mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-20 00:11:50 +00:00
17fde7a261
Replace the hand-rolled Meta realtime handler with a MetaRealtimeConfig that plugs into the shared realtime handler and RealTimeStreaming relay. Clients keep speaking the OpenAI realtime wire: session.update, input_audio_buffer.append/commit and the OpenAI transcription events. Unsupported transcription settings are logged and dropped, matching the Gemini realtime precedent, and the Meta-specific session.mode, keywords, language_bias, DIARIZATION and speaker extensions are removed. Drop the MODEL_API_KEY env var in favor of the standard META_API_KEY, remove the private-logging flag so spend logs record the transcript the same way other realtime models do, and add per-second pricing for muse-voice-transcribe-1.0. The relay now sends raw bytes from transform_realtime_request straight to the backend after pace_backend_send, and transcription sessions never trigger response.create.
432 lines
17 KiB
Python
432 lines
17 KiB
Python
import asyncio
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import time
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from types import TracebackType
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from typing import Final
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from unittest.mock import MagicMock, patch
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import pytest
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import litellm
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from litellm.realtime_api import main as realtime_main
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from litellm.realtime_api.main import _with_resolved_session_model
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class FakeLogging:
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def update_from_kwargs(self, **kwargs):
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pass
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def test_resolves_top_level_session_model():
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resolved = _with_resolved_session_model({"model": "alias/gpt-realtime"}, "gpt-realtime")
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assert resolved == {"model": "gpt-realtime"}
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def test_session_without_model_is_returned_unchanged():
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session = {"type": "realtime", "audio": {"input": {}}}
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assert _with_resolved_session_model(session, "gpt-realtime") == session
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def test_does_not_clobber_flat_transcription_model():
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"""The nested transcription model is a different model than the realtime
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conversation model and must not be overwritten with the routing model."""
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resolved = _with_resolved_session_model(
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{"model": "gpt-4o-realtime-preview", "input_audio_transcription": {"model": "whisper-1"}},
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"gpt-4o-realtime-preview",
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)
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assert resolved["input_audio_transcription"]["model"] == "whisper-1"
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def test_does_not_clobber_nested_audio_transcription_model():
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resolved = _with_resolved_session_model(
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{
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"model": "gpt-4o-realtime-preview",
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"audio": {"input": {"transcription": {"model": "whisper-1"}}},
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},
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"gpt-4o-realtime-preview",
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)
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assert resolved["audio"]["input"]["transcription"]["model"] == "whisper-1"
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def test_original_session_is_not_mutated():
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session = {"model": "alias/gpt-realtime"}
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_with_resolved_session_model(session, "gpt-realtime")
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assert session == {"model": "alias/gpt-realtime"}
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def _run_client_secret(session, model, monkeypatch):
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captured = {}
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async def mock_handler(**kwargs):
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captured.update(kwargs)
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return object()
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def mock_get_llm_provider(model, api_base, api_key):
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return model, "openai", None, api_base
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monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
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monkeypatch.setattr(
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realtime_main.base_llm_http_handler,
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"async_realtime_client_secret_handler",
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mock_handler,
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)
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asyncio.run(
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realtime_main.acreate_realtime_client_secret.__wrapped__(
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model=model,
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session=session,
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litellm_logging_obj=FakeLogging(),
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)
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)
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return captured
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def test_client_secret_session_model_takes_priority_over_top_level(monkeypatch):
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"""Backwards-compatible ordering: an explicit session.model wins over the
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top-level model, matching the proxy's own resolution order."""
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captured = _run_client_secret(
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session={"model": "gpt-realtime-session"},
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model="gpt-realtime-top-level",
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monkeypatch=monkeypatch,
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)
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assert captured["model"] == "gpt-realtime-session"
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assert captured["request_data"]["session"]["model"] == "gpt-realtime-session"
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async def _hanging_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
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await asyncio.sleep(30)
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return "", ""
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async def _thread_offloaded_hanging_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
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from litellm.litellm_core_utils.asyncify import asyncify
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await asyncify(time.sleep)(30)
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return "", ""
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async def _instant_resolver(credentials, project_id, custom_llm_provider) -> tuple[str, str]:
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return "token-abc", "resolved-project"
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@pytest.mark.asyncio
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async def test_vertex_credential_resolution_returns_the_resolved_token_and_project():
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assert await realtime_main._resolve_vertex_access_token_bounded(
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credentials="fake-credentials",
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project_id="fake-project",
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resolver=_instant_resolver,
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timeout_seconds=5,
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) == ("token-abc", "resolved-project")
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@pytest.mark.asyncio
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async def test_vertex_credential_resolution_times_out_instead_of_hanging():
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"""Regression for the realtime accept-then-silence hang: a stalled Google
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OAuth token refresh used to block the vertex branch unbounded (minutes of
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zero frames for the client). It must raise promptly and name the timeout."""
