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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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co-authored by
webjoin111
pre-commit-ci[bot]
webjoin111
pre-commit-ci[bot]
parent
0b32d69c9c
commit
922d092650
@@ -22,19 +22,23 @@ class StablePrefix:
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"""
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def __init__(self):
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"""初始化稳定前缀实例。"""
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self._snapshot: StablePrefixSnapshot | None = None
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self._version = 0
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@property
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def fingerprint(self) -> str:
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"""获取当前快照的唯一指纹。"""
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return self._snapshot.fingerprint if self._snapshot else "<unbuilt>"
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@property
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def version(self) -> int:
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"""获取当前前缀的版本号。"""
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return self._version
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@property
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def built(self) -> bool:
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"""判断当前是否已构建快照。"""
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return self._snapshot is not None
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def build(self, system_prompt: list[str], tools: list[Any]) -> bool:
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@@ -50,9 +54,11 @@ class StablePrefix:
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return True
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def invalidate(self):
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"""使当前前缀快照失效。"""
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self._snapshot = None
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def to_context(self) -> tuple[list[str], list[Any]]:
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"""将前缀快照导出为上下文元组。"""
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if not self._snapshot:
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raise RuntimeError("StablePrefix.to_context() called before build()")
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return self._snapshot.system_prompt, self._snapshot.tools
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@@ -60,6 +66,7 @@ class StablePrefix:
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def _take_snapshot(
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self, system_prompt: list[str], tools: list[Any]
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) -> StablePrefixSnapshot:
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"""为系统提示词与工具列表生成指纹快照。"""
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parsed_tools = []
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for t in tools:
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if hasattr(t, "name"):
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@@ -83,19 +90,24 @@ class AppendOnlyLog:
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"""追加写入模式 of 对话日志管理器"""
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def __init__(self):
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"""初始化追加对话日志。"""
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self._entries: list[Any] = []
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@property
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def length(self) -> int:
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"""获取当前对话日志的条数。"""
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return len(self._entries)
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def append(self, message: Any):
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"""向对话日志中追加单条消息。"""
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self._entries.append(message)
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def extend(self, messages: list[Any]):
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"""批量追加多条消息到对话日志。"""
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self._entries.extend(messages)
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def clear(self):
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"""清空所有对话日志。"""
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self._entries.clear()
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def to_messages(self) -> list[Any]:
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@@ -110,6 +122,7 @@ class AppendOnlyContextManager:
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"""
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def __init__(self):
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"""初始化上下文管理器。"""
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self.prefix = StablePrefix()
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self.log = AppendOnlyLog()
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self._last_sync_count = 0
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@@ -158,7 +171,7 @@ class AppendOnlyContextManager:
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def _compute_digest(self, messages: list[Any]) -> int:
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"""核心：计算消息列表 of 指纹，用于识别内容篡改。包含 role 与 content。"""
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from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
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from .context_renderer import ContextConverter
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payloads = []
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flattened = ContextConverter.flatten_to_llm_messages(messages)
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@@ -2,7 +2,7 @@ from collections.abc import Sequence
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from typing import Any
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from zhenxun.services.ai.core.messages import AgentEvent, AgentMessage, LLMMessage
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_core as logger
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class ContextConverter:
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@@ -14,7 +14,7 @@ from zhenxun.services.ai.core.models import ToolDefinition
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from zhenxun.services.ai.run.models import OutputDataT
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from zhenxun.services.ai.tools.core.tool import BaseTool
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from zhenxun.services.ai.tools.models import StructuredSubmissionResult, ToolResult
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_core as logger
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from zhenxun.utils.pydantic_compat import model_json_schema, model_validate
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DEFAULT_IVR_TEMPLATE = (
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@@ -2,7 +2,7 @@
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消息与响应域类型定义 - 统一导出门面
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"""
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from nonebot.compat import PYDANTIC_V2
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from zhenxun.utils.pydantic_compat import model_rebuild
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from .context_events import (
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AgentEvent,
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@@ -59,30 +59,25 @@ from .shared import (
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from .types import (
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AgentMessage,
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AnyLLMMessage,
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AssistantContentUnion,
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ContentT,
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PromptInput,
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RoleT,
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SystemContentUnion,
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ToolContentUnion,
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UserContentUnion,
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)
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if PYDANTIC_V2:
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ChatResponse.model_rebuild()
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LLMMessage.model_rebuild()
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SystemMessage.model_rebuild()
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UserMessage.model_rebuild()
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AssistantMessage.model_rebuild()
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ToolMessage.model_rebuild()
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RerankResponse.model_rebuild()
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model_rebuild(ChatResponse)
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model_rebuild(LLMMessage)
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model_rebuild(SystemMessage)
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model_rebuild(UserMessage)
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model_rebuild(AssistantMessage)
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model_rebuild(ToolMessage)
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model_rebuild(RerankResponse)
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__all__ = [
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"AgentEvent",
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"AgentMessage",
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"AnyLLMMessage",
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"AssistantContentUnion",
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"AssistantMessage",
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"AudioPart",
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"AudioResponse",
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@@ -112,7 +107,6 @@ __all__ = [
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"RerankResult",
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"RoleT",
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"SpeechRequest",
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"SystemContentUnion",
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"SystemMessage",
