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Rumioandwebjoin111
cd5fa065d3
- 统一使用 `run_context.run.messages` 作为消息历史的单一数据源，清理 `AgentState` 冗余字段 - 将工具消息装配逻辑 `assemble_tool_message` 提取并重构至 `ToolExecutor` - 引入 `token_drift` 动态校准偏移量，并精确计算工具与系统提示词的 Token 开销 - 重构 `ReflexionCapability` 自愈反思引擎，基于异常多态与模板字典动态生成反馈提示词 - 支持通过 `resolve_model_capabilities` 解析并合并用户自定义的模型能力覆盖 - 在执行器循环中支持 `should_reset_cycle`，以优雅处理外部干预（如用户追加指示） - 扩展 `capabilities` 中对 `gpt-[5-9]*` 等新型号模型的能力定义与上下文限制 Co-authored-by: webjoin111 <455457521@qq.com>
372 lines
11 KiB
Python
372 lines
11 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_1_05M = 1_050_000
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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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(
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["gpt-5.3-chat*", "gpt-5.3-instant*", "*codex-spark*"],
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CAP_OPENAI_MULTIMODAL,
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CTX_128K,
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),
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(
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["gpt-[5-9]*mini*", "gpt-[5-9]*nano*", "gpt-[5-9]*-instant*", "*codex*"],
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CAP_OPENAI_MULTIMODAL,
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CTX_400K,
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),
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(["gpt-[5-9]*"], CAP_OPENAI_MULTIMODAL, CTX_1_05M),
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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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["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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