 是否发送成功
 
-#### `image_to_user(image_base64, user_id, platform="qq", storage_message=True)`
+### 5. 发送自定义类型消息
-向用户发送图片
+```python
+async def custom_to_stream(
+    message_type: str,
+    content: str,
+    stream_id: str,
+    display_message: str = "",
+    typing: bool = False,
+    reply_to: str = "",
+    storage_message: bool = True,
+    show_log: bool = True,
+) -> bool:
+```
+向指定流发送自定义类型消息。
 
-### 4. 命令发送
+**Args:**
+- `message_type` (str): 消息类型，如"text"、"image"、"emoji"、"video"、"file"等
+- `content` (str): 消息内容（通常是base64编码或文本）
+- `stream_id` (str): 聊天流ID
+- `display_message` (str): 显示消息
+- `typing` (bool): 是否显示正在输入
+- `reply_to` (str): 回复消息，格式为"发送者:消息内容"
+- `storage_message` (bool): 是否存储消息到数据库
+- `show_log` (bool): 是否显示日志
 
-#### `command_to_group(command, group_id, platform="qq", storage_message=True)`
+**Returns:**
-向群聊发送命令
+- `bool` - 是否发送成功
-
-#### `command_to_user(command, user_id, platform="qq", storage_message=True)`
-向用户发送命令
-
-### 5. 自定义消息发送
-
-#### `custom_to_group(message_type, content, group_id, platform="qq", display_message="", typing=False, reply_to="", storage_message=True)`
-向群聊发送自定义类型消息
-
-#### `custom_to_user(message_type, content, user_id, platform="qq", display_message="", typing=False, reply_to="", storage_message=True)`
-向用户发送自定义类型消息
-
-#### `custom_message(message_type, content, target_id, is_group=True, platform="qq", display_message="", typing=False, reply_to="", storage_message=True)`
-通用的自定义消息发送
-
-**参数：**
-- `message_type`：消息类型，如"text"、"image"、"emoji"等
-- `content`：消息内容
-- `target_id`：目标ID（群ID或用户ID）
-- `is_group`：是否为群聊
-- `platform`：平台
-- `display_message`：显示消息
-- `typing`：是否显示正在输入
-- `reply_to`：回复消息
-- `storage_message`：是否存储
 
 ## 使用示例
 
-### 1. 基础文本发送
+### 1. 基础文本发送，并回复消息
 
 ```python
 from src.plugin_system.apis import send_api
@@ -93,57 +115,23 @@ from src.plugin_system.apis import send_api
 async def send_hello(chat_stream):
     """发送问候消息"""
     
-    if chat_stream.group_info:
+    success = await send_api.text_to_stream(
-        # 群聊
+        text="Hello, world!",
-        success = await send_api.text_to_group(
+        stream_id=chat_stream.stream_id,
-            text="大家好！",
+        typing=True,
-            group_id=chat_stream.group_info.group_id,
+        reply_to="User:How are you?",
-            typing=True
+        storage_message=True
-        )
+    )
-    else:
-        # 私聊
-        success = await send_api.text_to_user(
-            text="你好！",
-            user_id=chat_stream.user_info.user_id,
-            typing=True
-        )
     
     return success
 ```
 
-### 2. 回复特定消息
+### 2. 发送表情包
-
-```python
-async def reply_to_message(chat_stream, reply_text, original_sender, original_message):
-    """回复特定消息"""
-    
-    # 构建回复格式
-    reply_to = f"{original_sender}:{original_message}"
-    
-    if chat_stream.group_info:
-        success = await send_api.text_to_group(
-            text=reply_text,
-            group_id=chat_stream.group_info.group_id,
-            reply_to=reply_to
-        )
-    else:
-        success = await send_api.text_to_user(
-            text=reply_text,
-            user_id=chat_stream.user_info.user_id,
-            reply_to=reply_to
-        )
-    
-    return success
-```
-
-### 3. 发送表情包
 
 ```python
+from src.plugin_system.apis import emoji_api
 async def send_emoji_reaction(chat_stream, emotion):
     """根据情感发送表情包"""
-    
-    from src.plugin_system.apis import emoji_api
-    
     # 获取表情包
     emoji_result = await emoji_api.get_by_emotion(emotion)
     if not emoji_result:
@@ -152,107 +140,10 @@ async def send_emoji_reaction(chat_stream, emotion):
     emoji_base64, description, matched_emotion = emoji_result
     
     # 发送表情包
-    if chat_stream.group_info:
+    success = await send_api.emoji_to_stream(
-        success = await send_api.emoji_to_group(
+        emoji_base64=emoji_base64,
-            emoji_base64=emoji_base64,
+        stream_id=chat_stream.stream_id,
-            group_id=chat_stream.group_info.group_id
+        storage_message=False # 不存储到数据库
-        )
-    else:
-        success = await send_api.emoji_to_user(
-            emoji_base64=emoji_base64,
-            user_id=chat_stream.user_info.user_id
-        )
-    
-    return success
-```
-
-### 4. 在Action中发送消息
-
-```python
-from src.plugin_system.base import BaseAction
-
-class MessageAction(BaseAction):
-    async def execute(self, action_data, chat_stream):
-        message_type = action_data.get("type", "text")
-        content = action_data.get("content", "")
-        
-        if message_type == "text":
-            success = await self.send_text(chat_stream, content)
-        elif message_type == "emoji":
-            success = await self.send_emoji(chat_stream, content)
-        elif message_type == "image":
-            success = await self.send_image(chat_stream, content)
-        else:
-            success = False
-        
-        return {"success": success}
-    
-    async def send_text(self, chat_stream, text):
-        if chat_stream.group_info:
-            return await send_api.text_to_group(text, chat_stream.group_info.group_id)
-        else:
-            return await send_api.text_to_user(text, chat_stream.user_info.user_id)
-    
-    async def send_emoji(self, chat_stream, emoji_base64):
-        if chat_stream.group_info:
-            return await send_api.emoji_to_group(emoji_base64, chat_stream.group_info.group_id)
-        else:
-            return await send_api.emoji_to_user(emoji_base64, chat_stream.user_info.user_id)
-    
-    async def send_image(self, chat_stream, image_base64):
-        if chat_stream.group_info:
-            return await send_api.image_to_group(image_base64, chat_stream.group_info.group_id)
-        else:
-            return await send_api.image_to_user(image_base64, chat_stream.user_info.user_id)
-```
-
-### 5. 批量发送消息
-
-```python
-async def broadcast_message(message: str, target_groups: list):
-    """向多个群组广播消息"""
-    
-    results = {}
-    
-    for group_id in target_groups:
-        try:
-            success = await send_api.text_to_group(
-                text=message,
-                group_id=group_id,
-                typing=True
-            )
-            results[group_id] = success
-        except Exception as e:
-            results[group_id] = False
-            print(f"发送到群 {group_id} 失败: {e}")
-    
-    return results
-```
-
-### 6. 智能消息发送
-
-```python
-async def smart_send(chat_stream, message_data):
-    """智能发送不同类型的消息"""
-    
-    message_type = message_data.get("type", "text")
-    content = message_data.get("content", "")
-    options = message_data.get("options", {})
-    
-    # 根据聊天流类型选择发送方法
-    target_id = (chat_stream.group_info.group_id if chat_stream.group_info 
-                else chat_stream.user_info.user_id)
-    is_group = chat_stream.group_info is not None
-    
-    # 使用通用发送方法
-    success = await send_api.custom_message(
-        message_type=message_type,
-        content=content,
-        target_id=target_id,
-        is_group=is_group,
-        typing=options.get("typing", False),
-        reply_to=options.get("reply_to", ""),
-        display_message=options.get("display_message", "")
     )
     
     return success
@@ -273,90 +164,6 @@ async def smart_send(chat_stream, message_data):
 
 系统会自动查找匹配的原始消息并进行回复。
 
-## 高级用法
-
-### 1. 消息发送队列
-
-```python
-import asyncio
-
-class MessageQueue:
-    def __init__(self):
-        self.queue = asyncio.Queue()
-        self.running = False
-    
-    async def add_message(self, chat_stream, message_type, content, options=None):
-        """添加消息到队列"""
-        message_item = {
-            "chat_stream": chat_stream,
-            "type": message_type,
-            "content": content,
-            "options": options or {}
-        }
-        await self.queue.put(message_item)
-    
-    async def process_queue(self):
-        """处理消息队列"""
-        self.running = True
-        
-        while self.running:
-            try:
-                message_item = await asyncio.wait_for(self.queue.get(), timeout=1.0)
-                
-                # 发送消息
-                success = await smart_send(
-                    message_item["chat_stream"],
-                    {
-                        "type": message_item["type"],
-                        "content": message_item["content"],
-                        "options": message_item["options"]
-                    }
-                )
-                
-                # 标记任务完成
-                self.queue.task_done()
-                
-                # 发送间隔
-                await asyncio.sleep(0.5)
-                
-            except asyncio.TimeoutError:
-                continue
-            except Exception as e:
-                print(f"处理消息队列出错: {e}")
-```
-
-### 2. 消息模板系统
-
-```python
-class MessageTemplate:
-    def __init__(self):
-        self.templates = {
-            "welcome": "欢迎 {nickname} 加入群聊！",
-            "goodbye": "{nickname} 离开了群聊。",
-            "notification": "🔔 通知：{message}",
-            "error": "❌ 错误：{error_message}",
-            "success": "✅ 成功：{message}"
-        }
-    
-    def format_message(self, template_name: str, **kwargs) -> str:
-        """格式化消息模板"""
-        template = self.templates.get(template_name, "{message}")
-        return template.format(**kwargs)
-    
-    async def send_template(self, chat_stream, template_name: str, **kwargs):
-        """发送模板消息"""
-        message = self.format_message(template_name, **kwargs)
-        
-        if chat_stream.group_info:
-            return await send_api.text_to_group(message, chat_stream.group_info.group_id)
-        else:
-            return await send_api.text_to_user(message, chat_stream.user_info.user_id)
-
-# 使用示例
-template_system = MessageTemplate()
-await template_system.send_template(chat_stream, "welcome", nickname="张三")
-```
-
 ## 注意事项
 
 1. **异步操作**：所有发送函数都是异步的，必须使用`await`

docs/plugins/api/tool-api.md

@@ -0,0 +1,55 @@
+# 工具API
+
+工具API模块提供了获取和管理工具实例的功能，让插件能够访问系统中注册的工具。
+
+## 导入方式
+
+```python
+from src.plugin_system.apis import tool_api
+# 或者
+from src.plugin_system import tool_api
+```
+
+## 主要功能
+
+### 1. 获取工具实例
+
+```python
+def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
+```
+
+获取指定名称的工具实例。
+
+**Args**:
+- `tool_name`: 工具名称字符串
+
+**Returns**:
+- `Optional[BaseTool]`: 工具实例，如果工具不存在则返回 None
+
+### 2. 获取LLM可用的工具定义
+
+```python
+def get_llm_available_tool_definitions():
+```
+
+获取所有LLM可用的工具定义列表。
+
+**Returns**:
+- `List[Tuple[str, Dict[str, Any]]]`: 工具定义列表，每个元素为 `(工具名称, 工具定义字典)` 的元组
+  - 其具体定义请参照[tool-components.md](../tool-components.md)中的工具定义格式。
+#### 示例：
+
+```python
+# 获取所有LLM可用的工具定义
+tools = tool_api.get_llm_available_tool_definitions()
+for tool_name, tool_definition in tools:
+    print(f"工具: {tool_name}")
+    print(f"定义: {tool_definition}")
+```
+
+## 注意事项
+
+1. **工具存在性检查**：使用前请检查工具实例是否为 None
+2. **权限控制**：某些工具可能有使用权限限制
+3. **异步调用**：大多数工具方法是异步的，需要使用 await
+4. **错误处理**：调用工具时请做好异常处理

docs/plugins/api/utils-api.md

@@ -1,435 +0,0 @@
-# 工具API
-
-工具API模块提供了各种辅助功能，包括文件操作、时间处理、唯一ID生成等常用工具函数。
-
-## 导入方式
-
-```python
-from src.plugin_system.apis import utils_api
-```
-
-## 主要功能
-
-### 1. 文件操作
-
-#### `get_plugin_path(caller_frame=None) -> str`
-获取调用者插件的路径
-
-**参数：**
-- `caller_frame`：调用者的栈帧，默认为None（自动获取）
-
-**返回：**
-- `str`：插件目录的绝对路径
-
-**示例：**
-```python
-plugin_path = utils_api.get_plugin_path()
-print(f"插件路径: {plugin_path}")
-```
-
-#### `read_json_file(file_path: str, default: Any = None) -> Any`
-读取JSON文件
-
-**参数：**
-- `file_path`：文件路径，可以是相对于插件目录的路径
-- `default`：如果文件不存在或读取失败时返回的默认值
-
-**返回：**
-- `Any`：JSON数据或默认值
-
-**示例：**
-```python
-# 读取插件配置文件
-config = utils_api.read_json_file("config.json", {})
-settings = utils_api.read_json_file("data/settings.json", {"enabled": True})
-```
-
-#### `write_json_file(file_path: str, data: Any, indent: int = 2) -> bool`
-写入JSON文件
-
-**参数：**
-- `file_path`：文件路径，可以是相对于插件目录的路径
-- `data`：要写入的数据
-- `indent`：JSON缩进
-
-**返回：**
-- `bool`：是否写入成功
-
-**示例：**
-```python
-data = {"name": "test", "value": 123}
-success = utils_api.write_json_file("output.json", data)
-```
-
-### 2. 时间相关
-
-#### `get_timestamp() -> int`
-获取当前时间戳
-
-**返回：**
-- `int`：当前时间戳（秒）
-
-#### `format_time(timestamp: Optional[int] = None, format_str: str = "%Y-%m-%d %H:%M:%S") -> str`
-格式化时间
-
-**参数：**
-- `timestamp`：时间戳，如果为None则使用当前时间
-- `format_str`：时间格式字符串
-
-**返回：**
-- `str`：格式化后的时间字符串
-
-#### `parse_time(time_str: str, format_str: str = "%Y-%m-%d %H:%M:%S") -> int`
-解析时间字符串为时间戳
-
-**参数：**
-- `time_str`：时间字符串
-- `format_str`：时间格式字符串
-
-**返回：**
-- `int`：时间戳（秒）
-
-### 3. 其他工具
-
-#### `generate_unique_id() -> str`
-生成唯一ID
-
-**返回：**
-- `str`：唯一ID
-
-## 使用示例
-
-### 1. 插件数据管理
-
-```python
-from src.plugin_system.apis import utils_api
-
-class DataPlugin(BasePlugin):
-    def __init__(self):
-        self.plugin_path = utils_api.get_plugin_path()
-        self.data_file = "plugin_data.json"
-        self.load_data()
-    
-    def load_data(self):
-        """加载插件数据"""
-        default_data = {
-            "users": {},
-            "settings": {"enabled": True},
-            "stats": {"message_count": 0}
-        }
-        self.data = utils_api.read_json_file(self.data_file, default_data)
-    
-    def save_data(self):
-        """保存插件数据"""
-        return utils_api.write_json_file(self.data_file, self.data)
-    
-    async def handle_action(self, action_data, chat_stream):
-        # 更新统计信息
-        self.data["stats"]["message_count"] += 1
-        self.data["stats"]["last_update"] = utils_api.get_timestamp()
-        
-        # 保存数据
-        if self.save_data():
-            return {"success": True, "message": "数据已保存"}
-        else:
-            return {"success": False, "message": "数据保存失败"}
-```
-
-### 2. 日志记录系统
-
-```python
-class PluginLogger:
-    def __init__(self, plugin_name: str):
-        self.plugin_name = plugin_name
-        self.log_file = f"{plugin_name}_log.json"
-        self.logs = utils_api.read_json_file(self.log_file, [])
-    
-    def log_event(self, event_type: str, message: str, data: dict = None):
-        """记录事件"""
-        log_entry = {
-            "id": utils_api.generate_unique_id(),
-            "timestamp": utils_api.get_timestamp(),
-            "formatted_time": utils_api.format_time(),
-            "event_type": event_type,
-            "message": message,
-            "data": data or {}
-        }
-        
-        self.logs.append(log_entry)
-        
-        # 保持最新的100条记录
-        if len(self.logs) > 100:
-            self.logs = self.logs[-100:]
-        
-        # 保存到文件
-        utils_api.write_json_file(self.log_file, self.logs)
-    
-    def get_logs_by_type(self, event_type: str) -> list:
-        """获取指定类型的日志"""
-        return [log for log in self.logs if log["event_type"] == event_type]
-    
-    def get_recent_logs(self, count: int = 10) -> list:
-        """获取最近的日志"""
-        return self.logs[-count:]
-
-# 使用示例
-logger = PluginLogger("my_plugin")
-logger.log_event("user_action", "用户发送了消息", {"user_id": "123", "message": "hello"})
-```
-
-### 3. 配置管理系统
-
-```python
-class ConfigManager:
-    def __init__(self, config_file: str = "plugin_config.json"):
-        self.config_file = config_file
-        self.default_config = {
-            "enabled": True,
-            "debug": False,
-            "max_users": 100,
-            "response_delay": 1.0,
-            "features": {
-                "auto_reply": True,
-                "logging": True
-            }
-        }
-        self.config = self.load_config()
-    
-    def load_config(self) -> dict:
-        """加载配置"""
-        return utils_api.read_json_file(self.config_file, self.default_config)
-    
-    def save_config(self) -> bool:
-        """保存配置"""
-        return utils_api.write_json_file(self.config_file, self.config, indent=4)
-    
-    def get(self, key: str, default=None):
-        """获取配置值，支持嵌套访问"""
-        keys = key.split('.')
-        value = self.config
-        
-        for k in keys:
-            if isinstance(value, dict) and k in value:
-                value = value[k]
-            else:
-                return default
-        
-        return value
-    
-    def set(self, key: str, value):
-        """设置配置值，支持嵌套设置"""
-        keys = key.split('.')
-        config = self.config
-        
-        for k in keys[:-1]:
-            if k not in config:
-                config[k] = {}
-            config = config[k]
-        
-        config[keys[-1]] = value
-    
-    def update_config(self, updates: dict):
-        """批量更新配置"""
-        def deep_update(base, updates):
-            for key, value in updates.items():
-                if isinstance(value, dict) and key in base and isinstance(base[key], dict):
-                    deep_update(base[key], value)
-                else:
-                    base[key] = value
-        
-        deep_update(self.config, updates)
-
-# 使用示例
-config = ConfigManager()
-print(f"调试模式: {config.get('debug', False)}")
-print(f"自动回复: {config.get('features.auto_reply', True)}")
-
-config.set('features.new_feature', True)
-config.save_config()
-```
-
-### 4. 缓存系统
-
-```python
-class PluginCache:
-    def __init__(self, cache_file: str = "plugin_cache.json", ttl: int = 3600):
-        self.cache_file = cache_file
-        self.ttl = ttl  # 缓存过期时间（秒）
-        self.cache = self.load_cache()
-    
-    def load_cache(self) -> dict:
-        """加载缓存"""
-        return utils_api.read_json_file(self.cache_file, {})
-    
-    def save_cache(self):
-        """保存缓存"""
-        return utils_api.write_json_file(self.cache_file, self.cache)
-    
-    def get(self, key: str):
-        """获取缓存值"""
-        if key not in self.cache:
-            return None
-        
-        item = self.cache[key]
-        current_time = utils_api.get_timestamp()
-        
-        # 检查是否过期
-        if current_time - item["timestamp"] > self.ttl:
-            del self.cache[key]
-            return None
-        
-        return item["value"]
-    
-    def set(self, key: str, value):
-        """设置缓存值"""
-        self.cache[key] = {
-            "value": value,
-            "timestamp": utils_api.get_timestamp()
-        }
-        self.save_cache()
-    
-    def clear_expired(self):
-        """清理过期缓存"""
-        current_time = utils_api.get_timestamp()
-        expired_keys = []
-        
-        for key, item in self.cache.items():
-            if current_time - item["timestamp"] > self.ttl:
-                expired_keys.append(key)
-        
-        for key in expired_keys:
-            del self.cache[key]
-        
-        if expired_keys:
-            self.save_cache()
-        
-        return len(expired_keys)
-
-# 使用示例
-cache = PluginCache(ttl=1800)  # 30分钟过期
-cache.set("user_data_123", {"name": "张三", "score": 100})
-user_data = cache.get("user_data_123")
-```
-
-### 5. 时间处理工具
-
-```python
-class TimeHelper:
-    @staticmethod
-    def get_time_info():
-        """获取当前时间的详细信息"""
-        timestamp = utils_api.get_timestamp()
-        return {
-            "timestamp": timestamp,
-            "datetime": utils_api.format_time(timestamp),
-            "date": utils_api.format_time(timestamp, "%Y-%m-%d"),
-            "time": utils_api.format_time(timestamp, "%H:%M:%S"),
-            "year": utils_api.format_time(timestamp, "%Y"),
-            "month": utils_api.format_time(timestamp, "%m"),
-            "day": utils_api.format_time(timestamp, "%d"),
-            "weekday": utils_api.format_time(timestamp, "%A")
-        }
-    
-    @staticmethod
-    def time_ago(timestamp: int) -> str:
-        """计算时间差"""
-        current = utils_api.get_timestamp()
-        diff = current - timestamp
-        
-        if diff < 60:
-            return f"{diff}秒前"
-        elif diff < 3600:
-            return f"{diff // 60}分钟前"
-        elif diff < 86400:
-            return f"{diff // 3600}小时前"
-        else:
-            return f"{diff // 86400}天前"
-    
-    @staticmethod
-    def parse_duration(duration_str: str) -> int:
-        """解析时间段字符串，返回秒数"""
-        import re
-        
-        pattern = r'(\d+)([smhd])'
-        matches = re.findall(pattern, duration_str.lower())
-        
-        total_seconds = 0
-        for value, unit in matches:
-            value = int(value)
-            if unit == 's':
-                total_seconds += value
-            elif unit == 'm':
-                total_seconds += value * 60
-            elif unit == 'h':
-                total_seconds += value * 3600
-            elif unit == 'd':
-                total_seconds += value * 86400
-        
-        return total_seconds
-
-# 使用示例
-time_info = TimeHelper.get_time_info()
-print(f"当前时间: {time_info['datetime']}")
-
-last_seen = 1699000000
-print(f"最后见面: {TimeHelper.time_ago(last_seen)}")
-
-duration = TimeHelper.parse_duration("1h30m")  # 1小时30分钟 = 5400秒
-```
-
-## 最佳实践
-
-### 1. 错误处理
-```python
-def safe_file_operation(file_path: str, data: dict):
-    """安全的文件操作"""
-    try:
-        success = utils_api.write_json_file(file_path, data)
-        if not success:
-            logger.warning(f"文件写入失败: {file_path}")
-        return success
-    except Exception as e:
-        logger.error(f"文件操作出错: {e}")
-        return False
-```
-
-### 2. 路径处理
-```python
-import os
-
-def get_data_path(filename: str) -> str:
-    """获取数据文件的完整路径"""
-    plugin_path = utils_api.get_plugin_path()
-    data_dir = os.path.join(plugin_path, "data")
-    
-    # 确保数据目录存在
-    os.makedirs(data_dir, exist_ok=True)
-    
-    return os.path.join(data_dir, filename)
-```
-
-### 3. 定期清理
-```python
-async def cleanup_old_files():
-    """清理旧文件"""
-    plugin_path = utils_api.get_plugin_path()
-    current_time = utils_api.get_timestamp()
-    
-    for filename in os.listdir(plugin_path):
-        if filename.endswith('.tmp'):
-            file_path = os.path.join(plugin_path, filename)
-            file_time = os.path.getmtime(file_path)
-            
-            # 删除超过24小时的临时文件
-            if current_time - file_time > 86400:
-                os.remove(file_path)
-```
-
-## 注意事项
-
-1. **相对路径**：文件路径支持相对于插件目录的路径
-2. **自动创建目录**：写入文件时会自动创建必要的目录
-3. **错误处理**：所有函数都有错误处理，失败时返回默认值
-4. **编码格式**：文件读写使用UTF-8编码
-5. **时间格式**：时间戳使用秒为单位
-6. **JSON格式**：JSON文件使用可读性好的缩进格式 

docs/plugins/index.md

@@ -10,6 +10,7 @@
 
 - [🧱 Action组件详解](action-components.md) - 掌握最核心的Action组件
 - [💻 Command组件详解](command-components.md) - 学习直接响应命令的组件
+- [🔧 Tool组件详解](tool-components.md) - 了解如何扩展信息获取能力
 - [⚙️ 配置文件系统指南](configuration-guide.md) - 学会使用自动生成的插件配置文件
 - [📄 Manifest系统指南](manifest-guide.md) - 了解插件元数据管理和配置架构
 
@@ -43,24 +44,24 @@ Command vs Action 选择指南
 - [LLM API](api/llm-api.md) - 大语言模型交互接口，可以使用内置LLM生成内容
 - [✨ 回复生成器API](api/generator-api.md) - 智能回复生成接口，可以使用内置风格化生成器
 
-### 表情包api
+### 表情包API
 - [😊 表情包API](api/emoji-api.md) - 表情包选择和管理接口
 
-### 关系系统api
+### 关系系统API
 - [人物信息API](api/person-api.md) - 用户信息，处理麦麦认识的人和关系的接口
 
 ### 数据与配置API
 - [🗄️ 数据库API](api/database-api.md) - 数据库操作接口
 - [⚙️ 配置API](api/config-api.md) - 配置读取和用户信息接口
 
+### 插件和组件管理API
+- [🔌 插件API](api/plugin-manage-api.md) - 插件加载和管理接口
+- [🧩 组件API](api/component-manage-api.md) - 组件注册和管理接口
+
+### 日志API
+- [📜 日志API](api/logging-api.md) - logger实例获取接口
 ### 工具API
-- [工具API](api/utils-api.md) - 文件操作、时间处理等工具函数
+- [🔧 工具API](api/tool-api.md) - tool获取接口
-
-
-## 实验性
-
-这些功能将在未来重构或移除
-- [🔧 工具系统详解](tool-system.md) - 工具系统的使用和开发
 
 
 

docs/plugins/tool-components.md

@@ -1,10 +1,10 @@
-# 🔧 工具系统详解
+# 🔧 工具组件详解
 
-## 📖 什么是工具系统
+## 📖 什么是工具
 
-工具系统是MaiBot的信息获取能力扩展组件。如果说Action组件功能五花八门，可以拓展麦麦能做的事情，那么Tool就是在某个过程中拓宽了麦麦能够获得的信息量。
+工具是MaiBot的信息获取能力扩展组件。如果说Action组件功能五花八门，可以拓展麦麦能做的事情，那么Tool就是在某个过程中拓宽了麦麦能够获得的信息量。
 
-### 🎯 工具系统的特点
+### 🎯 工具的特点
 
 - 🔍 **信息获取增强**：扩展麦麦获取外部信息的能力
 - 📊 **数据丰富**：帮助麦麦获得更多背景信息和实时数据
@@ -20,14 +20,11 @@
 | **目标** | 让麦麦做更多事情 | 提供具体功能 | 让麦麦知道更多信息 |
 | **使用场景** | 增强交互体验 | 功能服务 | 信息查询和分析 |
 
-## 🏗️ 工具基本结构
+## 🏗️ Tool组件的基本结构
-
-### 必要组件
 
 每个工具必须继承 `BaseTool` 基类并实现以下属性和方法：
-
 ```python
-from src.tools.tool_can_use.base_tool import BaseTool, register_tool
+from src.plugin_system import BaseTool, ToolParamType
 
 class MyTool(BaseTool):
     # 工具名称，必须唯一
@@ -36,21 +33,29 @@ class MyTool(BaseTool):
     # 工具描述，告诉LLM这个工具的用途
     description = "这个工具用于获取特定类型的信息"
     
-    # 参数定义，遵循JSONSchema格式
+    # 参数定义，仅定义参数
-    parameters = {
+    # 比如想要定义一个类似下面的openai格式的参数表，则可以这么定义:
-        "type": "object",
+    # {
-        "properties": {
+    #     "type": "object",
-            "query": {
+    #     "properties": {
-                "type": "string",
+    #         "query": {
-                "description": "查询参数"
+    #             "type": "string",
-            },
+    #             "description": "查询参数"
-            "limit": {
+    #         },
-                "type": "integer", 
+    #         "limit": {
-                "description": "结果数量限制"
+    #             "type": "integer", 
-            }
+    #             "description": "结果数量限制"
-        },
+    #             "enum": [10, 20, 50]  # 可选值
-        "required": ["query"]
+    #         }
-    }
+    #     },
+    #     "required": ["query"]
+    # }
+    parameters = [
+        ("query", ToolParamType.STRING, "查询参数", True, None),  # 必填参数
+        ("limit", ToolParamType.INTEGER, "结果数量限制", False, ["10", "20", "50"])  # 可选参数
+    ]
+
+    available_for_llm = True  # 是否对LLM可用
     
     async def execute(self, function_args: Dict[str, Any]):
         """执行工具逻辑"""
@@ -69,7 +74,12 @@ class MyTool(BaseTool):
 |-----|------|------|
 | `name` | str | 工具的唯一标识名称 |
 | `description` | str | 工具功能描述，帮助LLM理解用途 |
-| `parameters` | dict | JSONSchema格式的参数定义 |
+| `parameters` | list[tuple] | 参数定义 |
+
+其构造而成的工具定义为:
+```python
+{"name": cls.name, "description": cls.description, "parameters": cls.parameters}
+```
 
 ### 方法说明
 
@@ -77,15 +87,6 @@ class MyTool(BaseTool):
 |-----|------|--------|------|
 | `execute` | `function_args` | `dict` | 执行工具核心逻辑 |
 
-## 🔄 自动注册机制
-
-工具系统采用自动发现和注册机制：
-
-1. **文件扫描**：系统自动遍历 `tool_can_use` 目录中的所有Python文件
-2. **类识别**：寻找继承自 `BaseTool` 的工具类
-3. **自动注册**：只需要实现对应的类并把文件放在正确文件夹中就可自动注册
-4. **即用即加载**：工具在需要时被实例化和调用
-
 ---
 
 ## 🎨 完整工具示例
@@ -93,7 +94,7 @@ class MyTool(BaseTool):
 完成一个天气查询工具
 
 ```python
-from src.tools.tool_can_use.base_tool import BaseTool, register_tool
+from src.plugin_system import BaseTool
 import aiohttp
 import json
 
@@ -102,23 +103,13 @@ class WeatherTool(BaseTool):
     
     name = "weather_query"
     description = "查询指定城市的实时天气信息，包括温度、湿度、天气状况等"
+    available_for_llm = True  # 允许LLM调用此工具
+    parameters = [
+        ("city", ToolParamType.STRING, "要查询天气的城市名称，如：北京、上海、纽约", True, None),
+        ("country", ToolParamType.STRING, "国家代码，如：CN、US，可选参数", False, None)
+    ]
     
-    parameters = {
+    async def execute(self, function_args: dict):
-        "type": "object",
-        "properties": {
-            "city": {
-                "type": "string",
-                "description": "要查询天气的城市名称，如：北京、上海、纽约"
-            },
-            "country": {
-                "type": "string", 
-                "description": "国家代码，如：CN、US，可选参数"
-            }
-        },
-        "required": ["city"]
-    }
-    
-    async def execute(self, function_args, message_txt=""):
         """执行天气查询"""
         try:
             city = function_args.get("city")
@@ -177,55 +168,12 @@ class WeatherTool(BaseTool):
 
 ---
 
-## 📊 工具开发步骤
-
-### 1. 创建工具文件
-
-在 `src/tools/tool_can_use/` 目录下创建新的Python文件：
-
-```bash
-# 例如创建 my_new_tool.py
-touch src/tools/tool_can_use/my_new_tool.py
-```
-
-### 2. 实现工具类
-
-```python
-from src.tools.tool_can_use.base_tool import BaseTool, register_tool
-
-class MyNewTool(BaseTool):
-    name = "my_new_tool"
-    description = "新工具的功能描述"
-    
-    parameters = {
-        "type": "object",
-        "properties": {
-            # 定义参数
-        },
-        "required": []
-    }
-    
-    async def execute(self, function_args, message_txt=""):
-        # 实现工具逻辑
-        return {
-            "name": self.name,
-            "content": "执行结果"
-        }
-```
-
-### 3. 系统集成
-
-工具创建完成后，系统会自动发现和注册，无需额外配置。
-
----
-
 ## 🚨 注意事项和限制
 
 ### 当前限制
 
-1. **独立开发**：需要单独编写，暂未完全融入插件系统
+1. **适用范围**：主要适用于信息获取场景
-2. **适用范围**：主要适用于信息获取场景
+2. **配置要求**：必须开启工具处理器
-3. **配置要求**：必须开启工具处理器
 
 ### 开发建议
 
@@ -238,66 +186,49 @@ class MyNewTool(BaseTool):
 ## 🎯 最佳实践
 
 ### 1. 工具命名规范
-
+#### ✅ 好的命名
 ```python
-# ✅ 好的命名
 name = "weather_query"        # 清晰表达功能
 name = "knowledge_search"     # 描述性强
 name = "stock_price_check"    # 功能明确
-
+```
-# ❌ 避免的命名
+#### ❌ 避免的命名
+```python
 name = "tool1"               # 无意义
 name = "wq"                  # 过于简短
 name = "weather_and_news"    # 功能过于复杂
 ```
 
 ### 2. 描述规范
-
+#### ✅ 良好的描述
 ```python
-# ✅ 好的描述
 description = "查询指定城市的实时天气信息，包括温度、湿度、天气状况"
-
+```
-# ❌ 避免的描述
+#### ❌ 避免的描述
+```python
 description = "天气"         # 过于简单
 description = "获取信息"      # 不够具体
 ```
 
 ### 3. 参数设计
 
+#### ✅ 合理的参数设计
 ```python
-# ✅ 合理的参数设计
+parameters = [
-parameters = {
+    ("city", ToolParamType.STRING, "城市名称，如：北京、上海", True, None),
-    "type": "object",
+    ("unit", ToolParamType.STRING, "温度单位：celsius 或 fahrenheit", False, ["celsius", "fahrenheit"])
-    "properties": {
+]
-        "city": {
+```
-            "type": "string",
+#### ❌ 避免的参数设计
-            "description": "城市名称，如：北京、上海"
+```python
-        },
+parameters = [
-        "unit": {
+    ("data", "string", "数据", True)  # 参数过于模糊
-            "type": "string",
+]
-            "description": "温度单位：celsius(摄氏度) 或 fahrenheit(华氏度)",
-            "enum": ["celsius", "fahrenheit"]
-        }
-    },
-    "required": ["city"]
-}
-
-# ❌ 避免的参数设计
-parameters = {
-    "type": "object",
-    "properties": {
-        "data": {
-            "type": "string",
-            "description": "数据"  # 描述不清晰
-        }
-    }
-}
 ```
 
 ### 4. 结果格式化
-
+#### ✅ 良好的结果格式
 ```python
-# ✅ 良好的结果格式
 def _format_result(self, data):
     return f"""
 🔍 查询结果
@@ -307,12 +238,9 @@ def _format_result(self, data):
 📝 说明: {data['description']}
 ━━━━━━━━━━━━
     """.strip()
-
+```
-# ❌ 避免的结果格式
+#### ❌ 避免的结果格式
+```python
 def _format_result(self, data):
     return str(data)  # 直接返回原始数据
 ```
-
----
-
-🎉 **工具系统为麦麦提供了强大的信息获取能力！合理使用工具可以让麦麦变得更加智能和博学。** 

plugins/hello_world_plugin/plugin.py

@@ -1,18 +1,55 @@
-from typing import List, Tuple, Type
+from typing import List, Tuple, Type, Any
 from src.plugin_system import (
     BasePlugin,
     register_plugin,
     BaseAction,
     BaseCommand,
+    BaseTool,
     ComponentInfo,
     ActionActivationType,
     ConfigField,
     BaseEventHandler,
     EventType,
     MaiMessages,
+    ToolParamType
 )
 
 
+class CompareNumbersTool(BaseTool):
+    """比较两个数大小的工具"""
+
+    name = "compare_numbers"
+    description = "使用工具 比较两个数的大小，返回较大的数"
+    parameters = [
+        ("num1", ToolParamType.FLOAT, "第一个数字", True, None),
+        ("num2", ToolParamType.FLOAT, "第二个数字", True, None),
+    ]
+
+    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
+        """执行比较两个数的大小
+
+        Args:
+            function_args: 工具参数
+
+        Returns:
+            dict: 工具执行结果
+        """
+        num1: int | float = function_args.get("num1")  # type: ignore
+        num2: int | float = function_args.get("num2")  # type: ignore
+
+        try:
+            if num1 > num2:
+                result = f"{num1} 大于 {num2}"
+            elif num1 < num2:
+                result = f"{num1} 小于 {num2}"
+            else:
+                result = f"{num1} 等于 {num2}"
+
+            return {"name": self.name, "content": result}
+        except Exception as e:
+            return {"name": self.name, "content": f"比较数字失败，炸了: {str(e)}"}
+
+
 # ===== Action组件 =====
 class HelloAction(BaseAction):
     """问候Action - 简单的问候动作"""
@@ -132,7 +169,9 @@ class HelloWorldPlugin(BasePlugin):
             "enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
         },
         "greeting": {
-            "message": ConfigField(type=str, default="嗨！很开心见到你！😊", description="默认问候消息"),
+            "message": ConfigField(
+                type=list, default=["嗨！很开心见到你！😊", "Ciallo～(∠・ω< )⌒★"], description="默认问候消息"
+            ),
             "enable_emoji": ConfigField(type=bool, default=True, description="是否启用表情符号"),
         },
         "time": {"format": ConfigField(type=str, default="%Y-%m-%d %H:%M:%S", description="时间显示格式")},
@@ -142,6 +181,7 @@ class HelloWorldPlugin(BasePlugin):
     def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
         return [
             (HelloAction.get_action_info(), HelloAction),
+            (CompareNumbersTool.get_tool_info(), CompareNumbersTool),  # 添加比较数字工具
             (ByeAction.get_action_info(), ByeAction),  # 添加告别Action
             (TimeCommand.get_command_info(), TimeCommand),
             (PrintMessage.get_handler_info(), PrintMessage),

requirements.txt

@@ -15,6 +15,7 @@ matplotlib
 networkx
 numpy
 openai
+google-genai
 pandas
 peewee
 pyarrow

scripts/import_openie.py

@@ -24,46 +24,6 @@ OPENIE_DIR = os.path.join(ROOT_PATH, "data", "openie")
 
 logger = get_logger("OpenIE导入")
 
-ENV_FILE = os.path.join(ROOT_PATH, ".env")
-
-if os.path.exists(".env"):
-    load_dotenv(".env", override=True)
-    print("成功加载环境变量配置")
-else:
-    print("未找到.env文件，请确保程序所需的环境变量被正确设置")
-    raise FileNotFoundError(".env 文件不存在，请创建并配置所需的环境变量")
-
-env_mask = {key: os.getenv(key) for key in os.environ}
-def scan_provider(env_config: dict):
-    provider = {}
-
-    # 利用未初始化 env 时获取的 env_mask 来对新的环境变量集去重
-    # 避免 GPG_KEY 这样的变量干扰检查
-    env_config = dict(filter(lambda item: item[0] not in env_mask, env_config.items()))
-
-    # 遍历 env_config 的所有键
-    for key in env_config:
-        # 检查键是否符合 {provider}_BASE_URL 或 {provider}_KEY 的格式
-        if key.endswith("_BASE_URL") or key.endswith("_KEY"):
-            # 提取 provider 名称
-            provider_name = key.split("_", 1)[0]  # 从左分割一次，取第一部分
-
-            # 初始化 provider 的字典（如果尚未初始化）
-            if provider_name not in provider:
-                provider[provider_name] = {"url": None, "key": None}
-
-            # 根据键的类型填充 url 或 key
-            if key.endswith("_BASE_URL"):
-                provider[provider_name]["url"] = env_config[key]
-            elif key.endswith("_KEY"):
-                provider[provider_name]["key"] = env_config[key]
-
-    # 检查每个 provider 是否同时存在 url 和 key
-    for provider_name, config in provider.items():
-        if config["url"] is None or config["key"] is None:
-            logger.error(f"provider 内容：{config}\nenv_config 内容：{env_config}")
-            raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量")
-
 def ensure_openie_dir():
     """确保OpenIE数据目录存在"""
     if not os.path.exists(OPENIE_DIR):
@@ -214,8 +174,6 @@ def handle_import_openie(openie_data: OpenIE, embed_manager: EmbeddingManager, k
 
 def main():  # sourcery skip: dict-comprehension
     # 新增确认提示
-    env_config = {key: os.getenv(key) for key in os.environ}
-    scan_provider(env_config)
     print("=== 重要操作确认 ===")
     print("OpenIE导入时会大量发送请求，可能会撞到请求速度上限，请注意选用的模型")
     print("同之前样例：在本地模型下，在70分钟内我们发送了约8万条请求，在网络允许下，速度会更快")

scripts/info_extraction.py

@@ -25,9 +25,8 @@ from rich.progress import (
     TextColumn,
 )
 from raw_data_preprocessor import RAW_DATA_PATH, load_raw_data
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
-from dotenv import load_dotenv
 
 logger = get_logger("LPMM知识库-信息提取")
 
@@ -36,45 +35,6 @@ ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 TEMP_DIR = os.path.join(ROOT_PATH, "temp")
 # IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data", "imported_lpmm_data")
 OPENIE_OUTPUT_DIR = os.path.join(ROOT_PATH, "data", "openie")
-ENV_FILE = os.path.join(ROOT_PATH, ".env")
-
-if os.path.exists(".env"):
-    load_dotenv(".env", override=True)
-    print("成功加载环境变量配置")
-else:
-    print("未找到.env文件，请确保程序所需的环境变量被正确设置")
-    raise FileNotFoundError(".env 文件不存在，请创建并配置所需的环境变量")
-
-env_mask = {key: os.getenv(key) for key in os.environ}
-def scan_provider(env_config: dict):
-    provider = {}
-
-    # 利用未初始化 env 时获取的 env_mask 来对新的环境变量集去重
-    # 避免 GPG_KEY 这样的变量干扰检查
-    env_config = dict(filter(lambda item: item[0] not in env_mask, env_config.items()))
-
-    # 遍历 env_config 的所有键
-    for key in env_config:
-        # 检查键是否符合 {provider}_BASE_URL 或 {provider}_KEY 的格式
-        if key.endswith("_BASE_URL") or key.endswith("_KEY"):
-            # 提取 provider 名称
-            provider_name = key.split("_", 1)[0]  # 从左分割一次，取第一部分
-
-            # 初始化 provider 的字典（如果尚未初始化）
-            if provider_name not in provider:
-                provider[provider_name] = {"url": None, "key": None}
-
-            # 根据键的类型填充 url 或 key
-            if key.endswith("_BASE_URL"):
-                provider[provider_name]["url"] = env_config[key]
-            elif key.endswith("_KEY"):
-                provider[provider_name]["key"] = env_config[key]
-
-    # 检查每个 provider 是否同时存在 url 和 key
-    for provider_name, config in provider.items():
-        if config["url"] is None or config["key"] is None:
-            logger.error(f"provider 内容：{config}\nenv_config 内容：{env_config}")
-            raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量")
 
 def ensure_dirs():
     """确保临时目录和输出目录存在"""
@@ -96,11 +56,11 @@ open_ie_doc_lock = Lock()
 shutdown_event = Event()
 
 lpmm_entity_extract_llm = LLMRequest(
-    model=global_config.model.lpmm_entity_extract,
+    model_set=model_config.model_task_config.lpmm_entity_extract,
     request_type="lpmm.entity_extract"
 )
 lpmm_rdf_build_llm = LLMRequest(
-    model=global_config.model.lpmm_rdf_build,
+    model_set=model_config.model_task_config.lpmm_rdf_build,
     request_type="lpmm.rdf_build"
 )
 def process_single_text(pg_hash, raw_data):
@@ -158,8 +118,6 @@ def main():  # sourcery skip: comprehension-to-generator, extract-method
     # 设置信号处理器
     signal.signal(signal.SIGINT, signal_handler)
     ensure_dirs()  # 确保目录存在
-    env_config = {key: os.getenv(key) for key in os.environ}
-    scan_provider(env_config)
     # 新增用户确认提示
     print("=== 重要操作确认，请认真阅读以下内容哦 ===")
     print("实体提取操作将会花费较多api余额和时间，建议在空闲时段执行。")

src/chat/chat_loop/heartFC_chat.py

@@ -414,7 +414,7 @@ class HeartFChatting:
                     else:
                         logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容")
 
-            action_message: Dict[str, Any] = message_data or target_message  # type: ignore
+            action_message = message_data or target_message
             if action_type == "reply":
                 # 等待回复生成完毕
                 if self.loop_mode == ChatMode.NORMAL:

src/chat/emoji_system/emoji_manager.py

@@ -8,15 +8,15 @@ import traceback
 import io
 import re
 import binascii
+
 from typing import Optional, Tuple, List, Any
 from PIL import Image
 from rich.traceback import install
 
-
 from src.common.database.database_model import Emoji
 from src.common.database.database import db as peewee_db
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.utils.utils_image import image_path_to_base64, get_image_manager
 from src.llm_models.utils_model import LLMRequest
 
@@ -379,9 +379,9 @@ class EmojiManager:
 
         self._scan_task = None
 
-        self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
+        self.vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="emoji")
         self.llm_emotion_judge = LLMRequest(
-            model=global_config.model.utils, max_tokens=600, request_type="emoji"
+            model_set=model_config.model_task_config.utils, request_type="emoji"
         )  # 更高的温度，更少的token（后续可以根据情绪来调整温度）
 
         self.emoji_num = 0
@@ -492,6 +492,7 @@ class EmojiManager:
             return None
 
     def _levenshtein_distance(self, s1: str, s2: str) -> int:
+        # sourcery skip: simplify-empty-collection-comparison, simplify-len-comparison, simplify-str-len-comparison
         """计算两个字符串的编辑距离
 
         Args:
@@ -629,11 +630,11 @@ class EmojiManager:
                         if success:
                             # 注册成功则跳出循环
                             break
-                        else:
+
-                            # 注册失败则删除对应文件
+                        # 注册失败则删除对应文件
-                            file_path = os.path.join(EMOJI_DIR, filename)
+                        file_path = os.path.join(EMOJI_DIR, filename)
-                            os.remove(file_path)
+                        os.remove(file_path)
-                            logger.warning(f"[清理] 删除注册失败的表情包文件: {filename}")
+                        logger.warning(f"[清理] 删除注册失败的表情包文件: {filename}")
                 except Exception as e:
                     logger.error(f"[错误] 扫描表情包目录失败: {str(e)}")
 
@@ -694,6 +695,7 @@ class EmojiManager:
             return []
 
     async def get_emoji_from_manager(self, emoji_hash: str) -> Optional["MaiEmoji"]:
+        # sourcery skip: use-next
         """从内存中的 emoji_objects 列表获取表情包
 
         参数:
@@ -709,10 +711,10 @@ class EmojiManager:
 
     async def get_emoji_description_by_hash(self, emoji_hash: str) -> Optional[str]:
         """根据哈希值获取已注册表情包的描述
-        
+
         Args:
             emoji_hash: 表情包的哈希值
-            
+
         Returns:
             Optional[str]: 表情包描述，如果未找到则返回None
         """
@@ -722,7 +724,7 @@ class EmojiManager:
             if emoji and emoji.description:
                 logger.info(f"[缓存命中] 从内存获取表情包描述: {emoji.description[:50]}...")