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"TaskLifecycleEvent",
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"TextDeltaPart",
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@@ -121,7 +115,6 @@ __all__ = [
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"ThoughtPart",
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"ToolCallDeltaPart",
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"ToolCallPart",
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"ToolContentUnion",
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"ToolMessage",
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"ToolReturnPart",
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"UsageInfo",
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@@ -7,6 +7,7 @@ from typing_extensions import Self
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from pydantic import BaseModel, Field
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from zhenxun.services.ai.utils.logger import log_core as logger
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from zhenxun.utils.pydantic_compat import model_validator
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@@ -309,7 +310,6 @@ class EmbedBatch(BaseModel):
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def to_text_only(self, context_name: str) -> list[str]:
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"""降级工具：将多模态的批量向量安全剔除图片等内容，回退为纯文本数组"""
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from zhenxun.services.log import logger
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texts = []
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for payload in self.payloads:
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@@ -12,33 +12,12 @@ from .parts import (
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ImagePart,
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LLMContentPart,
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TextPart,
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ThoughtPart,
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ToolCallPart,
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ToolReturnPart,
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VideoPart,
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)
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SystemContentUnion = TextPart
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"""系统消息允许的内容片段联合类型"""
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UserContentUnion = TextPart | ImagePart | AudioPart | VideoPart | FilePart
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"""用户消息允许的多模态内容片段联合类型"""
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AssistantContentUnion = (
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TextPart
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| ThoughtPart
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| ToolCallPart
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| ToolReturnPart
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| ImagePart
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| AudioPart
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| VideoPart
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| FilePart
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)
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"""助手回复允许的内容片段联合类型"""
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ToolContentUnion = ToolReturnPart | ImagePart | AudioPart | VideoPart | FilePart
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"""工具消息允许的内容片段联合类型"""
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RoleT = TypeVar("RoleT", default=str, covariant=True)
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"""泛型：消息参与者角色类型变量"""
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@@ -72,12 +51,9 @@ AgentMessage = LLMMessage | AgentEvent
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__all__ = [
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"AgentMessage",
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"AnyLLMMessage",
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"AssistantContentUnion",
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"ContentT",
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"LLMContentPart",
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"PromptInput",
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"RoleT",
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"SystemContentUnion",
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"ToolContentUnion",
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"UserContentUnion",
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]
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@@ -9,7 +9,7 @@ from typing import Any, Generic, Literal, TypeVar
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from pydantic import BaseModel, ConfigDict, Field
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from zhenxun.services.ai.core.options import GenerationConfig
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from .options import GenerationConfig
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ModelName = str | None
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@@ -131,6 +131,8 @@ class ModelCapabilities(BaseModel):
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"""模型支持的输出模态集合。"""
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supports_tool_calling: bool = False
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"""是否支持工具调用能力。"""
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supports_thinking_toggle: bool = False
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"""是否支持通过 {"thinking": {"type": "enabled/disabled"}} 显式控制思考模式。"""
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is_embedding_model: bool = False
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"""是否为嵌入模型。"""
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is_rerank_model: bool = False
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@@ -187,7 +189,7 @@ class ModelDetail(BaseModel):
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"""模型是否可用。"""
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temperature: float | None = None
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"""采样温度参数。"""
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generation_max_tokens: int | None = None
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max_output_tokens: int | None = None
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"""单次生成最大 Token 数。"""
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api_type: str | None = None
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"""API 类型标识。"""
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@@ -197,6 +199,10 @@ class ModelDetail(BaseModel):
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"""显式声明的主任务类型 (如 'image_generation')。"""
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path_prefix: str | None = Field(default=None)
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"""中转路由前缀，例如 '/cogvideox' 或 '/minimax'。"""
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max_input_tokens: int | None = None
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"""最大输入上下文窗口（用于控制记忆压缩策略）"""
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reasoning_effort: str | None = None
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"""该模型的默认思考/推理等级（如 'high', 'low', 'none'）"""
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__all__ = [
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@@ -212,13 +212,6 @@ class GeminiOptions(BaseProviderOption):
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"""检索定位配置，如 LBS 经纬度信息，配合 Google Maps 工具使用"""
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class DeepSeekOptions(BaseProviderOption):
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"""DeepSeek 专属特权参数"""
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thinking: bool | None = Field(default=None)
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"""是否强制开启或关闭 R1 模型的思维链过程"""
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class OpenAITTSOptions(BaseProviderOption):
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"""OpenAI TTS 专属特权参数"""
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@@ -331,8 +324,6 @@ class GenerationConfig(BaseModel):
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"""OpenAI 厂商专属请求参数集"""
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gemini_options: GeminiOptions = Field(default_factory=GeminiOptions)
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"""Gemini 厂商专属请求参数集"""
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deepseek_options: DeepSeekOptions = Field(default_factory=DeepSeekOptions)
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"""DeepSeek 厂商专属请求参数集"""
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enable_caching: bool | None = Field(default=None)
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"""是否在此次生成中开启上下文缓存 (Context Caching)"""
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@@ -67,8 +67,6 @@ class ToolProvider(Protocol):
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async def discover_tools(
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self,
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allowed_servers: list[str] | None = None,
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excluded_servers: list[str] | None = None,
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) -> dict[str, ToolExecutable]:
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"""
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异步发现此提供者提供的所有工具。
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@@ -8,7 +8,7 @@ from typing import Any, TypeVar
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from nonebot.utils import is_coroutine_callable
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from pydantic import BaseModel, ConfigDict
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_core as logger
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class AgentStreamEvent(BaseModel):
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@@ -4,7 +4,7 @@ from typing_extensions import TypeVar
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from jinja2 import Environment
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from zhenxun.services.log import logger
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from zhenxun.services.ai.utils.logger import log_core as logger
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from zhenxun.utils.pydantic_compat import model_dump
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if TYPE_CHECKING:
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