Adam SmithandClaude Opus 5
6a599e092a
The model field was one screen too late. Reported directly: "this is where I would have expected to see the model dropdown", on the AI Task init step that showed only Name and the recommended checkbox. - CONF_CHAT_MODEL moves from async_step_additional to async_step_init for conversation and ai_task_data, so all four endpoint types now ask on the first screen. stt and tts already did. - UNSUPPORTED_MODELS is validated there too, since the recommended path creates the subentry from that step and would otherwise skip the check. - model_list.default_model() picks what to preselect: RECOMMENDED_CHAT_MODEL only when the provider actually serves it (or discovery returned nothing), otherwise the first advertised model. Offering a stranger's gpt-4o-mini to a LiteLLM user preselects a model that fails on first use. This is the trade I got wrong last commit. I kept the field on the second step to avoid touching 71 ported tests, having judged them unfixable mechanically. They were not: the 71 were ~13 functions under parametrize, and test_subentry_switching's data is already a list of per-step dicts, so moving a model between steps is moving a key between two dicts. Two scripted passes moved 14 of them; 5 needed reasoning about (an error that now surfaces on init rather than the next step, and four cases whose expected data gains the now-always-recorded model). async_fetch_model_ids now also catches non-OpenAIError exceptions. Found via a ported test whose client is a MagicMock: the TypeError from awaiting it escaped and broke the whole form. A real proxy returning a payload the SDK cannot parse would do the same, and this runs inside a dialog with a working free-text fallback, so nothing it raises justifies taking the form down. Also fixes a string that was simply false on a third-party provider, again reported from the UI: "Store requests and responses in OpenAI" / "stored by OpenAI and visible in your OpenAI dashboard logs". It now describes the Responses API store flag and says support varies. Swept the other 43 OpenAI-mentioning strings while there -- the rest are [%key:...%] self-references or this fork's own action names, and no other option text makes a claim about who stores the data. Local: 170 passed. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NxJYWgGJtCw13qUHZtG8ki
96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
"""Model discovery for an OpenAI-compatible endpoint.
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Upstream hardcodes OpenAI's model names as defaults and validates them
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against lists of OpenAI models, which is useless against a proxy or a
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self-hosted server serving entirely different names. This asks the endpoint
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what it actually serves, so the config flow can offer a dropdown instead of
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a free-text box the user has to get exactly right.
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Kept separate from the vendored upstream files so re-syncing with
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home-assistant/core never conflicts with it.
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"""
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from __future__ import annotations
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from typing import Any
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import openai
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from homeassistant.helpers.selector import (
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SelectSelector,
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SelectSelectorConfig,
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SelectSelectorMode,
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)
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from .const import LOGGER, RECOMMENDED_CHAT_MODEL
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DEFAULT_TIMEOUT = 10.0
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async def async_fetch_model_ids(
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client: openai.AsyncOpenAI, *, timeout: float = DEFAULT_TIMEOUT
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) -> list[str]:
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"""Return the endpoint's model ids, sorted and de-duplicated.
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Returns an empty list if the endpoint cannot or will not list them. Model
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discovery is a convenience: an endpoint that serves /v1/responses but not
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/v1/models, or a key scoped so narrowly it cannot enumerate, must still be
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configurable by typing a model name in by hand.
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"""
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try:
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# max_retries=0: the SDK otherwise retries with backoff, and this call
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# runs while the user waits on a config-flow dialog. Discovery is a
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# convenience with a free-text fallback, so one quick attempt is the
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# right trade -- retrying would just stall the form.
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page = await client.with_options(
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max_retries=0, timeout=timeout
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).models.list()
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except openai.OpenAIError as err:
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LOGGER.debug("Could not list models: %s", err)
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return []
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except Exception: # noqa: BLE001 - see below
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# Deliberately broad. This runs inside a config-flow form and has a
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# working fallback (a free-text model field), so nothing it can raise
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# justifies taking the dialog down: a proxy may return a payload the
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# SDK cannot parse, and the failure modes of arbitrary
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# OpenAI-compatible servers are not enumerable in advance.
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LOGGER.debug("Unexpected error listing models", exc_info=True)
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return []
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return sorted({model.id for model in page.data if getattr(model, "id", None)})
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def model_field(model_ids: list[str]) -> Any:
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"""Return a schema field for choosing a model.
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A dropdown of what the endpoint advertises when that is known, otherwise a
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plain string field. The dropdown accepts a typed-in value too, since an
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endpoint may route a model it does not list (LiteLLM wildcard routes, for
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instance).
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"""
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if not model_ids:
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return str
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return SelectSelector(
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SelectSelectorConfig(
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options=model_ids,
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custom_value=True,
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mode=SelectSelectorMode.DROPDOWN,
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sort=True,
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)
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)
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def default_model(model_ids: list[str]) -> str:
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"""Return the model to preselect for a new endpoint.
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Upstream's default is RECOMMENDED_CHAT_MODEL, an OpenAI name. Offering it
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to someone configuring a third-party provider preselects a model that will
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fail on first use, so prefer it only when the provider actually serves it
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(or when discovery told us nothing) and otherwise start from what is
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advertised.
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"""
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if not model_ids or RECOMMENDED_CHAT_MODEL in model_ids:
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return RECOMMENDED_CHAT_MODEL
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return model_ids[0]
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