                 return emoji.description
-            
+
             # 如果内存中没有，从数据库查找
             self._ensure_db()
             try:
@@ -732,9 +734,9 @@ class EmojiManager:
                     return emoji_record.description
             except Exception as e:
                 logger.error(f"从数据库查询表情包描述时出错: {e}")
-            
+
             return None
-            
+
         except Exception as e:
             logger.error(f"获取表情包描述失败 (Hash: {emoji_hash}): {str(e)}")
             return None
@@ -779,6 +781,7 @@ class EmojiManager:
             return False
 
     async def replace_a_emoji(self, new_emoji: "MaiEmoji") -> bool:
+        # sourcery skip: use-getitem-for-re-match-groups
         """替换一个表情包
 
         Args:
@@ -820,7 +823,7 @@ class EmojiManager:
             )
 
             # 调用大模型进行决策
-            decision, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=0.8)
+            decision, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=0.8, max_tokens=600)
             logger.info(f"[决策] 结果: {decision}")
 
             # 解析决策结果
@@ -828,9 +831,7 @@ class EmojiManager:
                 logger.info("[决策] 不删除任何表情包")
                 return False
 
-            # 尝试从决策中提取表情包编号
+            if match := re.search(r"删除编号(\d+)", decision):
-            match = re.search(r"删除编号(\d+)", decision)
-            if match:
                 emoji_index = int(match.group(1)) - 1  # 转换为0-based索引
 
                 # 检查索引是否有效
@@ -889,6 +890,7 @@ class EmojiManager:
             existing_description = None
             try:
                 from src.common.database.database_model import Images
+
                 existing_image = Images.get_or_none((Images.emoji_hash == image_hash) & (Images.type == "emoji"))
                 if existing_image and existing_image.description:
                     existing_description = existing_image.description
@@ -902,15 +904,21 @@ class EmojiManager:
                 logger.info("[优化] 复用已有的详细描述，跳过VLM调用")
             else:
                 logger.info("[VLM分析] 生成新的详细描述")
-                if image_format == "gif" or image_format == "GIF":
+                if image_format in ["gif", "GIF"]:
                     image_base64 = get_image_manager().transform_gif(image_base64)  # type: ignore
                     if not image_base64:
                         raise RuntimeError("GIF表情包转换失败")
                     prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
-                    description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, "jpg")
+                    description, _ = await self.vlm.generate_response_for_image(
+                        prompt, image_base64, "jpg", temperature=0.3, max_tokens=1000
+                    )
                 else:
-                    prompt = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
+                    prompt = (
-                    description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
+                        "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
+                    )
+                    description, _ = await self.vlm.generate_response_for_image(
+                        prompt, image_base64, image_format, temperature=0.3, max_tokens=1000
+                    )
 
             # 审核表情包
             if global_config.emoji.content_filtration:
@@ -922,7 +930,9 @@ class EmojiManager:
                     4. 不要出现5个以上文字
                     请回答这个表情包是否满足上述要求，是则回答是，否则回答否，不要出现任何其他内容
                 '''
-                content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
+                content, _ = await self.vlm.generate_response_for_image(
+                    prompt, image_base64, image_format, temperature=0.3, max_tokens=1000
+                )
                 if content == "否":
                     return "", []
 
@@ -933,7 +943,9 @@ class EmojiManager:
             你可以关注其幽默和讽刺意味，动用贴吧，微博，小红书的知识，必须从互联网梗,meme的角度去分析
             请直接输出描述，不要出现任何其他内容，如果有多个描述，可以用逗号分隔
             """
-            emotions_text, _ = await self.llm_emotion_judge.generate_response_async(emotion_prompt, temperature=0.7)
+            emotions_text, _ = await self.llm_emotion_judge.generate_response_async(
+                emotion_prompt, temperature=0.7, max_tokens=600
+            )
 
             # 处理情感列表
             emotions = [e.strip() for e in emotions_text.split(",") if e.strip()]

src/chat/express/expression_learner.py

@@ -7,12 +7,12 @@ from datetime import datetime
 from typing import List, Dict, Optional, Any, Tuple
 
 from src.common.logger import get_logger
+from src.common.database.database_model import Expression
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import model_config
 from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_anonymous_messages
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
-from src.common.database.database_model import Expression
 
 
 MAX_EXPRESSION_COUNT = 300
@@ -80,11 +80,8 @@ def init_prompt() -> None:
 
 class ExpressionLearner:
     def __init__(self) -> None:
-        # TODO: API-Adapter修改标记
         self.express_learn_model: LLMRequest = LLMRequest(
-            model=global_config.model.replyer_1,
+            model_set=model_config.model_task_config.replyer_1, request_type="expressor.learner"
-            temperature=0.3,
-            request_type="expressor.learner",
         )
         self.llm_model = None
         self._ensure_expression_directories()
@@ -101,7 +98,7 @@ class ExpressionLearner:
             os.path.join(base_dir, "learnt_style"),
             os.path.join(base_dir, "learnt_grammar"),
         ]
-        
+
         for directory in directories_to_create:
             try:
                 os.makedirs(directory, exist_ok=True)
@@ -116,7 +113,7 @@ class ExpressionLearner:
         """
         base_dir = os.path.join("data", "expression")
         done_flag = os.path.join(base_dir, "done.done")
-        
+
         # 确保基础目录存在
         try:
             os.makedirs(base_dir, exist_ok=True)
@@ -124,28 +121,28 @@ class ExpressionLearner:
         except Exception as e:
             logger.error(f"创建表达方式目录失败: {e}")
             return
-        
+
         if os.path.exists(done_flag):
             logger.info("表达方式JSON已迁移，无需重复迁移。")
             return
-            
+
         logger.info("开始迁移表达方式JSON到数据库...")
         migrated_count = 0
-        
+
         for type in ["learnt_style", "learnt_grammar"]:
             type_str = "style" if type == "learnt_style" else "grammar"
             type_dir = os.path.join(base_dir, type)
             if not os.path.exists(type_dir):
                 logger.debug(f"目录不存在，跳过: {type_dir}")
                 continue
-                
+
             try:
                 chat_ids = os.listdir(type_dir)
                 logger.debug(f"在 {type_dir} 中找到 {len(chat_ids)} 个聊天ID目录")
             except Exception as e:
                 logger.error(f"读取目录失败 {type_dir}: {e}")
                 continue
-                
+
             for chat_id in chat_ids:
                 expr_file = os.path.join(type_dir, chat_id, "expressions.json")
                 if not os.path.exists(expr_file):
@@ -153,24 +150,24 @@ class ExpressionLearner:
                 try:
                     with open(expr_file, "r", encoding="utf-8") as f:
                         expressions = json.load(f)
-                    
+
                     if not isinstance(expressions, list):
                         logger.warning(f"表达方式文件格式错误，跳过: {expr_file}")
                         continue
-                        
+
                     for expr in expressions:
                         if not isinstance(expr, dict):
                             continue
-                            
+
                         situation = expr.get("situation")
                         style_val = expr.get("style")
                         count = expr.get("count", 1)
                         last_active_time = expr.get("last_active_time", time.time())
-                        
+
                         if not situation or not style_val:
                             logger.warning(f"表达方式缺少必要字段，跳过: {expr}")
                             continue
-                        
+
                         # 查重：同chat_id+type+situation+style
                         from src.common.database.database_model import Expression
 
@@ -201,7 +198,7 @@ class ExpressionLearner:
                     logger.error(f"JSON解析失败 {expr_file}: {e}")
                 except Exception as e:
                     logger.error(f"迁移表达方式 {expr_file} 失败: {e}")
-        
+
         # 标记迁移完成
         try:
             # 确保done.done文件的父目录存在
@@ -209,7 +206,7 @@ class ExpressionLearner:
             if not os.path.exists(done_parent_dir):
                 os.makedirs(done_parent_dir, exist_ok=True)
                 logger.debug(f"为done.done创建父目录: {done_parent_dir}")
-            
+
             with open(done_flag, "w", encoding="utf-8") as f:
                 f.write("done\n")
             logger.info(f"表达方式JSON迁移已完成，共迁移 {migrated_count} 个表达方式，已写入done.done标记文件")
@@ -229,13 +226,13 @@ class ExpressionLearner:
             # 查找所有create_date为空的表达方式
             old_expressions = Expression.select().where(Expression.create_date.is_null())
             updated_count = 0
-            
+
             for expr in old_expressions:
                 # 使用last_active_time作为create_date
                 expr.create_date = expr.last_active_time
                 expr.save()
                 updated_count += 1
-            
+
             if updated_count > 0:
                 logger.info(f"已为 {updated_count} 个老的表达方式设置创建日期")
         except Exception as e:
@@ -287,25 +284,29 @@ class ExpressionLearner:
         获取指定chat_id的表达方式创建信息，按创建日期排序
         """
         try:
-            expressions = (Expression.select()
+            expressions = (
-                         .where(Expression.chat_id == chat_id)
+                Expression.select()
-                         .order_by(Expression.create_date.desc())
+                .where(Expression.chat_id == chat_id)
-                         .limit(limit))
+                .order_by(Expression.create_date.desc())
-            
+                .limit(limit)
+            )
+
             result = []
             for expr in expressions:
                 create_date = expr.create_date if expr.create_date is not None else expr.last_active_time
-                result.append({
+                result.append(
-                    "situation": expr.situation,
+                    {
-                    "style": expr.style,
+                        "situation": expr.situation,
-                    "type": expr.type,
+                        "style": expr.style,
-                    "count": expr.count,
+                        "type": expr.type,
-                    "create_date": create_date,
+                        "count": expr.count,
-                    "create_date_formatted": format_create_date(create_date),
+                        "create_date": create_date,
-                    "last_active_time": expr.last_active_time,
+                        "create_date_formatted": format_create_date(create_date),
-                    "last_active_formatted": format_create_date(expr.last_active_time),
+                        "last_active_time": expr.last_active_time,
-                })
+                        "last_active_formatted": format_create_date(expr.last_active_time),
-            
+                    }
+                )
+
             return result
         except Exception as e:
             logger.error(f"获取表达方式创建信息失败: {e}")
@@ -355,19 +356,19 @@ class ExpressionLearner:
         try:
             # 获取所有表达方式
             all_expressions = Expression.select()
-            
+
             updated_count = 0
             deleted_count = 0
-            
+
             for expr in all_expressions:
                 # 计算时间差
                 last_active = expr.last_active_time
                 time_diff_days = (current_time - last_active) / (24 * 3600)  # 转换为天
-                
+
                 # 计算衰减值
                 decay_value = self.calculate_decay_factor(time_diff_days)
                 new_count = max(0.01, expr.count - decay_value)
-                
+
                 if new_count <= 0.01:
                     # 如果count太小，删除这个表达方式
                     expr.delete_instance()
@@ -377,10 +378,10 @@ class ExpressionLearner:
                     expr.count = new_count
                     expr.save()
                     updated_count += 1
-            
+
             if updated_count > 0 or deleted_count > 0:
                 logger.info(f"全局衰减完成：更新了 {updated_count} 个表达方式，删除了 {deleted_count} 个表达方式")
-                
+
         except Exception as e:
             logger.error(f"数据库全局衰减失败: {e}")
 
@@ -527,7 +528,7 @@ class ExpressionLearner:
         logger.debug(f"学习{type_str}的prompt: {prompt}")
 
         try:
-            response, _ = await self.express_learn_model.generate_response_async(prompt)
+            response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3)
         except Exception as e:
             logger.error(f"学习{type_str}失败: {e}")
             return None

src/chat/express/expression_selector.py

@@ -1,16 +1,17 @@
 import json
 import time
 import random
+import hashlib
 
 from typing import List, Dict, Tuple, Optional, Any
 from json_repair import repair_json
 
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.common.logger import get_logger
+from src.common.database.database_model import Expression
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from .expression_learner import get_expression_learner
-from src.common.database.database_model import Expression
 
 logger = get_logger("expression_selector")
 
@@ -75,10 +76,8 @@ def weighted_sample(population: List[Dict], weights: List[float], k: int) -> Lis
 class ExpressionSelector:
     def __init__(self):
         self.expression_learner = get_expression_learner()
-        # TODO: API-Adapter修改标记
         self.llm_model = LLMRequest(
-            model=global_config.model.utils_small,
+            model_set=model_config.model_task_config.utils_small, request_type="expression.selector"
-            request_type="expression.selector",
         )
 
     @staticmethod
@@ -92,7 +91,6 @@ class ExpressionSelector:
             id_str = parts[1]
             stream_type = parts[2]
             is_group = stream_type == "group"
-            import hashlib
             if is_group:
                 components = [platform, str(id_str)]
             else:
@@ -108,8 +106,7 @@ class ExpressionSelector:
         for group in groups:
             group_chat_ids = []
             for stream_config_str in group:
-                chat_id_candidate = self._parse_stream_config_to_chat_id(stream_config_str)
+                if chat_id_candidate := self._parse_stream_config_to_chat_id(stream_config_str):
-                if chat_id_candidate:
                     group_chat_ids.append(chat_id_candidate)
             if chat_id in group_chat_ids:
                 return group_chat_ids
@@ -118,9 +115,10 @@ class ExpressionSelector:
     def get_random_expressions(
         self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
     ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+        # sourcery skip: extract-duplicate-method, move-assign
         # 支持多chat_id合并抽选
         related_chat_ids = self.get_related_chat_ids(chat_id)
-        
+
         # 优化：一次性查询所有相关chat_id的表达方式
         style_query = Expression.select().where(
             (Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "style")
@@ -128,7 +126,7 @@ class ExpressionSelector:
         grammar_query = Expression.select().where(
             (Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "grammar")
         )
-        
+
         style_exprs = [
             {
                 "situation": expr.situation,
@@ -138,9 +136,10 @@ class ExpressionSelector:
                 "source_id": expr.chat_id,
                 "type": "style",
                 "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
-            } for expr in style_query
+            }
+            for expr in style_query
         ]
-        
+
         grammar_exprs = [
             {
                 "situation": expr.situation,
@@ -150,9 +149,10 @@ class ExpressionSelector:
                 "source_id": expr.chat_id,
                 "type": "grammar",
                 "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
-            } for expr in grammar_query
+            }
+            for expr in grammar_query
         ]
-        
+
         style_num = int(total_num * style_percentage)
         grammar_num = int(total_num * grammar_percentage)
         # 按权重抽样（使用count作为权重）
@@ -174,22 +174,22 @@ class ExpressionSelector:
             return
         updates_by_key = {}
         for expr in expressions_to_update:
-            source_id = expr.get("source_id")
+            source_id: str = expr.get("source_id")  # type: ignore
-            expr_type = expr.get("type", "style")
+            expr_type: str = expr.get("type", "style")
-            situation = expr.get("situation")
+            situation: str = expr.get("situation")  # type: ignore
-            style = expr.get("style")
+            style: str = expr.get("style")  # type: ignore
             if not source_id or not situation or not style:
                 logger.warning(f"表达方式缺少必要字段，无法更新: {expr}")
                 continue
             key = (source_id, expr_type, situation, style)
             if key not in updates_by_key:
                 updates_by_key[key] = expr
-        for (chat_id, expr_type, situation, style), _expr in updates_by_key.items():
+        for chat_id, expr_type, situation, style in updates_by_key:
             query = Expression.select().where(
-                (Expression.chat_id == chat_id) &
+                (Expression.chat_id == chat_id)
-                (Expression.type == expr_type) &
+                & (Expression.type == expr_type)
-                (Expression.situation == situation) &
+                & (Expression.situation == situation)
-                (Expression.style == style)
+                & (Expression.style == style)
             )
             if query.exists():
                 expr_obj = query.get()
@@ -264,7 +264,7 @@ class ExpressionSelector:
 
         # 4. 调用LLM
         try:
-            content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)
+            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
 
             # logger.info(f"{self.log_prefix} LLM返回结果: {content}")
 

src/chat/knowledge/embedding_store.py

@@ -3,6 +3,7 @@ import json
 import os
 import math
 import asyncio
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Dict, List, Tuple
 
 import numpy as np
@@ -11,8 +12,6 @@ import pandas as pd
 # import tqdm
 import faiss
 
-# from .llm_client import LLMClient
-# from .lpmmconfig import global_config
 from .utils.hash import get_sha256
 from .global_logger import logger
 from rich.traceback import install
@@ -26,12 +25,20 @@ from rich.progress import (
     SpinnerColumn,
     TextColumn,
 )
-from src.manager.local_store_manager import local_storage
 from src.chat.utils.utils import get_embedding
 from src.config.config import global_config
 
 
 install(extra_lines=3)
+
+# 多线程embedding配置常量
+DEFAULT_MAX_WORKERS = 10  # 默认最大线程数
+DEFAULT_CHUNK_SIZE = 10   # 默认每个线程处理的数据块大小
+MIN_CHUNK_SIZE = 1        # 最小分块大小
+MAX_CHUNK_SIZE = 50       # 最大分块大小
+MIN_WORKERS = 1           # 最小线程数
+MAX_WORKERS = 20          # 最大线程数
+
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
 EMBEDDING_DATA_DIR = os.path.join(ROOT_PATH, "data", "embedding")
 EMBEDDING_DATA_DIR_STR = str(EMBEDDING_DATA_DIR).replace("\\", "/")
@@ -87,13 +94,23 @@ class EmbeddingStoreItem:
 
 
 class EmbeddingStore:
-    def __init__(self, namespace: str, dir_path: str):
+    def __init__(self, namespace: str, dir_path: str, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
         self.namespace = namespace
         self.dir = dir_path
         self.embedding_file_path = f"{dir_path}/{namespace}.parquet"
         self.index_file_path = f"{dir_path}/{namespace}.index"
         self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json"
 
+        # 多线程配置参数验证和设置
+        self.max_workers = max(MIN_WORKERS, min(MAX_WORKERS, max_workers))
+        self.chunk_size = max(MIN_CHUNK_SIZE, min(MAX_CHUNK_SIZE, chunk_size))
+        
+        # 如果配置值被调整，记录日志
+        if self.max_workers != max_workers:
+            logger.warning(f"max_workers 已从 {max_workers} 调整为 {self.max_workers} (范围: {MIN_WORKERS}-{MAX_WORKERS})")
+        if self.chunk_size != chunk_size:
+            logger.warning(f"chunk_size 已从 {chunk_size} 调整为 {self.chunk_size} (范围: {MIN_CHUNK_SIZE}-{MAX_CHUNK_SIZE})")
+
         self.store = {}
 
         self.faiss_index = None
@@ -125,16 +142,134 @@ class EmbeddingStore:
                 return []
             return result
 
+    def _get_embeddings_batch_threaded(self, strs: List[str], chunk_size: int = 10, max_workers: int = 10, progress_callback=None) -> List[Tuple[str, List[float]]]:
+        """使用多线程批量获取嵌入向量
+        
+        Args:
+            strs: 要获取嵌入的字符串列表
+            chunk_size: 每个线程处理的数据块大小
+            max_workers: 最大线程数
+            progress_callback: 进度回调函数，接收一个参数表示完成的数量
+            
+        Returns:
+            包含(原始字符串, 嵌入向量)的元组列表，保持与输入顺序一致
+        """
+        if not strs:
+            return []
+            
+        # 分块
+        chunks = []
+        for i in range(0, len(strs), chunk_size):
+            chunk = strs[i:i + chunk_size]
+            chunks.append((i, chunk))  # 保存起始索引以维持顺序
+        
+        # 结果存储，使用字典按索引存储以保证顺序
+        results = {}
+        
+        def process_chunk(chunk_data):
+            """处理单个数据块的函数"""
+            start_idx, chunk_strs = chunk_data
+            chunk_results = []
+            
+            # 为每个线程创建独立的LLMRequest实例
+            from src.llm_models.utils_model import LLMRequest
+            from src.config.config import model_config
+            
+            try:
+                # 创建线程专用的LLM实例
+                llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
+                
+                for i, s in enumerate(chunk_strs):
+                    try:
+                        # 直接使用异步函数
+                        embedding = asyncio.run(llm.get_embedding(s))
+                        if embedding and len(embedding) > 0:
+                            chunk_results.append((start_idx + i, s, embedding[0]))  # embedding[0] 是实际的向量
+                        else:
+                            logger.error(f"获取嵌入失败: {s}")
+                            chunk_results.append((start_idx + i, s, []))
+                        
+                        # 每完成一个嵌入立即更新进度
+                        if progress_callback:
+                            progress_callback(1)
+                            
+                    except Exception as e:
+                        logger.error(f"获取嵌入时发生异常: {s}, 错误: {e}")
+                        chunk_results.append((start_idx + i, s, []))
+                        
+                        # 即使失败也要更新进度
+                        if progress_callback:
+                            progress_callback(1)
+                        
+            except Exception as e:
+                logger.error(f"创建LLM实例失败: {e}")
+                # 如果创建LLM实例失败，返回空结果
+                for i, s in enumerate(chunk_strs):
+                    chunk_results.append((start_idx + i, s, []))
+                    # 即使失败也要更新进度
+                    if progress_callback:
+                        progress_callback(1)
+                    
+            return chunk_results
+        
+        # 使用线程池处理
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            # 提交所有任务
+            future_to_chunk = {executor.submit(process_chunk, chunk): chunk for chunk in chunks}
+            
+            # 收集结果（进度已在process_chunk中实时更新）
+            for future in as_completed(future_to_chunk):
+                try:
+                    chunk_results = future.result()
+                    for idx, s, embedding in chunk_results:
+                        results[idx] = (s, embedding)
+                except Exception as e:
+                    chunk = future_to_chunk[future]
+                    logger.error(f"处理数据块时发生异常: {chunk}, 错误: {e}")
+                    # 为失败的块添加空结果
+                    start_idx, chunk_strs = chunk
+                    for i, s in enumerate(chunk_strs):
+                        results[start_idx + i] = (s, [])
+        
+        # 按原始顺序返回结果
+        ordered_results = []
+        for i in range(len(strs)):
+            if i in results:
+                ordered_results.append(results[i])
+            else:
+                # 防止遗漏
+                ordered_results.append((strs[i], []))
+                
+        return ordered_results
+
     def get_test_file_path(self):
         return EMBEDDING_TEST_FILE
 
     def save_embedding_test_vectors(self):
-        """保存测试字符串的嵌入到本地"""
+        """保存测试字符串的嵌入到本地（使用多线程优化）"""
+        logger.info("开始保存测试字符串的嵌入向量...")
+        
+        # 使用多线程批量获取测试字符串的嵌入
+        embedding_results = self._get_embeddings_batch_threaded(
+            EMBEDDING_TEST_STRINGS,
+            chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+        )
+        
+        # 构建测试向量字典
         test_vectors = {}
-        for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
+        for idx, (s, embedding) in enumerate(embedding_results):
-            test_vectors[str(idx)] = self._get_embedding(s)
+            if embedding:
+                test_vectors[str(idx)] = embedding
+            else:
+                logger.error(f"获取测试字符串嵌入失败: {s}")
+                # 使用原始单线程方法作为后备
+                test_vectors[str(idx)] = self._get_embedding(s)
+        
         with open(self.get_test_file_path(), "w", encoding="utf-8") as f:
             json.dump(test_vectors, f, ensure_ascii=False, indent=2)
+        
+        logger.info("测试字符串嵌入向量保存完成")
 
     def load_embedding_test_vectors(self):
         """加载本地保存的测试字符串嵌入"""
@@ -145,29 +280,64 @@ class EmbeddingStore:
             return json.load(f)
 
     def check_embedding_model_consistency(self):
-        """校验当前模型与本地嵌入模型是否一致"""
+        """校验当前模型与本地嵌入模型是否一致（使用多线程优化）"""
         local_vectors = self.load_embedding_test_vectors()
         if local_vectors is None:
             logger.warning("未检测到本地嵌入模型测试文件，将保存当前模型的测试嵌入。")
             self.save_embedding_test_vectors()
             return True
-        for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
+            
-            local_emb = local_vectors.get(str(idx))
+        # 检查本地向量完整性
-            if local_emb is None:
+        for idx in range(len(EMBEDDING_TEST_STRINGS)):
+            if local_vectors.get(str(idx)) is None:
                 logger.warning("本地嵌入模型测试文件缺失部分测试字符串，将重新保存。")
                 self.save_embedding_test_vectors()
                 return True
-            new_emb = self._get_embedding(s)
+        
+        logger.info("开始检验嵌入模型一致性...")
+        
+        # 使用多线程批量获取当前模型的嵌入
+        embedding_results = self._get_embeddings_batch_threaded(
+            EMBEDDING_TEST_STRINGS,
+            chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+        )
+        
+        # 检查一致性
+        for idx, (s, new_emb) in enumerate(embedding_results):
+            local_emb = local_vectors.get(str(idx))
+            if not new_emb:
+                logger.error(f"获取测试字符串嵌入失败: {s}")
+                return False
+                
             sim = cosine_similarity(local_emb, new_emb)
             if sim < EMBEDDING_SIM_THRESHOLD:
-                logger.error("嵌入模型一致性校验失败")
+                logger.error(f"嵌入模型一致性校验失败，字符串: {s}, 相似度: {sim:.4f}")
                 return False
+                
         logger.info("嵌入模型一致性校验通过。")
         return True
 
     def batch_insert_strs(self, strs: List[str], times: int) -> None:
-        """向库中存入字符串"""
+        """向库中存入字符串（使用多线程优化）"""
+        if not strs:
+            return
+            
         total = len(strs)
+        
+        # 过滤已存在的字符串
+        new_strs = []
+        for s in strs:
+            item_hash = self.namespace + "-" + get_sha256(s)
+            if item_hash not in self.store:
+                new_strs.append(s)
+        
+        if not new_strs:
+            logger.info(f"所有字符串已存在于{self.namespace}嵌入库中，跳过处理")
+            return
+            
+        logger.info(f"需要处理 {len(new_strs)}/{total} 个新字符串")
+        
         with Progress(
             SpinnerColumn(),
             TextColumn("[progress.description]{task.description}"),
@@ -181,19 +351,38 @@ class EmbeddingStore:
             transient=False,
         ) as progress:
             task = progress.add_task(f"存入嵌入库：({times}/{TOTAL_EMBEDDING_TIMES})", total=total)
-            for s in strs:
+            
-                # 计算hash去重
+            # 首先更新已存在项的进度
-                item_hash = self.namespace + "-" + get_sha256(s)
+            already_processed = total - len(new_strs)
-                if item_hash in self.store:
+            if already_processed > 0:
-                    progress.update(task, advance=1)
+                progress.update(task, advance=already_processed)
-                    continue
+            
-
+            if new_strs:
-                # 获取embedding
+                # 使用实例配置的参数，智能调整分块和线程数
-                embedding = self._get_embedding(s)
+                optimal_chunk_size = max(MIN_CHUNK_SIZE, min(self.chunk_size, len(new_strs) // self.max_workers if self.max_workers > 0 else self.chunk_size))
-
+                optimal_max_workers = min(self.max_workers, max(MIN_WORKERS, len(new_strs) // optimal_chunk_size if optimal_chunk_size > 0 else 1))
-                # 存入
+                
-                self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                logger.debug(f"使用多线程处理: chunk_size={optimal_chunk_size}, max_workers={optimal_max_workers}")
-                progress.update(task, advance=1)
+                
+                # 定义进度更新回调函数
+                def update_progress(count):
+                    progress.update(task, advance=count)
+                
+                # 批量获取嵌入，并实时更新进度
+                embedding_results = self._get_embeddings_batch_threaded(
+                    new_strs, 
+                    chunk_size=optimal_chunk_size, 
+                    max_workers=optimal_max_workers,
+                    progress_callback=update_progress
+                )
+                
+                # 存入结果（不再需要在这里更新进度，因为已经在回调中更新了）
+                for s, embedding in embedding_results:
+                    item_hash = self.namespace + "-" + get_sha256(s)
+                    if embedding:  # 只有成功获取到嵌入才存入
+                        self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                    else:
+                        logger.warning(f"跳过存储失败的嵌入: {s[:50]}...")
 
     def save_to_file(self) -> None:
         """保存到文件"""
@@ -316,31 +505,37 @@ class EmbeddingStore:
 
 
 class EmbeddingManager:
-    def __init__(self):
+    def __init__(self, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
+        """
+        初始化EmbeddingManager
+        
+        Args:
+            max_workers: 最大线程数
+            chunk_size: 每个线程处理的数据块大小
+        """
         self.paragraphs_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "paragraph",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.entities_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "entity",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.relation_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "relation",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.stored_pg_hashes = set()
 
     def check_all_embedding_model_consistency(self):
         """对所有嵌入库做模型一致性校验"""
-        for store in [
+        return self.paragraphs_embedding_store.check_embedding_model_consistency()
-            self.paragraphs_embedding_store,
-            self.entities_embedding_store,
-            self.relation_embedding_store,
-        ]:
-            if not store.check_embedding_model_consistency():
-                return False
-        return True
 
     def _store_pg_into_embedding(self, raw_paragraphs: Dict[str, str]):
         """将段落编码存入Embedding库"""

src/chat/knowledge/ie_process.py

@@ -8,12 +8,15 @@ from . import prompt_template
 from .knowledge_lib import INVALID_ENTITY
 from src.llm_models.utils_model import LLMRequest
 from json_repair import repair_json
+
+
 def _extract_json_from_text(text: str):
+    # sourcery skip: assign-if-exp, extract-method
     """从文本中提取JSON数据的高容错方法"""
     if text is None:
         logger.error("输入文本为None")
         return []
-        
+
     try:
         fixed_json = repair_json(text)
         if isinstance(fixed_json, str):
@@ -24,7 +27,7 @@ def _extract_json_from_text(text: str):
         # 如果是列表，直接返回
         if isinstance(parsed_json, list):
             return parsed_json
-            
+
         # 如果是字典且只有一个项目，可能包装了列表
         if isinstance(parsed_json, dict):
             # 如果字典只有一个键，并且值是列表，返回那个列表
@@ -33,7 +36,7 @@ def _extract_json_from_text(text: str):
                 if isinstance(value, list):
                     return value
             return parsed_json
-        
+
         # 其他情况，尝试转换为列表
         logger.warning(f"解析的JSON不是预期格式: {type(parsed_json)}, 内容: {parsed_json}")
         return []
@@ -42,44 +45,40 @@ def _extract_json_from_text(text: str):
         logger.error(f"JSON提取失败: {e}, 原始文本: {text[:100] if text else 'None'}...")
         return []
 
+
 def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
+    # sourcery skip: reintroduce-else, swap-if-else-branches, use-named-expression
     """对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
     entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
-    
+
     # 使用 asyncio.run 来运行异步方法
     try:
         # 如果当前已有事件循环在运行，使用它
         loop = asyncio.get_running_loop()
-        future = asyncio.run_coroutine_threadsafe(
+        future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(entity_extract_context), loop)
-            llm_req.generate_response_async(entity_extract_context), loop
+        response, _ = future.result()
-        )
-        response, (reasoning_content, model_name) = future.result()
     except RuntimeError:
         # 如果没有运行中的事件循环，直接使用 asyncio.run
-        response, (reasoning_content, model_name) = asyncio.run(
+        response, _ = asyncio.run(llm_req.generate_response_async(entity_extract_context))
-            llm_req.generate_response_async(entity_extract_context)
-        )
 
     # 添加调试日志
     logger.debug(f"LLM返回的原始响应: {response}")
-    
+
     entity_extract_result = _extract_json_from_text(response)
-    
+
     # 检查返回的是否为有效的实体列表
     if not isinstance(entity_extract_result, list):
-        # 如果不是列表，可能是字典格式，尝试从中提取列表
+        if not isinstance(entity_extract_result, dict):
-        if isinstance(entity_extract_result, dict):
+            raise ValueError(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
-            # 尝试常见的键名
+
-            for key in ['entities', 'result', 'data', 'items']:
+        # 尝试常见的键名
-                if key in entity_extract_result and isinstance(entity_extract_result[key], list):
+        for key in ["entities", "result", "data", "items"]:
-                    entity_extract_result = entity_extract_result[key]
+            if key in entity_extract_result and isinstance(entity_extract_result[key], list):
-                    break
+                entity_extract_result = entity_extract_result[key]
-            else:
+                break
-                # 如果找不到合适的列表，抛出异常
-                raise Exception(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
         else:
-            raise Exception(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
+            # 如果找不到合适的列表，抛出异常
-    
+            raise ValueError(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
     # 过滤无效实体
     entity_extract_result = [
         entity
@@ -87,8 +86,8 @@ def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
         if (entity is not None) and (entity != "") and (entity not in INVALID_ENTITY)
     ]
 
-    if len(entity_extract_result) == 0:
+    if not entity_extract_result:
-        raise Exception("实体提取结果为空")
+        raise ValueError("实体提取结果为空")
 
     return entity_extract_result
 
@@ -98,45 +97,44 @@ def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) ->
     rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
         paragraph, entities=json.dumps(entities, ensure_ascii=False)
     )
-    
+
     # 使用 asyncio.run 来运行异步方法
     try:
         # 如果当前已有事件循环在运行，使用它
         loop = asyncio.get_running_loop()
-        future = asyncio.run_coroutine_threadsafe(
+        future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(rdf_extract_context), loop)
-            llm_req.generate_response_async(rdf_extract_context), loop
+        response, _ = future.result()
-        )
-        response, (reasoning_content, model_name) = future.result()
     except RuntimeError:
         # 如果没有运行中的事件循环，直接使用 asyncio.run
-        response, (reasoning_content, model_name) = asyncio.run(
+        response, _ = asyncio.run(llm_req.generate_response_async(rdf_extract_context))
-            llm_req.generate_response_async(rdf_extract_context)
-        )
 
     # 添加调试日志
     logger.debug(f"RDF LLM返回的原始响应: {response}")
-    
+
     rdf_triple_result = _extract_json_from_text(response)
-    
+
     # 检查返回的是否为有效的三元组列表
     if not isinstance(rdf_triple_result, list):
-        # 如果不是列表，可能是字典格式，尝试从中提取列表
+        if not isinstance(rdf_triple_result, dict):
-        if isinstance(rdf_triple_result, dict):
+            raise ValueError(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
-            # 尝试常见的键名
+
-            for key in ['triples', 'result', 'data', 'items']:
+        # 尝试常见的键名
-                if key in rdf_triple_result and isinstance(rdf_triple_result[key], list):
+        for key in ["triples", "result", "data", "items"]:
-                    rdf_triple_result = rdf_triple_result[key]
+            if key in rdf_triple_result and isinstance(rdf_triple_result[key], list):
-                    break
+                rdf_triple_result = rdf_triple_result[key]
-            else:
+                break
-                # 如果找不到合适的列表，抛出异常
-                raise Exception(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
         else:
-            raise Exception(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
+            # 如果找不到合适的列表，抛出异常
-    
+            raise ValueError(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
     # 验证三元组格式
     for triple in rdf_triple_result:
-        if not isinstance(triple, list) or len(triple) != 3 or (triple[0] is None or triple[1] is None or triple[2] is None) or "" in triple:
+        if (
-            raise Exception("RDF提取结果格式错误")
+            not isinstance(triple, list)
+            or len(triple) != 3
+            or (triple[0] is None or triple[1] is None or triple[2] is None)
+            or "" in triple
+        ):
+            raise ValueError("RDF提取结果格式错误")
 
     return rdf_triple_result
 

src/chat/knowledge/kg_manager.py

@@ -20,8 +20,7 @@ from quick_algo import di_graph, pagerank
 
 from .utils.hash import get_sha256
 from .embedding_store import EmbeddingManager, EmbeddingStoreItem
-from .lpmmconfig import global_config
+from src.config.config import global_config
-from src.manager.local_store_manager import local_storage
 
 from .global_logger import logger
 
@@ -30,19 +29,9 @@ def _get_kg_dir():
     """
     安全地获取KG数据目录路径
     """
-    root_path: str = local_storage["root_path"]
+    current_dir = os.path.dirname(os.path.abspath(__file__))
-    if root_path is None:
+    root_path: str = os.path.abspath(os.path.join(current_dir, "..", "..", ".."))
