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* test(e2e): migrate access-control and inference-endpoint regression tests Move the access-control and non-chat inference-endpoint cases from litellm-regression-tests onto the shared e2e harness so a regression in either fails here first access_control/ asserts the gateway's authorization and error-shape contract: a key limited to one model is denied 403 (key_model_access_denied) when it calls another, a key scoped to allowed_routes=["llm_api_routes"] is forbidden 403 from a management route, and an unknown model is rejected 400 before any provider is called. The source asserted 401 for the disallowed-model case against an older proxy; the live contract is now a 403, so the guard tracks current behavior llm_translation/ gains one file per non-chat inference endpoint (/v1/responses, /v1/messages, /embeddings, /v1/rerank, /v1/audio/speech, /v1/images/generations). Each test registers the deployment it needs through /model/new, drives real provider traffic, asserts the parsed body carries real content instead of just a 200, then deletes the model on teardown, so nothing is hardcoded into the gateway config * Update endpoints_client.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
"""Live e2e: POST /embeddings returns a real vector.
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Registers an OpenAI embedding deployment at runtime and asserts a non-empty,
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non-zero vector came back. Migrated from
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litellm-regression-tests/tests/test_inference_endpoints.py; the LIT-3167 guard in
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tests/e2e/embeddings/ covers the Gemini embedding path.
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"""
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from __future__ import annotations
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import pytest
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from e2e_config import unique_marker
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from e2e_http import require_successful_call
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from endpoints_client import EmbeddingsResult, EndpointsClient
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from lifecycle import ResourceManager
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from models import LiteLLMParamsBody
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pytestmark = pytest.mark.e2e
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class TestEmbeddingsEndpoint:
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def test_embeddings_returns_vector(
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self, endpoints_client: EndpointsClient, resources: ResourceManager
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) -> None:
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model = f"e2e-embeddings-{unique_marker()}"
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model_id = endpoints_client.create_model(
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model,
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LiteLLMParamsBody(
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model="openai/text-embedding-3-small", api_key="os.environ/OPENAI_API_KEY"
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),
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)
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resources.defer(lambda: endpoints_client.delete_model(model_id))
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key = resources.key()
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result = endpoints_client.embeddings(key, model, "Say this is a test!")
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require_successful_call(result)
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parsed = EmbeddingsResult.model_validate_json(result.body)
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assert parsed.first_vector, f"/embeddings returned no vector: {result.body[:300]}"
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assert any(component != 0.0 for component in parsed.first_vector), (
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f"embedding vector is all zeros: {result.body[:300]}"
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)
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