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♻️ refactor(core): 重构 AI 能力与定时任务调度系统 (#2148)
* ♻️ refactor(core): 重构 AI 能力与定时任务调度系统 - 【AI 能力与工具】重构 Capability 注册与管理机制，引入 CapabilityManager 统一管理 - 移除全局能力注册表，改用声明式装饰器 `@capability` 进行解耦注册 - 重构工具解析器链，使用统一的 BaseToolResolver 代替原有的多个特定解析器 - 增强工具查询过滤，支持通配符匹配、工具箱过滤和排除标签 - 【定时任务调度】重构定时任务管理器，引入 SchedulerRegistry 统一管理任务元数据 - 引入 JobConfig 聚合定时任务配置，支持用户维度的定时任务调度 - 重构执行分发器，支持并发限制、串行间隔和随机延迟打散 - 【运行上下文】引入 ScheduledDeps 以支持后台和定时任务环境下的依赖注入 - 优化 RunContext，支持从定时任务上下文快速构造，并提供 emit 辅助方法 - 【日志与监控】引入 AILoggerProxy，实现 AI 各模块的专属日志输出 - 将各模块的全局 logger 替换为对应的模块专属日志代理 - 【其他优化】修复 Pydantic V1 兼容层中 model_validator 的装饰器兼容性问题 - 在非交互式环境（如定时任务）中自动隐藏 HITL 交互工具以节省 Token * ♻️ refactor(core): 优化内部导入路径并提升 Pydantic 兼容性 - 【重构】将 `services/ai` 模块内的绝对导入重构为相对导入，优化包结构 - 【重构】移除不必要的 `if TYPE_CHECKING` 保护，通过 `from __future__ import annotations` 直接导入类型 - 【清理】清理 `core/messages/types.py` 中未使用的 `AssistantContentUnion` 等联合类型定义 - 【优化】在 `utils/pydantic_compat.py` 中新增 `model_rebuild` 兼容函数，统一 Pydantic V1/V2 的模型重建逻辑 - 【优化】将部分函数内部的延迟导入提升至模块顶部，规范代码结构 * ♻️ refactor(imports): 优化导入路径为相对导入并清理冗余导入 - 【重构】将 AI 服务相关模块中的绝对导入路径修改为相对导入，提升模块内聚性与可移植性 - 【清理】移除多处函数内部或类方法中未使用的冗余导入，避免循环引用和资源浪费 - 【格式化】微调部分工具装饰器和返回语句的格式与尾随逗号 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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webjoin111
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parent
0b32d69c9c
commit
922d092650
@@ -6,9 +6,7 @@ from typing import Any, cast
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from nonebot.utils import is_coroutine_callable
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from zhenxun.services.ai.capabilities import (
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AbstractCapability,
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CombinedCapability,
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DynamicCapability,
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)
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from zhenxun.services.ai.context.memory.builder import MemoryBuilder
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from zhenxun.services.ai.context.memory.engine import MemoryReader, MemoryWriter
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@@ -22,13 +20,13 @@ from zhenxun.services.ai.flow.agent.capabilities import (
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TaskTrackingCapability,
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)
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from zhenxun.services.ai.flow.agent.models import Persona
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from zhenxun.services.ai.run import GLOBAL_CAPABILITIES, RunContext
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from zhenxun.services.ai.run import RunContext
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from zhenxun.services.ai.run.di import DependencyInjector
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from zhenxun.services.ai.tools.engine.registry import (
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ToolCollection,
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tool_provider_manager,
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)
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from zhenxun.services.ai.tools.models import GlobalToolFilter, ResolvedToolPayload
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from zhenxun.services.ai.tools.models import ResolvedToolPayload
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from zhenxun.services.ai.utils.scope import ScopeSelector
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from zhenxun.utils.pydantic_compat import model_copy
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@@ -135,22 +133,21 @@ class CapabilityBuilder:
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if task_obj:
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dynamic_caps.append(TaskTrackingCapability(task_obj, agent_name))
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run_level_caps = []
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if profile_capabilities:
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for cap in profile_capabilities:
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if isinstance(cap, AbstractCapability):
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run_level_caps.append(cap)
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elif callable(cap):
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run_level_caps.append(DynamicCapability(cap))
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from zhenxun.services.ai.capabilities.manager import capability_manager
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base_caps = GLOBAL_CAPABILITIES.get("global", []).copy()
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if namespace != "global" and namespace in GLOBAL_CAPABILITIES:
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base_caps.extend(GLOBAL_CAPABILITIES[namespace])
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run_level_caps = capability_manager.resolve_capabilities(
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profile_capabilities or [], namespace
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)
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agent_level_caps = capability_manager.resolve_capabilities(
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agent_capabilities or [], namespace
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)
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auto_caps = capability_manager.get_auto_apply_capabilities(namespace)
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combined_cap = CombinedCapability(
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base_caps
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auto_caps
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+ getattr(context, "capabilities", [])
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+ agent_capabilities
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+ agent_level_caps
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+ run_level_caps
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+ dynamic_caps
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)
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@@ -298,7 +295,6 @@ class ToolBuilder:
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toolset_funcs: list[Any],
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system_tools: list[Any],
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namespace: str,
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tool_filter: GlobalToolFilter | None,
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run_context: RunContext,
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run_scoped_cap: CombinedCapability,
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) -> ResolvedToolPayload:
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@@ -309,9 +305,8 @@ class ToolBuilder:
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参数：
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tool_definitions: 静态工具或工具集合的定义列表。
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toolset_funcs: 待依赖注入解析的工具集生成函数列表。
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system_tools: 系统默认强制集成的工具定义列表。
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system_tools: 系统默认强制集成的工具定义列表.