-        # 如果 local_storage 中没有 root_path，使用当前文件的相对路径作为备用
+    kg_dir = os.path.join(root_path, "data/rag")
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        root_path = os.path.abspath(os.path.join(current_dir, "..", "..", ".."))
-        logger.warning(f"local_storage 中未找到 root_path，使用备用路径: {root_path}")
-
-    # 获取RAG数据目录
-    rag_data_dir: str = global_config["persistence"]["rag_data_dir"]
-    if rag_data_dir is None:
-        kg_dir = os.path.join(root_path, "data/rag")
-    else:
-        kg_dir = os.path.join(root_path, rag_data_dir)
 
     return str(kg_dir).replace("\\", "/")
 
@@ -65,9 +54,9 @@ class KGManager:
 
         # 持久化相关 - 使用延迟初始化的路径
         self.dir_path = get_kg_dir_str()
-        self.graph_data_path = self.dir_path + "/" + local_storage["rag_graph_namespace"] + ".graphml"
+        self.graph_data_path = self.dir_path + "/" + "rag-graph" + ".graphml"
-        self.ent_cnt_data_path = self.dir_path + "/" + local_storage["rag_ent_cnt_namespace"] + ".parquet"
+        self.ent_cnt_data_path = self.dir_path + "/" + "rag-ent-cnt" + ".parquet"
-        self.pg_hash_file_path = self.dir_path + "/" + local_storage["rag_pg_hash_namespace"] + ".json"
+        self.pg_hash_file_path = self.dir_path + "/" + "rag-pg-hash" + ".json"
 
     def save_to_file(self):
         """将KG数据保存到文件"""
@@ -122,8 +111,8 @@ class KGManager:
                     # 避免自连接
                     continue
                 # 一个triple就是一条边（同时构建双向联系）
-                hash_key1 = local_storage["ent_namespace"] + "-" + get_sha256(triple[0])
+                hash_key1 = "entity" + "-" + get_sha256(triple[0])
-                hash_key2 = local_storage["ent_namespace"] + "-" + get_sha256(triple[2])
+                hash_key2 = "entity" + "-" + get_sha256(triple[2])
                 node_to_node[(hash_key1, hash_key2)] = node_to_node.get((hash_key1, hash_key2), 0) + 1.0
                 node_to_node[(hash_key2, hash_key1)] = node_to_node.get((hash_key2, hash_key1), 0) + 1.0
                 entity_set.add(hash_key1)
@@ -141,8 +130,8 @@ class KGManager:
         """构建实体节点与文段节点之间的关系"""
         for idx in triple_list_data:
             for triple in triple_list_data[idx]:
-                ent_hash_key = local_storage["ent_namespace"] + "-" + get_sha256(triple[0])
+                ent_hash_key = "entity" + "-" + get_sha256(triple[0])
-                pg_hash_key = local_storage["pg_namespace"] + "-" + str(idx)
+                pg_hash_key = "paragraph" + "-" + str(idx)
                 node_to_node[(ent_hash_key, pg_hash_key)] = node_to_node.get((ent_hash_key, pg_hash_key), 0) + 1.0
 
     @staticmethod
@@ -157,12 +146,12 @@ class KGManager:
         ent_hash_list = set()
         for triple_list in triple_list_data.values():
             for triple in triple_list:
-                ent_hash_list.add(local_storage["ent_namespace"] + "-" + get_sha256(triple[0]))
+                ent_hash_list.add("entity" + "-" + get_sha256(triple[0]))
-                ent_hash_list.add(local_storage["ent_namespace"] + "-" + get_sha256(triple[2]))
+                ent_hash_list.add("entity" + "-" + get_sha256(triple[2]))
         ent_hash_list = list(ent_hash_list)
 
         synonym_hash_set = set()
-        synonym_result = dict()
+        synonym_result = {}
 
         # rich 进度条
         total = len(ent_hash_list)
@@ -190,14 +179,14 @@ class KGManager:
                 assert isinstance(ent, EmbeddingStoreItem)
                 # 查询相似实体
                 similar_ents = embedding_manager.entities_embedding_store.search_top_k(
-                    ent.embedding, global_config["rag"]["params"]["synonym_search_top_k"]
+                    ent.embedding, global_config.lpmm_knowledge.rag_synonym_search_top_k
                 )
                 res_ent = []  # Debug
                 for res_ent_hash, similarity in similar_ents:
                     if res_ent_hash == ent_hash:
                         # 避免自连接
                         continue
-                    if similarity < global_config["rag"]["params"]["synonym_threshold"]:
+                    if similarity < global_config.lpmm_knowledge.rag_synonym_threshold:
                         # 相似度阈值
                         continue
                     node_to_node[(res_ent_hash, ent_hash)] = similarity
@@ -263,7 +252,7 @@ class KGManager:
         for src_tgt in node_to_node.keys():
             for node_hash in src_tgt:
                 if node_hash not in existed_nodes:
-                    if node_hash.startswith(local_storage["ent_namespace"]):
+                    if node_hash.startswith("entity"):
                         # 新增实体节点
                         node = embedding_manager.entities_embedding_store.store.get(node_hash)
                         if node is None:
@@ -275,7 +264,7 @@ class KGManager:
                         node_item["type"] = "ent"
                         node_item["create_time"] = now_time
                         self.graph.update_node(node_item)
-                    elif node_hash.startswith(local_storage["pg_namespace"]):
+                    elif node_hash.startswith("paragraph"):
                         # 新增文段节点
                         node = embedding_manager.paragraphs_embedding_store.store.get(node_hash)
                         if node is None:
@@ -359,7 +348,7 @@ class KGManager:
             # 关系三元组
             triple = relation[2:-2].split("', '")
             for ent in [(triple[0]), (triple[2])]:
-                ent_hash = local_storage["ent_namespace"] + "-" + get_sha256(ent)
+                ent_hash = "entity" + "-" + get_sha256(ent)
                 if ent_hash in existed_nodes:  # 该实体需在KG中存在
                     if ent_hash not in ent_sim_scores:  # 尚未记录的实体
                         ent_sim_scores[ent_hash] = []
@@ -380,7 +369,7 @@ class KGManager:
             for ent_hash in ent_weights.keys():
                 ent_weights[ent_hash] = 1.0
         else:
-            down_edge = global_config["qa"]["params"]["paragraph_node_weight"]
+            down_edge = global_config.lpmm_knowledge.qa_paragraph_node_weight
             # 缩放取值区间至[down_edge, 1]
             for ent_hash, score in ent_weights.items():
                 # 缩放相似度
@@ -389,7 +378,7 @@ class KGManager:
                 ) + down_edge
 
         # 取平均相似度的top_k实体
-        top_k = global_config["qa"]["params"]["ent_filter_top_k"]
+        top_k = global_config.lpmm_knowledge.qa_ent_filter_top_k
         if len(ent_mean_scores) > top_k:
             # 从大到小排序，取后len - k个
             ent_mean_scores = {k: v for k, v in sorted(ent_mean_scores.items(), key=lambda item: item[1], reverse=True)}
@@ -418,7 +407,7 @@ class KGManager:
 
         for pg_hash, score in pg_sim_scores.items():
             pg_weights[pg_hash] = (
-                score * global_config["qa"]["params"]["paragraph_node_weight"]
+                score * global_config.lpmm_knowledge.qa_paragraph_node_weight
             )  # 文段权重 = 归一化相似度 * 文段节点权重参数
         del pg_sim_scores
 
@@ -431,7 +420,7 @@ class KGManager:
             self.graph,
             personalization=ppr_node_weights,
             max_iter=100,
-            alpha=global_config["qa"]["params"]["ppr_damping"],
+            alpha=global_config.lpmm_knowledge.qa_ppr_damping,
         )
 
         # 获取最终结果
@@ -439,7 +428,7 @@ class KGManager:
         passage_node_res = [
             (node_key, score)
             for node_key, score in ppr_res.items()
-            if node_key.startswith(local_storage["pg_namespace"])
+            if node_key.startswith("paragraph")
         ]
         del ppr_res
 

src/chat/knowledge/knowledge_lib.py

@@ -1,12 +1,8 @@
-from src.chat.knowledge.lpmmconfig import global_config
 from src.chat.knowledge.embedding_store import EmbeddingManager
-from src.chat.knowledge.llm_client import LLMClient
-from src.chat.knowledge.mem_active_manager import MemoryActiveManager
 from src.chat.knowledge.qa_manager import QAManager
 from src.chat.knowledge.kg_manager import KGManager
 from src.chat.knowledge.global_logger import logger
-from src.config.config import global_config as bot_global_config
+from src.config.config import global_config
-from src.manager.local_store_manager import local_storage
 import os
 
 INVALID_ENTITY = [
@@ -21,9 +17,6 @@ INVALID_ENTITY = [
     "她们",
     "它们",
 ]
-PG_NAMESPACE = "paragraph"
-ENT_NAMESPACE = "entity"
-REL_NAMESPACE = "relation"
 
 RAG_GRAPH_NAMESPACE = "rag-graph"
 RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
@@ -34,67 +27,13 @@ ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..",
 DATA_PATH = os.path.join(ROOT_PATH, "data")
 
 
-def _initialize_knowledge_local_storage():
-    """
-    初始化知识库相关的本地存储配置
-    使用字典批量设置，避免重复的if判断
-    """
-    # 定义所有需要初始化的配置项
-    default_configs = {
-        # 路径配置
-        "root_path": ROOT_PATH,
-        "data_path": f"{ROOT_PATH}/data",
-        # 实体和命名空间配置
-        "lpmm_invalid_entity": INVALID_ENTITY,
-        "pg_namespace": PG_NAMESPACE,
-        "ent_namespace": ENT_NAMESPACE,
-        "rel_namespace": REL_NAMESPACE,
-        # RAG相关命名空间配置
-        "rag_graph_namespace": RAG_GRAPH_NAMESPACE,
-        "rag_ent_cnt_namespace": RAG_ENT_CNT_NAMESPACE,
-        "rag_pg_hash_namespace": RAG_PG_HASH_NAMESPACE,
-    }
-
-    # 日志级别映射：重要配置用info，其他用debug
-    important_configs = {"root_path", "data_path"}
-
-    # 批量设置配置项
-    initialized_count = 0
-    for key, default_value in default_configs.items():
-        if local_storage[key] is None:
-            local_storage[key] = default_value
-
-            # 根据重要性选择日志级别
-            if key in important_configs:
-                logger.info(f"设置{key}: {default_value}")
-            else:
-                logger.debug(f"设置{key}: {default_value}")
-
-            initialized_count += 1
-
-    if initialized_count > 0:
-        logger.info(f"知识库本地存储初始化完成，共设置 {initialized_count} 项配置")
-    else:
-        logger.debug("知识库本地存储配置已存在，跳过初始化")
-
-
-# 初始化本地存储路径
-# sourcery skip: dict-comprehension
-_initialize_knowledge_local_storage()
-
 qa_manager = None
 inspire_manager = None
 
 # 检查LPMM知识库是否启用
-if bot_global_config.lpmm_knowledge.enable:
+if global_config.lpmm_knowledge.enable:
     logger.info("正在初始化Mai-LPMM")
     logger.info("创建LLM客户端")
-    llm_client_list = {}
-    for key in global_config["llm_providers"]:
-        llm_client_list[key] = LLMClient(
-            global_config["llm_providers"][key]["base_url"],  # type: ignore
-            global_config["llm_providers"][key]["api_key"],  # type: ignore
-        )
 
     # 初始化Embedding库
     embed_manager = EmbeddingManager()
@@ -120,7 +59,7 @@ if bot_global_config.lpmm_knowledge.enable:
 
     # 数据比对：Embedding库与KG的段落hash集合
     for pg_hash in kg_manager.stored_paragraph_hashes:
-        key = f"{PG_NAMESPACE}-{pg_hash}"
+        key = f"paragraph-{pg_hash}"
         if key not in embed_manager.stored_pg_hashes:
             logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
 
@@ -130,11 +69,11 @@ if bot_global_config.lpmm_knowledge.enable:
         kg_manager,
     )
 
-    # 记忆激活（用于记忆库）
+    # # 记忆激活（用于记忆库）
-    inspire_manager = MemoryActiveManager(
+    # inspire_manager = MemoryActiveManager(
-        embed_manager,
+    #     embed_manager,
-        llm_client_list[global_config["embedding"]["provider"]],
+    #     llm_client_list[global_config["embedding"]["provider"]],
-    )
+    # )
 else:
     logger.info("LPMM知识库已禁用，跳过初始化")
     # 创建空的占位符对象，避免导入错误

src/chat/knowledge/llm_client.py

@@ -1,45 +0,0 @@
-from openai import OpenAI
-
-
-class LLMMessage:
-    def __init__(self, role, content):
-        self.role = role
-        self.content = content
-
-    def to_dict(self):
-        return {"role": self.role, "content": self.content}
-
-
-class LLMClient:
-    """LLM客户端，对应一个API服务商"""
-
-    def __init__(self, url, api_key):
-        self.client = OpenAI(
-            base_url=url,
-            api_key=api_key,
-        )
-
-    def send_chat_request(self, model, messages):
-        """发送对话请求，等待返回结果"""
-        response = self.client.chat.completions.create(model=model, messages=messages, stream=False)
-        if hasattr(response.choices[0].message, "reasoning_content"):
-            # 有单独的推理内容块
-            reasoning_content = response.choices[0].message.reasoning_content
-            content = response.choices[0].message.content
-        else:
-            # 无单独的推理内容块
-            response = response.choices[0].message.content.split("<think>")[-1].split("</think>")
-            # 如果有推理内容，则分割推理内容和内容
-            if len(response) == 2:
-                reasoning_content = response[0]
-                content = response[1]
-            else:
-                reasoning_content = None
-                content = response[0]
-
-        return reasoning_content, content
-
-    def send_embedding_request(self, model, text):
-        """发送嵌入请求，等待返回结果"""
-        text = text.replace("\n", " ")
-        return self.client.embeddings.create(input=[text], model=model).data[0].embedding

src/chat/knowledge/lpmmconfig.py

@@ -1,137 +0,0 @@
-import os
-import toml
-import sys
-
-# import argparse
-from .global_logger import logger
-
-PG_NAMESPACE = "paragraph"
-ENT_NAMESPACE = "entity"
-REL_NAMESPACE = "relation"
-
-RAG_GRAPH_NAMESPACE = "rag-graph"
-RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
-RAG_PG_HASH_NAMESPACE = "rag-pg-hash"
-
-# 无效实体
-INVALID_ENTITY = [
-    "",
-    "你",
-    "他",
-    "她",
-    "它",
-    "我们",
-    "你们",
-    "他们",
-    "她们",
-    "它们",
-]
-
-
-def _load_config(config, config_file_path):
-    """读取TOML格式的配置文件"""
-    if not os.path.exists(config_file_path):
-        return
-    with open(config_file_path, "r", encoding="utf-8") as f:
-        file_config = toml.load(f)
-
-    # Check if all top-level keys from default config exist in the file config
-    for key in config.keys():
-        if key not in file_config:
-            logger.critical(f"警告: 配置文件 '{config_file_path}' 缺少必需的顶级键: '{key}'。请检查配置文件。")
-            logger.critical("请通过template/lpmm_config_template.toml文件进行更新")
-            sys.exit(1)
-
-    if "llm_providers" in file_config:
-        for provider in file_config["llm_providers"]:
-            if provider["name"] not in config["llm_providers"]:
-                config["llm_providers"][provider["name"]] = {}
-            config["llm_providers"][provider["name"]]["base_url"] = provider["base_url"]
-            config["llm_providers"][provider["name"]]["api_key"] = provider["api_key"]
-
-    if "entity_extract" in file_config:
-        config["entity_extract"] = file_config["entity_extract"]
-
-    if "rdf_build" in file_config:
-        config["rdf_build"] = file_config["rdf_build"]
-
-    if "embedding" in file_config:
-        config["embedding"] = file_config["embedding"]
-
-    if "rag" in file_config:
-        config["rag"] = file_config["rag"]
-
-    if "qa" in file_config:
-        config["qa"] = file_config["qa"]
-
-    if "persistence" in file_config:
-        config["persistence"] = file_config["persistence"]
-    # print(config)
-    logger.info(f"从文件中读取配置: {config_file_path}")
-
-
-global_config = dict(
-    {
-        "lpmm": {
-            "version": "0.1.0",
-        },
-        "llm_providers": {
-            "localhost": {
-                "base_url": "https://api.siliconflow.cn/v1",
-                "api_key": "sk-ospynxadyorf",
-            }
-        },
-        "entity_extract": {
-            "llm": {
-                "provider": "localhost",
-                "model": "Pro/deepseek-ai/DeepSeek-V3",
-            }
-        },
-        "rdf_build": {
-            "llm": {
-                "provider": "localhost",
-                "model": "Pro/deepseek-ai/DeepSeek-V3",
-            }
-        },
-        "embedding": {
-            "provider": "localhost",
-            "model": "Pro/BAAI/bge-m3",
-            "dimension": 1024,
-        },
-        "rag": {
-            "params": {
-                "synonym_search_top_k": 10,
-                "synonym_threshold": 0.75,
-            }
-        },
-        "qa": {
-            "params": {
-                "relation_search_top_k": 10,
-                "relation_threshold": 0.75,
-                "paragraph_search_top_k": 10,
-                "paragraph_node_weight": 0.05,
-                "ent_filter_top_k": 10,
-                "ppr_damping": 0.8,
-                "res_top_k": 10,
-            },
-            "llm": {
-                "provider": "localhost",
-                "model": "qa",
-            },
-        },
-        "persistence": {
-            "data_root_path": "data",
-            "raw_data_path": "data/raw.json",
-            "openie_data_path": "data/openie.json",
-            "embedding_data_dir": "data/embedding",
-            "rag_data_dir": "data/rag",
-        },
-        "info_extraction": {
-            "workers": 10,
-        },
-    }
-)
-
-ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
-config_path = os.path.join(ROOT_PATH, "config", "lpmm_config.toml")
-_load_config(global_config, config_path)

src/chat/knowledge/mem_active_manager.py

@@ -1,3 +1,4 @@
+raise DeprecationWarning("MemoryActiveManager is not used yet, please do not import it")
 from .lpmmconfig import global_config
 from .embedding_store import EmbeddingManager
 from .llm_client import LLMClient

src/chat/knowledge/qa_manager.py

@@ -2,16 +2,14 @@ import time
 from typing import Tuple, List, Dict, Optional
 
 from .global_logger import logger
-
-# from . import prompt_template
 from .embedding_store import EmbeddingManager
-# from .llm_client import LLMClient
 from .kg_manager import KGManager
+
 # from .lpmmconfig import global_config
 from .utils.dyn_topk import dyn_select_top_k
 from src.llm_models.utils_model import LLMRequest
 from src.chat.utils.utils import get_embedding
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 
 MAX_KNOWLEDGE_LENGTH = 10000  # 最大知识长度
 
@@ -21,17 +19,12 @@ class QAManager:
         self,
         embed_manager: EmbeddingManager,
         kg_manager: KGManager,
-
     ):
         self.embed_manager = embed_manager
         self.kg_manager = kg_manager
-        # TODO: API-Adapter修改标记
+        self.qa_model = LLMRequest(model_set=model_config.model_task_config.lpmm_qa, request_type="lpmm.qa")
-        self.qa_model = LLMRequest(
-            model=global_config.model.lpmm_qa,
-            request_type="lpmm.qa"
-        )
 
-    async def process_query(self, question: str) -> Tuple[List[Tuple[str, float, float]], Optional[Dict[str, float]]]:
+    async def process_query(self, question: str) -> Optional[Tuple[List[Tuple[str, float, float]], Optional[Dict[str, float]]]]:
         """处理查询"""
 
         # 生成问题的Embedding
@@ -49,66 +42,70 @@ class QAManager:
             question_embedding,
             global_config.lpmm_knowledge.qa_relation_search_top_k,
         )
-        if relation_search_res is not None:
+        if relation_search_res is None:
-            # 过滤阈值
+            return None
-            # 考虑动态阈值：当存在显著数值差异的结果时，保留显著结果；否则，保留所有结果
+        # 过滤阈值
-            relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0)
+        # 考虑动态阈值：当存在显著数值差异的结果时，保留显著结果；否则，保留所有结果
-            if relation_search_res[0][1] < global_config.lpmm_knowledge.qa_relation_threshold:
+        relation_search_res = dyn_select_top_k(relation_search_res, 0.5, 1.0)
-                # 未找到相关关系
+        if not relation_search_res or relation_search_res[0][1] < global_config.lpmm_knowledge.qa_relation_threshold:
-                logger.debug("未找到相关关系，跳过关系检索")
+            # 未找到相关关系
-                relation_search_res = []
+            logger.debug("未找到相关关系，跳过关系检索")
+            relation_search_res = []
 
-            part_end_time = time.perf_counter()
+        part_end_time = time.perf_counter()
-            logger.debug(f"关系检索用时：{part_end_time - part_start_time:.5f}s")
+        logger.debug(f"关系检索用时：{part_end_time - part_start_time:.5f}s")
 
-            for res in relation_search_res:
+        for res in relation_search_res:
-                rel_str = self.embed_manager.relation_embedding_store.store.get(res[0]).str
+            rel_str = self.embed_manager.relation_embedding_store.store.get(res[0]).str
-                print(f"找到相关关系，相似度：{(res[1] * 100):.2f}%  -  {rel_str}")
+            print(f"找到相关关系，相似度：{(res[1] * 100):.2f}%  -  {rel_str}")
 
-            # TODO: 使用LLM过滤三元组结果
+        # TODO: 使用LLM过滤三元组结果
-            # logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s")
+        # logger.info(f"LLM过滤三元组用时：{time.time() - part_start_time:.2f}s")
-            # part_start_time = time.time()
+        # part_start_time = time.time()
 
-            # 根据问题Embedding查询Paragraph Embedding库
+        # 根据问题Embedding查询Paragraph Embedding库
+        part_start_time = time.perf_counter()
+        paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
+            question_embedding,
+            global_config.lpmm_knowledge.qa_paragraph_search_top_k,
+        )
+        part_end_time = time.perf_counter()
+        logger.debug(f"文段检索用时：{part_end_time - part_start_time:.5f}s")
+
+        if len(relation_search_res) != 0:
+            logger.info("找到相关关系，将使用RAG进行检索")
+            # 使用KG检索
             part_start_time = time.perf_counter()
-            paragraph_search_res = self.embed_manager.paragraphs_embedding_store.search_top_k(
+            result, ppr_node_weights = self.kg_manager.kg_search(
-                question_embedding,
+                relation_search_res, paragraph_search_res, self.embed_manager
-                global_config.lpmm_knowledge.qa_paragraph_search_top_k,
             )
             part_end_time = time.perf_counter()
-            logger.debug(f"文段检索用时：{part_end_time - part_start_time:.5f}s")
+            logger.info(f"RAG检索用时：{part_end_time - part_start_time:.5f}s")
-
-            if len(relation_search_res) != 0:
-                logger.info("找到相关关系，将使用RAG进行检索")
-                # 使用KG检索
-                part_start_time = time.perf_counter()
-                result, ppr_node_weights = self.kg_manager.kg_search(
-                    relation_search_res, paragraph_search_res, self.embed_manager
-                )
-                part_end_time = time.perf_counter()
-                logger.info(f"RAG检索用时：{part_end_time - part_start_time:.5f}s")
-            else:
-                logger.info("未找到相关关系，将使用文段检索结果")
-                result = paragraph_search_res
-                ppr_node_weights = None
-
-            # 过滤阈值
-            result = dyn_select_top_k(result, 0.5, 1.0)
-
-            for res in result:
-                raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
-                print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
-
-            return result, ppr_node_weights
         else:
-            return None
+            logger.info("未找到相关关系，将使用文段检索结果")
+            result = paragraph_search_res
+            ppr_node_weights = None
 
-    async def get_knowledge(self, question: str) -> str:
+        # 过滤阈值
+        result = dyn_select_top_k(result, 0.5, 1.0)
+
+        for res in result:
+            raw_paragraph = self.embed_manager.paragraphs_embedding_store.store[res[0]].str
+            print(f"找到相关文段，相关系数：{res[1]:.8f}\n{raw_paragraph}\n\n")
+
+        return result, ppr_node_weights
+
+    async def get_knowledge(self, question: str) -> Optional[str]:
         """获取知识"""
         # 处理查询
         processed_result = await self.process_query(question)
         if processed_result is not None:
             query_res = processed_result[0]
+            # 检查查询结果是否为空
+            if not query_res:
+                logger.debug("知识库查询结果为空，可能是知识库中没有相关内容")
+                return None
+                
             knowledge = [
                 (
                     self.embed_manager.paragraphs_embedding_store.store[res[0]].str,

src/chat/knowledge/raw_processing.py

@@ -1,48 +0,0 @@
-import json
-import os
-
-from .global_logger import logger
-from .lpmmconfig import global_config
-from src.chat.knowledge.utils.hash import get_sha256
-
-
-def load_raw_data(path: str = None) -> tuple[list[str], list[str]]:
-    """加载原始数据文件
-
-    读取原始数据文件，将原始数据加载到内存中
-
-    Args:
-        path: 可选，指定要读取的json文件绝对路径
-
-    Returns:
-        - raw_data: 原始数据列表
-        - sha256_list: 原始数据的SHA256集合
-    """
-    # 读取指定路径或默认路径的json文件
-    json_path = path if path else global_config["persistence"]["raw_data_path"]
-    if os.path.exists(json_path):
-        with open(json_path, "r", encoding="utf-8") as f:
-            import_json = json.loads(f.read())
-    else:
-        raise Exception(f"原始数据文件读取失败: {json_path}")
-    """
-    import_json 内容示例：
-    import_json = ["The capital of China is Beijing. The capital of France is Paris.",]
-    """
-    raw_data = []
-    sha256_list = []
-    sha256_set = set()
-    for item in import_json:
-        if not isinstance(item, str):
-            logger.warning("数据类型错误：{}".format(item))
-            continue
-        pg_hash = get_sha256(item)
-        if pg_hash in sha256_set:
-            logger.warning("重复数据：{}".format(item))
-            continue
-        sha256_set.add(pg_hash)
-        sha256_list.append(pg_hash)
-        raw_data.append(item)
-    logger.info("共读取到{}条数据".format(len(raw_data)))
-
-    return sha256_list, raw_data

src/chat/knowledge/utils/dyn_topk.py

@@ -5,6 +5,10 @@ def dyn_select_top_k(
     score: List[Tuple[Any, float]], jmp_factor: float, var_factor: float
 ) -> List[Tuple[Any, float, float]]:
     """动态TopK选择"""
+    # 检查输入列表是否为空
+    if not score:
+        return []
+    
     # 按照分数排序（降序）
     sorted_score = sorted(score, key=lambda x: x[1], reverse=True)
 

src/chat/knowledge/utils/visualize_graph.py

@@ -1,17 +0,0 @@
-import networkx as nx
-from matplotlib import pyplot as plt
-
-
-def draw_graph_and_show(graph):
-    """绘制图并显示，画布大小1280*1280"""
-    fig = plt.figure(1, figsize=(12.8, 12.8), dpi=100)
-    nx.draw_networkx(
-        graph,
-        node_size=100,
-        width=0.5,
-        with_labels=True,
-        labels=nx.get_node_attributes(graph, "content"),
-        font_family="Sarasa Mono SC",
-        font_size=8,
-    )
-    fig.show()

src/chat/memory_system/Hippocampus.py

@@ -5,25 +5,27 @@ import random
 import time
 import re
 import json
-from itertools import combinations
-
 import jieba
 import networkx as nx
 import numpy as np
+
+from itertools import combinations
+from typing import List, Tuple, Coroutine, Any, Set
 from collections import Counter
-from ...llm_models.utils_model import LLMRequest
+from rich.traceback import install
+
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config, model_config
+from src.common.database.database_model import Messages, GraphNodes, GraphEdges  # Peewee Models导入
 from src.common.logger import get_logger
 from src.chat.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器
-from ..utils.chat_message_builder import (
+from src.chat.utils.chat_message_builder import (
     get_raw_msg_by_timestamp,
     build_readable_messages,
     get_raw_msg_by_timestamp_with_chat,
 )  # 导入 build_readable_messages
-from ..utils.utils import translate_timestamp_to_human_readable
+from src.chat.utils.utils import translate_timestamp_to_human_readable
-from rich.traceback import install
 
-from ...config.config import global_config
-from src.common.database.database_model import Messages, GraphNodes, GraphEdges  # Peewee Models导入
 
 install(extra_lines=3)
 
@@ -198,8 +200,7 @@ class Hippocampus:
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        # TODO: API-Adapter修改标记
+        self.model_summary = LLMRequest(model_set=model_config.model_task_config.memory, request_type="memory.builder")
-        self.model_summary = LLMRequest(global_config.model.memory, request_type="memory.builder")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -339,9 +340,7 @@ class Hippocampus:
         else:
             topic_num = 5  # 51+字符: 5个关键词 (其余长文本)
 
-        topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
+        topics_response, _ = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
-            self.find_topic_llm(text, topic_num)
-        )
 
         # 提取关键词
         keywords = re.findall(r"<([^>]+)>", topics_response)
@@ -353,12 +352,11 @@ class Hippocampus:
                 for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
                 if keyword.strip()
             ]
-        
+
         if keywords:
             logger.info(f"提取关键词: {keywords}")
-        
+
-        return keywords 
+        return keywords
-        
 
     async def get_memory_from_text(
         self,
@@ -1245,7 +1243,7 @@ class ParahippocampalGyrus:
 
         # 2. 使用LLM提取关键主题
         topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
-        topics_response, (reasoning_content, model_name) = await self.hippocampus.model_summary.generate_response_async(
+        topics_response, _ = await self.hippocampus.model_summary.generate_response_async(
             self.hippocampus.find_topic_llm(input_text, topic_num)
         )
 
@@ -1269,7 +1267,7 @@ class ParahippocampalGyrus:
         logger.debug(f"过滤后话题: {filtered_topics}")
 
         # 4. 创建所有话题的摘要生成任务
-        tasks = []
+        tasks: List[Tuple[str, Coroutine[Any, Any, Tuple[str, Tuple[str, str, List | None]]]]] = []
         for topic in filtered_topics:
             # 调用修改后的 topic_what，不再需要 time_info
             topic_what_prompt = self.hippocampus.topic_what(input_text, topic)
@@ -1281,7 +1279,7 @@ class ParahippocampalGyrus:
                 continue
 
         # 等待所有任务完成
-        compressed_memory = set()
+        compressed_memory: Set[Tuple[str, str]] = set()
         similar_topics_dict = {}
 
         for topic, task in tasks:

src/chat/memory_system/instant_memory.py

@@ -3,13 +3,16 @@ import time
 import re
 import json
 import ast
-from json_repair import repair_json
-from src.llm_models.utils_model import LLMRequest
-from src.common.logger import get_logger
 import traceback
 
-from src.config.config import global_config
+from json_repair import repair_json
+from datetime import datetime, timedelta
+
+from src.llm_models.utils_model import LLMRequest
+from src.common.logger import get_logger
 from src.common.database.database_model import Memory  # Peewee Models导入
+from src.config.config import model_config
+
 
 logger = get_logger(__name__)
 
@@ -35,8 +38,7 @@ class InstantMemory:
         self.chat_id = chat_id
         self.last_view_time = time.time()
         self.summary_model = LLMRequest(
-            model=global_config.model.memory,
+            model_set=model_config.model_task_config.memory,
-            temperature=0.5,
             request_type="memory.summary",
         )
 
@@ -48,14 +50,11 @@ class InstantMemory:
         """
 
         try:
-            response, _ = await self.summary_model.generate_response_async(prompt)
+            response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
             print(prompt)
             print(response)
 
-            if "1" in response:
+            return "1" in response
-                return True
-            else:
-                return False
         except Exception as e:
             logger.error(f"判断是否需要记忆出现错误：{str(e)} {traceback.format_exc()}")
             return False
@@ -71,9 +70,9 @@ class InstantMemory:
         }}
         """
         try:
-            response, _ = await self.summary_model.generate_response_async(prompt)
+            response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
-            print(prompt)
+            # print(prompt)
-            print(response)
+            # print(response)
             if not response:
                 return None
             try:
@@ -142,7 +141,7 @@ class InstantMemory:
         请只输出json格式，不要输出其他多余内容
         """
         try:
-            response, _ = await self.summary_model.generate_response_async(prompt)
+            response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
             print(prompt)
             print(response)
             if not response:
@@ -177,7 +176,7 @@ class InstantMemory:
 
                 for mem in query:
                     # 对每条记忆
-                    mem_keywords = mem.keywords or []
+                    mem_keywords = mem.keywords or ""
                     parsed = ast.literal_eval(mem_keywords)
                     if isinstance(parsed, list):
                         mem_keywords = [str(k).strip() for k in parsed if str(k).strip()]
@@ -201,6 +200,7 @@ class InstantMemory:
             return None
 
     def _parse_time_range(self, time_str):
+        # sourcery skip: extract-duplicate-method, use-contextlib-suppress
         """
         支持解析如下格式：
         - 具体日期时间：YYYY-MM-DD HH:MM:SS
@@ -208,8 +208,6 @@ class InstantMemory:
         - 相对时间：今天，昨天，前天，N天前，N个月前
         - 空字符串：返回(None, None)
         """
-        from datetime import datetime, timedelta
-
         now = datetime.now()
         if not time_str:
             return 0, now
@@ -239,14 +237,12 @@ class InstantMemory:
             start = (now - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
             end = start + timedelta(days=1)
             return start, end
-        m = re.match(r"(\d+)天前", time_str)
+        if m := re.match(r"(\d+)天前", time_str):
-        if m:
             days = int(m.group(1))
             start = (now - timedelta(days=days)).replace(hour=0, minute=0, second=0, microsecond=0)
             end = start + timedelta(days=1)
             return start, end
-        m = re.match(r"(\d+)个月前", time_str)
+        if m := re.match(r"(\d+)个月前", time_str):
-        if m:
             months = int(m.group(1))
             # 近似每月30天
             start = (now - timedelta(days=months * 30)).replace(hour=0, minute=0, second=0, microsecond=0)

src/chat/memory_system/memory_activator.py

@@ -1,13 +1,15 @@
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
-from src.common.logger import get_logger
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from datetime import datetime
-from src.chat.memory_system.Hippocampus import hippocampus_manager
-from typing import List, Dict
 import difflib
 import json
+
 from json_repair import repair_json
+from typing import List, Dict
+from datetime import datetime
+
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config, model_config
+from src.common.logger import get_logger
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.memory_system.Hippocampus import hippocampus_manager
 
 
 logger = get_logger("memory_activator")
@@ -61,11 +63,8 @@ def init_prompt():
 
 class MemoryActivator:
     def __init__(self):
-        # TODO: API-Adapter修改标记
-
         self.key_words_model = LLMRequest(
-            model=global_config.model.utils_small,
+            model_set=model_config.model_task_config.utils_small,
-            temperature=0.5,
             request_type="memory.activator",
         )
 
@@ -92,7 +91,9 @@ class MemoryActivator:
 
         # logger.debug(f"prompt: {prompt}")
 
-        response, (reasoning_content, model_name) = await self.key_words_model.generate_response_async(prompt)
+        response, (reasoning_content, model_name, _) = await self.key_words_model.generate_response_async(
+            prompt, temperature=0.5
+        )
 
         keywords = list(get_keywords_from_json(response))
 

src/chat/message_receive/message.py

@@ -203,7 +203,7 @@ class MessageRecvS4U(MessageRecv):
         self.is_superchat = False
         self.gift_info = None
         self.gift_name = None
-        self.gift_count = None
+        self.gift_count: Optional[str] = None
         self.superchat_info = None
         self.superchat_price = None
         self.superchat_message_text = None
@@ -444,7 +444,7 @@ class MessageSending(MessageProcessBase):
         is_emoji: bool = False,
         thinking_start_time: float = 0,
         apply_set_reply_logic: bool = False,
-        reply_to: str = None,  # type: ignore
+        reply_to: Optional[str] = None,
     ):
         # 调用父类初始化
         super().__init__(

src/chat/planner_actions/action_manager.py

@@ -1,9 +1,10 @@
 from typing import Dict, Optional, Type
-from src.plugin_system.base.base_action import BaseAction
+
 from src.chat.message_receive.chat_stream import ChatStream
 from src.common.logger import get_logger
 from src.plugin_system.core.component_registry import component_registry
 from src.plugin_system.base.component_types import ComponentType, ActionInfo
+from src.plugin_system.base.base_action import BaseAction
 
 logger = get_logger("action_manager")
 

src/chat/planner_actions/action_modifier.py

@@ -5,7 +5,7 @@ import time
 from typing import List, Any, Dict, TYPE_CHECKING, Tuple
 
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from src.chat.message_receive.chat_stream import get_chat_manager, ChatMessageContext
 from src.chat.planner_actions.action_manager import ActionManager
@@ -36,10 +36,7 @@ class ActionModifier:
         self.action_manager = action_manager
 
         # 用于LLM判定的小模型
-        self.llm_judge = LLMRequest(
+        self.llm_judge = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="action.judge")
-            model=global_config.model.utils_small,
-            request_type="action.judge",
-        )
 
         # 缓存相关属性
         self._llm_judge_cache = {}  # 缓存LLM判定结果
@@ -438,4 +435,4 @@ class ActionModifier:
             return True
         else:
             logger.debug(f"{self.log_prefix}动作 {action_name} 未匹配到任何关键词: {activation_keywords}")
-            return False
+            return False

src/chat/planner_actions/planner.py

@@ -7,7 +7,7 @@ from datetime import datetime
 from json_repair import repair_json
 
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.common.logger import get_logger
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import (
@@ -36,8 +36,6 @@ def init_prompt():
 {chat_context_description}，以下是具体的聊天内容
 {chat_content_block}
 
-
-
 {moderation_prompt}
 
 现在请你根据{by_what}选择合适的action和触发action的消息:
@@ -73,10 +71,7 @@ class ActionPlanner:
         self.log_prefix = f"[{get_chat_manager().get_stream_name(chat_id) or chat_id}]"
         self.action_manager = action_manager
         # LLM规划器配置
-        self.planner_llm = LLMRequest(
+        self.planner_llm = LLMRequest(model_set=model_config.model_task_config.planner, request_type="planner")  # 用于动作规划
-            model=global_config.model.planner,
-            request_type="planner",  # 用于动作规划
-        )
 
         self.last_obs_time_mark = 0.0
 
@@ -140,7 +135,7 @@ class ActionPlanner:
             # --- 调用 LLM (普通文本生成) ---
             llm_content = None
             try:
-                llm_content, (reasoning_content, _) = await self.planner_llm.generate_response_async(prompt=prompt)
+                llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
 
                 if global_config.debug.show_prompt:
                     logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")

src/chat/replyer/default_generator.py

@@ -8,7 +8,8 @@ from typing import List, Optional, Dict, Any, Tuple
 from datetime import datetime
 from src.mais4u.mai_think import mai_thinking_manager
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
+from src.config.api_ada_configs import TaskConfig
 from src.individuality.individuality import get_individuality
 from src.llm_models.utils_model import LLMRequest
 from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
@@ -23,14 +24,13 @@ from src.chat.utils.chat_message_builder import (
     replace_user_references_sync,
 )
 from src.chat.express.expression_selector import expression_selector
-from src.chat.knowledge.knowledge_lib import qa_manager
 from src.chat.memory_system.memory_activator import MemoryActivator
 from src.chat.memory_system.instant_memory import InstantMemory
 from src.mood.mood_manager import mood_manager
 from src.person_info.relationship_fetcher import relationship_fetcher_manager
 from src.person_info.person_info import get_person_info_manager
-from src.tools.tool_executor import ToolExecutor
 from src.plugin_system.base.component_types import ActionInfo
+from src.plugin_system.apis import llm_api
 
 logger = get_logger("replyer")
 
@@ -40,7 +40,7 @@ def init_prompt():
     Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
     Prompt("在群里聊天", "chat_target_group2")
     Prompt("和{sender_name}聊天", "chat_target_private2")
-    
+
     Prompt(
         """
 {expression_habits_block}
@@ -102,36 +102,57 @@ def init_prompt():
         "s4u_style_prompt",
     )
 
+    Prompt(
+        """
+你是一个专门获取知识的助手。你的名字是{bot_name}。现在是{time_now}。
+群里正在进行的聊天内容：
+{chat_history}
+
+现在，{sender}发送了内容:{target_message},你想要回复ta。
+请仔细分析聊天内容，考虑以下几点：
+1. 内容中是否包含需要查询信息的问题
+2. 是否有明确的知识获取指令
+
+If you need to use the search tool, please directly call the function "lpmm_search_knowledge". If you do not need to use any tool, simply output "No tool needed".
