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ThearchyHelios
257 lines
9.7 KiB
Python
257 lines
9.7 KiB
Python
from __future__ import annotations
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from typing import Any
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# --------------------------------------------------------------------------
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# 模型目录
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#
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# pricing 单位是「美元 / 百万 token」，用于 run 的成本估算。
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# 新模型的价格会变，这份表只是开箱即用的默认值，用户可以在设置页改。
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# --------------------------------------------------------------------------
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ANTHROPIC_MODELS: list[dict[str, Any]] = [
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{"id": "claude-opus-5", "label": "Claude Opus 5", "context": 1_000_000,
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"pricing": {"input": 5.0, "output": 25.0}, "thinking": "adaptive"},
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{"id": "claude-fable-5-1", "label": "Claude Fable 5.1", "context": 1_000_000,
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"pricing": {"input": 10.0, "output": 50.0}, "thinking": "always"},
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{"id": "claude-opus-4-8", "label": "Claude Opus 4.8", "context": 1_000_000,
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"pricing": {"input": 5.0, "output": 25.0}, "thinking": "adaptive"},
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{"id": "claude-sonnet-5", "label": "Claude Sonnet 5", "context": 1_000_000,
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"pricing": {"input": 2.0, "output": 10.0}, "thinking": "adaptive"},
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{"id": "claude-haiku-4-5", "label": "Claude Haiku 4.5", "context": 200_000,
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"pricing": {"input": 1.0, "output": 5.0}, "thinking": "budget"},
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]
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OPENAI_MODELS: list[dict[str, Any]] = [
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{"id": "gpt-5.2", "label": "GPT-5.2", "context": 400_000,
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"pricing": {"input": 1.25, "output": 10.0}},
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{"id": "gpt-5.2-mini", "label": "GPT-5.2 mini", "context": 400_000,
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"pricing": {"input": 0.25, "output": 2.0}},
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{"id": "o4", "label": "o4", "context": 200_000,
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"pricing": {"input": 3.0, "output": 12.0}},
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]
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# 常见的 OpenAI 兼容服务，一键添加用
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COMPATIBLE_PRESETS: list[dict[str, Any]] = [
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{
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"key": "deepseek",
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"label": "DeepSeek",
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"base_url": "https://api.deepseek.com/v1",
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"models": [
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{"id": "deepseek-chat", "label": "DeepSeek Chat", "context": 128_000,
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"pricing": {"input": 0.27, "output": 1.1}},
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{"id": "deepseek-reasoner", "label": "DeepSeek Reasoner", "context": 128_000,
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"pricing": {"input": 0.55, "output": 2.19}},
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],
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},
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{
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"key": "moonshot",
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"label": "Moonshot / Kimi",
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"base_url": "https://api.moonshot.cn/v1",
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"models": [
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{"id": "kimi-k2-turbo-preview", "label": "Kimi K2 Turbo", "context": 256_000,
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"pricing": {"input": 0.6, "output": 2.5}},
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{"id": "moonshot-v1-128k", "label": "Moonshot v1 128k", "context": 128_000,
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"pricing": {"input": 0.8, "output": 3.0}},
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],
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},
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{
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"key": "dashscope",
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"label": "阿里云百炼 / 通义千问",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"models": [
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{"id": "qwen-max", "label": "Qwen Max", "context": 32_000,
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"pricing": {"input": 0.34, "output": 1.37}},
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{"id": "qwen-plus", "label": "Qwen Plus", "context": 131_000,
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"pricing": {"input": 0.11, "output": 0.28}},
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],
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},
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{
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"key": "zhipu",
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"label": "智谱 GLM",
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"base_url": "https://open.bigmodel.cn/api/paas/v4",
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"models": [
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{"id": "glm-4.6", "label": "GLM-4.6", "context": 200_000,
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"pricing": {"input": 0.6, "output": 2.2}},
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],
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},
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{
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"key": "siliconflow",
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"label": "硅基流动",
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"base_url": "https://api.siliconflow.cn/v1",
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"models": [
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{"id": "deepseek-ai/DeepSeek-V3", "label": "DeepSeek V3", "context": 128_000,
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"pricing": {"input": 0.27, "output": 1.1}},
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],
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},
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{
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"key": "openrouter",
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"label": "OpenRouter",
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"base_url": "https://openrouter.ai/api/v1",
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"models": [
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{"id": "anthropic/claude-opus-5", "label": "Claude Opus 5", "context": 1_000_000,
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"pricing": {"input": 5.0, "output": 25.0}},
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],
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},
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{
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"key": "ollama",
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"label": "Ollama（本地）",
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"base_url": "http://localhost:11434/v1",
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"api_key_optional": True,
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"models": [
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{"id": "qwen3:8b", "label": "Qwen3 8B（本地）", "context": 32_000,
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"pricing": {"input": 0.0, "output": 0.0}},
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{"id": "llama3.2", "label": "Llama 3.2（本地）", "context": 128_000,
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"pricing": {"input": 0.0, "output": 0.0}},
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],
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},
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]
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MOCK_MODELS: list[dict[str, Any]] = [
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{"id": "mock-fast", "label": "演示模型（无需 API Key）", "context": 128_000,
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"pricing": {"input": 0.0, "output": 0.0}},
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]
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PROVIDER_KINDS = [
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{
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"kind": "anthropic",
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"label": "Anthropic",
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"base_url_hint": "留空使用官方地址；填写后使用自定义网关（默认读取 ANTHROPIC_BASE_URL）",