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namespace: 会话命名空间。
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tool_filter: 全局的工具过滤条件，此处为占位。
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run_context: 运行上下文对象实例.
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run_scoped_cap: 运行域下的合并能力中间件，用于提供特定的能力工具。
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@@ -411,14 +406,14 @@ class ToolBuilder:
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current_tool_defs = list(_cap_res)
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final_defs_map = {d.name.lower(): d for d in current_tool_defs if d}
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final_effective_tools = ToolCollection()
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final_effective_tools_list = []
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for t_exec in effective_tools:
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t_name = getattr(t_exec, "name", "unknown")
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if t_name.lower() in final_defs_map:
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cloned_tool = copy.copy(t_exec)
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cloned_tool._dynamic_def = final_defs_map[t_name.lower()]
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final_effective_tools.append(cloned_tool)
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return final_effective_tools
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final_effective_tools_list.append(cloned_tool)
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return ToolCollection(final_effective_tools_list)
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class SessionBuilder:
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@@ -4,7 +4,7 @@ from collections.abc import Awaitable, Callable
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from zhenxun.services.ai.flow.agent.models import AgentRunResources, AgentState
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from zhenxun.services.ai.run.models import AgentRunResult, HandoffPayload
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from zhenxun.services.ai.tools.models import ToolResult
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_agent as logger
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from zhenxun.utils.pydantic_compat import model_construct
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from zhenxun.utils.utils import infer_plugin_namespace
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@@ -63,7 +63,7 @@ async def handle_submit_structured(
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parsed_obj = (
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tool_res.directive.payload.get("parsed_obj") if tool_res.directive else None
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)
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logger.info("✅ 拦截到结构化结果提交，结束循环。")
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logger.debug("✅ 拦截到结构化结果提交，结束循环。")
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state.is_finished = True
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state.final_result = model_construct(
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AgentRunResult,
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@@ -1,7 +1,7 @@
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from abc import ABC, abstractmethod
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import asyncio
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import json
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from typing import Any, cast
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from typing import Any
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from zhenxun.services.ai.capabilities import CombinedCapability
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from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
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@@ -15,7 +15,6 @@ from zhenxun.services.ai.core.exceptions import (
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)
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from zhenxun.services.ai.core.messages import (
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AgentMessage,
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AssistantContentUnion,
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AssistantMessage,
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AudioPart,
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ChatRequest,
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@@ -35,19 +34,20 @@ from zhenxun.services.ai.core.stream_events import (
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LLMStartEvent,
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ToolStreamChunkEvent,
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)
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from zhenxun.services.ai.flow.agent.engine.directive import (
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DirectiveHandlerFunc,
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directive_manager,
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)
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from zhenxun.services.ai.flow.agent.models import AgentRunResources, AgentState
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from zhenxun.services.ai.llm.engine.router import LLMOrchestrator
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from zhenxun.services.ai.run import AgentRunResult, RunContext
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from zhenxun.services.ai.run.session import session_manager
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from zhenxun.services.ai.tools.engine.executor import ToolExecutor
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from zhenxun.services.ai.tools.models import ToolResult
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_agent as logger
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from zhenxun.utils.pydantic_compat import dump_json_safely, model_construct
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from .directive import (
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DirectiveHandlerFunc,
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directive_manager,
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)
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class BaseAgentExecutor(ABC):
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"""
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@@ -219,13 +219,12 @@ class StandardAgentExecutor(BaseAgentExecutor):
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messages, run_context
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)
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if run_context.run.event_bus:
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await run_context.run.event_bus.emit(
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LLMStartEvent(
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model_name=resources.model_name or "unknown",
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messages=flattened_messages,
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)
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await run_context.run.emit(
