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* feat(llm): extra request-body fields via LLM_EXTRA_BODY Some provider parameters, such as OpenAI's service_tier and safety_identifier, are read only from the request body, so LLM_EXTRA_HEADERS can't set them. LLM_EXTRA_BODY merges a JSON object into the body of every request to the configured model, on both routing paths. A dedicated dedupe model gets its own DEDUPE_LLM_EXTRA_BODY instead, as with headers. * fix(config): decode saved JSON-object settings when loading persist_current() saves env values verbatim, so LLM_EXTRA_HEADERS, LLM_EXTRA_BODY and their DEDUPE_ variants land in cli-config.json as JSON text. The loader passed that text unchanged to the dict fields, so a later launch without the env var failed validation. Decode saved strings for dict fields, as pydantic-settings does for env vars. --------- Co-authored-by: Szymon Nastaly <szymon.nastaly@autoform.ch>
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""Tests for LLM_EXTRA_BODY: extra fields merged into every LLM request body."""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING, Any
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import httpx
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import litellm
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import openai
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import pytest
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from agents.extensions.models.litellm_model import LitellmModel
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from agents.models.interface import ModelTracing
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from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel
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from agents.models.openai_responses import OpenAIResponsesModel
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from strix.config import loader
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from strix.config.loader import load_settings
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from strix.core.inputs import make_model_settings
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if TYPE_CHECKING:
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from collections.abc import Iterator
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from agents.models.interface import Model
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_EXTRA_BODY = {"service_tier": "flex", "safety_identifier": "user-hash"}
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@pytest.fixture(autouse=True)
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def _reset(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]:
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monkeypatch.delenv("LLM_EXTRA_BODY", raising=False)
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monkeypatch.setattr(loader, "_cached", None)
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monkeypatch.setattr(loader, "_override", None)
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yield
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def test_extra_body_parsed_from_json_env(monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setenv("LLM_EXTRA_BODY", json.dumps({"service_tier": "flex", "n": 1}))
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assert load_settings().llm.extra_body == {"service_tier": "flex", "n": 1}
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async def _send(model: Model) -> None:
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await model.get_response(
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system_instructions=None,
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input="Reply with just 'OK'.",
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model_settings=make_model_settings(None, model_name="gpt-5", extra_body=_EXTRA_BODY),
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tools=[],
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output_schema=None,
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handoffs=[],
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tracing=ModelTracing.DISABLED,
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previous_response_id=None,
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conversation_id=None,
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prompt=None,
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)
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def _capturing_client(captured: dict[str, Any]) -> openai.AsyncOpenAI:
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def handler(request: httpx.Request) -> httpx.Response:
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captured.update(json.loads(request.content))
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return httpx.Response(400, json={"error": {"message": "captured"}})
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return openai.AsyncOpenAI(
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api_key="test",
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base_url="https://gateway.example/v1",
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max_retries=0,
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http_client=httpx.AsyncClient(transport=httpx.MockTransport(handler)),
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)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model_cls", [OpenAIChatCompletionsModel, OpenAIResponsesModel])
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async def test_extra_body_reaches_native_openai_request_body(model_cls: type) -> None:
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captured: dict[str, Any] = {}
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with pytest.raises(openai.BadRequestError):
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await _send(model_cls(model="gpt-5", openai_client=_capturing_client(captured)))
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assert {k: captured.get(k) for k in _EXTRA_BODY} == _EXTRA_BODY
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@pytest.mark.asyncio
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async def test_extra_body_passed_to_litellm(monkeypatch: pytest.MonkeyPatch) -> None:
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captured: dict[str, Any] = {}
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async def fake_acompletion(**kwargs: Any) -> Any:
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captured.update(kwargs)
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raise RuntimeError("captured")
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monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
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with pytest.raises(RuntimeError, match="captured"):
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await _send(LitellmModel(model="openai/gpt-5", base_url="https://gateway.example/v1"))
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assert captured["extra_body"] == _EXTRA_BODY
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