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"models": ANTHROPIC_MODELS,
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"default_model": "claude-opus-5",
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},
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{
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"kind": "openai",
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"label": "OpenAI",
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"base_url_hint": "留空使用官方地址",
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"models": OPENAI_MODELS,
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"default_model": "gpt-5.2",
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},
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{
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"kind": "openai_compatible",
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"label": "OpenAI 兼容（DeepSeek / Kimi / 通义 / Ollama…）",
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"base_url_hint": "必填，例如 https://api.deepseek.com/v1",
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"models": [],
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"presets": COMPATIBLE_PRESETS,
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"default_model": "",
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},
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{
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"kind": "mock",
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"label": "演示模型（无需 API Key）",
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"base_url_hint": "",
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"models": MOCK_MODELS,
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"default_model": "mock-fast",
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},
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]
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# --------------------------------------------------------------------------
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# 模型能力
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# --------------------------------------------------------------------------
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# Claude 4.6 及以后的模型移除了采样参数，传 temperature/top_p 会直接 400。
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_NO_SAMPLING_PREFIXES = (
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"claude-fable-5",
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"claude-mythos-5",
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"claude-opus-5",
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"claude-opus-4-8",
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"claude-opus-4-7",
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"claude-opus-4-6",
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"claude-sonnet-5",
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"claude-sonnet-4-6",
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)
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# 支持 adaptive thinking（而不是老的 budget_tokens）的模型
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_ADAPTIVE_THINKING_PREFIXES = _NO_SAMPLING_PREFIXES
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# 支持 output_config.effort
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_EFFORT_PREFIXES = _NO_SAMPLING_PREFIXES + ("claude-opus-4-5",)
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# 交错思考（工具结果回来之后再想一段，而不是一轮开头想完就只剩执行）。
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#
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# 4.6 及以后走 adaptive thinking，交错是自带的，不需要也不该再挂 beta header
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# ——在 Opus 4.6 上手动模式压根没有交错思考，只有 adaptive 有。上面那张
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# _ADAPTIVE_THINKING_PREFIXES 覆盖的都属于这一档。
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#
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# 需要显式开的是 Claude 4 / 4.5 这一代。目录里没有列它们，但模型名是放行的
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# （用户可以手填），所以这条路径要留着。Haiku 4.5 不支持，前缀也就不包含它。
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_INTERLEAVED_BETA_PREFIXES = ("claude-opus-4", "claude-sonnet-4")
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INTERLEAVED_BETA = "interleaved-thinking-2025-05-14"
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def needs_interleaved_beta(model: str) -> bool:
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"""这个模型要不要靠 beta header 才会在工具之间思考。"""
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name = _norm(model)
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if supports_adaptive_thinking(name):
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return False # adaptive 自带，多挂一个 header 只会是噪音
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return name.startswith(_INTERLEAVED_BETA_PREFIXES)
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def _norm(model: str) -> str:
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"""去掉 openrouter 之类的前缀，只看模型名本身。"""
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return (model or "").split("/")[-1].strip()
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def supports_sampling(model: str) -> bool:
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"""这个模型还能不能传 temperature / top_p。"""
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return not _norm(model).startswith(_NO_SAMPLING_PREFIXES)
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def supports_adaptive_thinking(model: str) -> bool:
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return _norm(model).startswith(_ADAPTIVE_THINKING_PREFIXES)
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def supports_effort(model: str) -> bool:
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return _norm(model).startswith(_EFFORT_PREFIXES)
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def find_pricing(model: str) -> dict[str, float] | None:
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target = _norm(model)
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pools = [ANTHROPIC_MODELS, OPENAI_MODELS, MOCK_MODELS]
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pools += [p["models"] for p in COMPATIBLE_PRESETS]
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for pool in pools:
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for entry in pool:
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if _norm(entry["id"]) == target:
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return entry.get("pricing")
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return None
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def find_context(model: str) -> int | None:
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"""目录里登记的上下文窗口（token）。没登记的返回 None，由调用方取保守值。"""
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target = _norm(model)
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pools = [ANTHROPIC_MODELS, OPENAI_MODELS, MOCK_MODELS]
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pools += [p["models"] for p in COMPATIBLE_PRESETS]
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for pool in pools:
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for entry in pool:
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if _norm(entry["id"]) == target:
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value = entry.get("context")
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return int(value) if isinstance(value, (int, float)) and value > 0 else None
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return None
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def unpriced_call(data: Any) -> bool:
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"""一次模型调用（llm.end 的数据）的金额估不出来：用了 token，模型却不在价格目录里，estimate_cost 只能给 0。
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这时写 $0 像是没花钱（真实运行里上百次调用、上百万 token，运行页写着 $0）。本地模型在目录里标的是 0 价，
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不算估不出来。事件自己标了 unpriced 的以它为准。"""
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if not isinstance(data, dict):
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return False
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if isinstance(data.get("unpriced"), bool):
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return data["unpriced"]
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tokens = sum(v for v in (data.get("input_tokens"), data.get("output_tokens"))
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if isinstance(v, (int, float)) and not isinstance(v, bool))
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model = data.get("model")
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return tokens > 0 and isinstance(model, str) and bool(model) and not data.get("cost_usd") \
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and find_pricing(model) is None
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def estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
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"""按目录价估算一次调用的花费（美元）。"""
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pricing = find_pricing(model)
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if not pricing:
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return 0.0
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return (
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input_tokens / 1_000_000 * pricing.get("input", 0.0)
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+ output_tokens / 1_000_000 * pricing.get("output", 0.0)
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)
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