+""",
+        name="lpmm_get_knowledge_prompt",
+    )
+
 
 class DefaultReplyer:
     def __init__(
         self,
         chat_stream: ChatStream,
-        model_configs: Optional[List[Dict[str, Any]]] = None,
+        model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
         request_type: str = "focus.replyer",
     ):
         self.request_type = request_type
 
-        if model_configs:
+        if model_set_with_weight:
-            self.express_model_configs = model_configs
+            # self.express_model_configs = model_configs
+            self.model_set: List[Tuple[TaskConfig, float]] = model_set_with_weight
         else:
             # 当未提供配置时，使用默认配置并赋予默认权重
 
-            model_config_1 = global_config.model.replyer_1.copy()
+            # model_config_1 = global_config.model.replyer_1.copy()
-            model_config_2 = global_config.model.replyer_2.copy()
+            # model_config_2 = global_config.model.replyer_2.copy()
             prob_first = global_config.chat.replyer_random_probability
 
-            model_config_1["weight"] = prob_first
+            # model_config_1["weight"] = prob_first
-            model_config_2["weight"] = 1.0 - prob_first
+            # model_config_2["weight"] = 1.0 - prob_first
 
-            self.express_model_configs = [model_config_1, model_config_2]
+            # self.express_model_configs = [model_config_1, model_config_2]
+            self.model_set = [
+                (model_config.model_task_config.replyer_1, prob_first),
+                (model_config.model_task_config.replyer_2, 1.0 - prob_first),
+            ]
 
-        if not self.express_model_configs:
+        # if not self.express_model_configs:
-            logger.warning("未找到有效的模型配置，回复生成可能会失败。")
+        #     logger.warning("未找到有效的模型配置，回复生成可能会失败。")
-            # 提供一个最终的回退，以防止在空列表上调用 random.choice
+        #     # 提供一个最终的回退，以防止在空列表上调用 random.choice
-            fallback_config = global_config.model.replyer_1.copy()
+        #     fallback_config = global_config.model.replyer_1.copy()
-            fallback_config.setdefault("weight", 1.0)
+        #     fallback_config.setdefault("weight", 1.0)
-            self.express_model_configs = [fallback_config]
+        #     self.express_model_configs = [fallback_config]
 
         self.chat_stream = chat_stream
         self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
@@ -139,13 +160,16 @@ class DefaultReplyer:
         self.heart_fc_sender = HeartFCSender()
         self.memory_activator = MemoryActivator()
         self.instant_memory = InstantMemory(chat_id=self.chat_stream.stream_id)
+
+        from src.plugin_system.core.tool_use import ToolExecutor  # 延迟导入ToolExecutor，不然会循环依赖
+
         self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
 
-    def _select_weighted_model_config(self) -> Dict[str, Any]:
+    def _select_weighted_models_config(self) -> Tuple[TaskConfig, float]:
         """使用加权随机选择来挑选一个模型配置"""
-        configs = self.express_model_configs
+        configs = self.model_set
         # 提取权重，如果模型配置中没有'weight'键，则默认为1.0
-        weights = [config.get("weight", 1.0) for config in configs]
+        weights = [weight for _, weight in configs]
 
         return random.choices(population=configs, weights=weights, k=1)[0]
 
@@ -155,18 +179,16 @@ class DefaultReplyer:
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
         enable_tool: bool = True,
-        enable_timeout: bool = False,
     ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
         回复器 (Replier): 负责生成回复文本的核心逻辑。
-        
+
         Args:
             reply_to: 回复对象，格式为 "发送者:消息内容"
             extra_info: 额外信息，用于补充上下文
             available_actions: 可用的动作信息字典
             enable_tool: 是否启用工具调用
-            enable_timeout: 是否启用超时处理
+
-            
         Returns:
             Tuple[bool, Optional[str], Optional[str]]: (是否成功, 生成的回复内容, 使用的prompt)
         """
@@ -177,13 +199,12 @@ class DefaultReplyer:
             # 3. 构建 Prompt
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_reply_context(
-                    reply_to = reply_to,
+                    reply_to=reply_to,
                     extra_info=extra_info,
                     available_actions=available_actions,
-                    enable_timeout=enable_timeout,
                     enable_tool=enable_tool,
                 )
-                
+
             if not prompt:
                 logger.warning("构建prompt失败，跳过回复生成")
                 return False, None, None
@@ -194,26 +215,8 @@ class DefaultReplyer:
             model_name = "unknown_model"
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
+                content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
-                    # 加权随机选择一个模型配置
+                logger.debug(f"replyer生成内容: {content}")
-                    selected_model_config = self._select_weighted_model_config()
-                    logger.info(
-                        f"使用模型生成回复: {selected_model_config.get('name', 'N/A')} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    if global_config.debug.show_prompt:
-                        logger.info(f"\n{prompt}\n")
-                    else:
-                        logger.debug(f"\n{prompt}\n")
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.debug(f"replyer生成内容: {content}")
 
             except Exception as llm_e:
                 # 精简报错信息
@@ -232,22 +235,21 @@ class DefaultReplyer:
         raw_reply: str = "",
         reason: str = "",
         reply_to: str = "",
-    ) -> Tuple[bool, Optional[str]]:
+        return_prompt: bool = False,
+    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
         表达器 (Expressor): 负责重写和优化回复文本。
-        
+
         Args:
             raw_reply: 原始回复内容
             reason: 回复原因
             reply_to: 回复对象，格式为 "发送者:消息内容"
             relation_info: 关系信息
-            
+
         Returns:
             Tuple[bool, Optional[str]]: (是否成功, 重写后的回复内容)
         """
         try:
-
-            
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_rewrite_context(
                     raw_reply=raw_reply,
@@ -260,36 +262,23 @@ class DefaultReplyer:
             model_name = "unknown_model"
             if not prompt:
                 logger.error("Prompt 构建失败，无法生成回复。")
-                return False, None
+                return False, None, None
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
+                content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
-                    # 加权随机选择一个模型配置
+                logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
-                    selected_model_config = self._select_weighted_model_config()
-                    logger.info(
-                        f"使用模型重写回复: {selected_model_config.get('name', 'N/A')} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
 
             except Exception as llm_e:
                 # 精简报错信息
                 logger.error(f"LLM 生成失败: {llm_e}")
-                return False, None  # LLM 调用失败则无法生成回复
+                return False, None, prompt if return_prompt else None  # LLM 调用失败则无法生成回复
 
-            return True, content
+            return True, content, prompt if return_prompt else None
 
         except Exception as e:
             logger.error(f"回复生成意外失败: {e}")
             traceback.print_exc()
-            return False, None
+            return False, None, prompt if return_prompt else None
 
     async def build_relation_info(self, reply_to: str = ""):
         if not global_config.relationship.enable_relationship:
@@ -313,11 +302,11 @@ class DefaultReplyer:
 
     async def build_expression_habits(self, chat_history: str, target: str) -> str:
         """构建表达习惯块
-        
+
         Args:
             chat_history: 聊天历史记录
             target: 目标消息内容
-            
+
         Returns:
             str: 表达习惯信息字符串
         """
@@ -366,17 +355,15 @@ class DefaultReplyer:
         if style_habits_str.strip() and grammar_habits_str.strip():
             expression_habits_title = "你可以参考以下的语言习惯和句法，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式结合到你的回复中："
 
-        expression_habits_block = f"{expression_habits_title}\n{expression_habits_block}"
+        return f"{expression_habits_title}\n{expression_habits_block}"
-
-        return expression_habits_block
 
     async def build_memory_block(self, chat_history: str, target: str) -> str:
         """构建记忆块
-        
+
         Args:
             chat_history: 聊天历史记录
             target: 目标消息内容
-            
+
         Returns:
             str: 记忆信息字符串
         """
@@ -441,7 +428,7 @@ class DefaultReplyer:
                 for tool_result in tool_results:
                     tool_name = tool_result.get("tool_name", "unknown")
                     content = tool_result.get("content", "")
-                    result_type = tool_result.get("type", "info")
+                    result_type = tool_result.get("type", "tool_result")
 
                     tool_info_str += f"- 【{tool_name}】{result_type}: {content}\n"
 
@@ -459,10 +446,10 @@ class DefaultReplyer:
 
     def _parse_reply_target(self, target_message: str) -> Tuple[str, str]:
         """解析回复目标消息
-        
+
         Args:
             target_message: 目标消息，格式为 "发送者:消息内容" 或 "发送者：消息内容"
-            
+
         Returns:
             Tuple[str, str]: (发送者名称, 消息内容)
         """
@@ -481,10 +468,10 @@ class DefaultReplyer:
 
     async def build_keywords_reaction_prompt(self, target: Optional[str]) -> str:
         """构建关键词反应提示
-        
+
         Args:
             target: 目标消息内容
-            
+
         Returns:
             str: 关键词反应提示字符串
         """
@@ -523,11 +510,11 @@ class DefaultReplyer:
 
     async def _time_and_run_task(self, coroutine, name: str) -> Tuple[str, Any, float]:
         """计时并运行异步任务的辅助函数
-        
+
         Args:
             coroutine: 要执行的协程
             name: 任务名称
-            
+
         Returns:
             Tuple[str, Any, float]: (任务名称, 任务结果, 执行耗时)
         """
@@ -537,7 +524,9 @@ class DefaultReplyer:
         duration = end_time - start_time
         return name, result, duration
 
-    def build_s4u_chat_history_prompts(self, message_list_before_now: List[Dict[str, Any]], target_user_id: str) -> Tuple[str, str]:
+    def build_s4u_chat_history_prompts(
+        self, message_list_before_now: List[Dict[str, Any]], target_user_id: str
+    ) -> Tuple[str, str]:
         """
         构建 s4u 风格的分离对话 prompt
 
@@ -612,7 +601,7 @@ class DefaultReplyer:
         chat_info: str,
     ) -> Any:
         """构建 mai_think 上下文信息
-        
+
         Args:
             chat_id: 聊天ID
             memory_block: 记忆块内容
@@ -625,7 +614,7 @@ class DefaultReplyer:
             sender: 发送者名称
             target: 目标消息内容
             chat_info: 聊天信息
-            
+
         Returns:
             Any: mai_think 实例
         """
@@ -647,19 +636,17 @@ class DefaultReplyer:
         reply_to: str,
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-        enable_timeout: bool = False,
         enable_tool: bool = True,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         """
         构建回复器上下文
 
         Args:
-            reply_data: 回复数据
+            reply_to: 回复对象，格式为 "发送者:消息内容"
-                replay_data 包含以下字段：
+            extra_info: 额外信息，用于补充上下文
-                    structured_info: 结构化信息，一般是工具调用获得的信息
-                    reply_to: 回复对象
-                    extra_info/extra_info_block: 额外信息
             available_actions: 可用动作
+            enable_timeout: 是否启用超时处理
+            enable_tool: 是否启用工具调用
 
         Returns:
             str: 构建好的上下文
@@ -727,7 +714,7 @@ class DefaultReplyer:
             self._time_and_run_task(
                 self.build_tool_info(chat_talking_prompt_short, reply_to, enable_tool=enable_tool), "tool_info"
             ),
-            self._time_and_run_task(get_prompt_info(target, threshold=0.38), "prompt_info"),
+            self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, reply_to), "prompt_info"),
         )
 
         # 任务名称中英文映射
@@ -877,7 +864,7 @@ class DefaultReplyer:
         raw_reply: str,
         reason: str,
         reply_to: str,
-    ) -> str:
+    ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
@@ -1011,6 +998,81 @@ class DefaultReplyer:
             display_message=display_message,
         )
 
+    async def llm_generate_content(self, prompt: str):
+        with Timer("LLM生成", {}):  # 内部计时器，可选保留
+            # 加权随机选择一个模型配置
+            selected_model_config, weight = self._select_weighted_models_config()
+            logger.info(f"使用模型集生成回复: {selected_model_config} (选中概率: {weight})")
+
+            express_model = LLMRequest(model_set=selected_model_config, request_type=self.request_type)
+
+            if global_config.debug.show_prompt:
+                logger.info(f"\n{prompt}\n")
+            else:
+                logger.debug(f"\n{prompt}\n")
+
+            content, (reasoning_content, model_name, tool_calls) = await express_model.generate_response_async(prompt)
+
+            logger.debug(f"replyer生成内容: {content}")
+        return content, reasoning_content, model_name, tool_calls
+
+    async def get_prompt_info(self, message: str, reply_to: str):
+        related_info = ""
+        start_time = time.time()
+        from src.plugins.built_in.knowledge.lpmm_get_knowledge import SearchKnowledgeFromLPMMTool
+        if not reply_to:
+            logger.debug("没有回复对象，跳过获取知识库内容")
+            return ""
+        sender, content = self._parse_reply_target(reply_to)
+        if not content:
+            logger.debug("回复对象内容为空，跳过获取知识库内容")
+            return ""
+        logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
+        # 从LPMM知识库获取知识
+        try:
+            # 检查LPMM知识库是否启用
+            if not global_config.lpmm_knowledge.enable:
+                logger.debug("LPMM知识库未启用，跳过获取知识库内容")
+                return ""
+            time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+
+            bot_name = global_config.bot.nickname
+
+            prompt = await global_prompt_manager.format_prompt(
+                "lpmm_get_knowledge_prompt",
+                bot_name=bot_name,
+                time_now=time_now,
+                chat_history=message,
+                sender=sender,
+                target_message=content,
+            )
+            _, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools(
+                prompt,
+                model_config=model_config.model_task_config.tool_use,
+                tool_options=[SearchKnowledgeFromLPMMTool.get_tool_definition()],
+            )
+            if tool_calls:
+                result = await self.tool_executor.execute_tool_call(tool_calls[0], SearchKnowledgeFromLPMMTool())
+                end_time = time.time()
+                if not result or not result.get("content"):
+                    logger.debug("从LPMM知识库获取知识失败，返回空知识...")
+                    return ""
+                found_knowledge_from_lpmm = result.get("content", "")
+                logger.debug(
+                    f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}"
+                )
+                related_info += found_knowledge_from_lpmm
+                logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
+                logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
+
+                return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
+            else:
+                logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
+                return ""
+        except Exception as e:
+            logger.error(f"获取知识库内容时发生异常: {str(e)}")
+            return ""
+
 
 def weighted_sample_no_replacement(items, weights, k) -> list:
     """
@@ -1046,38 +1108,4 @@ def weighted_sample_no_replacement(items, weights, k) -> list:
     return selected
 
 
-async def get_prompt_info(message: str, threshold: float):
-    related_info = ""
-    start_time = time.time()
-
-    logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
-    # 从LPMM知识库获取知识
-    try:
-        # 检查LPMM知识库是否启用
-        if qa_manager is None:
-            logger.debug("LPMM知识库已禁用，跳过知识获取")
-            return ""
-
-        found_knowledge_from_lpmm = await qa_manager.get_knowledge(message)
-
-        end_time = time.time()
-        if found_knowledge_from_lpmm is not None:
-            logger.debug(
-                f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}"
-            )
-            related_info += found_knowledge_from_lpmm
-            logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
-            logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
-
-            # 格式化知识信息
-            formatted_prompt_info = f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
-            return formatted_prompt_info
-        else:
-            logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
-            return ""
-    except Exception as e:
-        logger.error(f"获取知识库内容时发生异常: {str(e)}")
-        return ""
-
-
 init_prompt()

src/chat/replyer/replyer_manager.py

@@ -1,6 +1,7 @@
-from typing import Dict, Any, Optional, List
+from typing import Dict, Optional, List, Tuple
 
 from src.common.logger import get_logger
+from src.config.api_ada_configs import TaskConfig
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
 from src.chat.replyer.default_generator import DefaultReplyer
 
@@ -15,7 +16,7 @@ class ReplyerManager:
         self,
         chat_stream: Optional[ChatStream] = None,
         chat_id: Optional[str] = None,
-        model_configs: Optional[List[Dict[str, Any]]] = None,
+        model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
         request_type: str = "replyer",
     ) -> Optional[DefaultReplyer]:
         """
@@ -49,7 +50,7 @@ class ReplyerManager:
         # model_configs 只在此时（初始化时）生效
         replyer = DefaultReplyer(
             chat_stream=target_stream,
-            model_configs=model_configs,  # 可以是None，此时使用默认模型
+            model_set_with_weight=model_set_with_weight,  # 可以是None，此时使用默认模型
             request_type=request_type,
         )
         self._repliers[stream_id] = replyer

src/chat/utils/json_utils.py

@@ -1,223 +0,0 @@
-import ast
-import json
-import logging
-
-from typing import Any, Dict, TypeVar, List, Union, Tuple, Optional
-
-# 定义类型变量用于泛型类型提示
-T = TypeVar("T")
-
-# 获取logger
-logger = logging.getLogger("json_utils")
-
-
-def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
-    """
-    安全地解析JSON字符串，出错时返回默认值
-    现在尝试处理单引号和标准JSON
-
-    参数:
-        json_str: 要解析的JSON字符串
-        default_value: 解析失败时返回的默认值
-
-    返回:
-        解析后的Python对象，或在解析失败时返回default_value
-    """
-    if not json_str or not isinstance(json_str, str):
-        logger.warning(f"safe_json_loads 接收到非字符串输入: {type(json_str)}, 值: {json_str}")
-        return default_value
-
-    try:
-        # 尝试标准的 JSON 解析
-        return json.loads(json_str)
-    except json.JSONDecodeError:
-        # 如果标准解析失败，尝试用 ast.literal_eval 解析
-        try:
-            # logger.debug(f"标准JSON解析失败，尝试用 ast.literal_eval 解析: {json_str[:100]}...")
-            result = ast.literal_eval(json_str)
-            if isinstance(result, dict):
-                return result
-            logger.warning(f"ast.literal_eval 解析成功但结果不是字典: {type(result)}, 内容: {result}")
-            return default_value
-        except (ValueError, SyntaxError, MemoryError, RecursionError) as ast_e:
-            logger.error(f"使用 ast.literal_eval 解析失败: {ast_e}, 字符串: {json_str[:100]}...")
-            return default_value
-        except Exception as e:
-            logger.error(f"使用 ast.literal_eval 解析时发生意外错误: {e}, 字符串: {json_str[:100]}...")
-            return default_value
-    except Exception as e:
-        logger.error(f"JSON解析过程中发生意外错误: {e}, 字符串: {json_str[:100]}...")
-        return default_value
-
-
-def extract_tool_call_arguments(
-    tool_call: Dict[str, Any], default_value: Optional[Dict[str, Any]] = None
-) -> Dict[str, Any]:
-    """
-    从LLM工具调用对象中提取参数
-
-    参数:
-        tool_call: 工具调用对象字典
-        default_value: 解析失败时返回的默认值
-
-    返回:
-        解析后的参数字典，或在解析失败时返回default_value
-    """
-    default_result = default_value or {}
-
-    if not tool_call or not isinstance(tool_call, dict):
-        logger.error(f"无效的工具调用对象: {tool_call}")
-        return default_result
-
-    try:
-        # 提取function参数
-        function_data = tool_call.get("function", {})
-        if not function_data or not isinstance(function_data, dict):
-            logger.error(f"工具调用缺少function字段或格式不正确: {tool_call}")
-            return default_result
-
-        if arguments_str := function_data.get("arguments", "{}"):
-            # 解析JSON
-            return safe_json_loads(arguments_str, default_result)
-        else:
-            return default_result
-
-    except Exception as e:
-        logger.error(f"提取工具调用参数时出错: {e}")
-        return default_result
-
-
-def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False, pretty: bool = False) -> str:
-    """
-    安全地将Python对象序列化为JSON字符串
-
-    参数:
-        obj: 要序列化的Python对象
-        default_value: 序列化失败时返回的默认值
-        ensure_ascii: 是否确保ASCII编码(默认False，允许中文等非ASCII字符)
-        pretty: 是否美化输出JSON
-
-    返回:
-        序列化后的JSON字符串，或在序列化失败时返回default_value
-    """
-    try:
-        indent = 2 if pretty else None
-        return json.dumps(obj, ensure_ascii=ensure_ascii, indent=indent)
-    except TypeError as e:
-        logger.error(f"JSON序列化失败(类型错误): {e}")
-        return default_value
-    except Exception as e:
-        logger.error(f"JSON序列化过程中发生意外错误: {e}")
-        return default_value
-
-
-def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, List[Any], str]:
-    """
-    标准化LLM响应格式，将各种格式（如元组）转换为统一的列表格式
-
-    参数:
-        response: 原始LLM响应
-        log_prefix: 日志前缀
-
-    返回:
-        元组 (成功标志, 标准化后的响应列表, 错误消息)
-    """
-
-    logger.debug(f"{log_prefix}原始人 LLM响应: {response}")
-
-    # 检查是否为None
-    if response is None:
-        return False, [], "LLM响应为None"
-
-    # 记录原始类型
-    logger.debug(f"{log_prefix}LLM响应原始类型: {type(response).__name__}")
-
-    # 将元组转换为列表
-    if isinstance(response, tuple):
-        logger.debug(f"{log_prefix}将元组响应转换为列表")
-        response = list(response)
-
-    # 确保是列表类型
-    if not isinstance(response, list):
-        return False, [], f"无法处理的LLM响应类型: {type(response).__name__}"
-
-    # 处理工具调用部分（如果存在）
-    if len(response) == 3:
-        content, reasoning, tool_calls = response
-
-        # 将工具调用部分转换为列表（如果是元组）
-        if isinstance(tool_calls, tuple):
-            logger.debug(f"{log_prefix}将工具调用元组转换为列表")
-            tool_calls = list(tool_calls)
-            response[2] = tool_calls
-
-    return True, response, ""
-
-
-def process_llm_tool_calls(
-    tool_calls: List[Dict[str, Any]], log_prefix: str = ""
-) -> Tuple[bool, List[Dict[str, Any]], str]:
-    """
-    处理并验证LLM响应中的工具调用列表
-
-    参数:
-        tool_calls: 从LLM响应中直接获取的工具调用列表
-        log_prefix: 日志前缀
-
-    返回:
-        元组 (成功标志, 验证后的工具调用列表, 错误消息)
-    """
-
-    # 如果列表为空，表示没有工具调用，这不是错误
-    if not tool_calls:
-        return True, [], "工具调用列表为空"
-
-    # 验证每个工具调用的格式
-    valid_tool_calls = []
-    for i, tool_call in enumerate(tool_calls):
-        if not isinstance(tool_call, dict):
-            logger.warning(f"{log_prefix}工具调用[{i}]不是字典: {type(tool_call).__name__}, 内容: {tool_call}")
-            continue
-
-        # 检查基本结构
-        if tool_call.get("type") != "function":
-            logger.warning(
-                f"{log_prefix}工具调用[{i}]不是function类型: type={tool_call.get('type', '未定义')}, 内容: {tool_call}"
-            )
-            continue
-
-        if "function" not in tool_call or not isinstance(tool_call.get("function"), dict):
-            logger.warning(f"{log_prefix}工具调用[{i}]缺少'function'字段或其类型不正确: {tool_call}")
-            continue
-
-        func_details = tool_call["function"]
-        if "name" not in func_details or not isinstance(func_details.get("name"), str):
-            logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'name'或类型不正确: {func_details}")
-            continue
-
-        # 验证参数 'arguments'
-        args_value = func_details.get("arguments")
-
-        # 1. 检查 arguments 是否存在且是字符串
-        if args_value is None or not isinstance(args_value, str):
-            logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'字符串: {func_details}")
-            continue
-
-        # 2. 尝试安全地解析 arguments 字符串
-        parsed_args = safe_json_loads(args_value, None)
-
-        # 3. 检查解析结果是否为字典
-        if parsed_args is None or not isinstance(parsed_args, dict):
-            logger.warning(
-                f"{log_prefix}工具调用[{i}]的'arguments'无法解析为有效的JSON字典, "
-                f"原始字符串: {args_value[:100]}..., 解析结果类型: {type(parsed_args).__name__}"
-            )
-            continue
-
-        # 如果检查通过，将原始的 tool_call 加入有效列表
-        valid_tool_calls.append(tool_call)
-
-    if not valid_tool_calls and tool_calls:  # 如果原始列表不为空，但验证后为空
-        return False, [], "所有工具调用格式均无效"
-
-    return True, valid_tool_calls, ""

src/chat/utils/utils.py

@@ -11,7 +11,7 @@ from typing import Optional, Tuple, Dict, List, Any
 
 from src.common.logger import get_logger
 from src.common.message_repository import find_messages, count_messages
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.llm_models.utils_model import LLMRequest
@@ -109,13 +109,11 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
     return is_mentioned, reply_probability
 
 
-async def get_embedding(text, request_type="embedding"):
+async def get_embedding(text, request_type="embedding") -> Optional[List[float]]:
     """获取文本的embedding向量"""
-    # TODO: API-Adapter修改标记
+    llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type=request_type)
-    llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
-    # return llm.get_embedding_sync(text)
     try:
-        embedding = await llm.get_embedding(text)
+        embedding, _ = await llm.get_embedding(text)
     except Exception as e:
         logger.error(f"获取embedding失败: {str(e)}")
         embedding = None

src/chat/utils/utils_image.py

@@ -14,7 +14,7 @@ from rich.traceback import install
 from src.common.logger import get_logger
 from src.common.database.database import db
 from src.common.database.database_model import Images, ImageDescriptions
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 
 install(extra_lines=3)
@@ -37,7 +37,7 @@ class ImageManager:
             self._ensure_image_dir()
 
             self._initialized = True
-            self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image")
+            self.vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="image")
 
             try:
                 db.connect(reuse_if_open=True)
@@ -107,6 +107,7 @@ class ImageManager:
             # 优先使用EmojiManager查询已注册表情包的描述
             try:
                 from src.chat.emoji_system.emoji_manager import get_emoji_manager
+
                 emoji_manager = get_emoji_manager()
                 cached_emoji_description = await emoji_manager.get_emoji_description_by_hash(image_hash)
                 if cached_emoji_description:
@@ -116,13 +117,12 @@ class ImageManager:
                 logger.debug(f"查询EmojiManager时出错: {e}")
 
             # 查询ImageDescriptions表的缓存描述
-            cached_description = self._get_description_from_db(image_hash, "emoji")
+            if cached_description := self._get_description_from_db(image_hash, "emoji"):
-            if cached_description:
                 logger.info(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
                 return f"[表情包：{cached_description}]"
 
             # === 二步走识别流程 ===
-            
+
             # 第一步：VLM视觉分析 - 生成详细描述
             if image_format in ["gif", "GIF"]:
                 image_base64_processed = self.transform_gif(image_base64)
@@ -130,10 +130,16 @@ class ImageManager:
                     logger.warning("GIF转换失败，无法获取描述")
                     return "[表情包(GIF处理失败)]"
                 vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
-                detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg")
+                detailed_description, _ = await self.vlm.generate_response_for_image(
+                    vlm_prompt, image_base64_processed, "jpg", temperature=0.4, max_tokens=300
+                )
             else:
-                vlm_prompt = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
+                vlm_prompt = (
-                detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64, image_format)
+                    "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
+                )
+                detailed_description, _ = await self.vlm.generate_response_for_image(
+                    vlm_prompt, image_base64, image_format, temperature=0.4, max_tokens=300
+                )
 
             if detailed_description is None:
                 logger.warning("VLM未能生成表情包详细描述")
@@ -150,31 +156,32 @@ class ImageManager:
             3. 输出简短精准，不要解释
             4. 如果有多个词用逗号分隔
             """
-            
+
             # 使用较低温度确保输出稳定
-            emotion_llm = LLMRequest(model=global_config.model.utils, temperature=0.3, max_tokens=50, request_type="emoji")
+            emotion_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="emoji")
-            emotion_result, _ = await emotion_llm.generate_response_async(emotion_prompt)
+            emotion_result, _ = await emotion_llm.generate_response_async(
+                emotion_prompt, temperature=0.3, max_tokens=50
+            )
 
             if emotion_result is None:
                 logger.warning("LLM未能生成情感标签，使用详细描述的前几个词")
                 # 降级处理：从详细描述中提取关键词
                 import jieba
+
                 words = list(jieba.cut(detailed_description))
                 emotion_result = "，".join(words[:2]) if len(words) >= 2 else (words[0] if words else "表情")
 
             # 处理情感结果，取前1-2个最重要的标签
             emotions = [e.strip() for e in emotion_result.replace("，", ",").split(",") if e.strip()]
             final_emotion = emotions[0] if emotions else "表情"
-            
+
             # 如果有第二个情感且不重复，也包含进来
             if len(emotions) > 1 and emotions[1] != emotions[0]:
                 final_emotion = f"{emotions[0]}，{emotions[1]}"
 
             logger.info(f"[emoji识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}")
 
-            # 再次检查缓存，防止并发写入时重复生成
+            if cached_description := self._get_description_from_db(image_hash, "emoji"):
-            cached_description = self._get_description_from_db(image_hash, "emoji")
-            if cached_description:
                 logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}")
                 return f"[表情包：{cached_description}]"
 
@@ -242,9 +249,7 @@ class ImageManager:
                     logger.debug(f"[缓存命中] 使用Images表中的图片描述: {existing_image.description[:50]}...")
                     return f"[图片：{existing_image.description}]"
 
-            # 查询ImageDescriptions表的缓存描述
+            if cached_description := self._get_description_from_db(image_hash, "image"):
-            cached_description = self._get_description_from_db(image_hash, "image")
-            if cached_description:
                 logger.debug(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
                 return f"[图片：{cached_description}]"
 
@@ -252,7 +257,9 @@ class ImageManager:
             image_format = Image.open(io.BytesIO(image_bytes)).format.lower()  # type: ignore
             prompt = global_config.custom_prompt.image_prompt
             logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)")
-            description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
+            description, _ = await self.vlm.generate_response_for_image(
+                prompt, image_base64, image_format, temperature=0.4, max_tokens=300
+            )
 
             if description is None:
                 logger.warning("AI未能生成图片描述")
@@ -445,10 +452,7 @@ class ImageManager:
             image_bytes = base64.b64decode(image_base64)
             image_hash = hashlib.md5(image_bytes).hexdigest()
 
-            # 检查图片是否已存在
+            if existing_image := Images.get_or_none(Images.emoji_hash == image_hash):
-            existing_image = Images.get_or_none(Images.emoji_hash == image_hash)
-
-            if existing_image:
                 # 检查是否缺少必要字段，如果缺少则创建新记录
                 if (
                     not hasattr(existing_image, "image_id")
@@ -524,9 +528,7 @@ class ImageManager:
 
             # 优先检查是否已有其他相同哈希的图片记录包含描述
             existing_with_description = Images.get_or_none(
-                (Images.emoji_hash == image_hash) & 
+                (Images.emoji_hash == image_hash) & (Images.description.is_null(False)) & (Images.description != "")
-                (Images.description.is_null(False)) & 
-                (Images.description != "")
             )
             if existing_with_description and existing_with_description.id != image.id:
                 logger.debug(f"[缓存复用] 从其他相同图片记录复用描述: {existing_with_description.description[:50]}...")
@@ -538,8 +540,7 @@ class ImageManager:
                 return
 
             # 检查ImageDescriptions表的缓存描述
-            cached_description = self._get_description_from_db(image_hash, "image")
+            if cached_description := self._get_description_from_db(image_hash, "image"):
-            if cached_description:
                 logger.debug(f"[缓存复用] 从ImageDescriptions表复用描述: {cached_description[:50]}...")
                 image.description = cached_description
                 image.vlm_processed = True
@@ -554,15 +555,15 @@ class ImageManager:
 
             # 获取VLM描述
             logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)")
-            description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
+            description, _ = await self.vlm.generate_response_for_image(
+                prompt, image_base64, image_format, temperature=0.4, max_tokens=300
+            )
 
             if description is None:
                 logger.warning("VLM未能生成图片描述")
                 description = "无法生成描述"
 
-            # 再次检查缓存，防止并发写入时重复生成
+            if cached_description := self._get_description_from_db(image_hash, "image"):
-            cached_description = self._get_description_from_db(image_hash, "image")
-            if cached_description:
                 logger.warning(f"虽然生成了描述，但是找到缓存图片描述: {cached_description}")
                 description = cached_description
 
@@ -606,7 +607,7 @@ def image_path_to_base64(image_path: str) -> str:
         raise FileNotFoundError(f"图片文件不存在: {image_path}")
 
     with open(image_path, "rb") as f:
-        image_data = f.read()
+        if image_data := f.read():
-        if not image_data:
+            return base64.b64encode(image_data).decode("utf-8")
+        else:
             raise IOError(f"读取图片文件失败: {image_path}")
-        return base64.b64encode(image_data).decode("utf-8")

src/chat/utils/utils_voice.py

@@ -1,35 +1,29 @@
-import base64
+from src.config.config import global_config, model_config
-
-from src.config.config import global_config
 from src.llm_models.utils_model import LLMRequest
 
 from src.common.logger import get_logger
 from rich.traceback import install
+
 install(extra_lines=3)
 
 logger = get_logger("chat_voice")
 
+
 async def get_voice_text(voice_base64: str) -> str:
-    """获取音频文件描述"""
+    """获取音频文件转录文本"""
     if not global_config.voice.enable_asr:
         logger.warning("语音识别未启用，无法处理语音消息")
         return "[语音]"
     try:
-        # 解码base64音频数据
+        _llm = LLMRequest(model_set=model_config.model_task_config.voice, request_type="audio")
-        # 确保base64字符串只包含ASCII字符
+        text = await _llm.generate_response_for_voice(voice_base64)
-        if isinstance(voice_base64, str):
-            voice_base64 = voice_base64.encode("ascii", errors="ignore").decode("ascii")
-        voice_bytes = base64.b64decode(voice_base64)
-        _llm = LLMRequest(model=global_config.model.voice, request_type="voice")
-        text = await _llm.generate_response_for_voice(voice_bytes)
         if text is None:
             logger.warning("未能生成语音文本")
             return "[语音(文本生成失败)]"
-        
+
         logger.debug(f"描述是{text}")
 
         return f"[语音：{text}]"
     except Exception as e:
         logger.error(f"语音转文字失败: {str(e)}")
         return "[语音]"
-

src/chat/willing/mode_mxp.py

@@ -19,13 +19,13 @@ Mxp 模式：梦溪畔独家赞助
 下下策是询问一个菜鸟（@梦溪畔）
 """
 
-from .willing_manager import BaseWillingManager
 from typing import Dict
 import asyncio
 import time
 import math
 
 from src.chat.message_receive.chat_stream import ChatStream
+from .willing_manager import BaseWillingManager
 
 
 class MxpWillingManager(BaseWillingManager):

src/common/database/database_model.py

@@ -281,20 +281,6 @@ class Memory(BaseModel):
         table_name = "memory"
 
 
-class Knowledges(BaseModel):
-    """
-    用于存储知识库条目的模型。
-    """
-
-    content = TextField()  # 知识内容的文本
-    embedding = TextField()  # 知识内容的嵌入向量，存储为 JSON 字符串的浮点数列表
-    # 可以添加其他元数据字段，如 source, create_time 等
-
-    class Meta:
-        # database = db # 继承自 BaseModel
-        table_name = "knowledges"
-
-
 class Expression(BaseModel):
     """
     用于存储表达风格的模型。
@@ -382,7 +368,6 @@ def create_tables():
                 ImageDescriptions,
                 OnlineTime,
                 PersonInfo,
-                Knowledges,
                 Expression,
                 ThinkingLog,
                 GraphNodes,  # 添加图节点表
@@ -408,7 +393,6 @@ def initialize_database():
         ImageDescriptions,
         OnlineTime,
         PersonInfo,
-        Knowledges,
         Expression,
         Memory,
         ThinkingLog,

src/common/logger.py

@@ -334,7 +334,7 @@ MODULE_COLORS = {
     "llm_models": "\033[36m",  # 青色
     "remote": "\033[38;5;242m",  # 深灰色，更不显眼
     "planner": "\033[36m",
-    "memory": "\033[34m",
+    "memory": "\033[38;5;117m",  # 天蓝色
     "hfc": "\033[38;5;81m",  # 稍微暗一些的青色，保持可读
     "action_manager": "\033[38;5;208m",  # 橙色，不与replyer重复
     # 关系系统
@@ -352,7 +352,7 @@ MODULE_COLORS = {
     "expressor": "\033[38;5;166m",  # 橙色
     # 专注聊天模块
     "replyer": "\033[38;5;166m",  # 橙色
-    "memory_activator": "\033[34m",  # 绿色
+    "memory_activator": "\033[38;5;117m",  # 天蓝色
     # 插件系统
     "plugins": "\033[31m",  # 红色
     "plugin_api": "\033[33m",  # 黄色
@@ -451,7 +451,7 @@ class ModuleColoredConsoleRenderer:
         # 日志级别颜色
         self._level_colors = {
             "debug": "\033[38;5;208m",  # 橙色
-            "info": "\033[34m",  # 蓝色
+            "info": "\033[38;5;117m",  # 天蓝色
             "success": "\033[32m",  # 绿色
             "warning": "\033[33m",  # 黄色
             "error": "\033[31m",  # 红色

src/config/api_ada_configs.py

@@ -0,0 +1,142 @@
+from dataclasses import dataclass, field
+
+from .config_base import ConfigBase
+
+
+@dataclass
+class APIProvider(ConfigBase):
+    """API提供商配置类"""
+
+    name: str
+    """API提供商名称"""
+
+    base_url: str
+    """API基础URL"""
+
+    api_key: str = field(default_factory=str, repr=False)
+    """API密钥列表"""
+
+    client_type: str = field(default="openai")
+    """客户端类型（如openai/google等，默认为openai）"""
+
+    max_retry: int = 2
+    """最大重试次数（单个模型API调用失败，最多重试的次数）"""
+
+    timeout: int = 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: int = 10
+    """重试间隔（如果API调用失败，重试的间隔时间，单位：秒）"""
+
+    def get_api_key(self) -> str:
+        return self.api_key
+
+    def __post_init__(self):
+        """确保api_key在repr中不被显示"""
+        if not self.api_key:
+            raise ValueError("API密钥不能为空，请在配置中设置有效的API密钥。")
+        if not self.base_url:
+            raise ValueError("API基础URL不能为空，请在配置中设置有效的基础URL。")
+        if not self.name:
+            raise ValueError("API提供商名称不能为空，请在配置中设置有效的名称。")
+
+
+@dataclass
+class ModelInfo(ConfigBase):
+    """单个模型信息配置类"""
+
+    model_identifier: str
+    """模型标识符（用于URL调用）"""
+
+    name: str
+    """模型名称（用于模块调用）"""
+
+    api_provider: str
+    """API提供商（如OpenAI、Azure等）"""
+
+    price_in: float = field(default=0.0)
+    """每M token输入价格"""
+
+    price_out: float = field(default=0.0)
+    """每M token输出价格"""
+
+    force_stream_mode: bool = field(default=False)
+    """是否强制使用流式输出模式"""
+
+    extra_params: dict = field(default_factory=dict)
+    """额外参数（用于API调用时的额外配置）"""
+
+    def __post_init__(self):
+        if not self.model_identifier:
+            raise ValueError("模型标识符不能为空，请在配置中设置有效的模型标识符。")
+        if not self.name:
+            raise ValueError("模型名称不能为空，请在配置中设置有效的模型名称。")
+        if not self.api_provider:
+            raise ValueError("API提供商不能为空，请在配置中设置有效的API提供商。")
+
+
+@dataclass
+class TaskConfig(ConfigBase):
+    """任务配置类"""
+
+    model_list: list[str] = field(default_factory=list)
+    """任务使用的模型列表"""
+
+    max_tokens: int = 1024
+    """任务最大输出token数"""
+
+    temperature: float = 0.3
+    """模型温度"""
+
+
+@dataclass
+class ModelTaskConfig(ConfigBase):
+    """模型配置类"""
+
+    utils: TaskConfig
+    """组件模型配置"""
+
+    utils_small: TaskConfig
+    """组件小模型配置"""
+
+    replyer_1: TaskConfig
+    """normal_chat首要回复模型模型配置"""
+
+    replyer_2: TaskConfig
+    """normal_chat次要回复模型配置"""
+
+    memory: TaskConfig
+    """记忆模型配置"""
+
+    emotion: TaskConfig
+    """情绪模型配置"""
+
+    vlm: TaskConfig
+    """视觉语言模型配置"""
+
+    voice: TaskConfig
+    """语音识别模型配置"""
+
+    tool_use: TaskConfig
+    """专注工具使用模型配置"""
+
+    planner: TaskConfig
+    """规划模型配置"""
+
+    embedding: TaskConfig
+    """嵌入模型配置"""
+
+    lpmm_entity_extract: TaskConfig
+    """LPMM实体提取模型配置"""
+
+    lpmm_rdf_build: TaskConfig
+    """LPMM RDF构建模型配置"""
+
+    lpmm_qa: TaskConfig
+    """LPMM问答模型配置"""
+
+    def get_task(self, task_name: str) -> TaskConfig:
+        """获取指定任务的配置"""
+        if hasattr(self, task_name):
+            return getattr(self, task_name)
+        raise ValueError(f"任务 '{task_name}' 未找到对应的配置")

src/config/auto_update.py

@@ -1,162 +0,0 @@
-import shutil
-import tomlkit
-from tomlkit.items import Table, KeyType
-from pathlib import Path
-from datetime import datetime
-
-
-def get_key_comment(toml_table, key):
-    # 获取key的注释（如果有）
-    if hasattr(toml_table, "trivia") and hasattr(toml_table.trivia, "comment"):
-        return toml_table.trivia.comment
-    if hasattr(toml_table, "value") and isinstance(toml_table.value, dict):
-        item = toml_table.value.get(key)
-        if item is not None and hasattr(item, "trivia"):
-            return item.trivia.comment
-    if hasattr(toml_table, "keys"):
-        for k in toml_table.keys():
-            if isinstance(k, KeyType) and k.key == key:
-                return k.trivia.comment
-    return None
-
-
-def compare_dicts(new, old, path=None, new_comments=None, old_comments=None, logs=None):
-    # 递归比较两个dict，找出新增和删减项，收集注释
-    if path is None:
-        path = []
-    if logs is None:
-        logs = []
-    if new_comments is None:
-        new_comments = {}
-    if old_comments is None:
-        old_comments = {}
-    # 新增项
-    for key in new:
-        if key == "version":
-            continue
-        if key not in old:
-            comment = get_key_comment(new, key)
-            logs.append(f"新增: {'.'.join(path + [str(key)])}  注释: {comment or '无'}")
-        elif isinstance(new[key], (dict, Table)) and isinstance(old.get(key), (dict, Table)):
-            compare_dicts(new[key], old[key], path + [str(key)], new_comments, old_comments, logs)
-    # 删减项
-    for key in old:
-        if key == "version":
-            continue
-        if key not in new:
-            comment = get_key_comment(old, key)
-            logs.append(f"删减: {'.'.join(path + [str(key)])}  注释: {comment or '无'}")
-    return logs
-
-
-def update_config():
-    print("开始更新配置文件...")
