Teknium
f7d90c9410
The #89503/#70058/#74295/#87279 bug class kept regenerating because every transport and provider profile hand-rolled its own effort translation map (9 sites, 4 distinct policies). New agent/reasoning_effort.py is the single source of truth: - EFFORT_LADDER: canonical low->high ordering (superset check against VALID_REASONING_EFFORTS pinned by test) - clamp_effort(): one policy — supported passes verbatim, otherwise nearest WEAKER supported level (never escalate, never invert the ladder), floor when nothing weaker, 'none' never a degradation target, declared vendor-documented overrides win, bespoke names pass through - declared wire vocabularies as data: OpenAI-compat, Codex Responses, xAI (4.6/legacy), Actual relays, Kimi K3/K2, TokenHub, GLM-5.2, DeepSeek V4, Ollama Cloud, Meta, Solar Converted sites (all behavior-preserving except noted): - chat_completions chokepoint, Kimi + TokenHub paths - codex transport (backend branches now pick a declared set) - auxiliary_client Responses path - hermes_cli.models clamp_reasoning_effort_to_supported -> thin wrapper - plugins: kimi-coding, zai, opencode-zen, deepseek, ollama-cloud, meta-ai, upstage, custom (copilot already routes via the wrapper) Behavior fixes the shared policy surfaces: - ollama-cloud/opencode-go 'minimal' now degrades to 'low' instead of being dropped (drop left the server default = MORE thinking than asked) New tests: ladder contract (every configurable level is clamped by every declared wire set; monotonicity across the full ladder for every set).
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
"""Ollama Cloud provider profile.
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Ollama Cloud's OpenAI-compatible ``/v1/chat/completions`` endpoint
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supports top-level ``reasoning_effort`` with values ``none``, ``low``,
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``medium``, ``high``, and ``max`` (the last being undocumented but
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empirically confirmed for DeepSeek V4 — ``max`` produces ~2.5× more
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thinking tokens than ``high``).
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This profile maps Hermes's ``xhigh`` → ``max`` to unlock DeepSeek V4's
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"Max thinking" tier through Ollama Cloud. ``low`` / ``medium`` / ``high``
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pass through unchanged.
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When reasoning is explicitly disabled (``enabled: false`` or
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``effort: "none"``), ``reasoning_effort`` is omitted entirely so the
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model runs in non-thinking mode.
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"""
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from __future__ import annotations
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from typing import Any
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from providers import register_provider
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from providers.base import ProviderProfile
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class OllamaCloudProfile(ProviderProfile):
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"""Ollama Cloud — maps xhigh→max via top-level reasoning_effort."""
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def build_api_kwargs_extras(
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self,
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*,
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reasoning_config: dict | None = None,
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supports_reasoning: bool = False,
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**ctx: Any,
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) -> tuple[dict[str, Any], dict[str, Any]]:
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"""Emit top-level ``reasoning_effort`` for Ollama Cloud thinking models.
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Gated on ``supports_reasoning``, which the transport resolves from the
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model's native ``/api/show`` ``capabilities`` (``thinking``). Models
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without the thinking capability (e.g. ``gemma3``, ``qwen3-coder``) get
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no ``reasoning_effort`` at all — emitting it there is a no-op the API
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ignores, and gating avoids sending a meaningless field.
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"""
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top_level: dict[str, Any] = {}
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if not supports_reasoning:
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return {}, {}
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if reasoning_config and isinstance(reasoning_config, dict):
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enabled = reasoning_config.get("enabled", True)
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if enabled is False:
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# Ollama Cloud defaults to thinking ON, and ignores the
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# extra_body.thinking:{type:disabled} shape (verified live).
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# The ONLY way to actually suppress thinking on its
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# /v1/chat/completions endpoint is top-level
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# reasoning_effort:"none" — omitting the field leaves
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# thinking on.
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return {}, {"reasoning_effort": "none"}
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effort = (reasoning_config.get("effort") or "").strip().lower()
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if not effort:
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# No explicit effort requested — let the model decide
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# (Ollama Cloud's server default is thinking ON).
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return {}, {}
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if effort == "none":
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return {}, {"reasoning_effort": "none"} # explicit off switch
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# Accepted set {none, low, medium, high, max} is declared in
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# agent.reasoning_effort ("minimal" is rejected with HTTP 400 →
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# clamps to low; xhigh rounds up to max). Bespoke levels outside
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# the ladder are omitted so the model applies its own default
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# rather than triggering a hard 400.
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from agent.reasoning_effort import (
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OLLAMA_CLOUD_EFFORTS,
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OLLAMA_CLOUD_OVERRIDES,
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clamp_effort,
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)
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clamped = clamp_effort(
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effort, OLLAMA_CLOUD_EFFORTS, OLLAMA_CLOUD_OVERRIDES
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)
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if clamped in OLLAMA_CLOUD_EFFORTS:
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top_level["reasoning_effort"] = clamped
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return {}, top_level
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ollama_cloud = OllamaCloudProfile(
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name="ollama-cloud",
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aliases=("ollama_cloud",),
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default_aux_model="nemotron-3-nano:30b",
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env_vars=("OLLAMA_API_KEY",),
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base_url="https://ollama.com/v1",
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)
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register_provider(ollama_cloud)
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