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start = time.monotonic()
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with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
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await realtime_main._resolve_vertex_access_token_bounded(
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credentials="fake-credentials",
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project_id="fake-project",
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resolver=_hanging_resolver,
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timeout_seconds=0.05,
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)
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assert time.monotonic() - start < 5
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@pytest.mark.asyncio
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async def test_vertex_credential_resolution_bounds_a_thread_offloaded_refresh():
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"""The real stall is a blocking google-auth refresh that runs in a worker
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thread via asyncify, not a plain awaitable sleep. A timeout that only bounds
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cancellable awaits would leave that shape hanging, so bound the shape the
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proxy actually runs."""
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start = time.monotonic()
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with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
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await realtime_main._resolve_vertex_access_token_bounded(
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credentials="fake-credentials",
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project_id="fake-project",
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resolver=_thread_offloaded_hanging_resolver,
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timeout_seconds=0.05,
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)
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assert time.monotonic() - start < 5
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@pytest.mark.asyncio
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async def test_meta_realtime_dispatches_to_base_handler_with_meta_config(monkeypatch: pytest.MonkeyPatch):
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from litellm.llms.meta.realtime.transformation import MetaRealtimeConfig
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captured: dict[str, object] = {}
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def mock_get_llm_provider(model, api_base, api_key):
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return model.removeprefix("meta/"), "meta", None, api_base
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async def mock_async_realtime(**kwargs):
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captured.update(kwargs)
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monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
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monkeypatch.setattr(realtime_main.base_llm_http_handler, "async_realtime", mock_async_realtime)
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await realtime_main._arealtime.__wrapped__(
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model="meta/muse-voice-transcribe-1.0",
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websocket=MagicMock(),
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litellm_logging_obj=FakeLogging(),
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query_params={"model": "meta/muse-voice-transcribe-1.0", "intent": "transcription"},
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)
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assert isinstance(captured["provider_config"], MetaRealtimeConfig)
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assert captured["model"] == "muse-voice-transcribe-1.0"
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assert captured["query_params"] == {"model": "muse-voice-transcribe-1.0", "intent": "transcription"}
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@pytest.mark.asyncio
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async def test_arealtime_vertex_branch_resolves_credentials_under_a_bound(monkeypatch):
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"""The wiring half of the regression: the vertex branch of _arealtime must
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go through the bounded resolver, so a hung token refresh surfaces as a
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prompt error there rather than as an accepted-then-silent websocket."""
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async def hanging_token_refresh(**kwargs):
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await asyncio.sleep(30)
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def mock_get_llm_provider(model, api_base, api_key):
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return model, "vertex_ai", None, api_base
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monkeypatch.setattr(realtime_main, "get_llm_provider", mock_get_llm_provider)
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monkeypatch.setattr(realtime_main, "vertex_access_token_resolver", hanging_token_refresh)
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monkeypatch.setattr(realtime_main, "REALTIME_CREDENTIAL_RESOLUTION_TIMEOUT_SECONDS", 0.05)
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start = time.monotonic()
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with pytest.raises(ValueError, match="timed out fetching Google OAuth access token"):
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await realtime_main._arealtime.__wrapped__(
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model="gemini-live-2.5-flash",
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websocket=MagicMock(),
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litellm_logging_obj=FakeLogging(),
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vertex_credentials="fake-credentials",
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vertex_project="fake-project",
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vertex_location="us-central1",
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)
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assert time.monotonic() - start < 5
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def test_client_secret_forwards_nested_transcription_model_untouched(monkeypatch):
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captured = _run_client_secret(
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session={
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"model": "gpt-4o-realtime-preview",
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"input_audio_transcription": {"model": "whisper-1"},
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},
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model=None,
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monkeypatch=monkeypatch,
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)
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session = captured["request_data"]["session"]
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assert session["model"] == "gpt-4o-realtime-preview"
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assert session["input_audio_transcription"]["model"] == "whisper-1"
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class _CapturingConnect:
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def __init__(self) -> None:
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self.url: str | None = None
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def __call__(self, url: str, **kwargs: object) -> "_CapturingConnect":
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self.url = url
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return self
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async def __aenter__(self) -> MagicMock:
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return MagicMock()
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async def __aexit__(
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self,
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exc_type: type[BaseException] | None,
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exc: BaseException | None,
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tb: TracebackType | None,
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) -> None:
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return None
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@pytest.mark.asyncio
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async def test_azure_health_check_probes_ga_transcription_url_for_transcription_model(local_model_cost_map):
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"""Regression for LIT-6240: transcription-only models (mode audio_transcription
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in the cost map) are GA-only and 400 on the beta path, so the health probe
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must hit /openai/v1/realtime?intent=transcription like real calls do."""