-    # 获取根目录路径
-    root_dir = Path(__file__).parent.parent.parent.parent
-    template_dir = root_dir / "template"
-    config_dir = root_dir / "config"
-    old_config_dir = config_dir / "old"
-
-    # 创建old目录（如果不存在）
-    old_config_dir.mkdir(exist_ok=True)
-
-    # 定义文件路径
-    template_path = template_dir / "bot_config_template.toml"
-    old_config_path = config_dir / "bot_config.toml"
-    new_config_path = config_dir / "bot_config.toml"
-
-    # 读取旧配置文件
-    old_config = {}
-    if old_config_path.exists():
-        print(f"发现旧配置文件: {old_config_path}")
-        with open(old_config_path, "r", encoding="utf-8") as f:
-            old_config = tomlkit.load(f)
-
-        # 生成带时间戳的新文件名
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml"
-
-        # 移动旧配置文件到old目录
-        shutil.move(old_config_path, old_backup_path)
-        print(f"已备份旧配置文件到: {old_backup_path}")
-
-    # 复制模板文件到配置目录
-    print(f"从模板文件创建新配置: {template_path}")
-    shutil.copy2(template_path, new_config_path)
-
-    # 读取新配置文件
-    with open(new_config_path, "r", encoding="utf-8") as f:
-        new_config = tomlkit.load(f)
-
-    # 检查version是否相同
-    if old_config and "inner" in old_config and "inner" in new_config:
-        old_version = old_config["inner"].get("version")  # type: ignore
-        new_version = new_config["inner"].get("version")  # type: ignore
-        if old_version and new_version and old_version == new_version:
-            print(f"检测到版本号相同 (v{old_version})，跳过更新")
-            # 如果version相同，恢复旧配置文件并返回
-            shutil.move(old_backup_path, old_config_path)  # type: ignore
-            return
-        else:
-            print(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}")
-
-    # 输出新增和删减项及注释
-    if old_config:
-        print("配置项变动如下：")
-        logs = compare_dicts(new_config, old_config)
-        if logs:
-            for log in logs:
-                print(log)
-        else:
-            print("无新增或删减项")
-
-    # 递归更新配置
-    def update_dict(target, source):
-        for key, value in source.items():
-            # 跳过version字段的更新
-            if key == "version":
-                continue
-            if key in target:
-                if isinstance(value, dict) and isinstance(target[key], (dict, Table)):
-                    update_dict(target[key], value)
-                else:
-                    try:
-                        # 对数组类型进行特殊处理
-                        if isinstance(value, list):
-                            # 如果是空数组，确保它保持为空数组
-                            if not value:
-                                target[key] = tomlkit.array()
-                            else:
-                                # 特殊处理正则表达式数组和包含正则表达式的结构
-                                if key == "ban_msgs_regex":
-                                    # 直接使用原始值，不进行额外处理
-                                    target[key] = value
-                                elif key == "regex_rules":
-                                    # 对于regex_rules，需要特殊处理其中的regex字段
-                                    target[key] = value
-                                else:
-                                    # 检查是否包含正则表达式相关的字典项
-                                    contains_regex = False
-                                    if value and isinstance(value[0], dict) and "regex" in value[0]:
-                                        contains_regex = True
-
-                                    target[key] = value if contains_regex else tomlkit.array(str(value))
-                        else:
-                            # 其他类型使用item方法创建新值
-                            target[key] = tomlkit.item(value)
-                    except (TypeError, ValueError):
-                        # 如果转换失败，直接赋值
-                        target[key] = value
-
-    # 将旧配置的值更新到新配置中
-    print("开始合并新旧配置...")
-    update_dict(new_config, old_config)
-
-    # 保存更新后的配置（保留注释和格式）
-    with open(new_config_path, "w", encoding="utf-8") as f:
-        f.write(tomlkit.dumps(new_config))
-    print("配置文件更新完成")
-
-
-if __name__ == "__main__":
-    update_config()

src/config/config.py

@@ -1,12 +1,14 @@
 import os
 import tomlkit
 import shutil
+import sys
 
 from datetime import datetime
 from tomlkit import TOMLDocument
 from tomlkit.items import Table, KeyType
 from dataclasses import field, dataclass
 from rich.traceback import install
+from typing import List, Optional
 
 from src.common.logger import get_logger
 from src.config.config_base import ConfigBase
@@ -25,7 +27,6 @@ from src.config.official_configs import (
     ResponseSplitterConfig,
     TelemetryConfig,
     ExperimentalConfig,
-    ModelConfig,
     MessageReceiveConfig,
     MaimMessageConfig,
     LPMMKnowledgeConfig,
@@ -36,6 +37,13 @@ from src.config.official_configs import (
     CustomPromptConfig,
 )
 
+from .api_ada_configs import (
+    ModelTaskConfig,
+    ModelInfo,
+    APIProvider,
+)
+
+
 install(extra_lines=3)
 
 
@@ -49,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
 
 # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
-MMC_VERSION = "0.9.1"
+MMC_VERSION = "0.10.0-snapshot.4"
 
 
 def get_key_comment(toml_table, key):
@@ -79,7 +87,7 @@ def compare_dicts(new, old, path=None, logs=None):
             continue
         if key not in old:
             comment = get_key_comment(new, key)
-            logs.append(f"新增: {'.'.join(path + [str(key)])}  注释: {comment if comment else '无'}")
+            logs.append(f"新增: {'.'.join(path + [str(key)])}  注释: {comment or '无'}")
         elif isinstance(new[key], (dict, Table)) and isinstance(old.get(key), (dict, Table)):
             compare_dicts(new[key], old[key], path + [str(key)], logs)
     # 删减项
@@ -88,7 +96,7 @@ def compare_dicts(new, old, path=None, logs=None):
             continue
         if key not in new:
             comment = get_key_comment(old, key)
-            logs.append(f"删减: {'.'.join(path + [str(key)])}  注释: {comment if comment else '无'}")
+            logs.append(f"删减: {'.'.join(path + [str(key)])}  注释: {comment or '无'}")
     return logs
 
 
@@ -123,67 +131,110 @@ def compare_default_values(new, old, path=None, logs=None, changes=None):
         if key in old:
             if isinstance(new[key], (dict, Table)) and isinstance(old[key], (dict, Table)):
                 compare_default_values(new[key], old[key], path + [str(key)], logs, changes)
-            else:
+            elif new[key] != old[key]:
-                # 只要值发生变化就记录
+                logs.append(f"默认值变化: {'.'.join(path + [str(key)])}  旧默认值: {old[key]}  新默认值: {new[key]}")
-                if new[key] != old[key]:
+                changes.append((path + [str(key)], old[key], new[key]))
-                    logs.append(
-                        f"默认值变化: {'.'.join(path + [str(key)])}  旧默认值: {old[key]}  新默认值: {new[key]}"
-                    )
-                    changes.append((path + [str(key)], old[key], new[key]))
     return logs, changes
 
 
-def update_config():
+def _get_version_from_toml(toml_path) -> Optional[str]:
+    """从TOML文件中获取版本号"""
+    if not os.path.exists(toml_path):
+        return None
+    with open(toml_path, "r", encoding="utf-8") as f:
+        doc = tomlkit.load(f)
+    if "inner" in doc and "version" in doc["inner"]:  # type: ignore
+        return doc["inner"]["version"]  # type: ignore
+    return None
+
+
+def _version_tuple(v):
+    """将版本字符串转换为元组以便比较"""
+    if v is None:
+        return (0,)
+    return tuple(int(x) if x.isdigit() else 0 for x in str(v).replace("v", "").split("-")[0].split("."))
+
+
+def _update_dict(target: TOMLDocument | dict | Table, source: TOMLDocument | dict):
+    """
+    将source字典的值更新到target字典中（如果target中存在相同的键）
+    """
+    for key, value in source.items():
+        # 跳过version字段的更新
+        if key == "version":
+            continue
+        if key in target:
+            target_value = target[key]
+            if isinstance(value, dict) and isinstance(target_value, (dict, Table)):
+                _update_dict(target_value, value)
+            else:
+                try:
+                    # 对数组类型进行特殊处理
+                    if isinstance(value, list):
+                        # 如果是空数组，确保它保持为空数组
+                        target[key] = tomlkit.array(str(value)) if value else tomlkit.array()
+                    else:
+                        # 其他类型使用item方法创建新值
+                        target[key] = tomlkit.item(value)
+                except (TypeError, ValueError):
+                    # 如果转换失败，直接赋值
+                    target[key] = value
+
+
+def _update_config_generic(config_name: str, template_name: str):
+    """
+    通用的配置文件更新函数
+
+    Args:
+        config_name: 配置文件名（不含扩展名），如 'bot_config' 或 'model_config'
+        template_name: 模板文件名（不含扩展名），如 'bot_config_template' 或 'model_config_template'
+    """
     # 获取根目录路径
     old_config_dir = os.path.join(CONFIG_DIR, "old")
     compare_dir = os.path.join(TEMPLATE_DIR, "compare")
 
     # 定义文件路径
-    template_path = os.path.join(TEMPLATE_DIR, "bot_config_template.toml")
+    template_path = os.path.join(TEMPLATE_DIR, f"{template_name}.toml")
-    old_config_path = os.path.join(CONFIG_DIR, "bot_config.toml")
+    old_config_path = os.path.join(CONFIG_DIR, f"{config_name}.toml")
-    new_config_path = os.path.join(CONFIG_DIR, "bot_config.toml")
+    new_config_path = os.path.join(CONFIG_DIR, f"{config_name}.toml")
-    compare_path = os.path.join(compare_dir, "bot_config_template.toml")
+    compare_path = os.path.join(compare_dir, f"{template_name}.toml")
 
     # 创建compare目录（如果不存在）
     os.makedirs(compare_dir, exist_ok=True)
 
-    # 处理compare下的模板文件
+    template_version = _get_version_from_toml(template_path)
-    def get_version_from_toml(toml_path):
+    compare_version = _get_version_from_toml(compare_path)
-        if not os.path.exists(toml_path):
-            return None
-        with open(toml_path, "r", encoding="utf-8") as f:
-            doc = tomlkit.load(f)
-        if "inner" in doc and "version" in doc["inner"]:  # type: ignore
-            return doc["inner"]["version"]  # type: ignore
-        return None
 
-    template_version = get_version_from_toml(template_path)
+    # 检查配置文件是否存在
-    compare_version = get_version_from_toml(compare_path)
+    if not os.path.exists(old_config_path):
+        logger.info(f"{config_name}.toml配置文件不存在，从模板创建新配置")
+        os.makedirs(CONFIG_DIR, exist_ok=True)  # 创建文件夹
+        shutil.copy2(template_path, old_config_path)  # 复制模板文件
+        logger.info(f"已创建新{config_name}配置文件，请填写后重新运行: {old_config_path}")
+        # 新创建配置文件，退出
+        sys.exit(0)
 
-    def version_tuple(v):
+    compare_config = None
-        if v is None:
+    new_config = None
-            return (0,)
+    old_config = None
-        return tuple(int(x) if x.isdigit() else 0 for x in str(v).replace("v", "").split("-")[0].split("."))
 
     # 先读取 compare 下的模板（如果有），用于默认值变动检测
     if os.path.exists(compare_path):
         with open(compare_path, "r", encoding="utf-8") as f:
             compare_config = tomlkit.load(f)
-    else:
-        compare_config = None
 
     # 读取当前模板
     with open(template_path, "r", encoding="utf-8") as f:
         new_config = tomlkit.load(f)
 
     # 检查默认值变化并处理（只有 compare_config 存在时才做）
-    if compare_config is not None:
+    if compare_config:
         # 读取旧配置
         with open(old_config_path, "r", encoding="utf-8") as f:
             old_config = tomlkit.load(f)
         logs, changes = compare_default_values(new_config, compare_config)
         if logs:
-            logger.info("检测到模板默认值变动如下：")
+            logger.info(f"检测到{config_name}模板默认值变动如下：")
             for log in logs:
                 logger.info(log)
             # 检查旧配置是否等于旧默认值，如果是则更新为新默认值
@@ -192,33 +243,20 @@ def update_config():
                 if old_value == old_default:
                     set_value_by_path(old_config, path, new_default)
                     logger.info(
-                        f"已自动将配置 {'.'.join(path)} 的值从旧默认值 {old_default} 更新为新默认值 {new_default}"
+                        f"已自动将{config_name}配置 {'.'.join(path)} 的值从旧默认值 {old_default} 更新为新默认值 {new_default}"
                     )
         else:
-            logger.info("未检测到模板默认值变动")
+            logger.info(f"未检测到{config_name}模板默认值变动")
-        # 保存旧配置的变更（后续合并逻辑会用到 old_config）
-    else:
-        old_config = None
 
     # 检查 compare 下没有模板，或新模板版本更高，则复制
     if not os.path.exists(compare_path):
         shutil.copy2(template_path, compare_path)
-        logger.info(f"已将模板文件复制到: {compare_path}")
+        logger.info(f"已将{config_name}模板文件复制到: {compare_path}")
+    elif _version_tuple(template_version) > _version_tuple(compare_version):
+        shutil.copy2(template_path, compare_path)
+        logger.info(f"{config_name}模板版本较新，已替换compare下的模板: {compare_path}")
     else:
-        if version_tuple(template_version) > version_tuple(compare_version):
+        logger.debug(f"compare下的{config_name}模板版本不低于当前模板，无需替换: {compare_path}")
-            shutil.copy2(template_path, compare_path)
-            logger.info(f"模板版本较新，已替换compare下的模板: {compare_path}")
-        else:
-            logger.debug(f"compare下的模板版本不低于当前模板，无需替换: {compare_path}")
-
-    # 检查配置文件是否存在
-    if not os.path.exists(old_config_path):
-        logger.info("配置文件不存在，从模板创建新配置")
-        os.makedirs(CONFIG_DIR, exist_ok=True)  # 创建文件夹
-        shutil.copy2(template_path, old_config_path)  # 复制模板文件
-        logger.info(f"已创建新配置文件，请填写后重新运行: {old_config_path}")
-        # 如果是新创建的配置文件,直接返回
-        quit()
 
     # 读取旧配置文件和模板文件（如果前面没读过 old_config，这里再读一次）
     if old_config is None:
@@ -226,79 +264,60 @@ def update_config():
             old_config = tomlkit.load(f)
     # new_config 已经读取
 
-    # 读取 compare_config 只用于默认值变动检测，后续合并逻辑不再用
-
     # 检查version是否相同
     if old_config and "inner" in old_config and "inner" in new_config:
         old_version = old_config["inner"].get("version")  # type: ignore
         new_version = new_config["inner"].get("version")  # type: ignore
         if old_version and new_version and old_version == new_version:
-            logger.info(f"检测到配置文件版本号相同 (v{old_version})，跳过更新")
+            logger.info(f"检测到{config_name}配置文件版本号相同 (v{old_version})，跳过更新")
             return
         else:
             logger.info(
-                f"\n----------------------------------------\n检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}\n----------------------------------------"
+                f"\n----------------------------------------\n检测到{config_name}版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}\n----------------------------------------"
             )
     else:
-        logger.info("已有配置文件未检测到版本号，可能是旧版本。将进行更新")
+        logger.info(f"已有{config_name}配置文件未检测到版本号，可能是旧版本。将进行更新")
 
     # 创建old目录（如果不存在）
     os.makedirs(old_config_dir, exist_ok=True)  # 生成带时间戳的新文件名
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    old_backup_path = os.path.join(old_config_dir, f"bot_config_{timestamp}.toml")
+    old_backup_path = os.path.join(old_config_dir, f"{config_name}_{timestamp}.toml")
 
     # 移动旧配置文件到old目录
     shutil.move(old_config_path, old_backup_path)
-    logger.info(f"已备份旧配置文件到: {old_backup_path}")
+    logger.info(f"已备份旧{config_name}配置文件到: {old_backup_path}")
 
     # 复制模板文件到配置目录
     shutil.copy2(template_path, new_config_path)
-    logger.info(f"已创建新配置文件: {new_config_path}")
+    logger.info(f"已创建新{config_name}配置文件: {new_config_path}")
 
     # 输出新增和删减项及注释
     if old_config:
-        logger.info("配置项变动如下：\n----------------------------------------")
+        logger.info(f"{config_name}配置项变动如下：\n----------------------------------------")
-        logs = compare_dicts(new_config, old_config)
+        if logs := compare_dicts(new_config, old_config):
-        if logs:
             for log in logs:
                 logger.info(log)
         else:
             logger.info("无新增或删减项")
 
-    def update_dict(target: TOMLDocument | dict | Table, source: TOMLDocument | dict):
-        """
-        将source字典的值更新到target字典中（如果target中存在相同的键）
-        """
-        for key, value in source.items():
-            # 跳过version字段的更新
-            if key == "version":
-                continue
-            if key in target:
-                target_value = target[key]
-                if isinstance(value, dict) and isinstance(target_value, (dict, Table)):
-                    update_dict(target_value, value)
-                else:
-                    try:
-                        # 对数组类型进行特殊处理
-                        if isinstance(value, list):
-                            # 如果是空数组，确保它保持为空数组
-                            target[key] = tomlkit.array(str(value)) if value else tomlkit.array()
-                        else:
-                            # 其他类型使用item方法创建新值
-                            target[key] = tomlkit.item(value)
-                    except (TypeError, ValueError):
-                        # 如果转换失败，直接赋值
-                        target[key] = value
-
     # 将旧配置的值更新到新配置中
-    logger.info("开始合并新旧配置...")
+    logger.info(f"开始合并{config_name}新旧配置...")
-    update_dict(new_config, old_config)
+    _update_dict(new_config, old_config)
 
     # 保存更新后的配置（保留注释和格式）
     with open(new_config_path, "w", encoding="utf-8") as f:
         f.write(tomlkit.dumps(new_config))
-    logger.info("配置文件更新完成，建议检查新配置文件中的内容，以免丢失重要信息")
+    logger.info(f"{config_name}配置文件更新完成，建议检查新配置文件中的内容，以免丢失重要信息")
-    quit()
+
+
+def update_config():
+    """更新bot_config.toml配置文件"""
+    _update_config_generic("bot_config", "bot_config_template")
+
+
+def update_model_config():
+    """更新model_config.toml配置文件"""
+    _update_config_generic("model_config", "model_config_template")
 
 
 @dataclass
@@ -323,7 +342,6 @@ class Config(ConfigBase):
     response_splitter: ResponseSplitterConfig
     telemetry: TelemetryConfig
     experimental: ExperimentalConfig
-    model: ModelConfig
     maim_message: MaimMessageConfig
     lpmm_knowledge: LPMMKnowledgeConfig
     tool: ToolConfig
@@ -331,11 +349,69 @@ class Config(ConfigBase):
     custom_prompt: CustomPromptConfig
     voice: VoiceConfig
 
+
+@dataclass
+class APIAdapterConfig(ConfigBase):
+    """API Adapter配置类"""
+
+    models: List[ModelInfo]
+    """模型列表"""
+
+    model_task_config: ModelTaskConfig
+    """模型任务配置"""
+
+    api_providers: List[APIProvider] = field(default_factory=list)
+    """API提供商列表"""
+
+    def __post_init__(self):
+        if not self.models:
+            raise ValueError("模型列表不能为空，请在配置中设置有效的模型列表。")
+        if not self.api_providers:
+            raise ValueError("API提供商列表不能为空，请在配置中设置有效的API提供商列表。")
+
+        # 检查API提供商名称是否重复
+        provider_names = [provider.name for provider in self.api_providers]
+        if len(provider_names) != len(set(provider_names)):
+            raise ValueError("API提供商名称存在重复，请检查配置文件。")
+
+        # 检查模型名称是否重复
+        model_names = [model.name for model in self.models]
+        if len(model_names) != len(set(model_names)):
+            raise ValueError("模型名称存在重复，请检查配置文件。")
+
+        self.api_providers_dict = {provider.name: provider for provider in self.api_providers}
+        self.models_dict = {model.name: model for model in self.models}
+
+        for model in self.models:
+            if not model.model_identifier:
+                raise ValueError(f"模型 '{model.name}' 的 model_identifier 不能为空")
+            if not model.api_provider or model.api_provider not in self.api_providers_dict:
+                raise ValueError(f"模型 '{model.name}' 的 api_provider '{model.api_provider}' 不存在")
+
+    def get_model_info(self, model_name: str) -> ModelInfo:
+        """根据模型名称获取模型信息"""
+        if not model_name:
+            raise ValueError("模型名称不能为空")
+        if model_name not in self.models_dict:
+            raise KeyError(f"模型 '{model_name}' 不存在")
+        return self.models_dict[model_name]
+
+    def get_provider(self, provider_name: str) -> APIProvider:
+        """根据提供商名称获取API提供商信息"""
+        if not provider_name:
+            raise ValueError("API提供商名称不能为空")
+        if provider_name not in self.api_providers_dict:
+            raise KeyError(f"API提供商 '{provider_name}' 不存在")
+        return self.api_providers_dict[provider_name]
+
+
 def load_config(config_path: str) -> Config:
     """
     加载配置文件
-    :param config_path: 配置文件路径
+    Args:
-    :return: Config对象
+        config_path: 配置文件路径
+    Returns:
+        Config对象
     """
     # 读取配置文件
     with open(config_path, "r", encoding="utf-8") as f:
@@ -349,18 +425,32 @@ def load_config(config_path: str) -> Config:
         raise e
 
 
-def get_config_dir() -> str:
+def api_ada_load_config(config_path: str) -> APIAdapterConfig:
     """
-    获取配置目录
+    加载API适配器配置文件
-    :return: 配置目录路径
+    Args:
+        config_path: 配置文件路径
+    Returns:
+        APIAdapterConfig对象
     """
-    return CONFIG_DIR
+    # 读取配置文件
+    with open(config_path, "r", encoding="utf-8") as f:
+        config_data = tomlkit.load(f)
+
+    # 创建APIAdapterConfig对象
+    try:
+        return APIAdapterConfig.from_dict(config_data)
+    except Exception as e:
+        logger.critical("API适配器配置文件解析失败")
+        raise e
 
 
 # 获取配置文件路径
 logger.info(f"MaiCore当前版本: {MMC_VERSION}")
 update_config()
+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！")

src/config/official_configs.py

@@ -1,7 +1,7 @@
 import re
 
 from dataclasses import dataclass, field
-from typing import Any, Literal, Optional
+from typing import Literal, Optional
 
 from src.config.config_base import ConfigBase
 
@@ -598,51 +598,3 @@ class LPMMKnowledgeConfig(ConfigBase):
     embedding_dimension: int = 1024
     """嵌入向量维度，应该与模型的输出维度一致"""
 
-
-@dataclass
-class ModelConfig(ConfigBase):
-    """模型配置类"""
-
-    model_max_output_length: int = 800  # 最大回复长度
-
-    utils: dict[str, Any] = field(default_factory=lambda: {})
-    """组件模型配置"""
-
-    utils_small: dict[str, Any] = field(default_factory=lambda: {})
-    """组件小模型配置"""
-
-    replyer_1: dict[str, Any] = field(default_factory=lambda: {})
-    """normal_chat首要回复模型模型配置"""
-
-    replyer_2: dict[str, Any] = field(default_factory=lambda: {})
-    """normal_chat次要回复模型配置"""
-
-    memory: dict[str, Any] = field(default_factory=lambda: {})
-    """记忆模型配置"""
-
-    emotion: dict[str, Any] = field(default_factory=lambda: {})
-    """情绪模型配置"""
-
-    vlm: dict[str, Any] = field(default_factory=lambda: {})
-    """视觉语言模型配置"""
-
-    voice: dict[str, Any] = field(default_factory=lambda: {})
-    """语音识别模型配置"""
-
-    tool_use: dict[str, Any] = field(default_factory=lambda: {})
-    """专注工具使用模型配置"""
-
-    planner: dict[str, Any] = field(default_factory=lambda: {})
-    """规划模型配置"""
-
-    embedding: dict[str, Any] = field(default_factory=lambda: {})
-    """嵌入模型配置"""
-
-    lpmm_entity_extract: dict[str, Any] = field(default_factory=lambda: {})
-    """LPMM实体提取模型配置"""
-
-    lpmm_rdf_build: dict[str, Any] = field(default_factory=lambda: {})
-    """LPMM RDF构建模型配置"""
-
-    lpmm_qa: dict[str, Any] = field(default_factory=lambda: {})
-    """LPMM问答模型配置"""

src/individuality/individuality.py

@@ -4,7 +4,7 @@ import hashlib
 import time
 
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from src.person_info.person_info import get_person_info_manager
 from rich.traceback import install
@@ -23,10 +23,7 @@ class Individuality:
         self.meta_info_file_path = "data/personality/meta.json"
         self.personality_data_file_path = "data/personality/personality_data.json"
 
-        self.model = LLMRequest(
+        self.model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="individuality.compress")
-            model=global_config.model.utils,
-            request_type="individuality.compress",
-        )
 
     async def initialize(self) -> None:
         """初始化个体特征"""
@@ -35,7 +32,6 @@ class Individuality:
         personality_side = global_config.personality.personality_side
         identity = global_config.personality.identity
 
-
         person_info_manager = get_person_info_manager()
         self.bot_person_id = person_info_manager.get_person_id("system", "bot_id")
         self.name = bot_nickname
@@ -85,16 +81,16 @@ class Individuality:
             bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
         else:
             bot_nickname = ""
-        
+
         # 从文件获取 short_impression
         personality, identity = self._get_personality_from_file()
-        
+
         # 确保short_impression是列表格式且有足够的元素
         if not personality or not identity:
             logger.warning(f"personality或identity为空: {personality}, {identity}, 使用默认值")
             personality = "友好活泼"
             identity = "人类"
-        
+
         prompt_personality = f"{personality}\n{identity}"
         return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
 
@@ -215,7 +211,7 @@ class Individuality:
 
     def _get_personality_from_file(self) -> tuple[str, str]:
         """从文件获取personality数据
-        
+
         Returns:
             tuple: (personality, identity)
         """
@@ -226,7 +222,7 @@ class Individuality:
 
     def _save_personality_to_file(self, personality: str, identity: str):
         """保存personality数据到文件
-        
+
         Args:
             personality: 压缩后的人格描述
             identity: 压缩后的身份描述
@@ -235,7 +231,7 @@ class Individuality:
             "personality": personality,
             "identity": identity,
             "bot_nickname": self.name,
-            "last_updated": int(time.time())
+            "last_updated": int(time.time()),
         }
         self._save_personality_data(personality_data)
 
@@ -269,7 +265,7 @@ class Individuality:
 2. 尽量简洁，不超过30字
 3. 直接输出压缩后的内容，不要解释"""
 
-            response, (_, _) = await self.model.generate_response_async(
+            response, _ = await self.model.generate_response_async(
                 prompt=prompt,
             )
 
@@ -281,7 +277,7 @@ class Individuality:
                 # 压缩失败时使用原始内容
                 if personality_side:
                     personality_parts.append(personality_side)
-            
+
             if personality_parts:
                 personality_result = "。".join(personality_parts)
             else:
@@ -308,7 +304,7 @@ class Individuality:
 2. 尽量简洁，不超过30字
 3. 直接输出压缩后的内容，不要解释"""
 
-            response, (_, _) = await self.model.generate_response_async(
+            response, _ = await self.model.generate_response_async(
                 prompt=prompt,
             )
 

src/llm_models/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Mai.To.The.Gate
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/llm_models/exceptions.py

@@ -0,0 +1,98 @@
+from typing import Any
+
+
+# 常见Error Code Mapping (以OpenAI API为例)
+error_code_mapping = {
+    400: "参数不正确",
+    401: "API-Key错误，认证失败，请检查/config/model_list.toml中的配置是否正确",
+    402: "账号余额不足",
+    403: "模型拒绝访问，可能需要实名或余额不足",
+    404: "Not Found",
+    413: "请求体过大，请尝试压缩图片或减少输入内容",
+    429: "请求过于频繁，请稍后再试",
+    500: "服务器内部故障",
+    503: "服务器负载过高",
+}
+
+
+class NetworkConnectionError(Exception):
+    """连接异常，常见于网络问题或服务器不可用"""
+
+    def __init__(self):
+        super().__init__()
+
+    def __str__(self):
+        return "连接异常，请检查网络连接状态或URL是否正确"
+
+
+class ReqAbortException(Exception):
+    """请求异常退出，常见于请求被中断或取消"""
+
+    def __init__(self, message: str | None = None):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return self.message or "请求因未知原因异常终止"
+
+
+class RespNotOkException(Exception):
+    """请求响应异常，见于请求未能成功响应（非 '200 OK'）"""
+
+    def __init__(self, status_code: int, message: str | None = None):
+        super().__init__(message)
+        self.status_code = status_code
+        self.message = message
+
+    def __str__(self):
+        if self.status_code in error_code_mapping:
+            return error_code_mapping[self.status_code]
+        elif self.message:
+            return self.message
+        else:
+            return f"未知的异常响应代码：{self.status_code}"
+
+
+class RespParseException(Exception):
+    """响应解析错误，常见于响应格式不正确或解析方法不匹配"""
+
+    def __init__(self, ext_info: Any, message: str | None = None):
+        super().__init__(message)
+        self.ext_info = ext_info
+        self.message = message
+
+    def __str__(self):
+        return self.message or "解析响应内容时发生未知错误，请检查是否配置了正确的解析方法"
+
+
+class PayLoadTooLargeError(Exception):
+    """自定义异常类，用于处理请求体过大错误"""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return "请求体过大，请尝试压缩图片或减少输入内容。"
+
+
+class RequestAbortException(Exception):
+    """自定义异常类，用于处理请求中断异常"""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return self.message
+
+
+class PermissionDeniedException(Exception):
+    """自定义异常类，用于处理访问拒绝的异常"""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return self.message

src/llm_models/model_client/__init__.py

@@ -0,0 +1,8 @@
+from src.config.config import model_config
+
+used_client_types = {provider.client_type for provider in model_config.api_providers}
+
+if "openai" in used_client_types:
+    from . import openai_client  # noqa: F401
+if "gemini" in used_client_types:
+    from . import gemini_client  # noqa: F401

src/llm_models/model_client/base_client.py

@@ -0,0 +1,172 @@
+import asyncio
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+from typing import Callable, Any, Optional
+
+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
+
+
+@dataclass
+class UsageRecord:
+    """
+    使用记录类
+    """
+
+    model_name: str
+    """模型名称"""
+
+    provider_name: str
+    """提供商名称"""
+
+    prompt_tokens: int
+    """提示token数"""
+
+    completion_tokens: int
+    """完成token数"""
+
+    total_tokens: int
+    """总token数"""
+
+
+@dataclass
+class APIResponse:
+    """
+    API响应类
+    """
+
+    content: str | None = None
+    """响应内容"""
+
+    reasoning_content: str | None = None
+    """推理内容"""
+
+    tool_calls: list[ToolCall] | None = None
+    """工具调用 [(工具名称, 工具参数), ...]"""
