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* ♻️ 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>
366 lines
10 KiB
Python
366 lines
10 KiB
Python
import fnmatch
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from zhenxun.services.ai.core.models import (
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ModelCapabilities,
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ModelModality,
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ReasoningMode,
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)
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from zhenxun.utils.pydantic_compat import model_copy
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CTX_1M = 1_000_000
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CTX_400K = 400_000
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CTX_256K = 256_000
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CTX_200K = 204_800
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CTX_128K = 128_000
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CTX_8K = 8_192
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CAP_MULTIMODAL_EMBEDDING = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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ModelModality.FILE,
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},
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is_embedding_model=True,
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supports_tool_calling=False,
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)
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STANDARD_TEXT_TOOL_CAPABILITIES = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supported_native_tools={
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"web_search",
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"code_execution",
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"computer_use",
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"file_search",
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},
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)
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CAP_GEMINI_2_5 = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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reasoning_mode=ReasoningMode.BUDGET,
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reasoning_visibility="visible",
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supported_native_tools={
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"web_search",
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"code_execution",
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"google_map",
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"url_context",
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},
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)
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CAP_GEMINI_3_BASE = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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reasoning_mode=ReasoningMode.LEVEL,
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reasoning_visibility="visible",
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supported_native_tools={
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"web_search",
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"code_execution",
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"google_map",
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"url_context",
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},
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features={
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"mixed_tools",
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"server_side_tool_invocations",
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},
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)
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CAP_GEMINI_3_PRO = model_copy(
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CAP_GEMINI_3_BASE,
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update={
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"reasoning_effort_map": {
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"max": "high",
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"xhigh": "high",
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"minimal": "low",
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"none": "low",
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}
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},
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)
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CAP_GEMINI_3_FLASH = model_copy(
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CAP_GEMINI_3_BASE,
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update={
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"reasoning_effort_map": {"max": "high", "xhigh": "high", "none": "minimal"}
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},
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)
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CAP_OPENAI_REASONING = ModelCapabilities(
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input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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reasoning_mode=ReasoningMode.EFFORT,
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reasoning_visibility="hidden",
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supported_native_tools={
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"web_search",
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"code_execution",
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"computer_use",
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"file_search",
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},
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reasoning_effort_map={"max": "xhigh", "minimal": "none"},
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)
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CAP_OPENAI_MULTIMODAL = ModelCapabilities(
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input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supported_native_tools={
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"web_search",
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"computer_use",
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"file_search",
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},
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reasoning_effort_map={"max": "xhigh", "minimal": "none"},
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)
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CAP_DEEPSEEK_V4 = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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reasoning_mode=ReasoningMode.EFFORT,
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reasoning_visibility="visible",
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reasoning_effort_map={"minimal": "low"},
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)
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CAP_MINIMAX_REASONING = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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reasoning_mode=ReasoningMode.EFFORT,
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reasoning_visibility="visible",
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)
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CAP_GLM_MULTIMODAL = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.VIDEO,
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ModelModality.FILE,
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},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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)
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CAP_GLM_REASONING = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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reasoning_mode=ReasoningMode.EFFORT,
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)
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CAP_MINIMAX_MULTIMODAL = ModelCapabilities(
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input_modalities={ModelModality.TEXT, ModelModality.IMAGE, ModelModality.VIDEO},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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)
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CAP_MIMO_TEXT = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.TEXT},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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supported_native_tools={"web_search"},
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reasoning_effort_map={"max": "high", "xhigh": "high", "minimal": "low"},
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)
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CAP_MIMO_MULTIMODAL = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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},
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output_modalities={ModelModality.TEXT, ModelModality.AUDIO},
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supports_tool_calling=True,
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supports_thinking_toggle=True,
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supported_native_tools={"web_search"},
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reasoning_effort_map={"max": "high", "xhigh": "high", "minimal": "low"},
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)
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CAP_OPENAI_TTS = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.AUDIO},
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supports_tool_calling=False,
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default_voice_id="alloy",
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)
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CAP_GEMINI_TTS = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.AUDIO},
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supports_tool_calling=False,
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default_voice_id="Aoede",
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)
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CAP_MINIMAX_TTS = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.AUDIO},
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supports_tool_calling=False,
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default_voice_id="female-shaonv",
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)
