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Providers: omit temperature for OpenAI reasoning models

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* fix: omit temperature for OpenAI reasoning models (o1/o3/o4/gpt-5)

These models only accept the default temperature; sending any explicit
value (even 0.0) returns HTTP 400 "Only the default (1) value is
supported". This broke two paths:

- Endpoint probing in _probe_single_model hardcodes temperature: 0.0, so
  a perfectly valid o3/gpt-5 endpoint is reported as failing in the
  Model Endpoints health check.
- Chat/stream payloads send temperature unconditionally, so a non-default
  temperature preset 400s on these models.

The code already special-cases the same model family for
max_completion_tokens, so this adds a sibling _restricts_temperature()
helper and omits the field for those models, letting the API use its
required default. gpt-4.5 is intentionally excluded (not a reasoning
model; accepts temperature normally).

Adds tests/test_llm_core_temperature.py covering the predicate and the
synchronous payload builder.

* fix: also omit temperature for reasoning models on the direct-POST paths

The first commit only covered llm_call/llm_call_async/stream_llm and the
endpoint probe. Email auto-summary, urgency-less spam classification, the
email reply-summary endpoint, and gallery vision tagging build their
OpenAI payloads inline and POST them directly (requests/httpx), bypassing
llm_core — so a reasoning model configured there would still 400 on the
temperature field. These sites already branch on _uses_max_completion_tokens,
so they're the same class; added the matching _restricts_temperature guard.

gallery_routes also gains the max_completion_tokens branch it was missing,
so gpt-5 vision tagging works end to end.

Note: email_pollers urgency scoring goes through llm_call_async and was
already covered.
This commit is contained in:
SurprisedDuck
2026-06-02 13:58:33 +02:00
committed by GitHub
parent 119075f368
commit 934bca9e48
6 changed files with 113 additions and 6 deletions
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routes/model_routes.py
+6 -2
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@@ -251,9 +251,13 @@ def _probe_single_model(base: str, api_key: str, model_id: str, timeout: int = 1
target_url = build_chat_url(base)
h = build_headers(api_key, base)
h["Content-Type"] = "application/json"
from src.llm_core import _uses_max_completion_tokens
from src.llm_core import _uses_max_completion_tokens, _restricts_temperature
_max_key = "max_completion_tokens" if _uses_max_completion_tokens(model_id) else "max_tokens"
payload = {"model": model_id, "messages": messages, _max_key: 5, "temperature": 0.0}
payload = {"model": model_id, "messages": messages, _max_key: 5}
# Reasoning models (o1/o3/o4/gpt-5) reject an explicit temperature, so a
# probe that hardcodes one falsely reports a working endpoint as failing.
if not _restricts_temperature(model_id):
payload["temperature"] = 0.0
if _test_tools:
payload["tools"] = _test_tools