+
+    embedding: list[float] | None = None
+    """嵌入向量"""
+
+    usage: UsageRecord | None = None
+    """使用情况 (prompt_tokens, completion_tokens, total_tokens)"""
+
+    raw_data: Any = None
+    """响应原始数据"""
+
+
+class BaseClient(ABC):
+    """
+    基础客户端
+    """
+
+    api_provider: APIProvider
+
+    def __init__(self, api_provider: APIProvider):
+        self.api_provider = api_provider
+
+    @abstractmethod
+    async def get_response(
+        self,
+        model_info: ModelInfo,
+        message_list: list[Message],
+        tool_options: list[ToolOption] | None = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+        response_format: RespFormat | None = None,
+        stream_response_handler: Optional[
+            Callable[[Any, asyncio.Event | None], tuple[APIResponse, tuple[int, int, int]]]
+        ] = None,
+        async_response_parser: Callable[[Any], tuple[APIResponse, tuple[int, int, int]]] | None = None,
+        interrupt_flag: asyncio.Event | None = None,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取对话响应
+        :param model_info: 模型信息
+        :param message_list: 对话体
+        :param tool_options: 工具选项（可选，默认为None）
+        :param max_tokens: 最大token数（可选，默认为1024）
+        :param temperature: 温度（可选，默认为0.7）
+        :param response_format: 响应格式（可选，默认为 NotGiven ）
+        :param stream_response_handler: 流式响应处理函数（可选）
+        :param async_response_parser: 响应解析函数（可选）
+        :param interrupt_flag: 中断信号量（可选，默认为None）
+        :return: (响应文本, 推理文本, 工具调用, 其他数据)
+        """
+        raise NotImplementedError("'get_response' method should be overridden in subclasses")
+
+    @abstractmethod
+    async def get_embedding(
+        self,
+        model_info: ModelInfo,
+        embedding_input: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取文本嵌入
+        :param model_info: 模型信息
+        :param embedding_input: 嵌入输入文本
+        :return: 嵌入响应
+        """
+        raise NotImplementedError("'get_embedding' method should be overridden in subclasses")
+
+    @abstractmethod
+    async def get_audio_transcriptions(
+        self,
+        model_info: ModelInfo,
+        audio_base64: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取音频转录
+        :param model_info: 模型信息
+        :param audio_base64: base64编码的音频数据
+        :extra_params: 附加的请求参数
+        :return: 音频转录响应
+        """
+        raise NotImplementedError("'get_audio_transcriptions' method should be overridden in subclasses")
+
+    @abstractmethod
+    def get_support_image_formats(self) -> list[str]:
+        """
+        获取支持的图片格式
+        :return: 支持的图片格式列表
+        """
+        raise NotImplementedError("'get_support_image_formats' method should be overridden in subclasses")
+
+
+class ClientRegistry:
+    def __init__(self) -> None:
+        self.client_registry: dict[str, type[BaseClient]] = {}
+
+    def register_client_class(self, client_type: str):
+        """
+        注册API客户端类
+        Args:
+            client_class: API客户端类
+        """
+
+        def decorator(cls: type[BaseClient]) -> type[BaseClient]:
+            if not issubclass(cls, BaseClient):
+                raise TypeError(f"{cls.__name__} is not a subclass of BaseClient")
+            self.client_registry[client_type] = cls
+            return cls
+
+        return decorator
+
+    def get_client_class(self, client_type: str) -> type[BaseClient]:
+        """
+        获取注册的API客户端类
+        Args:
+            client_type: 客户端类型
+        Returns:
+            type[BaseClient]: 注册的API客户端类
+        """
+        if client_type not in self.client_registry:
+            raise KeyError(f"'{client_type}' 类型的 Client 未注册")
+        return self.client_registry[client_type]
+
+
+client_registry = ClientRegistry()

src/llm_models/model_client/gemini_client.py

@@ -0,0 +1,496 @@
+import asyncio
+import io
+import base64
+from typing import Callable, AsyncIterator, Optional, Coroutine, Any, List
+
+from google import genai
+from google.genai.types import (
+    Content,
+    Part,
+    FunctionDeclaration,
+    GenerateContentResponse,
+    ContentListUnion,
+    ContentUnion,
+    ThinkingConfig,
+    Tool,
+    GenerateContentConfig,
+    EmbedContentResponse,
+    EmbedContentConfig,
+)
+from google.genai.errors import (
+    ClientError,
+    ServerError,
+    UnknownFunctionCallArgumentError,
+    UnsupportedFunctionError,
+    FunctionInvocationError,
+)
+
+from src.config.api_ada_configs import ModelInfo, APIProvider
+from src.common.logger import get_logger
+
+from .base_client import APIResponse, UsageRecord, BaseClient, client_registry
+from ..exceptions import (
+    RespParseException,
+    NetworkConnectionError,
+    RespNotOkException,
+    ReqAbortException,
+)
+from ..payload_content.message import Message, RoleType
+from ..payload_content.resp_format import RespFormat, RespFormatType
+from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
+
+logger = get_logger("Gemini客户端")
+
+
+def _convert_messages(
+    messages: list[Message],
+) -> tuple[ContentListUnion, list[str] | None]:
+    """
+    转换消息格式 - 将消息转换为Gemini API所需的格式
+    :param messages: 消息列表
+    :return: 转换后的消息列表(和可能存在的system消息)
+    """
+
+    def _convert_message_item(message: Message) -> Content:
+        """
+        转换单个消息格式，除了system和tool类型的消息
+        :param message: 消息对象
+        :return: 转换后的消息字典
+        """
+
+        # 将openai格式的角色重命名为gemini格式的角色
+        if message.role == RoleType.Assistant:
+            role = "model"
+        elif message.role == RoleType.User:
+            role = "user"
+
+        # 添加Content
+        if isinstance(message.content, str):
+            content = [Part.from_text(text=message.content)]
+        elif isinstance(message.content, list):
+            content: List[Part] = []
+            for item in message.content:
+                if isinstance(item, tuple):
+                    content.append(
+                        Part.from_bytes(data=base64.b64decode(item[1]), mime_type=f"image/{item[0].lower()}")
+                    )
+                elif isinstance(item, str):
+                    content.append(Part.from_text(text=item))
+        else:
+            raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
+
+        return Content(role=role, parts=content)
+
+    temp_list: list[ContentUnion] = []
+    system_instructions: list[str] = []
+    for message in messages:
+        if message.role == RoleType.System:
+            if isinstance(message.content, str):
+                system_instructions.append(message.content)
+            else:
+                raise ValueError("你tm怎么往system里面塞图片base64？")
+        elif message.role == RoleType.Tool:
+            if not message.tool_call_id:
+                raise ValueError("无法触及的代码：请使用MessageBuilder类构建消息对象")
+        else:
+            temp_list.append(_convert_message_item(message))
+    if system_instructions:
+        # 如果有system消息，就把它加上去
+        ret: tuple = (temp_list, system_instructions)
+    else:
+        # 如果没有system消息，就直接返回
+        ret: tuple = (temp_list, None)
+
+    return ret
+
+
+def _convert_tool_options(tool_options: list[ToolOption]) -> list[FunctionDeclaration]:
+    """
+    转换工具选项格式 - 将工具选项转换为Gemini API所需的格式
+    :param tool_options: 工具选项列表
+    :return: 转换后的工具对象列表
+    """
+
+    def _convert_tool_param(tool_option_param: ToolParam) -> dict:
+        """
+        转换单个工具参数格式
+        :param tool_option_param: 工具参数对象
+        :return: 转换后的工具参数字典
+        """
+        return_dict: dict[str, Any] = {
+            "type": tool_option_param.param_type.value,
+            "description": tool_option_param.description,
+        }
+        if tool_option_param.enum_values:
+            return_dict["enum"] = tool_option_param.enum_values
+        return return_dict
+
+    def _convert_tool_option_item(tool_option: ToolOption) -> FunctionDeclaration:
+        """
+        转换单个工具项格式
+        :param tool_option: 工具选项对象
+        :return: 转换后的Gemini工具选项对象
+        """
+        ret: dict[str, Any] = {
+            "name": tool_option.name,
+            "description": tool_option.description,
+        }
+        if tool_option.params:
+            ret["parameters"] = {
+                "type": "object",
+                "properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
+                "required": [param.name for param in tool_option.params if param.required],
+            }
+        ret1 = FunctionDeclaration(**ret)
+        return ret1
+
+    return [_convert_tool_option_item(tool_option) for tool_option in tool_options]
+
+
+def _process_delta(
+    delta: GenerateContentResponse,
+    fc_delta_buffer: io.StringIO,
+    tool_calls_buffer: list[tuple[str, str, dict[str, Any]]],
+):
+    if not hasattr(delta, "candidates") or not delta.candidates:
+        raise RespParseException(delta, "响应解析失败，缺失candidates字段")
+
+    if delta.text:
+        fc_delta_buffer.write(delta.text)
+
+    if delta.function_calls:  # 为什么不用hasattr呢，是因为这个属性一定有，即使是个空的
+        for call in delta.function_calls:
+            try:
+                if not isinstance(call.args, dict):  # gemini返回的function call参数就是dict格式的了
+                    raise RespParseException(delta, "响应解析失败，工具调用参数无法解析为字典类型")
+                if not call.id or not call.name:
+                    raise RespParseException(delta, "响应解析失败，工具调用缺失id或name字段")
+                tool_calls_buffer.append(
+                    (
+                        call.id,
+                        call.name,
+                        call.args or {},  # 如果args是None，则转换为一个空字典
+                    )
+                )
+            except Exception as e:
+                raise RespParseException(delta, "响应解析失败，无法解析工具调用参数") from e
+
+
+def _build_stream_api_resp(
+    _fc_delta_buffer: io.StringIO,
+    _tool_calls_buffer: list[tuple[str, str, dict]],
+) -> APIResponse:
+    # sourcery skip: simplify-len-comparison, use-assigned-variable
+    resp = APIResponse()
+
+    if _fc_delta_buffer.tell() > 0:
+        # 如果正式内容缓冲区不为空，则将其写入APIResponse对象
+        resp.content = _fc_delta_buffer.getvalue()
+    _fc_delta_buffer.close()
+    if len(_tool_calls_buffer) > 0:
+        # 如果工具调用缓冲区不为空，则将其解析为ToolCall对象列表
+        resp.tool_calls = []
+        for call_id, function_name, arguments_buffer in _tool_calls_buffer:
+            if arguments_buffer is not None:
+                arguments = arguments_buffer
+                if not isinstance(arguments, dict):
+                    raise RespParseException(
+                        None,
+                        f"响应解析失败，工具调用参数无法解析为字典类型。工具调用参数原始响应：\n{arguments_buffer}",
+                    )
+            else:
+                arguments = None
+
+            resp.tool_calls.append(ToolCall(call_id, function_name, arguments))
+
+    return resp
+
+
+async def _default_stream_response_handler(
+    resp_stream: AsyncIterator[GenerateContentResponse],
+    interrupt_flag: asyncio.Event | None,
+) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
+    """
+    流式响应处理函数 - 处理Gemini API的流式响应
+    :param resp_stream: 流式响应对象,是一个神秘的iterator，我完全不知道这个玩意能不能跑，不过遍历一遍之后它就空了，如果跑不了一点的话可以考虑改成别的东西
+    :return: APIResponse对象
+    """
+    _fc_delta_buffer = io.StringIO()  # 正式内容缓冲区，用于存储接收到的正式内容
+    _tool_calls_buffer: list[tuple[str, str, dict]] = []  # 工具调用缓冲区，用于存储接收到的工具调用
+    _usage_record = None  # 使用情况记录
+
+    def _insure_buffer_closed():
+        if _fc_delta_buffer and not _fc_delta_buffer.closed:
+            _fc_delta_buffer.close()
+
+    async for chunk in resp_stream:
+        # 检查是否有中断量
+        if interrupt_flag and interrupt_flag.is_set():
+            # 如果中断量被设置，则抛出ReqAbortException
+            raise ReqAbortException("请求被外部信号中断")
+
+        _process_delta(
+            chunk,
+            _fc_delta_buffer,
+            _tool_calls_buffer,
+        )
+
+        if chunk.usage_metadata:
+            # 如果有使用情况，则将其存储在APIResponse对象中
+            _usage_record = (
+                chunk.usage_metadata.prompt_token_count or 0,
+                (chunk.usage_metadata.candidates_token_count or 0) + (chunk.usage_metadata.thoughts_token_count or 0),
+                chunk.usage_metadata.total_token_count or 0,
+            )
+    try:
+        return _build_stream_api_resp(
+            _fc_delta_buffer,
+            _tool_calls_buffer,
+        ), _usage_record
+    except Exception:
+        # 确保缓冲区被关闭
+        _insure_buffer_closed()
+        raise
+
+
+def _default_normal_response_parser(
+    resp: GenerateContentResponse,
+) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
+    """
+    解析对话补全响应 - 将Gemini API响应解析为APIResponse对象
+    :param resp: 响应对象
+    :return: APIResponse对象
+    """
+    api_response = APIResponse()
+
+    if not hasattr(resp, "candidates") or not resp.candidates:
+        raise RespParseException(resp, "响应解析失败，缺失candidates字段")
+    try:
+        if resp.candidates[0].content and resp.candidates[0].content.parts:
+            for part in resp.candidates[0].content.parts:
+                if not part.text:
+                    continue
+                if part.thought:
+                    api_response.reasoning_content = (
+                        api_response.reasoning_content + part.text if api_response.reasoning_content else part.text
+                    )
+    except Exception as e:
+        logger.warning(f"解析思考内容时发生错误: {e}，跳过解析")
+
+    if resp.text:
+        api_response.content = resp.text
+
+    if resp.function_calls:
+        api_response.tool_calls = []
+        for call in resp.function_calls:
+            try:
+                if not isinstance(call.args, dict):
+                    raise RespParseException(resp, "响应解析失败，工具调用参数无法解析为字典类型")
+                if not call.name:
+                    raise RespParseException(resp, "响应解析失败，工具调用缺失name字段")
+                api_response.tool_calls.append(ToolCall(call.id or "gemini-tool_call", call.name, call.args or {}))
+            except Exception as e:
+                raise RespParseException(resp, "响应解析失败，无法解析工具调用参数") from e
+
+    if resp.usage_metadata:
+        _usage_record = (
+            resp.usage_metadata.prompt_token_count or 0,
+            (resp.usage_metadata.candidates_token_count or 0) + (resp.usage_metadata.thoughts_token_count or 0),
+            resp.usage_metadata.total_token_count or 0,
+        )
+    else:
+        _usage_record = None
+
+    api_response.raw_data = resp
+
+    return api_response, _usage_record
+
+
+@client_registry.register_client_class("gemini")
+class GeminiClient(BaseClient):
+    client: genai.Client
+
+    def __init__(self, api_provider: APIProvider):
+        super().__init__(api_provider)
+        self.client = genai.Client(
+            api_key=api_provider.api_key,
+        )  # 这里和openai不一样，gemini会自己决定自己是否需要retry
+
+    async def get_response(
+        self,
+        model_info: ModelInfo,
+        message_list: list[Message],
+        tool_options: list[ToolOption] | None = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+        response_format: RespFormat | None = None,
+        stream_response_handler: Optional[
+            Callable[
+                [AsyncIterator[GenerateContentResponse], asyncio.Event | None],
+                Coroutine[Any, Any, tuple[APIResponse, Optional[tuple[int, int, int]]]],
+            ]
+        ] = None,
+        async_response_parser: Optional[
+            Callable[[GenerateContentResponse], tuple[APIResponse, Optional[tuple[int, int, int]]]]
+        ] = None,
+        interrupt_flag: asyncio.Event | None = None,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取对话响应
+        Args:
+            model_info: 模型信息
+            message_list: 对话体
+            tool_options: 工具选项（可选，默认为None）
+            max_tokens: 最大token数（可选，默认为1024）
+            temperature: 温度（可选，默认为0.7）
+            response_format: 响应格式（默认为text/plain,如果是输入的JSON Schema则必须遵守OpenAPI3.0格式,理论上和openai是一样的，暂不支持其它相应格式输入）
+            stream_response_handler: 流式响应处理函数（可选，默认为default_stream_response_handler）
+            async_response_parser: 响应解析函数（可选，默认为default_response_parser）
+            interrupt_flag: 中断信号量（可选，默认为None）
+        Returns:
+            APIResponse对象，包含响应内容、推理内容、工具调用等信息
+        """
+        if stream_response_handler is None:
+            stream_response_handler = _default_stream_response_handler
+
+        if async_response_parser is None:
+            async_response_parser = _default_normal_response_parser
+
+        # 将messages构造为Gemini API所需的格式
+        messages = _convert_messages(message_list)
+        # 将tool_options转换为Gemini API所需的格式
+        tools = _convert_tool_options(tool_options) if tool_options else None
+        # 将response_format转换为Gemini API所需的格式
+        generation_config_dict = {
+            "max_output_tokens": max_tokens,
+            "temperature": temperature,
+            "response_modalities": ["TEXT"],
+            "thinking_config": ThinkingConfig(
+                include_thoughts=True,
+                thinking_budget=(
+                    extra_params["thinking_budget"] if extra_params and "thinking_budget" in extra_params else None
+                ),
+            ),
+        }
+        if tools:
+            generation_config_dict["tools"] = Tool(function_declarations=tools)
+        if messages[1]:
+            # 如果有system消息，则将其添加到配置中
+            generation_config_dict["system_instructions"] = messages[1]
+        if response_format and response_format.format_type == RespFormatType.TEXT:
+            generation_config_dict["response_mime_type"] = "text/plain"
+        elif response_format and response_format.format_type in (RespFormatType.JSON_OBJ, RespFormatType.JSON_SCHEMA):
+            generation_config_dict["response_mime_type"] = "application/json"
+            generation_config_dict["response_schema"] = response_format.to_dict()
+
+        generation_config = GenerateContentConfig(**generation_config_dict)
+
+        try:
+            if model_info.force_stream_mode:
+                req_task = asyncio.create_task(
+                    self.client.aio.models.generate_content_stream(
+                        model=model_info.model_identifier,
+                        contents=messages[0],
+                        config=generation_config,
+                    )
+                )
+                while not req_task.done():
+                    if interrupt_flag and interrupt_flag.is_set():
+                        # 如果中断量存在且被设置，则取消任务并抛出异常
+                        req_task.cancel()
+                        raise ReqAbortException("请求被外部信号中断")
+                    await asyncio.sleep(0.1)  # 等待0.1秒后再次检查任务&中断信号量状态
+                resp, usage_record = await stream_response_handler(req_task.result(), interrupt_flag)
+            else:
+                req_task = asyncio.create_task(
+                    self.client.aio.models.generate_content(
+                        model=model_info.model_identifier,
+                        contents=messages[0],
+                        config=generation_config,
+                    )
+                )
+                while not req_task.done():
+                    if interrupt_flag and interrupt_flag.is_set():
+                        # 如果中断量存在且被设置，则取消任务并抛出异常
+                        req_task.cancel()
+                        raise ReqAbortException("请求被外部信号中断")
+                    await asyncio.sleep(0.5)  # 等待0.5秒后再次检查任务&中断信号量状态
+
+                resp, usage_record = async_response_parser(req_task.result())
+        except (ClientError, ServerError) as e:
+            # 重封装ClientError和ServerError为RespNotOkException
+            raise RespNotOkException(e.code, e.message) from None
+        except (
+            UnknownFunctionCallArgumentError,
+            UnsupportedFunctionError,
+            FunctionInvocationError,
+        ) as e:
+            raise ValueError(f"工具类型错误：请检查工具选项和参数：{str(e)}") from None
+        except Exception as e:
+            raise NetworkConnectionError() from e
+
+        if usage_record:
+            resp.usage = UsageRecord(
+                model_name=model_info.name,
+                provider_name=model_info.api_provider,
+                prompt_tokens=usage_record[0],
+                completion_tokens=usage_record[1],
+                total_tokens=usage_record[2],
+            )
+
+        return resp
+
+    async def get_embedding(
+        self,
+        model_info: ModelInfo,
+        embedding_input: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取文本嵌入
+        :param model_info: 模型信息
+        :param embedding_input: 嵌入输入文本
+        :return: 嵌入响应
+        """
+        try:
+            raw_response: EmbedContentResponse = await self.client.aio.models.embed_content(
+                model=model_info.model_identifier,
+                contents=embedding_input,
+                config=EmbedContentConfig(task_type="SEMANTIC_SIMILARITY"),
+            )
+        except (ClientError, ServerError) as e:
+            # 重封装ClientError和ServerError为RespNotOkException
+            raise RespNotOkException(e.code) from None
+        except Exception as e:
+            raise NetworkConnectionError() from e
+
+        response = APIResponse()
+
+        # 解析嵌入响应和使用情况
+        if hasattr(raw_response, "embeddings") and raw_response.embeddings:
+            response.embedding = raw_response.embeddings[0].values
+        else:
+            raise RespParseException(raw_response, "响应解析失败，缺失embeddings字段")
+
+        response.usage = UsageRecord(
+            model_name=model_info.name,
+            provider_name=model_info.api_provider,
+            prompt_tokens=len(embedding_input),
+            completion_tokens=0,
+            total_tokens=len(embedding_input),
+        )
+
+        return response
+
+    def get_audio_transcriptions(
+        self, model_info: ModelInfo, audio_base64: str, extra_params: dict[str, Any] | None = None
+    ) -> APIResponse:
+        raise NotImplementedError("尚未实现音频转录功能")
+
+    def get_support_image_formats(self) -> list[str]:
+        """
+        获取支持的图片格式
+        :return: 支持的图片格式列表
+        """
+        return ["png", "jpg", "jpeg", "webp", "heic", "heif"]

src/llm_models/model_client/openai_client.py

@@ -0,0 +1,580 @@
+import asyncio
+import io
+import json
+import re
+import base64
+from collections.abc import Iterable
+from typing import Callable, Any, Coroutine, Optional
+from json_repair import repair_json
+
+from openai import (
+    AsyncOpenAI,
+    APIConnectionError,
+    APIStatusError,
+    NOT_GIVEN,
+    AsyncStream,
+)
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionChunk,
+    ChatCompletionMessageParam,
+    ChatCompletionToolParam,
+)
+from openai.types.chat.chat_completion_chunk import ChoiceDelta
+
+from src.config.api_ada_configs import ModelInfo, APIProvider
+from src.common.logger import get_logger
+from .base_client import APIResponse, UsageRecord, BaseClient, client_registry
+from ..exceptions import (
+    RespParseException,
+    NetworkConnectionError,
+    RespNotOkException,
+    ReqAbortException,
+)
+from ..payload_content.message import Message, RoleType
+from ..payload_content.resp_format import RespFormat
+from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
+
+logger = get_logger("OpenAI客户端")
+
+
+def _convert_messages(messages: list[Message]) -> list[ChatCompletionMessageParam]:
+    """
+    转换消息格式 - 将消息转换为OpenAI API所需的格式
+    :param messages: 消息列表
+    :return: 转换后的消息列表
+    """
+
+    def _convert_message_item(message: Message) -> ChatCompletionMessageParam:
+        """
+        转换单个消息格式
+        :param message: 消息对象
+        :return: 转换后的消息字典
+        """
+
+        # 添加Content
+        content: str | list[dict[str, Any]]
+        if isinstance(message.content, str):
+            content = message.content
+        elif isinstance(message.content, list):
+            content = []
+            for item in message.content:
+                if isinstance(item, tuple):
+                    content.append(
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/{item[0].lower()};base64,{item[1]}"},
+                        }
+                    )
+                elif isinstance(item, str):
+                    content.append({"type": "text", "text": item})
+        else:
+            raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
+
+        ret = {
+            "role": message.role.value,
+            "content": content,
+        }
+
+        # 添加工具调用ID
+        if message.role == RoleType.Tool:
+            if not message.tool_call_id:
+                raise ValueError("无法触及的代码：请使用MessageBuilder类构建消息对象")
+            ret["tool_call_id"] = message.tool_call_id
+
+        return ret  # type: ignore
+
+    return [_convert_message_item(message) for message in messages]
+
+
+def _convert_tool_options(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
+    """
+    转换工具选项格式 - 将工具选项转换为OpenAI API所需的格式
+    :param tool_options: 工具选项列表
+    :return: 转换后的工具选项列表
+    """
+
+    def _convert_tool_param(tool_option_param: ToolParam) -> dict[str, Any]:
+        """
+        转换单个工具参数格式
+        :param tool_option_param: 工具参数对象
+        :return: 转换后的工具参数字典
+        """
+        return_dict: dict[str, Any] = {
+            "type": tool_option_param.param_type.value,
+            "description": tool_option_param.description,
+        }
+        if tool_option_param.enum_values:
+            return_dict["enum"] = tool_option_param.enum_values
+        return return_dict
+
+    def _convert_tool_option_item(tool_option: ToolOption) -> dict[str, Any]:
+        """
+        转换单个工具项格式
+        :param tool_option: 工具选项对象
+        :return: 转换后的工具选项字典
+        """
+        ret: dict[str, Any] = {
+            "name": tool_option.name,
+            "description": tool_option.description,
+        }
+        if tool_option.params:
+            ret["parameters"] = {
+                "type": "object",
+                "properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
+                "required": [param.name for param in tool_option.params if param.required],
+            }
+        return ret
+
+    return [
+        {
+            "type": "function",
+            "function": _convert_tool_option_item(tool_option),
+        }
+        for tool_option in tool_options
+    ]
+
+
+def _process_delta(
+    delta: ChoiceDelta,
+    has_rc_attr_flag: bool,
+    in_rc_flag: bool,
+    rc_delta_buffer: io.StringIO,
+    fc_delta_buffer: io.StringIO,
+    tool_calls_buffer: list[tuple[str, str, io.StringIO]],
+) -> bool:
+    # 接收content
+    if has_rc_attr_flag:
+        # 有独立的推理内容块，则无需考虑content内容的判读
+        if hasattr(delta, "reasoning_content") and delta.reasoning_content:  # type: ignore
+            # 如果有推理内容，则将其写入推理内容缓冲区
+            assert isinstance(delta.reasoning_content, str)  # type: ignore
+            rc_delta_buffer.write(delta.reasoning_content)  # type: ignore
+        elif delta.content:
+            # 如果有正式内容，则将其写入正式内容缓冲区
+            fc_delta_buffer.write(delta.content)
+    elif hasattr(delta, "content") and delta.content is not None:
+        # 没有独立的推理内容块，但有正式内容
+        if in_rc_flag:
+            # 当前在推理内容块中
+            if delta.content == "</think>":
+                # 如果当前内容是</think>，则将其视为推理内容的结束标记，退出推理内容块
+                in_rc_flag = False
+            else:
+                # 其他情况视为推理内容，加入推理内容缓冲区
+                rc_delta_buffer.write(delta.content)
+        elif delta.content == "<think>" and not fc_delta_buffer.getvalue():
+            # 如果当前内容是<think>，且正式内容缓冲区为空，说明<think>为输出的首个token
+            # 则将其视为推理内容的开始标记，进入推理内容块
+            in_rc_flag = True
+        else:
+            # 其他情况视为正式内容，加入正式内容缓冲区
+            fc_delta_buffer.write(delta.content)
+    # 接收tool_calls
+    if hasattr(delta, "tool_calls") and delta.tool_calls:
+        tool_call_delta = delta.tool_calls[0]
+
+        if tool_call_delta.index >= len(tool_calls_buffer):
+            # 调用索引号大于等于缓冲区长度，说明是新的工具调用
+            if tool_call_delta.id and tool_call_delta.function and tool_call_delta.function.name:
+                tool_calls_buffer.append(
+                    (
+                        tool_call_delta.id,
+                        tool_call_delta.function.name,
+                        io.StringIO(),
+                    )
+                )
+            else:
+                logger.warning("工具调用索引号大于等于缓冲区长度，但缺少ID或函数信息。")
+
+        if tool_call_delta.function and tool_call_delta.function.arguments:
+            # 如果有工具调用参数，则添加到对应的工具调用的参数串缓冲区中
+            tool_calls_buffer[tool_call_delta.index][2].write(tool_call_delta.function.arguments)
+
+    return in_rc_flag
+
+
+def _build_stream_api_resp(
+    _fc_delta_buffer: io.StringIO,
+    _rc_delta_buffer: io.StringIO,
+    _tool_calls_buffer: list[tuple[str, str, io.StringIO]],
+) -> APIResponse:
+    resp = APIResponse()
+
+    if _rc_delta_buffer.tell() > 0:
+        # 如果推理内容缓冲区不为空，则将其写入APIResponse对象
+        resp.reasoning_content = _rc_delta_buffer.getvalue()
+    _rc_delta_buffer.close()
+    if _fc_delta_buffer.tell() > 0:
+        # 如果正式内容缓冲区不为空，则将其写入APIResponse对象
+        resp.content = _fc_delta_buffer.getvalue()
+    _fc_delta_buffer.close()
+    if _tool_calls_buffer:
+        # 如果工具调用缓冲区不为空，则将其解析为ToolCall对象列表
+        resp.tool_calls = []
+        for call_id, function_name, arguments_buffer in _tool_calls_buffer:
+            if arguments_buffer.tell() > 0:
+                # 如果参数串缓冲区不为空，则解析为JSON对象
+                raw_arg_data = arguments_buffer.getvalue()
+                arguments_buffer.close()
+                try:
+                    arguments = json.loads(repair_json(raw_arg_data))
+                    if not isinstance(arguments, dict):
+                        raise RespParseException(
+                            None,
+                            f"响应解析失败，工具调用参数无法解析为字典类型。工具调用参数原始响应：\n{raw_arg_data}",
+                        )
+                except json.JSONDecodeError as e:
+                    raise RespParseException(
+                        None,
+                        f"响应解析失败，无法解析工具调用参数。工具调用参数原始响应：{raw_arg_data}",
+                    ) from e
+            else:
+                arguments_buffer.close()
+                arguments = None
+
+            resp.tool_calls.append(ToolCall(call_id, function_name, arguments))
+
+    return resp
+
+
+async def _default_stream_response_handler(
+    resp_stream: AsyncStream[ChatCompletionChunk],
+    interrupt_flag: asyncio.Event | None,
+) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
+    """
+    流式响应处理函数 - 处理OpenAI API的流式响应
+    :param resp_stream: 流式响应对象
+    :return: APIResponse对象
+    """
+
+    _has_rc_attr_flag = False  # 标记是否有独立的推理内容块
+    _in_rc_flag = False  # 标记是否在推理内容块中
+    _rc_delta_buffer = io.StringIO()  # 推理内容缓冲区，用于存储接收到的推理内容
+    _fc_delta_buffer = io.StringIO()  # 正式内容缓冲区，用于存储接收到的正式内容
+    _tool_calls_buffer: list[tuple[str, str, io.StringIO]] = []  # 工具调用缓冲区，用于存储接收到的工具调用
+    _usage_record = None  # 使用情况记录
+
+    def _insure_buffer_closed():
+        # 确保缓冲区被关闭
+        if _rc_delta_buffer and not _rc_delta_buffer.closed:
+            _rc_delta_buffer.close()
+        if _fc_delta_buffer and not _fc_delta_buffer.closed:
+            _fc_delta_buffer.close()
+        for _, _, buffer in _tool_calls_buffer:
+            if buffer and not buffer.closed:
+                buffer.close()
+
+    async for event in resp_stream:
+        if interrupt_flag and interrupt_flag.is_set():
+            # 如果中断量被设置，则抛出ReqAbortException
+            _insure_buffer_closed()
+            raise ReqAbortException("请求被外部信号中断")
+
+        delta = event.choices[0].delta  # 获取当前块的delta内容
+
+        if hasattr(delta, "reasoning_content") and delta.reasoning_content:  # type: ignore
+            # 标记：有独立的推理内容块
+            _has_rc_attr_flag = True
+
+        _in_rc_flag = _process_delta(
+            delta,
+            _has_rc_attr_flag,
+            _in_rc_flag,
+            _rc_delta_buffer,
+            _fc_delta_buffer,
+            _tool_calls_buffer,
+        )
+
+        if event.usage:
+            # 如果有使用情况，则将其存储在APIResponse对象中
+            _usage_record = (
+                event.usage.prompt_tokens or 0,
+                event.usage.completion_tokens or 0,
+                event.usage.total_tokens or 0,
+            )
+
+    try:
+        return _build_stream_api_resp(
+            _fc_delta_buffer,
+            _rc_delta_buffer,
+            _tool_calls_buffer,
+        ), _usage_record
+    except Exception:
+        # 确保缓冲区被关闭
+        _insure_buffer_closed()
+        raise
+
+
+pattern = re.compile(
+    r"<think>(?P<think>.*?)</think>(?P<content>.*)|<think>(?P<think_unclosed>.*)|(?P<content_only>.+)",
+    re.DOTALL,
+)
+"""用于解析推理内容的正则表达式"""
+
+
+def _default_normal_response_parser(
+    resp: ChatCompletion,
+) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
+    """
+    解析对话补全响应 - 将OpenAI API响应解析为APIResponse对象
+    :param resp: 响应对象
+    :return: APIResponse对象
+    """
+    api_response = APIResponse()
+
+    if not hasattr(resp, "choices") or len(resp.choices) == 0:
+        raise RespParseException(resp, "响应解析失败，缺失choices字段")
+    message_part = resp.choices[0].message
+
+    if hasattr(message_part, "reasoning_content") and message_part.reasoning_content:  # type: ignore
+        # 有有效的推理字段
+        api_response.content = message_part.content
+        api_response.reasoning_content = message_part.reasoning_content  # type: ignore
+    elif message_part.content:
+        # 提取推理和内容
+        match = pattern.match(message_part.content)
+        if not match:
+            raise RespParseException(resp, "响应解析失败，无法捕获推理内容和输出内容")
+        if match.group("think") is not None:
+            result = match.group("think").strip(), match.group("content").strip()
+        elif match.group("think_unclosed") is not None:
+            result = match.group("think_unclosed").strip(), None
+        else:
+            result = None, match.group("content_only").strip()
+        api_response.reasoning_content, api_response.content = result
+
+    # 提取工具调用
+    if message_part.tool_calls:
+        api_response.tool_calls = []
+        for call in message_part.tool_calls:
+            try:
+                arguments = json.loads(repair_json(call.function.arguments))
+                if not isinstance(arguments, dict):
+                    raise RespParseException(resp, "响应解析失败，工具调用参数无法解析为字典类型")
+                api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments))
+            except json.JSONDecodeError as e:
+                raise RespParseException(resp, "响应解析失败，无法解析工具调用参数") from e
+
+    # 提取Usage信息
+    if resp.usage:
+        _usage_record = (
+            resp.usage.prompt_tokens or 0,
+            resp.usage.completion_tokens or 0,
+            resp.usage.total_tokens or 0,
+        )
+    else:
+        _usage_record = None
+
+    # 将原始响应存储在原始数据中
+    api_response.raw_data = resp
+
+    return api_response, _usage_record
+
+
+@client_registry.register_client_class("openai")
+class OpenaiClient(BaseClient):
+    def __init__(self, api_provider: APIProvider):
+        super().__init__(api_provider)
+        self.client: AsyncOpenAI = AsyncOpenAI(
+            base_url=api_provider.base_url,
+            api_key=api_provider.api_key,
+            max_retries=0,
+        )
+
+    async def get_response(
+        self,
+        model_info: ModelInfo,
+        message_list: list[Message],
+        tool_options: list[ToolOption] | None = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+        response_format: RespFormat | None = None,
+        stream_response_handler: Optional[
+            Callable[
+                [AsyncStream[ChatCompletionChunk], asyncio.Event | None],
+                Coroutine[Any, Any, tuple[APIResponse, Optional[tuple[int, int, int]]]],
+            ]
+        ] = None,
+        async_response_parser: Optional[
+            Callable[[ChatCompletion], tuple[APIResponse, Optional[tuple[int, int, int]]]]
+        ] = None,
+        interrupt_flag: asyncio.Event | None = None,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取对话响应
+        Args:
+            model_info: 模型信息
+            message_list: 对话体
+            tool_options: 工具选项（可选，默认为None）
+            max_tokens: 最大token数（可选，默认为1024）
+            temperature: 温度（可选，默认为0.7）
+            response_format: 响应格式（可选，默认为 NotGiven ）
+            stream_response_handler: 流式响应处理函数（可选，默认为default_stream_response_handler）
+            async_response_parser: 响应解析函数（可选，默认为default_response_parser）
+            interrupt_flag: 中断信号量（可选，默认为None）
+        Returns:
+            (响应文本, 推理文本, 工具调用, 其他数据)
+        """
+        if stream_response_handler is None:
+            stream_response_handler = _default_stream_response_handler
+
+        if async_response_parser is None:
+            async_response_parser = _default_normal_response_parser
+
+        # 将messages构造为OpenAI API所需的格式
+        messages: Iterable[ChatCompletionMessageParam] = _convert_messages(message_list)
+        # 将tool_options转换为OpenAI API所需的格式
+        tools: Iterable[ChatCompletionToolParam] = _convert_tool_options(tool_options) if tool_options else NOT_GIVEN  # type: ignore
+
+        try:
+            if model_info.force_stream_mode:
+                req_task = asyncio.create_task(
+                    self.client.chat.completions.create(
+                        model=model_info.model_identifier,
+                        messages=messages,
+                        tools=tools,
+                        temperature=temperature,
+                        max_tokens=max_tokens,
+                        stream=True,
+                        response_format=NOT_GIVEN,
+                        extra_body=extra_params,
+                    )
+                )
+                while not req_task.done():
+                    if interrupt_flag and interrupt_flag.is_set():
+                        # 如果中断量存在且被设置，则取消任务并抛出异常
+                        req_task.cancel()
+                        raise ReqAbortException("请求被外部信号中断")
+                    await asyncio.sleep(0.1)  # 等待0.1秒后再次检查任务&中断信号量状态
+
+                resp, usage_record = await stream_response_handler(req_task.result(), interrupt_flag)
+            else:
+                # 发送请求并获取响应
+                req_task = asyncio.create_task(
+                    self.client.chat.completions.create(
+                        model=model_info.model_identifier,
+                        messages=messages,
+                        tools=tools,
+                        temperature=temperature,
+                        max_tokens=max_tokens,
+                        stream=False,
+                        response_format=NOT_GIVEN,
+                        extra_body=extra_params,
+                    )
+                )
+                while not req_task.done():
+                    if interrupt_flag and interrupt_flag.is_set():
+                        # 如果中断量存在且被设置，则取消任务并抛出异常
+                        req_task.cancel()
+                        raise ReqAbortException("请求被外部信号中断")
+                    await asyncio.sleep(0.5)  # 等待0.5秒后再次检查任务&中断信号量状态
+
+                resp, usage_record = async_response_parser(req_task.result())
+        except APIConnectionError as e:
+            # 重封装APIConnectionError为NetworkConnectionError
+            raise NetworkConnectionError() from e
+        except APIStatusError as e:
+            # 重封装APIError为RespNotOkException
+            raise RespNotOkException(e.status_code, e.message) from e
+
+        if usage_record:
+            resp.usage = UsageRecord(
+                model_name=model_info.name,
+                provider_name=model_info.api_provider,
+                prompt_tokens=usage_record[0],
+                completion_tokens=usage_record[1],
+                total_tokens=usage_record[2],
+            )
+
+        return resp
+
+    async def get_embedding(
+        self,
+        model_info: ModelInfo,
+        embedding_input: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取文本嵌入
+        :param model_info: 模型信息
+        :param embedding_input: 嵌入输入文本
+        :return: 嵌入响应
+        """
+        try:
+            raw_response = await self.client.embeddings.create(
+                model=model_info.model_identifier,
+                input=embedding_input,
+                extra_body=extra_params,
+            )
+        except APIConnectionError as e:
+            raise NetworkConnectionError() from e
+        except APIStatusError as e:
+            # 重封装APIError为RespNotOkException
+            raise RespNotOkException(e.status_code) from e
+
+        response = APIResponse()
+
+        # 解析嵌入响应
+        if len(raw_response.data) > 0:
+            response.embedding = raw_response.data[0].embedding
+        else:
+            raise RespParseException(
+                raw_response,
+                "响应解析失败，缺失嵌入数据。",
+            )
+
+        # 解析使用情况
+        if hasattr(raw_response, "usage"):
+            response.usage = UsageRecord(
+                model_name=model_info.name,
+                provider_name=model_info.api_provider,
+                prompt_tokens=raw_response.usage.prompt_tokens or 0,
+                completion_tokens=raw_response.usage.completion_tokens or 0,  # type: ignore
+                total_tokens=raw_response.usage.total_tokens or 0,
+            )
+
+        return response
+
+    async def get_audio_transcriptions(
+        self,
+        model_info: ModelInfo,
+        audio_base64: str,
+        extra_params: dict[str, Any] | None = None,
+    ) -> APIResponse:
+        """
+        获取音频转录
+        :param model_info: 模型信息
+        :param audio_base64: base64编码的音频数据
+        :extra_params: 附加的请求参数
+        :return: 音频转录响应
+        """
+        try:
+            raw_response = await self.client.audio.transcriptions.create(
+                model=model_info.model_identifier,
+                file=("audio.wav", io.BytesIO(base64.b64decode(audio_base64))),
+                extra_body=extra_params,
+            )
+        except APIConnectionError as e:
+            raise NetworkConnectionError() from e
+        except APIStatusError as e:
+            # 重封装APIError为RespNotOkException
+            raise RespNotOkException(e.status_code) from e
+        response = APIResponse()
+        # 解析转录响应
+        if hasattr(raw_response, "text"):
+            response.content = raw_response.text
+        else:
+            raise RespParseException(
+                raw_response,
+                "响应解析失败，缺失转录文本。",
+            )
+        return response
+
+    def get_support_image_formats(self) -> list[str]:
+        """
+        获取支持的图片格式
+        :return: 支持的图片格式列表
+        """
+        return ["jpg", "jpeg", "png", "webp", "gif"]

src/llm_models/payload_content/__init__.py

@@ -0,0 +1,3 @@
+from .tool_option import ToolCall
+
+__all__ = ["ToolCall"]

src/llm_models/payload_content/message.py

@@ -0,0 +1,107 @@
+from enum import Enum
+
+
+# 设计这系列类的目的是为未来可能的扩展做准备
+
+
+class RoleType(Enum):
+    System = "system"
+    User = "user"
+    Assistant = "assistant"
+    Tool = "tool"
+
+
+SUPPORTED_IMAGE_FORMATS = ["jpg", "jpeg", "png", "webp", "gif"]  # openai支持的图片格式
+
+
+class Message:
+    def __init__(
+        self,
+        role: RoleType,
+        content: str | list[tuple[str, str] | str],
+        tool_call_id: str | None = None,
+    ):
+        """
+        初始化消息对象
+        （不应直接修改Message类，而应使用MessageBuilder类来构建对象）
+        """
+        self.role: RoleType = role
+        self.content: str | list[tuple[str, str] | str] = content
+        self.tool_call_id: str | None = tool_call_id
+
+
+class MessageBuilder:
+    def __init__(self):
+        self.__role: RoleType = RoleType.User
+        self.__content: list[tuple[str, str] | str] = []
+        self.__tool_call_id: str | None = None
+
+    def set_role(self, role: RoleType = RoleType.User) -> "MessageBuilder":
+        """
+        设置角色（默认为User）
+        :param role: 角色
+        :return: MessageBuilder对象
+        """
+        self.__role = role
+        return self
+
+    def add_text_content(self, text: str) -> "MessageBuilder":
+        """
+        添加文本内容
+        :param text: 文本内容
+        :return: MessageBuilder对象
+        """
+        self.__content.append(text)
+        return self
+
+    def add_image_content(
+        self,
+        image_format: str,
+        image_base64: str,
+        support_formats: list[str] = SUPPORTED_IMAGE_FORMATS,  # 默认支持格式
+    ) -> "MessageBuilder":
+        """
+        添加图片内容
+        :param image_format: 图片格式
+        :param image_base64: 图片的base64编码
+        :return: MessageBuilder对象
+        """
+        if image_format.lower() not in support_formats:
+            raise ValueError("不受支持的图片格式")
+        if not image_base64:
+            raise ValueError("图片的base64编码不能为空")
+        self.__content.append((image_format, image_base64))
+        return self
+
+    def add_tool_call(self, tool_call_id: str) -> "MessageBuilder":
+        """
+        添加工具调用指令（调用时请确保已设置为Tool角色）
+        :param tool_call_id: 工具调用指令的id
+        :return: MessageBuilder对象
+        """
+        if self.__role != RoleType.Tool:
+            raise ValueError("仅当角色为Tool时才能添加工具调用ID")
+        if not tool_call_id:
+            raise ValueError("工具调用ID不能为空")
+        self.__tool_call_id = tool_call_id
+        return self
+
+    def build(self) -> Message:
+        """
+        构建消息对象
+        :return: Message对象
+        """
+        if len(self.__content) == 0:
+            raise ValueError("内容不能为空")
+        if self.__role == RoleType.Tool and self.__tool_call_id is None:
+            raise ValueError("Tool角色的工具调用ID不能为空")
+
+        return Message(
+            role=self.__role,
+            content=(
+                self.__content[0]
+                if (len(self.__content) == 1 and isinstance(self.__content[0], str))
+                else self.__content
+            ),
+            tool_call_id=self.__tool_call_id,
+        )

src/llm_models/payload_content/resp_format.py

@@ -0,0 +1,223 @@
+from enum import Enum
+from typing import Optional, Any
+
+from pydantic import BaseModel
+from typing_extensions import TypedDict, Required
+
+
+class RespFormatType(Enum):
+    TEXT = "text"  # 文本
+    JSON_OBJ = "json_object"  # JSON
+    JSON_SCHEMA = "json_schema"  # JSON Schema
+
+
+class JsonSchema(TypedDict, total=False):
+    name: Required[str]
+    """
+    The name of the response format.
+
+    Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length
+    of 64.
+    """
+
+    description: Optional[str]
+    """
+    A description of what the response format is for, used by the model to determine
+    how to respond in the format.
+    """
+
+    schema: dict[str, object]
+    """
+    The schema for the response format, described as a JSON Schema object. Learn how
+    to build JSON schemas [here](https://json-schema.org/).