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connect = _CapturingConnect()
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with patch("websockets.connect", connect):
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assert await realtime_main._realtime_health_check(
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model="gpt-realtime-whisper",
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custom_llm_provider="azure",
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api_key="fake-key",
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api_base="https://my-endpoint.openai.azure.com",
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api_version="2025-04-01-preview",
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)
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assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?intent=transcription"
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@pytest.mark.asyncio
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async def test_azure_health_check_stays_on_ga_when_deployment_registration_overwrites_mode(
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local_model_cost_map, monkeypatch
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):
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"""In a live proxy, Router._register_deployment_in_model_cost writes the
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operator's deployment model_info (mode: realtime) over the catalog entry for
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azure/gpt-realtime-whisper, so mode alone misreads the model as speech-capable
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and the probe regresses to the beta path. supported_endpoints survives that
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registration and must keep the probe on the GA transcription path."""
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polluted = {**litellm.model_cost["azure/gpt-realtime-whisper"], "mode": "realtime"}
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monkeypatch.setitem(litellm.model_cost, "azure/gpt-realtime-whisper", polluted)
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connect = _CapturingConnect()
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with patch("websockets.connect", connect):
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assert await realtime_main._realtime_health_check(
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model="gpt-realtime-whisper",
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custom_llm_provider="azure",
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api_key="fake-key",
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api_base="https://my-endpoint.openai.azure.com",
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api_version="2025-04-01-preview",
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)
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assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?intent=transcription"
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def test_transcription_only_detection_falls_back_to_mode(local_model_cost_map):
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"""azure/whisper-1 declares mode audio_transcription but no supported_endpoints,
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so only the mode signal can classify it as transcription-only."""
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assert realtime_main._is_transcription_only_realtime_model("whisper-1", "azure") is True
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def test_transcription_only_detection_rejects_speech_model(local_model_cost_map):
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assert realtime_main._is_transcription_only_realtime_model("gpt-realtime-mini", "azure") is False
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@pytest.mark.asyncio
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async def test_azure_health_check_probes_the_ga_upstream_for_an_unconfigured_speech_model(monkeypatch):
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monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
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connect = _CapturingConnect()
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with patch("websockets.connect", connect):
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assert await realtime_main._realtime_health_check(
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model="gpt-4o-realtime-preview",
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custom_llm_provider="azure",
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api_key="fake-key",
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api_base="https://my-endpoint.openai.azure.com",
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api_version="2024-10-01-preview",
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)
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assert connect.url == "wss://my-endpoint.openai.azure.com/openai/v1/realtime?model=gpt-4o-realtime-preview"
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_AZURE_BETA_HEALTH_URL: Final = (
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"wss://my-endpoint.openai.azure.com/openai/realtime"
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"?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview"
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)
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@pytest.mark.asyncio
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async def test_azure_health_check_honors_deployment_realtime_protocol(monkeypatch):
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monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
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connect = _CapturingConnect()
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with patch("websockets.connect", connect):
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assert await realtime_main._realtime_health_check(
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model="gpt-4o-realtime-preview",
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custom_llm_provider="azure",
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api_key="fake-key",
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api_base="https://my-endpoint.openai.azure.com",
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api_version="2024-10-01-preview",
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model_params={"realtime_protocol": "beta"},
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)
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assert connect.url == _AZURE_BETA_HEALTH_URL
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@pytest.mark.asyncio
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async def test_azure_health_check_honors_env_realtime_protocol(monkeypatch):
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monkeypatch.setenv("LITELLM_AZURE_REALTIME_PROTOCOL", "beta")
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connect = _CapturingConnect()
