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e1316686f8
M2.x tool_choice is enum-only (none/auto), so route through the no-tool_choice dispatch. MinimaxChatOpenAI injects reasoning_split so <think> blocks stay out of content. Catalog rounded out to the full official M2.x lineup plus forward-compat regex.
121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
"""Declarative per-model capability table for OpenAI-compatible providers.
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This is the single place that knows which model IDs reject which API
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parameters or require which structured-output method. The LLM client
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subclasses consult ``get_capabilities(model_name)`` instead of hardcoding
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model-name ``if`` ladders, so adding a new model (or a new provider quirk)
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means editing this table — not the client code.
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Pattern adapted from the per-model ``compat:`` flags DeepSeek themselves
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publish in their integration guides (e.g. the Oh My Pi config schema
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documents ``supportsToolChoice``, ``requiresReasoningContentForToolCalls``
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as declarative per-model fields).
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass
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from typing import Literal
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StructuredMethod = Literal[
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"function_calling", # uses tools; respects supports_tool_choice
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"json_mode", # uses response_format={"type":"json_object"}
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"json_schema", # uses response_format={"type":"json_schema",...}
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"none", # no structured output available; caller falls back to free-text
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]
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@dataclass(frozen=True)
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class ModelCapabilities:
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"""What an OpenAI-compatible model accepts at the API level."""
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supports_tool_choice: bool
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supports_json_mode: bool
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supports_json_schema: bool
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preferred_structured_method: StructuredMethod
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# DeepSeek thinking-mode models 400 if reasoning_content from prior
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# assistant turns is not echoed back on the next request.
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requires_reasoning_content_roundtrip: bool = False
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# DeepSeek's thinking models accept the ``tools`` array but reject the
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# ``tool_choice`` parameter (official Oh My Pi integration guide and the
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# 400 response in issue #678). Their official tool-calling examples
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# (api-docs.deepseek.com/guides/tool_calls) pass ``tools=[...]`` without
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# ``tool_choice`` — we mirror that pattern by setting supports_tool_choice
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# to False and letting the client suppress the kwarg.
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_DEEPSEEK_THINKING = ModelCapabilities(
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supports_tool_choice=False,
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supports_json_mode=True,
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supports_json_schema=False,
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preferred_structured_method="function_calling",
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requires_reasoning_content_roundtrip=True,
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)
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_DEEPSEEK_CHAT = ModelCapabilities(
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supports_tool_choice=True,
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supports_json_mode=True,
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supports_json_schema=False,
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preferred_structured_method="function_calling",
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)
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# MiniMax M2.x reasoning models accept the tools array, but their
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# tool_choice parameter is restricted to the enum {"none", "auto"}
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# (platform.minimax.io/docs/api-reference/text-post). Langchain's
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# function_calling path sends tool_choice as a function-spec dict, which
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# MiniMax 400s — same shape as the DeepSeek bug. supports_tool_choice=False
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# makes the dispatch in NormalizedChatOpenAI suppress the kwarg; the schema
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# still ships as a tool. json_mode response_format is only for
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# MiniMax-Text-01, not M2.x.
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_MINIMAX_THINKING = ModelCapabilities(
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supports_tool_choice=False,
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supports_json_mode=False,
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supports_json_schema=False,
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preferred_structured_method="function_calling",
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)
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_DEFAULT = ModelCapabilities(
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supports_tool_choice=True,
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supports_json_mode=True,
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supports_json_schema=True,
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preferred_structured_method="function_calling",
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)
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# Exact-ID matches take precedence over pattern matches.
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_BY_ID: dict[str, ModelCapabilities] = {
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"deepseek-chat": _DEEPSEEK_CHAT,
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"deepseek-reasoner": _DEEPSEEK_THINKING,
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"deepseek-v4-flash": _DEEPSEEK_THINKING,
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"deepseek-v4-pro": _DEEPSEEK_THINKING,
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# MiniMax — full official model lineup per
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# platform.minimax.io/docs/api-reference/text-openai-api
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"MiniMax-M2.7": _MINIMAX_THINKING,
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"MiniMax-M2.7-highspeed": _MINIMAX_THINKING,
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"MiniMax-M2.5": _MINIMAX_THINKING,
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"MiniMax-M2.5-highspeed": _MINIMAX_THINKING,
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"MiniMax-M2.1": _MINIMAX_THINKING,
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"MiniMax-M2.1-highspeed": _MINIMAX_THINKING,
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"MiniMax-M2": _MINIMAX_THINKING,
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}
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# Forward-compat patterns. New ``deepseek-v5-*`` / ``deepseek-reasoner-*``
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# or ``MiniMax-M3*`` variants inherit the thinking-mode quirks automatically.
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_BY_PATTERN: list[tuple[re.Pattern[str], ModelCapabilities]] = [
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(re.compile(r"^deepseek-v\d"), _DEEPSEEK_THINKING),
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(re.compile(r"^deepseek-reasoner"), _DEEPSEEK_THINKING),
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(re.compile(r"^MiniMax-M\d"), _MINIMAX_THINKING),
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]
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def get_capabilities(model_name: str) -> ModelCapabilities:
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"""Resolve capabilities by exact ID, then pattern, then default."""
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if model_name in _BY_ID:
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return _BY_ID[model_name]
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for pattern, caps in _BY_PATTERN:
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if pattern.match(model_name):
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return caps
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return _DEFAULT
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