+    """
+
+    strict: Optional[bool]
+    """
+    Whether to enable strict schema adherence when generating the output. If set to
+    true, the model will always follow the exact schema defined in the `schema`
+    field. Only a subset of JSON Schema is supported when `strict` is `true`. To
+    learn more, read the
+    [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
+    """
+
+
+def _json_schema_type_check(instance) -> str | None:
+    if "name" not in instance:
+        return "schema必须包含'name'字段"
+    elif not isinstance(instance["name"], str) or instance["name"].strip() == "":
+        return "schema的'name'字段必须是非空字符串"
+    if "description" in instance and (
+        not isinstance(instance["description"], str)
+        or instance["description"].strip() == ""
+    ):
+        return "schema的'description'字段只能填入非空字符串"
+    if "schema" not in instance:
+        return "schema必须包含'schema'字段"
+    elif not isinstance(instance["schema"], dict):
+        return "schema的'schema'字段必须是字典，详见https://json-schema.org/"
+    if "strict" in instance and not isinstance(instance["strict"], bool):
+        return "schema的'strict'字段只能填入布尔值"
+
+    return None
+
+
+def _remove_title(schema: dict[str, Any] | list[Any]) -> dict[str, Any] | list[Any]:
+    """
+    递归移除JSON Schema中的title字段
+    """
+    if isinstance(schema, list):
+        # 如果当前Schema是列表，则对所有dict/list子元素递归调用
+        for idx, item in enumerate(schema):
+            if isinstance(item, (dict, list)):
+                schema[idx] = _remove_title(item)
+    elif isinstance(schema, dict):
+        # 是字典，移除title字段，并对所有dict/list子元素递归调用
+        if "title" in schema:
+            del schema["title"]
+        for key, value in schema.items():
+            if isinstance(value, (dict, list)):
+                schema[key] = _remove_title(value)
+
+    return schema
+
+
+def _link_definitions(schema: dict[str, Any]) -> dict[str, Any]:
+    """
+    链接JSON Schema中的definitions字段
+    """
+
+    def link_definitions_recursive(
+        path: str, sub_schema: list[Any] | dict[str, Any], defs: dict[str, Any]
+    ) -> dict[str, Any]:
+        """
+        递归链接JSON Schema中的definitions字段
+        :param path: 当前路径
+        :param sub_schema: 子Schema
+        :param defs: Schema定义集
+        :return:
+        """
+        if isinstance(sub_schema, list):
+            # 如果当前Schema是列表，则遍历每个元素
+            for i in range(len(sub_schema)):
+                if isinstance(sub_schema[i], dict):
+                    sub_schema[i] = link_definitions_recursive(
+                        f"{path}/{str(i)}", sub_schema[i], defs
+                    )
+        else:
+            # 否则为字典
+            if "$defs" in sub_schema:
+                # 如果当前Schema有$def字段，则将其添加到defs中
+                key_prefix = f"{path}/$defs/"
+                for key, value in sub_schema["$defs"].items():
+                    def_key = key_prefix + key
+                    if def_key not in defs:
+                        defs[def_key] = value
+                del sub_schema["$defs"]
+            if "$ref" in sub_schema:
+                # 如果当前Schema有$ref字段，则将其替换为defs中的定义
+                def_key = sub_schema["$ref"]
+                if def_key in defs:
+                    sub_schema = defs[def_key]
+                else:
+                    raise ValueError(f"Schema中引用的定义'{def_key}'不存在")
+            # 遍历键值对
+            for key, value in sub_schema.items():
+                if isinstance(value, (dict, list)):
+                    # 如果当前值是字典或列表，则递归调用
+                    sub_schema[key] = link_definitions_recursive(
+                        f"{path}/{key}", value, defs
+                    )
+
+        return sub_schema
+
+    return link_definitions_recursive("#", schema, {})
+
+
+def _remove_defs(schema: dict[str, Any]) -> dict[str, Any]:
+    """
+    递归移除JSON Schema中的$defs字段
+    """
+    if isinstance(schema, list):
+        # 如果当前Schema是列表，则对所有dict/list子元素递归调用
+        for idx, item in enumerate(schema):
+            if isinstance(item, (dict, list)):
+                schema[idx] = _remove_title(item)
+    elif isinstance(schema, dict):
+        # 是字典，移除title字段，并对所有dict/list子元素递归调用
+        if "$defs" in schema:
+            del schema["$defs"]
+        for key, value in schema.items():
+            if isinstance(value, (dict, list)):
+                schema[key] = _remove_title(value)
+
+    return schema
+
+
+class RespFormat:
+    """
+    响应格式
+    """
+
+    @staticmethod
+    def _generate_schema_from_model(schema):
+        json_schema = {
+            "name": schema.__name__,
+            "schema": _remove_defs(
+                _link_definitions(_remove_title(schema.model_json_schema()))
+            ),
+            "strict": False,
+        }
+        if schema.__doc__:
+            json_schema["description"] = schema.__doc__
+        return json_schema
+
+    def __init__(
+        self,
+        format_type: RespFormatType = RespFormatType.TEXT,
+        schema: type | JsonSchema | None = None,
+    ):
+        """
+        响应格式
+        :param format_type: 响应格式类型（默认为文本）
+        :param schema: 模板类或JsonSchema（仅当format_type为JSON Schema时有效）
+        """
+        self.format_type: RespFormatType = format_type
+
+        if format_type == RespFormatType.JSON_SCHEMA:
+            if schema is None:
+                raise ValueError("当format_type为'JSON_SCHEMA'时，schema不能为空")
+            if isinstance(schema, dict):
+                if check_msg := _json_schema_type_check(schema):
+                    raise ValueError(f"schema格式不正确，{check_msg}")
+
+                self.schema = schema
+            elif issubclass(schema, BaseModel):
+                try:
+                    json_schema = self._generate_schema_from_model(schema)
+
+                    self.schema = json_schema
+                except Exception as e:
+                    raise ValueError(
+                        f"自动生成JSON Schema时发生异常，请检查模型类{schema.__name__}的定义，详细信息：\n"
+                        f"{schema.__name__}:\n"
+                    ) from e
+            else:
+                raise ValueError("schema必须是BaseModel的子类或JsonSchema")
+        else:
+            self.schema = None
+
+    def to_dict(self):
+        """
+        将响应格式转换为字典
+        :return: 字典
+        """
+        if self.schema:
+            return {
+                "format_type": self.format_type.value,
+                "schema": self.schema,
+            }
+        else:
+            return {
+                "format_type": self.format_type.value,
+            }

src/llm_models/payload_content/tool_option.py

@@ -0,0 +1,163 @@
+from enum import Enum
+
+
+class ToolParamType(Enum):
+    """
+    工具调用参数类型
+    """
+
+    STRING = "string"  # 字符串
+    INTEGER = "integer"  # 整型
+    FLOAT = "float"  # 浮点型
+    BOOLEAN = "bool"  # 布尔型
+
+
+class ToolParam:
+    """
+    工具调用参数
+    """
+
+    def __init__(
+        self,
+        name: str,
+        param_type: ToolParamType,
+        description: str,
+        required: bool,
+        enum_values: list[str] | None = None,
+    ):
+        """
+        初始化工具调用参数
+        （不应直接修改ToolParam类，而应使用ToolOptionBuilder类来构建对象）
+        :param name: 参数名称
+        :param param_type: 参数类型
+        :param description: 参数描述
+        :param required: 是否必填
+        """
+        self.name: str = name
+        self.param_type: ToolParamType = param_type
+        self.description: str = description
+        self.required: bool = required
+        self.enum_values: list[str] | None = enum_values
+
+
+class ToolOption:
+    """
+    工具调用项
+    """
+
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        params: list[ToolParam] | None = None,
+    ):
+        """
+        初始化工具调用项
+        （不应直接修改ToolOption类，而应使用ToolOptionBuilder类来构建对象）
+        :param name: 工具名称
+        :param description: 工具描述
+        :param params: 工具参数列表
+        """
+        self.name: str = name
+        self.description: str = description
+        self.params: list[ToolParam] | None = params
+
+
+class ToolOptionBuilder:
+    """
+    工具调用项构建器
+    """
+
+    def __init__(self):
+        self.__name: str = ""
+        self.__description: str = ""
+        self.__params: list[ToolParam] = []
+
+    def set_name(self, name: str) -> "ToolOptionBuilder":
+        """
+        设置工具名称
+        :param name: 工具名称
+        :return: ToolBuilder实例
+        """
+        if not name:
+            raise ValueError("工具名称不能为空")
+        self.__name = name
+        return self
+
+    def set_description(self, description: str) -> "ToolOptionBuilder":
+        """
+        设置工具描述
+        :param description: 工具描述
+        :return: ToolBuilder实例
+        """
+        if not description:
+            raise ValueError("工具描述不能为空")
+        self.__description = description
+        return self
+
+    def add_param(
+        self,
+        name: str,
+        param_type: ToolParamType,
+        description: str,
+        required: bool = False,
+        enum_values: list[str] | None = None,
+    ) -> "ToolOptionBuilder":
+        """
+        添加工具参数
+        :param name: 参数名称
+        :param param_type: 参数类型
+        :param description: 参数描述
+        :param required: 是否必填（默认为False）
+        :return: ToolBuilder实例
+        """
+        if not name or not description:
+            raise ValueError("参数名称/描述不能为空")
+
+        self.__params.append(
+            ToolParam(
+                name=name,
+                param_type=param_type,
+                description=description,
+                required=required,
+                enum_values=enum_values,
+            )
+        )
+
+        return self
+
+    def build(self):
+        """
+        构建工具调用项
+        :return: 工具调用项
+        """
+        if self.__name == "" or self.__description == "":
+            raise ValueError("工具名称/描述不能为空")
+
+        return ToolOption(
+            name=self.__name,
+            description=self.__description,
+            params=None if len(self.__params) == 0 else self.__params,
+        )
+
+
+class ToolCall:
+    """
+    来自模型反馈的工具调用
+    """
+
+    def __init__(
+        self,
+        call_id: str,
+        func_name: str,
+        args: dict | None = None,
+    ):
+        """
+        初始化工具调用
+        :param call_id: 工具调用ID
+        :param func_name: 要调用的函数名称
+        :param args: 工具调用参数
+        """
+        self.call_id: str = call_id
+        self.func_name: str = func_name
+        self.args: dict | None = args

src/llm_models/utils.py

@@ -0,0 +1,186 @@
+import base64
+import io
+
+from PIL import Image
+from datetime import datetime
+
+from src.common.logger import get_logger
+from src.common.database.database import db  # 确保 db 被导入用于 create_tables
+from src.common.database.database_model import LLMUsage
+from src.config.api_ada_configs import ModelInfo
+from .payload_content.message import Message, MessageBuilder
+from .model_client.base_client import UsageRecord
+
+logger = get_logger("消息压缩工具")
+
+
+def compress_messages(messages: list[Message], img_target_size: int = 1 * 1024 * 1024) -> list[Message]:
+    """
+    压缩消息列表中的图片
+    :param messages: 消息列表
+    :param img_target_size: 图片目标大小，默认1MB
+    :return: 压缩后的消息列表
+    """
+
+    def reformat_static_image(image_data: bytes) -> bytes:
+        """
+        将静态图片转换为JPEG格式
+        :param image_data: 图片数据
+        :return: 转换后的图片数据
+        """
+        try:
+            image = Image.open(image_data)
+
+            if image.format and (image.format.upper() in ["JPEG", "JPG", "PNG", "WEBP"]):
+                # 静态图像，转换为JPEG格式
+                reformated_image_data = io.BytesIO()
+                image.save(reformated_image_data, format="JPEG", quality=95, optimize=True)
+                image_data = reformated_image_data.getvalue()
+
+            return image_data
+        except Exception as e:
+            logger.error(f"图片转换格式失败: {str(e)}")
+            return image_data
+
+    def rescale_image(image_data: bytes, scale: float) -> tuple[bytes, tuple[int, int] | None, tuple[int, int] | None]:
+        """
+        缩放图片
+        :param image_data: 图片数据
+        :param scale: 缩放比例
+        :return: 缩放后的图片数据
+        """
+        try:
+            image = Image.open(image_data)
+
+            # 原始尺寸
+            original_size = (image.width, image.height)
+
+            # 计算新的尺寸
+            new_size = (int(original_size[0] * scale), int(original_size[1] * scale))
+
+            output_buffer = io.BytesIO()
+
+            if getattr(image, "is_animated", False):
+                # 动态图片，处理所有帧
+                frames = []
+                new_size = (new_size[0] // 2, new_size[1] // 2)  # 动图，缩放尺寸再打折
+                for frame_idx in range(getattr(image, "n_frames", 1)):
+                    image.seek(frame_idx)
+                    new_frame = image.copy()
+                    new_frame = new_frame.resize(new_size, Image.Resampling.LANCZOS)
+                    frames.append(new_frame)
+
+                # 保存到缓冲区
+                frames[0].save(
+                    output_buffer,
+                    format="GIF",
+                    save_all=True,
+                    append_images=frames[1:],
+                    optimize=True,
+                    duration=image.info.get("duration", 100),
+                    loop=image.info.get("loop", 0),
+                )
+            else:
+                # 静态图片，直接缩放保存
+                resized_image = image.resize(new_size, Image.Resampling.LANCZOS)
+                resized_image.save(output_buffer, format="JPEG", quality=95, optimize=True)
+
+            return output_buffer.getvalue(), original_size, new_size
+
+        except Exception as e:
+            logger.error(f"图片缩放失败: {str(e)}")
+            import traceback
+
+            logger.error(traceback.format_exc())
+            return image_data, None, None
+
+    def compress_base64_image(base64_data: str, target_size: int = 1 * 1024 * 1024) -> str:
+        original_b64_data_size = len(base64_data)  # 计算原始数据大小
+
+        image_data = base64.b64decode(base64_data)
+
+        # 先尝试转换格式为JPEG
+        image_data = reformat_static_image(image_data)
+        base64_data = base64.b64encode(image_data).decode("utf-8")
+        if len(base64_data) <= target_size:
+            # 如果转换后小于目标大小，直接返回
+            logger.info(f"成功将图片转为JPEG格式，编码后大小: {len(base64_data) / 1024:.1f}KB")
+            return base64_data
+
+        # 如果转换后仍然大于目标大小，进行尺寸压缩
+        scale = min(1.0, target_size / len(base64_data))
+        image_data, original_size, new_size = rescale_image(image_data, scale)
+        base64_data = base64.b64encode(image_data).decode("utf-8")
+
+        if original_size and new_size:
+            logger.info(
+                f"压缩图片: {original_size[0]}x{original_size[1]} -> {new_size[0]}x{new_size[1]}\n"
+                f"压缩前大小: {original_b64_data_size / 1024:.1f}KB, 压缩后大小: {len(base64_data) / 1024:.1f}KB"
+            )
+
+        return base64_data
+
+    compressed_messages = []
+    for message in messages:
+        if isinstance(message.content, list):
+            # 检查content，如有图片则压缩
+            message_builder = MessageBuilder()
+            for content_item in message.content:
+                if isinstance(content_item, tuple):
+                    # 图片，进行压缩
+                    message_builder.add_image_content(
+                        content_item[0],
+                        compress_base64_image(content_item[1], target_size=img_target_size),
+                    )
+                else:
+                    message_builder.add_text_content(content_item)
+            compressed_messages.append(message_builder.build())
+        else:
+            compressed_messages.append(message)
+
+    return compressed_messages
+
+
+class LLMUsageRecorder:
+    """
+    LLM使用情况记录器
+    """
+
+    def __init__(self):
+        try:
+            # 使用 Peewee 创建表，safe=True 表示如果表已存在则不会抛出错误
+            db.create_tables([LLMUsage], safe=True)
+            # logger.debug("LLMUsage 表已初始化/确保存在。")
+        except Exception as e:
+            logger.error(f"创建 LLMUsage 表失败: {str(e)}")
+
+    def record_usage_to_database(
+        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str
+    ):
+        input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
+        output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
+        total_cost = round(input_cost + output_cost, 6)
+        try:
+            # 使用 Peewee 模型创建记录
+            LLMUsage.create(
+                model_name=model_info.model_identifier,
+                user_id=user_id,
+                request_type=request_type,
+                endpoint=endpoint,
+                prompt_tokens=model_usage.prompt_tokens or 0,
+                completion_tokens=model_usage.completion_tokens or 0,
+                total_tokens=model_usage.total_tokens or 0,
+                cost=total_cost or 0.0,
+                status="success",
+                timestamp=datetime.now(),  # Peewee 会处理 DateTimeField
+            )
+            logger.debug(
+                f"Token使用情况 - 模型: {model_usage.model_name}, "
+                f"用户: {user_id}, 类型: {request_type}, "
+                f"提示词: {model_usage.prompt_tokens}, 完成: {model_usage.completion_tokens}, "
+                f"总计: {model_usage.total_tokens}"
+            )
+        except Exception as e:
+            logger.error(f"记录token使用情况失败: {str(e)}")
+
+llm_usage_recorder = LLMUsageRecorder()

src/mais4u/mai_think.py

@@ -2,13 +2,15 @@ from src.chat.message_receive.chat_stream import get_chat_manager
 import time
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import model_config
 from src.chat.message_receive.message import MessageRecvS4U
 from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor
 from src.mais4u.mais4u_chat.internal_manager import internal_manager
 from src.common.logger import get_logger
+
 logger = get_logger(__name__)
 
+
 def init_prompt():
     Prompt(
         """
@@ -32,10 +34,8 @@ def init_prompt():
     )
 
 
-
-
 class MaiThinking:
-    def __init__(self,chat_id):
+    def __init__(self, chat_id):
         self.chat_id = chat_id
         self.chat_stream = get_chat_manager().get_stream(chat_id)
         self.platform = self.chat_stream.platform
@@ -44,11 +44,11 @@ class MaiThinking:
             self.is_group = True
         else:
             self.is_group = False
-        
+
         self.s4u_message_processor = S4UMessageProcessor()
-        
+
         self.mind = ""
-        
+
         self.memory_block = ""
         self.relation_info_block = ""
         self.time_block = ""
@@ -59,17 +59,13 @@ class MaiThinking:
         self.identity = ""
         self.sender = ""
         self.target = ""
-        
+
-        self.thinking_model = LLMRequest(
+        self.thinking_model = LLMRequest(model_set=model_config.model_task_config.replyer_1, request_type="thinking")
-                        model=global_config.model.replyer_1,
-                        request_type="thinking",
-                    )
 
     async def do_think_before_response(self):
         pass
 
-    async def do_think_after_response(self,reponse:str):
+    async def do_think_after_response(self, reponse: str):
-        
         prompt = await global_prompt_manager.format_prompt(
             "after_response_think_prompt",
             mind=self.mind,
@@ -85,47 +81,44 @@ class MaiThinking:
             sender=self.sender,
             target=self.target,
         )
-        
+
         result, _ = await self.thinking_model.generate_response_async(prompt)
         self.mind = result
-        
+
         logger.info(f"[{self.chat_id}] 思考前想法：{self.mind}")
         # logger.info(f"[{self.chat_id}] 思考前prompt：{prompt}")
         logger.info(f"[{self.chat_id}] 思考后想法：{self.mind}")
-        
+
-        
         msg_recv = await self.build_internal_message_recv(self.mind)
         await self.s4u_message_processor.process_message(msg_recv)
         internal_manager.set_internal_state(self.mind)
-        
+
-    
     async def do_think_when_receive_message(self):
         pass
-    
+
-    async def build_internal_message_recv(self,message_text:str):
+    async def build_internal_message_recv(self, message_text: str):
-        
         msg_id = f"internal_{time.time()}"
-        
+
         message_dict = {
             "message_info": {
                 "message_id": msg_id,
                 "time": time.time(),
                 "user_info": {
-                    "user_id": "internal",         # 内部用户ID
+                    "user_id": "internal",  # 内部用户ID
-                    "user_nickname": "内心",            # 内部昵称
+                    "user_nickname": "内心",  # 内部昵称
-                    "platform": self.platform,             # 平台标记为 internal
+                    "platform": self.platform,  # 平台标记为 internal
                     # 其他 user_info 字段按需补充
                 },
-                "platform": self.platform,                 # 平台
+                "platform": self.platform,  # 平台
                 # 其他 message_info 字段按需补充
             },
             "message_segment": {
-                "type": "text",                         # 消息类型
+                "type": "text",  # 消息类型
-                "data": message_text,              # 消息内容
+                "data": message_text,  # 消息内容
                 # 其他 segment 字段按需补充
             },
-            "raw_message": message_text,           # 原始消息内容
+            "raw_message": message_text,  # 原始消息内容
-            "processed_plain_text": message_text,   # 处理后的纯文本
+            "processed_plain_text": message_text,  # 处理后的纯文本
             # 下面这些字段可选，根据 MessageRecv 需要
             "is_emoji": False,
             "has_emoji": False,
@@ -139,45 +132,36 @@ class MaiThinking:
             "priority_info": {"message_priority": 10.0},  # 内部消息可设高优先级
             "interest_value": 1.0,
         }
-        
+
         if self.is_group:
             message_dict["message_info"]["group_info"] = {
                 "platform": self.platform,
                 "group_id": self.chat_stream.group_info.group_id,
                 "group_name": self.chat_stream.group_info.group_name,
             }
-        
+
         msg_recv = MessageRecvS4U(message_dict)
         msg_recv.chat_info = self.chat_info
         msg_recv.chat_stream = self.chat_stream
         msg_recv.is_internal = True
-        
+
         return msg_recv
-        
+
-        
-    
 
 class MaiThinkingManager:
     def __init__(self):
         self.mai_think_list = []
-    
+
-    def get_mai_think(self,chat_id):
+    def get_mai_think(self, chat_id):
         for mai_think in self.mai_think_list:
             if mai_think.chat_id == chat_id:
                 return mai_think
         mai_think = MaiThinking(chat_id)
         self.mai_think_list.append(mai_think)
         return mai_think
-    
+
+
 mai_thinking_manager = MaiThinkingManager()
-        
+
 
 init_prompt()
-
-
-
-
-
-
-
-

src/mais4u/mais4u_chat/body_emotion_action_manager.py

@@ -1,14 +1,16 @@
 import json
 import time
+
+from json_repair import repair_json
 from src.chat.message_receive.message import MessageRecv
 from src.llm_models.utils_model import LLMRequest
 from src.common.logger import get_logger
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.manager.async_task_manager import AsyncTask, async_task_manager
 from src.plugin_system.apis import send_api
-from json_repair import repair_json
+
 from src.mais4u.s4u_config import s4u_config
 
 logger = get_logger("action")
@@ -32,7 +34,7 @@ BODY_CODE = {
     "帅气的姿势": "010_0190",
     "另一个帅气的姿势": "010_0191",
     "手掌朝前可爱": "010_0210",
-    "平静，双手后放":"平静，双手后放",
+    "平静，双手后放": "平静，双手后放",
     "思考": "思考",
     "优雅，左手放在腰上": "优雅，左手放在腰上",
     "一般": "一般",
@@ -94,19 +96,15 @@ class ChatAction:
         self.body_action_cooldown: dict[str, int] = {}
 
         print(s4u_config.models.motion)
-        print(global_config.model.emotion)
+        print(model_config.model_task_config.emotion)
-        
-        self.action_model = LLMRequest(
-            model=global_config.model.emotion,
-            temperature=0.7,
-            request_type="motion",
-        )
 
-        self.last_change_time = 0
+        self.action_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="motion")
+
+        self.last_change_time: float = 0
 
     async def send_action_update(self):
         """发送动作更新到前端"""
-        
+
         body_code = BODY_CODE.get(self.body_action, "")
         await send_api.custom_to_stream(
             message_type="body_action",
@@ -115,13 +113,11 @@ class ChatAction:
             storage_message=False,
             show_log=True,
         )
-        
-        
 
     async def update_action_by_message(self, message: MessageRecv):
         self.regression_count = 0
 
-        message_time = message.message_info.time
+        message_time: float = message.message_info.time  # type: ignore
         message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive(
             chat_id=self.chat_id,
             timestamp_start=self.last_change_time,
@@ -147,13 +143,13 @@ class ChatAction:
 
         prompt_personality = global_config.personality.personality_core
         indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
-        
+
         try:
             # 冷却池处理：过滤掉冷却中的动作
             self._update_body_action_cooldown()
             available_actions = [k for k in BODY_CODE.keys() if k not in self.body_action_cooldown]
             all_actions = "\n".join(available_actions)
-            
+
             prompt = await global_prompt_manager.format_prompt(
                 "change_action_prompt",
                 chat_talking_prompt=chat_talking_prompt,
@@ -163,19 +159,18 @@ class ChatAction:
             )
 
             logger.info(f"prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.action_model.generate_response_async(prompt=prompt)
+            response, (reasoning_content, _, _) = await self.action_model.generate_response_async(
+                prompt=prompt, temperature=0.7
+            )
             logger.info(f"response: {response}")
             logger.info(f"reasoning_content: {reasoning_content}")
 
-            action_data = json.loads(repair_json(response))
+            if action_data := json.loads(repair_json(response)):
-
-            if action_data:
                 # 记录原动作，切换后进入冷却
                 prev_body_action = self.body_action
                 new_body_action = action_data.get("body_action", self.body_action)
-                if new_body_action != prev_body_action:
+                if new_body_action != prev_body_action and prev_body_action:
-                    if prev_body_action:
+                    self.body_action_cooldown[prev_body_action] = 3
-                        self.body_action_cooldown[prev_body_action] = 3
                 self.body_action = new_body_action
                 self.head_action = action_data.get("head_action", self.head_action)
                 # 发送动作更新
@@ -213,7 +208,6 @@ class ChatAction:
         prompt_personality = global_config.personality.personality_core
         indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
         try:
-
             # 冷却池处理：过滤掉冷却中的动作
             self._update_body_action_cooldown()
             available_actions = [k for k in BODY_CODE.keys() if k not in self.body_action_cooldown]
@@ -228,17 +222,17 @@ class ChatAction:
             )
 
             logger.info(f"prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.action_model.generate_response_async(prompt=prompt)
+            response, (reasoning_content, _, _) = await self.action_model.generate_response_async(
+                prompt=prompt, temperature=0.7
+            )
             logger.info(f"response: {response}")
             logger.info(f"reasoning_content: {reasoning_content}")
 
-            action_data = json.loads(repair_json(response))
+            if action_data := json.loads(repair_json(response)):
-            if action_data:
                 prev_body_action = self.body_action
                 new_body_action = action_data.get("body_action", self.body_action)
-                if new_body_action != prev_body_action:
+                if new_body_action != prev_body_action and prev_body_action:
-                    if prev_body_action:
+                    self.body_action_cooldown[prev_body_action] = 6
-                        self.body_action_cooldown[prev_body_action] = 6
                 self.body_action = new_body_action
                 # 发送动作更新
                 await self.send_action_update()
@@ -306,9 +300,6 @@ class ActionManager:
         return new_action_state
 
 
-
-
-
 init_prompt()
 
 action_manager = ActionManager()

src/mais4u/mais4u_chat/s4u_chat.py

@@ -137,7 +137,7 @@ class MessageSenderContainer:
                 await self.storage.store_message(bot_message, self.chat_stream)
 
             except Exception as e:
-                logger.error(f"[{self.chat_stream.get_stream_name()}] 消息发送或存储时出现错误: {e}", exc_info=True)
+                logger.error(f"[消息流: {self.chat_stream.stream_id}] 消息发送或存储时出现错误: {e}", exc_info=True)
 
             finally:
                 # CRUCIAL: Always call task_done() for any item that was successfully retrieved.

src/mais4u/mais4u_chat/s4u_mood_manager.py

@@ -6,7 +6,7 @@ from src.chat.message_receive.message import MessageRecv
 from src.llm_models.utils_model import LLMRequest
 from src.common.logger import get_logger
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.manager.async_task_manager import AsyncTask, async_task_manager
 from src.plugin_system.apis import send_api
@@ -114,18 +114,12 @@ class ChatMood:
 
         self.regression_count: int = 0
 
-        self.mood_model = LLMRequest(
+        self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood_text")
-            model=global_config.model.emotion,
-            temperature=0.7,
-            request_type="mood_text",
-        )
         self.mood_model_numerical = LLMRequest(
-            model=global_config.model.emotion,
+            model_set=model_config.model_task_config.emotion, request_type="mood_numerical"
-            temperature=0.4,
-            request_type="mood_numerical",
         )
 
-        self.last_change_time = 0
+        self.last_change_time: float = 0
 
         # 发送初始情绪状态到ws端
         asyncio.create_task(self.send_emotion_update(self.mood_values))
@@ -164,7 +158,7 @@ class ChatMood:
     async def update_mood_by_message(self, message: MessageRecv):
         self.regression_count = 0
 
-        message_time = message.message_info.time
+        message_time: float = message.message_info.time  # type: ignore
         message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive(
             chat_id=self.chat_id,
             timestamp_start=self.last_change_time,
@@ -199,7 +193,9 @@ class ChatMood:
                 mood_state=self.mood_state,
             )
             logger.debug(f"text mood prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
+            response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
+                prompt=prompt, temperature=0.7
+            )
             logger.info(f"text mood response: {response}")
             logger.debug(f"text mood reasoning_content: {reasoning_content}")
             return response
@@ -216,8 +212,8 @@ class ChatMood:
                 fear=self.mood_values["fear"],
             )
             logger.debug(f"numerical mood prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.mood_model_numerical.generate_response_async(
+            response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
-                prompt=prompt
+                prompt=prompt, temperature=0.4
             )
             logger.info(f"numerical mood response: {response}")
             logger.debug(f"numerical mood reasoning_content: {reasoning_content}")
@@ -276,7 +272,9 @@ class ChatMood:
                 mood_state=self.mood_state,
             )
             logger.debug(f"text regress prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
+            response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
+                prompt=prompt, temperature=0.7
+            )
             logger.info(f"text regress response: {response}")
             logger.debug(f"text regress reasoning_content: {reasoning_content}")
             return response
@@ -293,8 +291,9 @@ class ChatMood:
                 fear=self.mood_values["fear"],
             )
             logger.debug(f"numerical regress prompt: {prompt}")
-            response, (reasoning_content, model_name) = await self.mood_model_numerical.generate_response_async(
+            response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
-                prompt=prompt
+                prompt=prompt,
+                temperature=0.4,
             )
             logger.info(f"numerical regress response: {response}")
             logger.debug(f"numerical regress reasoning_content: {reasoning_content}")
@@ -447,6 +446,7 @@ class MoodManager:
         # 发送初始情绪状态到ws端
         asyncio.create_task(new_mood.send_emotion_update(new_mood.mood_values))
 
+
 if ENABLE_S4U:
     init_prompt()
     mood_manager = MoodManager()

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -150,19 +150,18 @@ class PromptBuilder:
         relation_prompt = ""
         if global_config.relationship.enable_relationship and who_chat_in_group:
             relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_stream.stream_id)
-            
+
             # 将 (platform, user_id, nickname) 转换为 person_id
             person_ids = []
             for person in who_chat_in_group:
                 person_id = PersonInfoManager.get_person_id(person[0], person[1])
                 person_ids.append(person_id)
-            
+
             # 使用 RelationshipFetcher 的 build_relation_info 方法，设置 points_num=3 保持与原来相同的行为
             relation_info_list = await asyncio.gather(
                 *[relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
             )
-            relation_info = "".join(relation_info_list)
+            if relation_info := "".join(relation_info_list):
-            if relation_info:
                 relation_prompt = await global_prompt_manager.format_prompt(
                     "relation_prompt", relation_info=relation_info
                 )
@@ -186,9 +185,9 @@ class PromptBuilder:
             timestamp=time.time(),
             limit=300,
         )
-        
 
-        talk_type = message.message_info.platform + ":" + str(message.chat_stream.user_info.user_id)
+
+        talk_type = f"{message.message_info.platform}:{str(message.chat_stream.user_info.user_id)}"
 
         core_dialogue_list = []
         background_dialogue_list = []
@@ -258,19 +257,19 @@ class PromptBuilder:
             all_msg_seg_list.append(msg_seg_str)
             for msg in all_msg_seg_list:
                 core_msg_str += msg
-                
+
-                
+
         all_dialogue_prompt = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
             limit=20,
-        )        
+        )
         all_dialogue_prompt_str = build_readable_messages(
             all_dialogue_prompt,
             timestamp_mode="normal_no_YMD",
             show_pic=False,
         )
-        
+
 
         return core_msg_str, background_dialogue_prompt,all_dialogue_prompt_str
 

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -1,7 +1,7 @@
 import os
 from typing import AsyncGenerator
 from src.mais4u.openai_client import AsyncOpenAIClient
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.message_receive.message import MessageRecvS4U
 from src.mais4u.mais4u_chat.s4u_prompt import prompt_builder
 from src.common.logger import get_logger
@@ -14,24 +14,27 @@ logger = get_logger("s4u_stream_generator")
 
 class S4UStreamGenerator:
     def __init__(self):
-        replyer_1_config = global_config.model.replyer_1
+        replyer_1_config = model_config.model_task_config.replyer_1
-        provider = replyer_1_config.get("provider")
+        model_to_use = replyer_1_config.model_list[0]
-        if not provider:
+        model_info = model_config.get_model_info(model_to_use)
-            logger.error("`replyer_1` 在配置文件中缺少 `provider` 字段")
+        if not model_info:
-            raise ValueError("`replyer_1` 在配置文件中缺少 `provider` 字段")
+            logger.error(f"模型 {model_to_use} 在配置中未找到")
+            raise ValueError(f"模型 {model_to_use} 在配置中未找到")
+        provider_name = model_info.api_provider
+        provider_info = model_config.get_provider(provider_name)
+        if not provider_info:
+            logger.error("`replyer_1` 找不到对应的Provider")
+            raise ValueError("`replyer_1` 找不到对应的Provider")
 
-        api_key = os.environ.get(f"{provider.upper()}_KEY")
+        api_key = provider_info.api_key
-        base_url = os.environ.get(f"{provider.upper()}_BASE_URL")
+        base_url = provider_info.base_url
 
         if not api_key:
-            logger.error(f"环境变量 {provider.upper()}_KEY 未设置")
+            logger.error(f"{provider_name}没有配置API KEY")
-            raise ValueError(f"环境变量 {provider.upper()}_KEY 未设置")
+            raise ValueError(f"{provider_name}没有配置API KEY")
 
         self.client_1 = AsyncOpenAIClient(api_key=api_key, base_url=base_url)
-        self.model_1_name = replyer_1_config.get("name")
+        self.model_1_name = model_to_use
-        if not self.model_1_name:
-            logger.error("`replyer_1` 在配置文件中缺少 `model_name` 字段")
-            raise ValueError("`replyer_1` 在配置文件中缺少 `model_name` 字段")
         self.replyer_1_config = replyer_1_config
 
         self.current_model_name = "unknown model"
@@ -44,10 +47,10 @@ class S4UStreamGenerator:
             r'[^.。!?？！\n\r]+(?:[.。!?？！\n\r](?![\'"])|$))',  # 匹配直到句子结束符
             re.UNICODE | re.DOTALL,
         )
-        
+
-        self.chat_stream =None
+        self.chat_stream = None
-        
+
-    async def build_last_internal_message(self,message:MessageRecvS4U,previous_reply_context:str = ""):
+    async def build_last_internal_message(self, message: MessageRecvS4U, previous_reply_context: str = ""):
         # person_id = PersonInfoManager.get_person_id(
         #     message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id
         # )
@@ -71,14 +74,10 @@ class S4UStreamGenerator:
             [这是用户发来的新消息, 你需要结合上下文，对此进行回复]:
             {message.processed_plain_text}
             """
-            return True,message_txt
+            return True, message_txt
         else:
             message_txt = message.processed_plain_text
-            return False,message_txt
+            return False, message_txt
-        
-
-            
-    
 
     async def generate_response(
         self, message: MessageRecvS4U, previous_reply_context: str = ""
@@ -88,7 +87,7 @@ class S4UStreamGenerator:
         self.partial_response = ""
         message_txt = message.processed_plain_text
         if not message.is_internal:
-            interupted,message_txt_added = await self.build_last_internal_message(message,previous_reply_context)
+            interupted, message_txt_added = await self.build_last_internal_message(message, previous_reply_context)
             if interupted:
                 message_txt = message_txt_added
 
@@ -105,7 +104,6 @@ class S4UStreamGenerator:
         current_client = self.client_1
         self.current_model_name = self.model_1_name
 
-
         extra_kwargs = {}
         if self.replyer_1_config.get("enable_thinking") is not None:
             extra_kwargs["enable_thinking"] = self.replyer_1_config.get("enable_thinking")

src/mais4u/mais4u_chat/super_chat_manager.py

@@ -214,51 +214,49 @@ class SuperChatManager:
     def build_superchat_display_string(self, chat_id: str, max_count: int = 10) -> str:
         """构建SuperChat显示字符串"""
         superchats = self.get_superchats_by_chat(chat_id)
-        
+
         if not superchats:
             return ""
-        
+
         # 限制显示数量
         display_superchats = superchats[:max_count]
-        
+
-        lines = []
+        lines = ["📢 当前有效超级弹幕："]
-        lines.append("📢 当前有效超级弹幕：")
-        
         for i, sc in enumerate(display_superchats, 1):
             remaining_minutes = int(sc.remaining_time() / 60)
             remaining_seconds = int(sc.remaining_time() % 60)
-            
+
             time_display = f"{remaining_minutes}分{remaining_seconds}秒" if remaining_minutes > 0 else f"{remaining_seconds}秒"
-            
+
             line = f"{i}. 【{sc.price}元】{sc.user_nickname}: {sc.message_text}"
             if len(line) > 100:  # 限制单行长度
-                line = line[:97] + "..."
+                line = f"{line[:97]}..."
             line += f" (剩余{time_display})"
             lines.append(line)
-        
+
         if len(superchats) > max_count:
             lines.append(f"... 还有{len(superchats) - max_count}条SuperChat")
-        
+
         return "\n".join(lines)
     
     def build_superchat_summary_string(self, chat_id: str) -> str:
         """构建SuperChat摘要字符串"""
         superchats = self.get_superchats_by_chat(chat_id)
-        
+
         if not superchats:
             return "当前没有有效的超级弹幕"
         lines = []
         for sc in superchats:
             single_sc_str = f"{sc.user_nickname} - {sc.price}元 - {sc.message_text}"
             if len(single_sc_str) > 100:
-                single_sc_str = single_sc_str[:97] + "..."
+                single_sc_str = f"{single_sc_str[:97]}..."
             single_sc_str += f" (剩余{int(sc.remaining_time())}秒)"
             lines.append(single_sc_str)
-        
+
         total_amount = sum(sc.price for sc in superchats)
         count = len(superchats)
         highest_amount = max(sc.price for sc in superchats)
-        
+
         final_str = f"当前有{count}条超级弹幕，总金额{total_amount}元，最高单笔{highest_amount}元"
         if lines:
             final_str += "\n" + "\n".join(lines)
@@ -287,7 +285,7 @@ class SuperChatManager:
             "lowest_amount": min(amounts)
         }
     
-    async def shutdown(self):
+    async def shutdown(self):  # sourcery skip: use-contextlib-suppress
         """关闭管理器，清理资源"""
         if self._cleanup_task and not self._cleanup_task.done():
             self._cleanup_task.cancel()
@@ -300,6 +298,7 @@ class SuperChatManager:
 
 
 
+# sourcery skip: assign-if-exp
 if ENABLE_S4U:
     super_chat_manager = SuperChatManager()
 else:

src/mais4u/mais4u_chat/yes_or_no.py

@@ -1,19 +1,14 @@
 from src.llm_models.utils_model import LLMRequest
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import model_config
 from src.plugin_system.apis import send_api
+
 logger = get_logger(__name__)
 
-head_actions_list = [
+head_actions_list = ["不做额外动作", "点头一次", "点头两次", "摇头", "歪脑袋", "低头望向一边"]
-    "不做额外动作",
-    "点头一次",
-    "点头两次",
-    "摇头",
-    "歪脑袋",
-    "低头望向一边"
-]
 
-async def yes_or_no_head(text: str,emotion: str = "",chat_history: str = "",chat_id: str = ""):
+
+async def yes_or_no_head(text: str, emotion: str = "", chat_history: str = "", chat_id: str = ""):
     prompt = f"""
 {chat_history}
 以上是对方的发言：
@@ -30,22 +25,14 @@ async def yes_or_no_head(text: str,emotion: str = "",chat_history: str = "",chat
 低头望向一边
 
 请从上面的动作中选择一个，并输出，请只输出你选择的动作就好，不要输出其他内容。"""
-    model = LLMRequest(
+    model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="motion")
-            model=global_config.model.emotion,
+
-            temperature=0.7,
-            request_type="motion",
-    )
-    
     try:
         # logger.info(f"prompt: {prompt}")
-        response, (reasoning_content, model_name) = await model.generate_response_async(prompt=prompt)
+        response, _ = await model.generate_response_async(prompt=prompt, temperature=0.7)
         logger.info(f"response: {response}")
-        
+
-        if response in head_actions_list:
+        head_action = response if response in head_actions_list else "不做额外动作"
-            head_action = response
-        else:
-            head_action = "不做额外动作"
-        
         await send_api.custom_to_stream(
             message_type="head_action",
             content=head_action,
@@ -53,11 +40,7 @@ async def yes_or_no_head(text: str,emotion: str = "",chat_history: str = "",chat
             storage_message=False,
             show_log=True,
         )
-        
+
-        
-        
     except Exception as e:
         logger.error(f"yes_or_no_head error: {e}")
         return "不做额外动作"
-    
-

src/mood/mood_manager.py

@@ -3,13 +3,14 @@ import random
 import time
 
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.message_receive.message import MessageRecv
+from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
 from src.llm_models.utils_model import LLMRequest
 from src.manager.async_task_manager import AsyncTask, async_task_manager
-from src.chat.message_receive.chat_stream import get_chat_manager
+
 
 logger = get_logger("mood")
 
@@ -49,7 +50,7 @@ class ChatMood:
 
         chat_manager = get_chat_manager()
         self.chat_stream = chat_manager.get_stream(self.chat_id)
-        
+
         if not self.chat_stream:
             raise ValueError(f"Chat stream for chat_id {chat_id} not found")
 
@@ -59,11 +60,7 @@ class ChatMood:
 
         self.regression_count: int = 0
 
-        self.mood_model = LLMRequest(
+        self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood")
-            model=global_config.model.emotion,
-            temperature=0.7,
-            request_type="mood",
-        )
 
         self.last_change_time: float = 0
 
@@ -83,12 +80,16 @@ class ChatMood:
         logger.debug(
             f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"
         )
-        update_probability = global_config.mood.mood_update_threshold * min(1.0, base_probability * time_multiplier * interest_multiplier)
+        update_probability = global_config.mood.mood_update_threshold * min(
+            1.0, base_probability * time_multiplier * interest_multiplier
+        )
 
         if random.random() > update_probability:
             return
 
-        logger.debug(f"{self.log_prefix} 更新情绪状态，感兴趣度: {interested_rate:.2f}, 更新概率: {update_probability:.2f}")
+        logger.debug(
+            f"{self.log_prefix} 更新情绪状态，感兴趣度: {interested_rate:.2f}, 更新概率: {update_probability:.2f}"
+        )
 
         message_time: float = message.message_info.time  # type: ignore
         message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive(
@@ -124,7 +125,9 @@ class ChatMood:
             mood_state=self.mood_state,
         )
 
-        response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
+        response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
+            prompt=prompt, temperature=0.7
+        )
         if global_config.debug.show_prompt:
             logger.info(f"{self.log_prefix} prompt: {prompt}")
             logger.info(f"{self.log_prefix} response: {response}")
@@ -171,7 +174,9 @@ class ChatMood:
             mood_state=self.mood_state,
         )
 
-        response, (reasoning_content, model_name) = await self.mood_model.generate_response_async(prompt=prompt)
+        response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
+            prompt=prompt, temperature=0.7
+        )
 
         if global_config.debug.show_prompt:
             logger.info(f"{self.log_prefix} prompt: {prompt}")

src/person_info/person_info.py

@@ -11,7 +11,7 @@ from src.common.logger import get_logger
 from src.common.database.database import db
 from src.common.database.database_model import PersonInfo
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 
 
 """
@@ -54,11 +54,7 @@ person_info_default = {
 class PersonInfoManager:
     def __init__(self):
         self.person_name_list = {}
-        # TODO: API-Adapter修改标记
+        self.qv_name_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="relation.qv_name")
-        self.qv_name_llm = LLMRequest(
-            model=global_config.model.utils,
-            request_type="relation.qv_name",
-        )
         try:
             db.connect(reuse_if_open=True)
             # 设置连接池参数
@@ -199,7 +195,7 @@ class PersonInfoManager:
                 if existing:
                     logger.debug(f"用户 {p_data['person_id']} 已存在，跳过创建")
                     return True
-                
+
                 # 尝试创建
                 PersonInfo.create(**p_data)
                 return True
@@ -376,7 +372,7 @@ class PersonInfoManager:
                 "nickname": "昵称",
                 "reason": "理由"
             }"""
-            response, (reasoning_content, model_name) = await self.qv_name_llm.generate_response_async(qv_name_prompt)
+            response, _ = await self.qv_name_llm.generate_response_async(qv_name_prompt)
             # logger.info(f"取名提示词：{qv_name_prompt}\n取名回复：{response}")
             result = self._extract_json_from_text(response)
 
@@ -592,7 +588,7 @@ class PersonInfoManager:
             record = PersonInfo.get_or_none(PersonInfo.person_id == p_id)
             if record:
                 return record, False  # 记录存在，未创建
-                
+
             # 记录不存在，尝试创建
             try:
                 PersonInfo.create(**init_data)
@@ -622,7 +618,7 @@ class PersonInfoManager:
             "points": [],
             "forgotten_points": [],
         }
-        
+
         # 序列化JSON字段
         for key in JSON_SERIALIZED_FIELDS:
             if key in initial_data:
@@ -630,12 +626,12 @@ class PersonInfoManager:
                     initial_data[key] = json.dumps(initial_data[key], ensure_ascii=False)
                 elif initial_data[key] is None:
                     initial_data[key] = json.dumps([], ensure_ascii=False)
-        
+
         model_fields = PersonInfo._meta.fields.keys()  # type: ignore
         filtered_initial_data = {k: v for k, v in initial_data.items() if v is not None and k in model_fields}
 
         record, was_created = await asyncio.to_thread(_db_get_or_create_sync, person_id, filtered_initial_data)
-        
+
         if was_created:
             logger.info(f"用户 {platform}:{user_id} (person_id: {person_id}) 不存在，将创建新记录 (Peewee)。")
             logger.info(f"已为 {person_id} 创建新记录，初始数据 (filtered for model): {filtered_initial_data}")

src/person_info/relationship_fetcher.py

@@ -7,7 +7,7 @@ from typing import List, Dict, Any
 from json_repair import repair_json
 
 from src.common.logger import get_logger
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
@@ -73,14 +73,12 @@ class RelationshipFetcher:
 
         # LLM模型配置
         self.llm_model = LLMRequest(
-            model=global_config.model.utils_small,
+            model_set=model_config.model_task_config.utils_small, request_type="relation.fetcher"
-            request_type="relation.fetcher",
         )
 
         # 小模型用于即时信息提取
         self.instant_llm_model = LLMRequest(
-            model=global_config.model.utils_small,
+            model_set=model_config.model_task_config.utils_small, request_type="relation.fetch"
-            request_type="relation.fetch",
         )
 
         name = get_chat_manager().get_stream_name(self.chat_id)
@@ -96,7 +94,7 @@ class RelationshipFetcher:
             if not self.info_fetched_cache[person_id]:
                 del self.info_fetched_cache[person_id]
 
-    async def build_relation_info(self, person_id, points_num = 3):
+    async def build_relation_info(self, person_id, points_num=3):
         # 清理过期的信息缓存
         self._cleanup_expired_cache()
 
@@ -361,7 +359,6 @@ class RelationshipFetcher:
             logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}")
             logger.error(traceback.format_exc())
 
-
     async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
         # sourcery skip: use-next
         """将提取到的信息保存到 person_info 的 info_list 字段中

src/person_info/relationship_manager.py

@@ -3,7 +3,7 @@ from .person_info import PersonInfoManager, get_person_info_manager
 import time
 import random
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.chat.utils.chat_message_builder import build_readable_messages
 import json
 from json_repair import repair_json
@@ -20,9 +20,8 @@ logger = get_logger("relation")
 class RelationshipManager:
     def __init__(self):
         self.relationship_llm = LLMRequest(
-            model=global_config.model.utils,
+            model_set=model_config.model_task_config.utils, request_type="relationship"
-            request_type="relationship",  # 用于动作规划
+        )  # 用于动作规划
-        )
 
     @staticmethod
     async def is_known_some_one(platform, user_id):
@@ -181,18 +180,14 @@ class RelationshipManager:
         try:
             points = repair_json(points)
             points_data = json.loads(points)
-            
+
             # 只处理正确的格式，错误格式直接跳过
             if points_data == "none" or not points_data:
                 points_list = []
             elif isinstance(points_data, str) and points_data.lower() == "none":
                 points_list = []
             elif isinstance(points_data, list):
-                # 正确格式：数组格式 [{"point": "...", "weight": 10}, ...]
