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* ♻️ refactor(pydantic): 提取 Pydantic 兼容函数到独立模块 * ♻️ refactor!(llm): 重构LLM服务，引入现代化工具和执行器架构 🏗️ **架构变更** - 引入ToolProvider/ToolExecutable协议，取代ToolRegistry - 新增LLMToolExecutor，分离工具调用逻辑 - 新增BaseMemory抽象，解耦会话状态管理 🔄 **API重构** - 移除：analyze, analyze_multimodal, pipeline_chat - 新增：generate_structured, run_with_tools - 重构：chat, search, code变为无状态调用 🛠️ **工具系统** - 新增@function_tool装饰器 - 统一工具定义到ToolExecutable协议 - 移除MCP工具系统和mcp_tools.json --------- Co-authored-by: webjoin111 <455457521@qq.com>
77 lines
1.8 KiB
Python
77 lines
1.8 KiB
Python
"""
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Zhenxun Bot - 核心服务模块
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主要服务包括：
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- 数据库上下文 (db_context): 提供数据库模型基类和连接管理。
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- 日志服务 (log): 提供增强的、带上下文的日志记录器。
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- LLM服务 (llm): 提供与大语言模型交互的统一API。
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- 插件生命周期管理 (plugin_init): 支持插件安装和卸载时的钩子函数。
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- 定时任务调度器 (scheduler): 提供持久化的、可管理的定时任务服务。
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"""
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from nonebot import require
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require("nonebot_plugin_apscheduler")
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require("nonebot_plugin_alconna")
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require("nonebot_plugin_session")
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require("nonebot_plugin_htmlrender")
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require("nonebot_plugin_uninfo")
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require("nonebot_plugin_waiter")
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from .db_context import Model, disconnect, with_db_timeout
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from .llm import (
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AI,
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AIConfig,
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CommonOverrides,
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LLMContentPart,
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LLMException,
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LLMGenerationConfig,
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LLMMessage,
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chat,
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clear_model_cache,
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code,
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create_multimodal_message,
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embed,
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generate,
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generate_structured,
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get_cache_stats,
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get_model_instance,
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list_available_models,
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list_embedding_models,
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search,
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set_global_default_model_name,
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)
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from .log import logger
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from .plugin_init import PluginInit, PluginInitManager
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from .scheduler import scheduler_manager
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__all__ = [
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"AI",
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"AIConfig",
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"CommonOverrides",
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"LLMContentPart",
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"LLMException",
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"LLMGenerationConfig",
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"LLMMessage",
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"Model",
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"PluginInit",
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"PluginInitManager",
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"chat",
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"clear_model_cache",
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"code",
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"create_multimodal_message",
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"disconnect",
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"embed",
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"generate",
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"generate_structured",
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"get_cache_stats",
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"get_model_instance",
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"list_available_models",
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"list_embedding_models",
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"logger",
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"scheduler_manager",
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"search",
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"set_global_default_model_name",
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"with_db_timeout",
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]
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