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with patch("websockets.connect", connect):
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assert await realtime_main._realtime_health_check(
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model="gpt-4o-realtime-preview",
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custom_llm_provider="azure",
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api_key="fake-key",
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api_base="https://my-endpoint.openai.azure.com",
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api_version="2024-10-01-preview",
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)
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assert connect.url == _AZURE_BETA_HEALTH_URL
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class _ConnectThatStopsAfterCapturingTheUrl:
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url: str | None = None
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def __call__(self, url: str, **kwargs: object) -> "_ConnectThatStopsAfterCapturingTheUrl":
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self.url = url
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return self
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async def __aenter__(self) -> None:
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raise RuntimeError("backend url captured, nothing to bridge")
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async def __aexit__(
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self,
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exc_type: type[BaseException] | None,
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exc: BaseException | None,
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tb: TracebackType | None,
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) -> None:
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return None
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@pytest.mark.asyncio
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async def test_arealtime_azure_ai_on_a_foundry_host_connects_to_the_azure_openai_realtime_route():
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connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
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with patch("websockets.connect", connect):
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await realtime_main._arealtime.__wrapped__(
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model="azure_ai/gpt-realtime-mini",
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websocket=MagicMock(),
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api_base="https://my-project.services.ai.azure.com",
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api_key="fake-key",
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litellm_logging_obj=FakeLogging(),
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)
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assert connect.url == "wss://my-project.services.ai.azure.com/openai/v1/realtime?model=gpt-realtime-mini"
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class _ClientWebSocketWithHeaders:
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def __init__(self, headers: tuple[tuple[bytes, bytes], ...]) -> None:
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self.scope: Final = {"headers": headers}
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_GA_CLIENT: Final = _ClientWebSocketWithHeaders(headers=())
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_BETA_CLIENT: Final = _ClientWebSocketWithHeaders(headers=((b"openai-beta", b"realtime=v1"),))
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async def _azure_backend_url_dialed_for(websocket: _ClientWebSocketWithHeaders, **kwargs: object) -> str | None:
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connect: Final = _ConnectThatStopsAfterCapturingTheUrl()
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with patch("websockets.connect", connect):
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await realtime_main._arealtime.__wrapped__(
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model="azure/gpt-realtime",
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websocket=websocket,
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api_base="https://my-endpoint.openai.azure.com",
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api_key="fake-key",
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litellm_logging_obj=FakeLogging(),
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**kwargs,
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)
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return connect.url
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@pytest.mark.asyncio
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async def test_arealtime_azure_ga_client_without_beta_header_dials_the_ga_upstream(monkeypatch):
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monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
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assert (
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await _azure_backend_url_dialed_for(_GA_CLIENT)
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== "wss://my-endpoint.openai.azure.com/openai/v1/realtime?model=gpt-realtime"
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)
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@pytest.mark.asyncio
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async def test_arealtime_azure_beta_header_client_keeps_the_beta_upstream(monkeypatch):
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monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
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assert await _azure_backend_url_dialed_for(_BETA_CLIENT) == (
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"wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
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)
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@pytest.mark.asyncio
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async def test_arealtime_azure_explicit_beta_protocol_wins_over_a_ga_client(monkeypatch):
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monkeypatch.delenv("LITELLM_AZURE_REALTIME_PROTOCOL", raising=False)
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assert await _azure_backend_url_dialed_for(_GA_CLIENT, realtime_protocol="beta") == (
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"wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
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)
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@pytest.mark.asyncio
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async def test_arealtime_azure_env_beta_protocol_wins_over_a_ga_client(monkeypatch):
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monkeypatch.setenv("LITELLM_AZURE_REALTIME_PROTOCOL", "beta")
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assert await _azure_backend_url_dialed_for(_GA_CLIENT) == (
|
|
"wss://my-endpoint.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-realtime"
|
|
)
|