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CAP_MIMO_TTS = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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output_modalities={ModelModality.AUDIO},
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supports_tool_calling=False,
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default_voice_id="mimo_default",
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)
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CAP_TEXT_EMBEDDING = ModelCapabilities(
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input_modalities={ModelModality.TEXT},
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is_embedding_model=True,
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supports_tool_calling=False,
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)
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CAP_RERANK_ONLY = ModelCapabilities(
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input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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is_rerank_model=True,
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)
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CAP_OPENAI_IMAGE = ModelCapabilities(
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input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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output_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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supports_tool_calling=False,
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)
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CAP_GEMINI_IMAGE = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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},
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output_modalities={ModelModality.TEXT, ModelModality.IMAGE},
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supports_tool_calling=True,
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supported_native_tools={
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"web_search",
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},
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)
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DEFAULT_PERMISSIVE_CAPABILITIES = ModelCapabilities(
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input_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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ModelModality.VIDEO,
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},
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output_modalities={
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ModelModality.TEXT,
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ModelModality.IMAGE,
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ModelModality.AUDIO,
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},
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supports_tool_calling=True,
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)
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MODEL_ALIAS_MAPPING: dict[str, str] = {
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"*DeepSeek-V4-Pro*": "deepseek-v4-pro",
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"*DeepSeek-V4-Flash*": "deepseek-v4-flash",
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}
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_ROUTING_TABLE: list[tuple[list[str], ModelCapabilities, int]] = [
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(["mimo-*tts*"], CAP_MIMO_TTS, CTX_8K),
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(["gemini-*tts*"], CAP_GEMINI_TTS, CTX_8K),
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(["*minimax-*tts*", "*MiniMax-*tts*"], CAP_MINIMAX_TTS, CTX_8K),
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(["*tts*"], CAP_OPENAI_TTS, CTX_8K),
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(["*gpt*image*"], CAP_OPENAI_IMAGE, CTX_128K),
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(["*gemini*image*", "*nano-banana*"], CAP_GEMINI_IMAGE, CTX_128K),
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(["glm-4.6v*"], CAP_GLM_MULTIMODAL, CTX_128K),
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(["glm-4.7-flash*"], STANDARD_TEXT_TOOL_CAPABILITIES, CTX_128K),
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(["deepseek-v4-pro*", "deepseek-v4-flash*"], CAP_DEEPSEEK_V4, CTX_1M),
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(["glm-4-long*"], STANDARD_TEXT_TOOL_CAPABILITIES, CTX_1M),
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(["*MiniMax-M3*"], CAP_MINIMAX_MULTIMODAL, CTX_1M),
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(["mimo-v2.5-pro*", "mimo-v2-pro*", "mimo-v2-flash*"], CAP_MIMO_TEXT, CTX_1M),
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(["mimo-v2.5", "mimo-v2-omni*"], CAP_MIMO_MULTIMODAL, CTX_1M),
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(["gpt-5.5*", "gpt-5.4*"], CAP_OPENAI_MULTIMODAL, CTX_1M),
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(["gemini-3*pro*"], CAP_GEMINI_3_PRO, CTX_1M),
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(["gemini-3*"], CAP_GEMINI_3_FLASH, CTX_1M),
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(
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["gemini-2.5-pro*", "gemini-2.5-flash*"],
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CAP_GEMINI_2_5,
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CTX_1M,
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),
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(
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["gpt-5*", "gpt-5-mini*", "gpt-5-nano*", "*codex*"],
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CAP_OPENAI_MULTIMODAL,
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CTX_400K,
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),
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(
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["kimi-k2.7*", "kimi-k2.6*", "kimi-k2.5*"],
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DEFAULT_PERMISSIVE_CAPABILITIES,
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CTX_256K,
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),
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(["glm-5v*"], CAP_GLM_MULTIMODAL, CTX_200K),
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(["glm-5*", "glm-4.7*", "glm-4.6*"], CAP_GLM_REASONING, CTX_200K),
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(["*MiniMax-M2*", "*minimax-m2*"], CAP_MINIMAX_REASONING, CTX_200K),
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(["gpt-4*", "gpt-3.5*", "gpt-*"], CAP_OPENAI_MULTIMODAL, CTX_128K),
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(["o1-*", "o3-*"], CAP_OPENAI_REASONING, CTX_128K),
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(["glm-4v*"], CAP_GLM_MULTIMODAL, CTX_128K),
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(
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["glm-4.5*", "glm-4-flashx-*", "glm-4*"],
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STANDARD_TEXT_TOOL_CAPABILITIES,
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CTX_128K,
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),
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(
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["gemini-embedding-2*", "jina-embeddings-v5-omni*"],
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CAP_MULTIMODAL_EMBEDDING,
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CTX_8K,
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),
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(
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["*embedding*", "*Embedding*", "jina-embeddings-*", "bge-m3*", "*bge-large*"],
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CAP_TEXT_EMBEDDING,
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CTX_8K,
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),
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(["*reranker*", "*rerank*", "jina-colbert-*"], CAP_RERANK_ONLY, CTX_8K),
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]
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def _build_registry() -> dict[str, ModelCapabilities]:
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"""构建模型能力注册表 (基于声明式路由表)"""
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registry: dict[str, ModelCapabilities] = {}
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for patterns, cap_template, ctx_limit in _ROUTING_TABLE:
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cap_instance = model_copy(cap_template, update={"max_input_tokens": ctx_limit})
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for pattern in patterns:
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registry[pattern] = cap_instance
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return registry
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MODEL_CAPABILITIES_REGISTRY = _build_registry()
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def get_model_capabilities(model_name: str) -> ModelCapabilities:
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"""
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从注册表获取模型能力，支持别名映射和通配符匹配。
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"""
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canonical_name = model_name
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for alias_pattern, c_name in MODEL_ALIAS_MAPPING.items():
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if fnmatch.fnmatch(model_name, alias_pattern):
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canonical_name = c_name
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break
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parts = canonical_name.split("/")
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names_to_check = ["/".join(parts[i:]) for i in range(len(parts))]
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for name in names_to_check:
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if name in MODEL_CAPABILITIES_REGISTRY:
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return MODEL_CAPABILITIES_REGISTRY[name]
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for pattern, capabilities in MODEL_CAPABILITIES_REGISTRY.items():
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if "*" in pattern and fnmatch.fnmatch(name, pattern):
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return capabilities
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return DEFAULT_PERMISSIVE_CAPABILITIES
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