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LLMStartEvent(
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model_name=resources.model_name or "unknown",
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messages=flattened_messages,
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)
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)
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response = await self._execute_model_request(
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model_name=resources.model_name,
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@@ -238,8 +237,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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cancellation_token=cancellation_token,
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)
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if run_context.run.event_bus:
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await run_context.run.event_bus.emit(LLMEndEvent(response=response))
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await run_context.run.emit(LLMEndEvent(response=response))
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assistant_content = (
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response.content_parts if response.content_parts else response.text
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@@ -252,9 +250,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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part.metadata["thought_signature"] = response.thought_signature
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break
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assistant_message = AssistantMessage(
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content=cast(list[AssistantContentUnion], response.content_parts)
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)
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assistant_message = AssistantMessage(content=response.content_parts)
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if hasattr(response, "parsed_obj") and response.parsed_obj is not None:
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if not isinstance(response.parsed_obj, str):
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@@ -376,7 +372,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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state.messages, resources.model_name or "", base_overhead=0
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)
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logger.debug(
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f"[TokenTracker] (Iter {state.current_cycle + 1}) "
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f"(Iter {state.current_cycle + 1}) "
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f"预估将消耗 {est_tokens} Token "
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f"(Model: {resources.model_name or 'Unknown'})"
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)
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@@ -444,7 +440,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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return
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if not response.tool_calls:
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logger.debug("✅ AgentExecutor：模型未请求工具调用，推理循环结束。")
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logger.debug("✅ 模型未请求工具调用，推理循环结束。")
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state.is_finished = True
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state.final_result = model_construct(
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AgentRunResult,
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@@ -476,8 +472,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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if is_server_side:
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logger.debug(
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"☁️ [AgentExecutor] 检测到云端工具调用: "
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f"{call.tool_name}，已跳过本地执行。"
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f"☁️ 检测到云端工具调用: {call.tool_name}，已跳过本地执行。"
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)
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async with self.tool_executor._tool_stream_scope(
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event_bus,
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@@ -500,7 +495,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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client_tool_calls.append(call)
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if not client_tool_calls:
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logger.info("✅ AgentExecutor：无本地客户端工具需执行，推理循环平滑结束。")
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logger.info("✅ 无本地客户端工具需执行，推理循环平滑结束。")
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state.is_finished = True
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state.final_result = model_construct(
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@@ -638,7 +633,6 @@ class StandardAgentExecutor(BaseAgentExecutor):
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self, state: AgentState, resources: AgentRunResources
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) -> AgentRunResult[Any]:
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run_context = resources.run_context
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event_bus = run_context.run.event_bus
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if not resources.config.enable_fallback_summary:
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raise UpstreamServerException(
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@@ -646,17 +640,15 @@ class StandardAgentExecutor(BaseAgentExecutor):
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)
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logger.warning(
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f"AgentExecutor 达到最大循环次数 ({resources.config.max_cycles})，"
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"触发兜底总结机制。"
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f"达到最大循环次数 ({resources.config.max_cycles})，触发兜底总结机制。"
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)
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if event_bus:
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await event_bus.emit(
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ToolStreamChunkEvent(
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tool_name="System",
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content="⏳ 思考过程过于复杂，正在强制生成最终总结...",
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)
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await run_context.run.emit(
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ToolStreamChunkEvent(
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tool_name="System",
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content="⏳ 思考过程过于复杂，正在强制生成最终总结...",
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)
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)
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fallback_msg = LLMMessage.user(
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"### 🚨 [系统强制指令]\n"
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