wehub-resource-sync
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
"""Unit tests for the workflow-level ``_apply_request_model_override`` helper.
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Mirrors the shape of :mod:`tests.runtime.test_reasoning_effort_validation`
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in scope (workflow-internal helper, no DBOS / no FastAPI). The helper
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backs the ``/model`` slash command's server-side substitution step:
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the request's optional ``model_override`` becomes the effective
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``LLMConfig.model`` for that single execution while also being
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stashed in ``extra["model_override"]`` so the harness path can
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forward it.
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"""
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from __future__ import annotations
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from omnigent.runtime.workflow import _apply_request_model_override
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from omnigent.spec.types import LLMConfig
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def test_apply_request_model_override_none_returns_input_unchanged() -> None:
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"""Passing ``None`` is a no-op — the spec's model and extra survive.
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Critical to the design contract: when no per-request override
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is set, the agent spec's configured model wins. Any leak here
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would silently swap models on every request.
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"""
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original = LLMConfig(
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model="databricks-gpt-5-4",
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extra={"temperature": 0.5},
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)
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result = _apply_request_model_override(original, None)
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# The returned value is functionally identical to the input.
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assert result.model == "databricks-gpt-5-4"
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assert result.extra == {"temperature": 0.5}
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# ``extra["model_override"]`` is absent — downstream harness
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# propagation must distinguish "no override" from "override
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# set to the spec's own model".
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assert "model_override" not in result.extra
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def test_apply_request_model_override_substitutes_model_field() -> None:
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"""A non-None override becomes the effective ``llm_config.model``.
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The harness subprocess reads ``llm_config.model`` when calling
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the LLM; substituting it here is what makes the override
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actually take effect on the wire.
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"""
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original = LLMConfig(
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model="databricks-gpt-5-4",
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extra={"temperature": 0.5},
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)
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result = _apply_request_model_override(original, "openai/gpt-5.4-mini")
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assert result.model == "openai/gpt-5.4-mini"
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def test_apply_request_model_override_stashes_override_in_extra() -> None:
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"""The override is also recorded in ``extra["model_override"]``.
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The harness path can't distinguish "spec default model" from
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"user-set override" by looking at ``llm_config.model`` alone —
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both produce the same string after substitution. ``extra`` is
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the unambiguous signal that ``the harness HTTP client`` reads to
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decide whether to emit ``body["model_override"]`` on the wire.
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"""
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original = LLMConfig(model="databricks-gpt-5-4", extra={})
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result = _apply_request_model_override(original, "openai/gpt-5.4-mini")
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assert result.extra["model_override"] == "openai/gpt-5.4-mini"
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def test_apply_request_model_override_preserves_other_extra_keys() -> None:
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"""Existing ``extra`` keys (temperature, reasoning_effort) survive.
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``_apply_request_model_override`` runs AFTER
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``_apply_request_reasoning``, so any reasoning_effort the
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request layered in must still be present after the model
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substitution. If this regresses, ``/effort`` and ``/model``
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silently fight when used together.
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"""
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original = LLMConfig(
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model="databricks-gpt-5-4",
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extra={"temperature": 0.5, "reasoning_effort": "high"},
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)
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result = _apply_request_model_override(original, "openai/gpt-5.4-mini")
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# Pre-existing keys still there.
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assert result.extra["temperature"] == 0.5
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assert result.extra["reasoning_effort"] == "high"
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# New key added on top.
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assert result.extra["model_override"] == "openai/gpt-5.4-mini"
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def test_apply_request_model_override_does_not_mutate_input() -> None:
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"""The helper returns a new LLMConfig — the input is untouched.
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Mirrors ``_apply_request_reasoning``'s contract. Mutation of
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the agent's cached ``spec.llm`` would leak the override into
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every subsequent request hitting the same spec, which is
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exactly the bug ``LLMConfig``'s frozen-by-convention contract
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exists to prevent.
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"""
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original = LLMConfig(
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model="databricks-gpt-5-4",
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extra={"temperature": 0.5},
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)
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_apply_request_model_override(original, "openai/gpt-5.4-mini")
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# Original is unchanged after the call.
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assert original.model == "databricks-gpt-5-4"
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assert original.extra == {"temperature": 0.5}
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