+                points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data]
-                if not points_data:  # 空数组
-                    points_list = []
-                else:
-                    points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data]
             else:
                 # 错误格式，直接跳过不解析
                 logger.warning(f"LLM返回了错误的JSON格式，跳过解析: {type(points_data)}, 内容: {points_data}")

src/plugin_system/__init__.py

@@ -9,6 +9,7 @@ from .base import (
     BasePlugin,
     BaseAction,
     BaseCommand,
+    BaseTool,
     ConfigField,
     ComponentType,
     ActionActivationType,
@@ -17,11 +18,13 @@ from .base import (
     ActionInfo,
     CommandInfo,
     PluginInfo,
+    ToolInfo,
     PythonDependency,
     BaseEventHandler,
     EventHandlerInfo,
     EventType,
     MaiMessages,
+    ToolParamType,
 )
 
 # 导入工具模块
@@ -34,6 +37,7 @@ from .utils import (
 
 from .apis import (
     chat_api,
+    tool_api,  
     component_manage_api,
     config_api,
     database_api,
@@ -44,17 +48,17 @@ from .apis import (
     person_api,
     plugin_manage_api,
     send_api,
-    utils_api,
     register_plugin,
     get_logger,
 )
 
 
-__version__ = "1.0.0"
+__version__ = "2.0.0"
 
 __all__ = [
     # API 模块
     "chat_api",
+    "tool_api",
     "component_manage_api",
     "config_api",
     "database_api",
@@ -65,13 +69,13 @@ __all__ = [
     "person_api",
     "plugin_manage_api",
     "send_api",
-    "utils_api",
     "register_plugin",
     "get_logger",
     # 基础类
     "BasePlugin",
     "BaseAction",
     "BaseCommand",
+    "BaseTool",
     "BaseEventHandler",
     # 类型定义
     "ComponentType",
@@ -81,9 +85,11 @@ __all__ = [
     "ActionInfo",
     "CommandInfo",
     "PluginInfo",
+    "ToolInfo",
     "PythonDependency",
     "EventHandlerInfo",
     "EventType",
+    "ToolParamType",
     # 消息
     "MaiMessages",
     # 装饰器

src/plugin_system/apis/__init__.py

@@ -17,7 +17,7 @@ from src.plugin_system.apis import (
     person_api,
     plugin_manage_api,
     send_api,
-    utils_api,
+    tool_api,
 )
 from .logging_api import get_logger
 from .plugin_register_api import register_plugin
@@ -35,7 +35,7 @@ __all__ = [
     "person_api",
     "plugin_manage_api",
     "send_api",
-    "utils_api",
     "get_logger",
     "register_plugin",
+    "tool_api",
 ]

src/plugin_system/apis/component_manage_api.py

@@ -5,6 +5,7 @@ from src.plugin_system.base.component_types import (
     EventHandlerInfo,
     PluginInfo,
     ComponentType,
+    ToolInfo,
 )
 
 
@@ -119,6 +120,21 @@ def get_registered_command_info(command_name: str) -> Optional[CommandInfo]:
     return component_registry.get_registered_command_info(command_name)
 
 
+def get_registered_tool_info(tool_name: str) -> Optional[ToolInfo]:
+    """
+    获取指定 Tool 的注册信息。
+
+    Args:
+        tool_name (str): Tool 名称。
+
+    Returns:
+        ToolInfo: Tool 信息对象，如果 Tool 不存在则返回 None。
+    """
+    from src.plugin_system.core.component_registry import component_registry
+
+    return component_registry.get_registered_tool_info(tool_name)
+
+
 # === EventHandler 特定查询方法 ===
 def get_registered_event_handler_info(
     event_handler_name: str,
@@ -191,6 +207,8 @@ def locally_enable_component(component_name: str, component_type: ComponentType,
             return global_announcement_manager.enable_specific_chat_action(stream_id, component_name)
         case ComponentType.COMMAND:
             return global_announcement_manager.enable_specific_chat_command(stream_id, component_name)
+        case ComponentType.TOOL:
+            return global_announcement_manager.enable_specific_chat_tool(stream_id, component_name)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.enable_specific_chat_event_handler(stream_id, component_name)
         case _:
@@ -216,11 +234,14 @@ def locally_disable_component(component_name: str, component_type: ComponentType
             return global_announcement_manager.disable_specific_chat_action(stream_id, component_name)
         case ComponentType.COMMAND:
             return global_announcement_manager.disable_specific_chat_command(stream_id, component_name)
+        case ComponentType.TOOL:
+            return global_announcement_manager.disable_specific_chat_tool(stream_id, component_name)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.disable_specific_chat_event_handler(stream_id, component_name)
         case _:
             raise ValueError(f"未知 component type: {component_type}")
 
+
 def get_locally_disabled_components(stream_id: str, component_type: ComponentType) -> list[str]:
     """
     获取指定消息流中禁用的组件列表。
@@ -239,7 +260,9 @@ def get_locally_disabled_components(stream_id: str, component_type: ComponentTyp
             return global_announcement_manager.get_disabled_chat_actions(stream_id)
         case ComponentType.COMMAND:
             return global_announcement_manager.get_disabled_chat_commands(stream_id)
+        case ComponentType.TOOL:
+            return global_announcement_manager.get_disabled_chat_tools(stream_id)
         case ComponentType.EVENT_HANDLER:
             return global_announcement_manager.get_disabled_chat_event_handlers(stream_id)
         case _:
-            raise ValueError(f"未知 component type: {component_type}")
+            raise ValueError(f"未知 component type: {component_type}")

src/plugin_system/apis/database_api.py

@@ -152,10 +152,7 @@ async def db_query(
 
     except DoesNotExist:
         # 记录不存在
-        if query_type == "get" and single_result:
+        return None if query_type == "get" and single_result else []
-            return None
-        return []
-
     except Exception as e:
         logger.error(f"[DatabaseAPI] 数据库操作出错: {e}")
         traceback.print_exc()
@@ -170,7 +167,8 @@ async def db_query(
 
 async def db_save(
     model_class: Type[Model], data: Dict[str, Any], key_field: Optional[str] = None, key_value: Optional[Any] = None
-) -> Union[Dict[str, Any], None]:
+) -> Optional[Dict[str, Any]]:
+    # sourcery skip: inline-immediately-returned-variable
     """保存数据到数据库（创建或更新）
 
     如果提供了key_field和key_value，会先尝试查找匹配的记录进行更新；
@@ -203,10 +201,9 @@ async def db_save(
     try:
         # 如果提供了key_field和key_value，尝试更新现有记录
         if key_field and key_value is not None:
-            # 查找现有记录
+            if existing_records := list(
-            existing_records = list(model_class.select().where(getattr(model_class, key_field) == key_value).limit(1))
+                model_class.select().where(getattr(model_class, key_field) == key_value).limit(1)
-
+            ):
-            if existing_records:
                 # 更新现有记录
                 existing_record = existing_records[0]
                 for field, value in data.items():
@@ -244,8 +241,8 @@ async def db_get(
     Args:
         model_class: Peewee模型类
         filters: 过滤条件，字段名和值的字典
-        order_by: 排序字段，前缀'-'表示降序，例如'-time'表示按时间字段（即time字段）降序
         limit: 结果数量限制
+        order_by: 排序字段，前缀'-'表示降序，例如'-time'表示按时间字段（即time字段）降序
         single_result: 是否只返回单个结果，如果为True，则返回单个记录字典或None；否则返回记录字典列表或空列表
 
     Returns:
@@ -310,7 +307,7 @@ async def store_action_info(
     thinking_id: str = "",
     action_data: Optional[dict] = None,
     action_name: str = "",
-) -> Union[Dict[str, Any], None]:
+) -> Optional[Dict[str, Any]]:
     """存储动作信息到数据库
 
     将Action执行的相关信息保存到ActionRecords表中，用于后续的记忆和上下文构建。

src/plugin_system/apis/emoji_api.py

@@ -65,14 +65,14 @@ async def get_by_description(description: str) -> Optional[Tuple[str, str, str]]
         return None
 
 
-async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str, str]]]:
+async def get_random(count: Optional[int] = 1) -> List[Tuple[str, str, str]]:
     """随机获取指定数量的表情包
 
     Args:
         count: 要获取的表情包数量，默认为1
 
     Returns:
-        Optional[List[Tuple[str, str, str]]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表，如果失败则为None
+        List[Tuple[str, str, str]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表，失败则返回空列表
 
     Raises:
         TypeError: 如果count不是整数类型
@@ -94,13 +94,13 @@ async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str,
 
         if not all_emojis:
             logger.warning("[EmojiAPI] 没有可用的表情包")
-            return None
+            return []
 
         # 过滤有效表情包
         valid_emojis = [emoji for emoji in all_emojis if not emoji.is_deleted]
         if not valid_emojis:
             logger.warning("[EmojiAPI] 没有有效的表情包")
-            return None
+            return []
 
         if len(valid_emojis) < count:
             logger.warning(
@@ -127,14 +127,14 @@ async def get_random(count: Optional[int] = 1) -> Optional[List[Tuple[str, str,
 
         if not results and count > 0:
             logger.warning("[EmojiAPI] 随机获取表情包失败，没有一个可以成功处理")
-            return None
+            return []
 
         logger.info(f"[EmojiAPI] 成功获取 {len(results)} 个随机表情包")
         return results
 
     except Exception as e:
         logger.error(f"[EmojiAPI] 获取随机表情包失败: {e}")
-        return None
+        return []
 
 
 async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
@@ -162,10 +162,11 @@ async def get_by_emotion(emotion: str) -> Optional[Tuple[str, str, str]]:
 
         # 筛选匹配情感的表情包
         matching_emojis = []
-        for emoji_obj in all_emojis:
+        matching_emojis.extend(
-            if not emoji_obj.is_deleted and emotion.lower() in [e.lower() for e in emoji_obj.emotion]:
+            emoji_obj
-                matching_emojis.append(emoji_obj)
+            for emoji_obj in all_emojis
-
+            if not emoji_obj.is_deleted and emotion.lower() in [e.lower() for e in emoji_obj.emotion]
+        )
         if not matching_emojis:
             logger.warning(f"[EmojiAPI] 未找到匹配情感 '{emotion}' 的表情包")
             return None
@@ -256,10 +257,11 @@ def get_descriptions() -> List[str]:
         emoji_manager = get_emoji_manager()
         descriptions = []
 
-        for emoji_obj in emoji_manager.emoji_objects:
+        descriptions.extend(
-            if not emoji_obj.is_deleted and emoji_obj.description:
+            emoji_obj.description
-                descriptions.append(emoji_obj.description)
+            for emoji_obj in emoji_manager.emoji_objects
-
+            if not emoji_obj.is_deleted and emoji_obj.description
+        )
         return descriptions
     except Exception as e:
         logger.error(f"[EmojiAPI] 获取表情包描述失败: {e}")

src/plugin_system/apis/generator_api.py

@@ -12,6 +12,7 @@ import traceback
 from typing import Tuple, Any, Dict, List, Optional
 from rich.traceback import install
 from src.common.logger import get_logger
+from src.config.api_ada_configs import TaskConfig
 from src.chat.replyer.default_generator import DefaultReplyer
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.utils.utils import process_llm_response
@@ -31,7 +32,7 @@ logger = get_logger("generator_api")
 def get_replyer(
     chat_stream: Optional[ChatStream] = None,
     chat_id: Optional[str] = None,
-    model_configs: Optional[List[Dict[str, Any]]] = None,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
     request_type: str = "replyer",
 ) -> Optional[DefaultReplyer]:
     """获取回复器对象
@@ -42,7 +43,7 @@ def get_replyer(
     Args:
         chat_stream: 聊天流对象（优先）
         chat_id: 聊天ID（实际上就是stream_id）
-        model_configs: 模型配置列表
+        model_set_with_weight: 模型配置列表，每个元素为 (TaskConfig, weight) 元组
         request_type: 请求类型
 
     Returns:
@@ -58,7 +59,7 @@ def get_replyer(
         return replyer_manager.get_replyer(
             chat_stream=chat_stream,
             chat_id=chat_id,
-            model_configs=model_configs,
+            model_set_with_weight=model_set_with_weight,
             request_type=request_type,
         )
     except Exception as e:
@@ -83,31 +84,36 @@ async def generate_reply(
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
     return_prompt: bool = False,
-    model_configs: Optional[List[Dict[str, Any]]] = None,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
-    request_type: str = "",
+    request_type: str = "generator_api",
-    enable_timeout: bool = False,
 ) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
     """生成回复
 
     Args:
         chat_stream: 聊天流对象（优先）
         chat_id: 聊天ID（备用）
-        action_data: 动作数据
+        action_data: 动作数据（向下兼容，包含reply_to和extra_info）
+        reply_to: 回复对象，格式为 "发送者:消息内容"
+        extra_info: 额外信息，用于补充上下文
+        available_actions: 可用动作
+        enable_tool: 是否启用工具调用
         enable_splitter: 是否启用消息分割器
         enable_chinese_typo: 是否启用错字生成器
         return_prompt: 是否返回提示词
+        model_set_with_weight: 模型配置列表，每个元素为 (TaskConfig, weight) 元组
+        request_type: 请求类型（可选，记录LLM使用）
     Returns:
         Tuple[bool, List[Tuple[str, Any]], Optional[str]]: (是否成功, 回复集合, 提示词)
     """
     try:
         # 获取回复器
-        replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs, request_type=request_type)
+        replyer = get_replyer(chat_stream, chat_id, model_set_with_weight=model_set_with_weight, request_type=request_type)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
             return False, [], None
 
         logger.debug("[GeneratorAPI] 开始生成回复")
-        
+
         if not reply_to and action_data:
             reply_to = action_data.get("reply_to", "")
         if not extra_info and action_data:
@@ -118,7 +124,6 @@ async def generate_reply(
             reply_to=reply_to,
             extra_info=extra_info,
             available_actions=available_actions,
-            enable_timeout=enable_timeout,
             enable_tool=enable_tool,
         )
         reply_set = []
@@ -150,33 +155,35 @@ async def rewrite_reply(
     chat_id: Optional[str] = None,
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
-    model_configs: Optional[List[Dict[str, Any]]] = None,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
     raw_reply: str = "",
     reason: str = "",
     reply_to: str = "",
-) -> Tuple[bool, List[Tuple[str, Any]]]:
+    return_prompt: bool = False,
+) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
     """重写回复
 
     Args:
         chat_stream: 聊天流对象（优先）
-        reply_data: 回复数据字典（备用，当其他参数缺失时从此获取）
+        reply_data: 回复数据字典（向下兼容备用，当其他参数缺失时从此获取）
         chat_id: 聊天ID（备用）
         enable_splitter: 是否启用消息分割器
         enable_chinese_typo: 是否启用错字生成器
-        model_configs: 模型配置列表
+        model_set_with_weight: 模型配置列表，每个元素为 (TaskConfig, weight) 元组
         raw_reply: 原始回复内容
         reason: 回复原因
         reply_to: 回复对象
+        return_prompt: 是否返回提示词
 
     Returns:
         Tuple[bool, List[Tuple[str, Any]]]: (是否成功, 回复集合)
     """
     try:
         # 获取回复器
-        replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs)
+        replyer = get_replyer(chat_stream, chat_id, model_set_with_weight=model_set_with_weight)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
-            return False, []
+            return False, [], None
 
         logger.info("[GeneratorAPI] 开始重写回复")
 
@@ -187,10 +194,11 @@ async def rewrite_reply(
             reply_to = reply_to or reply_data.get("reply_to", "")
 
         # 调用回复器重写回复
-        success, content = await replyer.rewrite_reply_with_context(
+        success, content, prompt = await replyer.rewrite_reply_with_context(
             raw_reply=raw_reply,
             reason=reason,
             reply_to=reply_to,
+            return_prompt=return_prompt,
         )
         reply_set = []
         if content:
@@ -201,14 +209,14 @@ async def rewrite_reply(
         else:
             logger.warning("[GeneratorAPI] 重写回复失败")
 
-        return success, reply_set
+        return success, reply_set, prompt if return_prompt else None
 
     except ValueError as ve:
         raise ve
 
     except Exception as e:
         logger.error(f"[GeneratorAPI] 重写回复时出错: {e}")
-        return False, []
+        return False, [], None
 
 
 async def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]:
@@ -234,3 +242,27 @@ async def process_human_text(content: str, enable_splitter: bool, enable_chinese
     except Exception as e:
         logger.error(f"[GeneratorAPI] 处理人形文本时出错: {e}")
         return []
+
+async def generate_response_custom(
+    chat_stream: Optional[ChatStream] = None,
+    chat_id: Optional[str] = None,
+    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+    prompt: str = "",
+) -> Optional[str]:
+    replyer = get_replyer(chat_stream, chat_id, model_set_with_weight=model_set_with_weight)
+    if not replyer:
+        logger.error("[GeneratorAPI] 无法获取回复器")
+        return None
+
+    try:
+        logger.debug("[GeneratorAPI] 开始生成自定义回复")
+        response, _, _, _ = await replyer.llm_generate_content(prompt)
+        if response:
+            logger.debug("[GeneratorAPI] 自定义回复生成成功")
+            return response
+        else:
+            logger.warning("[GeneratorAPI] 自定义回复生成失败")
+            return None
+    except Exception as e:
+        logger.error(f"[GeneratorAPI] 生成自定义回复时出错: {e}")
+        return None

src/plugin_system/apis/llm_api.py

@@ -7,10 +7,12 @@
     success, response, reasoning, model_name = await llm_api.generate_with_model(prompt, model_config)
 """
 
-from typing import Tuple, Dict, Any
+from typing import Tuple, Dict, List, Any, Optional
 from src.common.logger import get_logger
+from src.llm_models.payload_content.tool_option import ToolCall
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
+from src.config.config import global_config, model_config
+from src.config.api_ada_configs import TaskConfig
 
 logger = get_logger("llm_api")
 
@@ -19,9 +21,7 @@ logger = get_logger("llm_api")
 # =============================================================================
 
 
-
+def get_available_models() -> Dict[str, TaskConfig]:
-
-def get_available_models() -> Dict[str, Any]:
     """获取所有可用的模型配置
 
     Returns:
@@ -33,14 +33,14 @@ def get_available_models() -> Dict[str, Any]:
             return {}
 
         # 自动获取所有属性并转换为字典形式
-        rets = {}
+        models = model_config.model_task_config
-        models = global_config.model
         attrs = dir(models)
+        rets: Dict[str, TaskConfig] = {}
         for attr in attrs:
             if not attr.startswith("__"):
                 try:
                     value = getattr(models, attr)
-                    if not callable(value):  # 排除方法
+                    if not callable(value) and isinstance(value, TaskConfig):
                         rets[attr] = value
                 except Exception as e:
                     logger.debug(f"[LLMAPI] 获取属性 {attr} 失败: {e}")
@@ -53,7 +53,11 @@ def get_available_models() -> Dict[str, Any]:
 
 
 async def generate_with_model(
-    prompt: str, model_config: Dict[str, Any], request_type: str = "plugin.generate", **kwargs
+    prompt: str,
+    model_config: TaskConfig,
+    request_type: str = "plugin.generate",
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
 ) -> Tuple[bool, str, str, str]:
     """使用指定模型生成内容
 
@@ -61,22 +65,62 @@ async def generate_with_model(
         prompt: 提示词
         model_config: 模型配置（从 get_available_models 获取的模型配置）
         request_type: 请求类型标识
-        **kwargs: 其他模型特定参数，如temperature、max_tokens等
 
     Returns:
         Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
     """
     try:
-        model_name = model_config.get("name")
+        model_name_list = model_config.model_list
-        logger.info(f"[LLMAPI] 使用模型 {model_name} 生成内容")
+        logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容")
         logger.debug(f"[LLMAPI] 完整提示词: {prompt}")
 
-        llm_request = LLMRequest(model=model_config, request_type=request_type, **kwargs)
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
 
-        response, (reasoning, model_name) = await llm_request.generate_response_async(prompt)
+        response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(prompt, temperature=temperature, max_tokens=max_tokens)
-        return True, response, reasoning, model_name
+        return True, response, reasoning_content, model_name
 
     except Exception as e:
         error_msg = f"生成内容时出错: {str(e)}"
         logger.error(f"[LLMAPI] {error_msg}")
         return False, error_msg, "", ""
+
+async def generate_with_model_with_tools(
+    prompt: str,
+    model_config: TaskConfig,
+    tool_options: List[Dict[str, Any]] | None = None,
+    request_type: str = "plugin.generate",
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
+) -> Tuple[bool, str, str, str, List[ToolCall] | None]:
+    """使用指定模型和工具生成内容
+
+    Args:
+        prompt: 提示词
+        model_config: 模型配置（从 get_available_models 获取的模型配置）
+        tool_options: 工具选项列表
+        request_type: 请求类型标识
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Returns:
+        Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
+    """
+    try:
+        model_name_list = model_config.model_list
+        logger.info(f"[LLMAPI] 使用模型集合 {model_name_list} 生成内容")
+        logger.debug(f"[LLMAPI] 完整提示词: {prompt}")
+
+        llm_request = LLMRequest(model_set=model_config, request_type=request_type)
+
+        response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_async(
+            prompt,
+            tools=tool_options,
+            temperature=temperature,
+            max_tokens=max_tokens
+        )
+        return True, response, reasoning_content, model_name, tool_call
+
+    except Exception as e:
+        error_msg = f"生成内容时出错: {str(e)}"
+        logger.error(f"[LLMAPI] {error_msg}")
+        return False, error_msg, "", "", None

src/plugin_system/apis/message_api.py

@@ -207,7 +207,7 @@ def get_random_chat_messages(
 
 
 def get_messages_by_time_for_users(
-    start_time: float, end_time: float, person_ids: list, limit: int = 0, limit_mode: str = "latest"
+    start_time: float, end_time: float, person_ids: List[str], limit: int = 0, limit_mode: str = "latest"
 ) -> List[Dict[str, Any]]:
     """
     获取指定用户在所有聊天中指定时间范围内的消息
@@ -287,7 +287,7 @@ def get_messages_before_time_in_chat(
     return get_raw_msg_before_timestamp_with_chat(chat_id, timestamp, limit)
 
 
-def get_messages_before_time_for_users(timestamp: float, person_ids: list, limit: int = 0) -> List[Dict[str, Any]]:
+def get_messages_before_time_for_users(timestamp: float, person_ids: List[str], limit: int = 0) -> List[Dict[str, Any]]:
     """
     获取指定用户在指定时间戳之前的消息
 
@@ -372,7 +372,7 @@ def count_new_messages(chat_id: str, start_time: float = 0.0, end_time: Optional
     return num_new_messages_since(chat_id, start_time, end_time)
 
 
-def count_new_messages_for_users(chat_id: str, start_time: float, end_time: float, person_ids: list) -> int:
+def count_new_messages_for_users(chat_id: str, start_time: float, end_time: float, person_ids: List[str]) -> int:
     """
     计算指定聊天中指定用户从开始时间到结束时间的新消息数量
 

src/plugin_system/apis/plugin_manage_api.py

@@ -1,10 +1,12 @@
 from typing import Tuple, List
+
+
 def list_loaded_plugins() -> List[str]:
     """
     列出所有当前加载的插件。
 
     Returns:
-        list: 当前加载的插件名称列表。
+        List[str]: 当前加载的插件名称列表。
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
@@ -16,17 +18,38 @@ def list_registered_plugins() -> List[str]:
     列出所有已注册的插件。
 
     Returns:
-        list: 已注册的插件名称列表。
+        List[str]: 已注册的插件名称列表。
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
     return plugin_manager.list_registered_plugins()
 
 
+def get_plugin_path(plugin_name: str) -> str:
+    """
+    获取指定插件的路径。
+
+    Args:
+        plugin_name (str): 插件名称。
+
+    Returns:
+        str: 插件目录的绝对路径。
+        
+    Raises:
+        ValueError: 如果插件不存在。
+    """
+    from src.plugin_system.core.plugin_manager import plugin_manager
+
+    if plugin_path := plugin_manager.get_plugin_path(plugin_name):
+        return plugin_path
+    else:
+        raise ValueError(f"插件 '{plugin_name}' 不存在。")
+
+
 async def remove_plugin(plugin_name: str) -> bool:
     """
     卸载指定的插件。
-    
+
     **此函数是异步的，确保在异步环境中调用。**
 
     Args:
@@ -43,7 +66,7 @@ async def remove_plugin(plugin_name: str) -> bool:
 async def reload_plugin(plugin_name: str) -> bool:
     """
     重新加载指定的插件。
-    
+
     **此函数是异步的，确保在异步环境中调用。**
 
     Args:
@@ -71,6 +94,7 @@ def load_plugin(plugin_name: str) -> Tuple[bool, int]:
 
     return plugin_manager.load_registered_plugin_classes(plugin_name)
 
+
 def add_plugin_directory(plugin_directory: str) -> bool:
     """
     添加插件目录。
@@ -84,6 +108,7 @@ def add_plugin_directory(plugin_directory: str) -> bool:
 
     return plugin_manager.add_plugin_directory(plugin_directory)
 
+
 def rescan_plugin_directory() -> Tuple[int, int]:
     """
     重新扫描插件目录，加载新插件。
@@ -92,4 +117,4 @@ def rescan_plugin_directory() -> Tuple[int, int]:
     """
     from src.plugin_system.core.plugin_manager import plugin_manager
 
-    return plugin_manager.rescan_plugin_directory()
+    return plugin_manager.rescan_plugin_directory()

src/plugin_system/apis/send_api.py

@@ -49,7 +49,7 @@ async def _send_to_target(
     display_message: str = "",
     typing: bool = False,
     reply_to: str = "",
-    reply_to_platform_id: str = "",
+    reply_to_platform_id: Optional[str] = None,
     storage_message: bool = True,
     show_log: bool = True,
 ) -> bool:
@@ -60,8 +60,11 @@ async def _send_to_target(
         content: 消息内容
         stream_id: 目标流ID
         display_message: 显示消息
-        typing: 是否显示正在输入
+        typing: 是否模拟打字等待。
-        reply_to: 回复消息的格式，如"发送者:消息内容"
+        reply_to: 回复消息，格式为"发送者:消息内容"
+        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
+        storage_message: 是否存储消息到数据库
+        show_log: 发送是否显示日志
 
     Returns:
         bool: 是否发送成功
@@ -97,6 +100,10 @@ async def _send_to_target(
         anchor_message = None
         if reply_to:
             anchor_message = await _find_reply_message(target_stream, reply_to)
+            if anchor_message and anchor_message.message_info.user_info and not reply_to_platform_id:
+                reply_to_platform_id = (
+                    f"{anchor_message.message_info.platform}:{anchor_message.message_info.user_info.user_id}"
+                )
 
         # 构建发送消息对象
         bot_message = MessageSending(
@@ -262,12 +269,22 @@ async def text_to_stream(
         stream_id: 聊天流ID
         typing: 是否显示正在输入
         reply_to: 回复消息，格式为"发送者:消息内容"
+        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
         storage_message: 是否存储消息到数据库
 
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, reply_to_platform_id, storage_message)
+    return await _send_to_target(
+        "text",
+        text,
+        stream_id,
+        "",
+        typing,
+        reply_to,
+        reply_to_platform_id=reply_to_platform_id,
+        storage_message=storage_message,
+    )
 
 
 async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True) -> bool:
@@ -318,7 +335,7 @@ async def command_to_stream(
 
 async def custom_to_stream(
     message_type: str,
-    content: str,
+    content: str | dict,
     stream_id: str,
     display_message: str = "",
     typing: bool = False,
@@ -350,249 +367,3 @@ async def custom_to_stream(
         storage_message=storage_message,
         show_log=show_log,
     )
-
-
-async def text_to_group(
-    text: str,
-    group_id: str,
-    platform: str = "qq",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向群聊发送文本消息
-
-    Args:
-        text: 要发送的文本内容
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, storage_message=storage_message)
-
-
-async def text_to_user(
-    text: str,
-    user_id: str,
-    platform: str = "qq",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向用户发送私聊文本消息
-
-    Args:
-        text: 要发送的文本内容
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("text", text, stream_id, "", typing, reply_to, storage_message=storage_message)
-
-
-async def emoji_to_group(emoji_base64: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送表情包
-
-    Args:
-        emoji_base64: 表情包的base64编码
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def emoji_to_user(emoji_base64: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送表情包
-
-    Args:
-        emoji_base64: 表情包的base64编码
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def image_to_group(image_base64: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送图片
-
-    Args:
-        image_base64: 图片的base64编码
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def image_to_user(image_base64: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送图片
-
-    Args:
-        image_base64: 图片的base64编码
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False)
-
-
-async def command_to_group(command: str, group_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向群聊发送命令
-
-    Args:
-        command: 命令
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target("command", command, stream_id, "", typing=False, storage_message=storage_message)
-
-
-async def command_to_user(command: str, user_id: str, platform: str = "qq", storage_message: bool = True) -> bool:
-    """向用户发送命令
-
-    Args:
-        command: 命令
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target("command", command, stream_id, "", typing=False, storage_message=storage_message)
-
-
-# =============================================================================
-# 通用发送函数 - 支持任意消息类型
-# =============================================================================
-
-
-async def custom_to_group(
-    message_type: str,
-    content: str,
-    group_id: str,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向群聊发送自定义类型消息
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"等
-        content: 消息内容（通常是base64编码或文本）
-        group_id: 群聊ID
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, group_id, True)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )
-
-
-async def custom_to_user(
-    message_type: str,
-    content: str,
-    user_id: str,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """向用户发送自定义类型消息
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"等
-        content: 消息内容（通常是base64编码或文本）
-        user_id: 用户ID
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, user_id, False)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )
-
-
-async def custom_message(
-    message_type: str,
-    content: str,
-    target_id: str,
-    is_group: bool = True,
-    platform: str = "qq",
-    display_message: str = "",
-    typing: bool = False,
-    reply_to: str = "",
-    storage_message: bool = True,
-) -> bool:
-    """发送自定义消息的通用接口
-
-    Args:
-        message_type: 消息类型，如"text"、"image"、"emoji"、"video"、"file"、"audio"等
-        content: 消息内容
-        target_id: 目标ID（群ID或用户ID）
-        is_group: 是否为群聊，True为群聊，False为私聊
-        platform: 平台，默认为"qq"
-        display_message: 显示消息
-        typing: 是否显示正在输入
-        reply_to: 回复消息，格式为"发送者:消息内容"
-
-    Returns:
-        bool: 是否发送成功
-
-    示例:
-        # 发送视频到群聊
-        await send_api.custom_message("video", video_base64, "123456", True)
-
-        # 发送文件到用户
-        await send_api.custom_message("file", file_base64, "987654", False)
-
-        # 发送音频到群聊并回复特定消息
-        await send_api.custom_message("audio", audio_base64, "123456", True, reply_to="张三:你好")
-    """
-    stream_id = get_chat_manager().get_stream_id(platform, target_id, is_group)
-    return await _send_to_target(
-        message_type, content, stream_id, display_message, typing, reply_to, storage_message=storage_message
-    )

src/plugin_system/apis/tool_api.py

@@ -0,0 +1,27 @@
+from typing import Optional, Type
+from src.plugin_system.base.base_tool import BaseTool
+from src.plugin_system.base.component_types import ComponentType
+
+from src.common.logger import get_logger
+
+logger = get_logger("tool_api")
+
+
+def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
+    """获取公开工具实例"""
+    from src.plugin_system.core import component_registry
+
+    tool_class: Type[BaseTool] = component_registry.get_component_class(tool_name, ComponentType.TOOL)  # type: ignore
+    return tool_class() if tool_class else None
+
+
+def get_llm_available_tool_definitions():
+    """获取LLM可用的工具定义列表
+    
+    Returns:
+        List[Tuple[str, Dict[str, Any]]]: 工具定义列表，为[("tool_name", 定义)]
+    """
+    from src.plugin_system.core import component_registry
+
+    llm_available_tools = component_registry.get_llm_available_tools()
+    return [(name, tool_class.get_tool_definition()) for name, tool_class in llm_available_tools.items()]

src/plugin_system/apis/utils_api.py

@@ -1,168 +0,0 @@
-"""工具类API模块
-
-提供了各种辅助功能
-使用方式：
-    from src.plugin_system.apis import utils_api
-    plugin_path = utils_api.get_plugin_path()
-    data = utils_api.read_json_file("data.json")
-    timestamp = utils_api.get_timestamp()
-"""
-
-import os
-import json
-import time
-import inspect
-import datetime
-import uuid
-from typing import Any, Optional
-from src.common.logger import get_logger
-
-logger = get_logger("utils_api")
-
-
-# =============================================================================
-# 文件操作API函数
-# =============================================================================
-
-
-def get_plugin_path(caller_frame=None) -> str:
-    """获取调用者插件的路径
-
-    Args:
-        caller_frame: 调用者的栈帧，默认为None（自动获取）
-
-    Returns:
-        str: 插件目录的绝对路径
-    """
-    try:
-        if caller_frame is None:
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-
-        plugin_module_path = inspect.getfile(caller_frame)  # type: ignore
-        plugin_dir = os.path.dirname(plugin_module_path)
-        return plugin_dir
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 获取插件路径失败: {e}")
-        return ""
-
-
-def read_json_file(file_path: str, default: Any = None) -> Any:
-    """读取JSON文件
-
-    Args:
-        file_path: 文件路径，可以是相对于插件目录的路径
-        default: 如果文件不存在或读取失败时返回的默认值
-
-    Returns:
-        Any: JSON数据或默认值
-    """
-    try:
-        # 如果是相对路径，则相对于调用者的插件目录
-        if not os.path.isabs(file_path):
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-            plugin_dir = get_plugin_path(caller_frame)
-            file_path = os.path.join(plugin_dir, file_path)
-
-        if not os.path.exists(file_path):
-            logger.warning(f"[UtilsAPI] 文件不存在: {file_path}")
-            return default
-
-        with open(file_path, "r", encoding="utf-8") as f:
-            return json.load(f)
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 读取JSON文件出错: {e}")
-        return default
-
-
-def write_json_file(file_path: str, data: Any, indent: int = 2) -> bool:
-    """写入JSON文件
-
-    Args:
-        file_path: 文件路径，可以是相对于插件目录的路径
-        data: 要写入的数据
-        indent: JSON缩进
-
-    Returns:
-        bool: 是否写入成功
-    """
-    try:
-        # 如果是相对路径，则相对于调用者的插件目录
-        if not os.path.isabs(file_path):
-            caller_frame = inspect.currentframe().f_back  # type: ignore
-            plugin_dir = get_plugin_path(caller_frame)
-            file_path = os.path.join(plugin_dir, file_path)
-
-        # 确保目录存在
-        os.makedirs(os.path.dirname(file_path), exist_ok=True)
-
-        with open(file_path, "w", encoding="utf-8") as f:
-            json.dump(data, f, ensure_ascii=False, indent=indent)
-        return True
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 写入JSON文件出错: {e}")
-        return False
-
-
-# =============================================================================
-# 时间相关API函数
-# =============================================================================
-
-
-def get_timestamp() -> int:
-    """获取当前时间戳
-
-    Returns:
-        int: 当前时间戳（秒）
-    """
-    return int(time.time())
-
-
-def format_time(timestamp: Optional[int | float] = None, format_str: str = "%Y-%m-%d %H:%M:%S") -> str:
-    """格式化时间
-
-    Args:
-        timestamp: 时间戳，如果为None则使用当前时间
-        format_str: 时间格式字符串
-
-    Returns:
-        str: 格式化后的时间字符串
-    """
-    try:
-        if timestamp is None:
-            timestamp = time.time()
-        return datetime.datetime.fromtimestamp(timestamp).strftime(format_str)
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 格式化时间失败: {e}")
-        return ""
-
-
-def parse_time(time_str: str, format_str: str = "%Y-%m-%d %H:%M:%S") -> int:
-    """解析时间字符串为时间戳
-
-    Args:
-        time_str: 时间字符串
-        format_str: 时间格式字符串
-
-    Returns:
-        int: 时间戳（秒）
-    """
-    try:
-        dt = datetime.datetime.strptime(time_str, format_str)
-        return int(dt.timestamp())
-    except Exception as e:
-        logger.error(f"[UtilsAPI] 解析时间失败: {e}")
-        return 0
-
-
-# =============================================================================
-# 其他工具函数
-# =============================================================================
-
-
-def generate_unique_id() -> str:
-    """生成唯一ID
-
-    Returns:
-        str: 唯一ID
-    """
-    return str(uuid.uuid4())

src/plugin_system/base/__init__.py

@@ -6,6 +6,7 @@
 
 from .base_plugin import BasePlugin
 from .base_action import BaseAction
+from .base_tool import BaseTool
 from .base_command import BaseCommand
 from .base_events_handler import BaseEventHandler
 from .component_types import (
@@ -15,11 +16,13 @@ from .component_types import (
     ComponentInfo,
     ActionInfo,
     CommandInfo,
+    ToolInfo,
     PluginInfo,
     PythonDependency,
     EventHandlerInfo,
     EventType,
     MaiMessages,
+    ToolParamType,
 )
 from .config_types import ConfigField
 
@@ -27,12 +30,14 @@ __all__ = [
     "BasePlugin",
     "BaseAction",
     "BaseCommand",
+    "BaseTool",
     "ComponentType",
     "ActionActivationType",
     "ChatMode",
     "ComponentInfo",
     "ActionInfo",
     "CommandInfo",
+    "ToolInfo",
     "PluginInfo",
     "PythonDependency",
     "ConfigField",
@@ -40,4 +45,5 @@ __all__ = [
     "EventType",
     "BaseEventHandler",
     "MaiMessages",
+    "ToolParamType",
 ]