litellm/tests/e2e/batches/COVERAGE.md
mubashir1osmani ed07aec89f
Merge pull request #32166 from BerriAI/litellm_e2e_batches_ocr_model_registration
fix(e2e): register batch + rust OCR deployments via /model/new
2026-07-05 02:15:10 +00:00

4.8 KiB

Batches Test Coverage Matrix

Live e2e coverage of the Batches API over a real proxy, real provider keys, and real cost. Synchronous tier only: a batch's completion window is 24h, so these tests never wait for completed. They assert the proxy accepts, routes, retrieves, cancels, and lists a batch; everything created is deleted on teardown.

Provider x operation

Only supported cells are tested. The capability table in capabilities.py holds one row per supported (provider, scenario) pair, so there are no skipped cells in the parametrized run. The batches suite never skips: missing provider creds or upstream failures are hard test failures (see tests/e2e/CLAUDE.md).

Provider create retrieve cancel list file backing
OpenAI yes yes yes yes OpenAI Files
Azure yes yes yes yes Azure Files
Vertex AI yes yes yes yes GCS bucket (GCS_BUCKET_NAME via files_settings)
Bedrock yes yes no (limited upstream) no S3 bucket (AWS_BATCH_S3_BUCKET + AWS_BATCH_ROLE_ARN on model)

Bedrock cancel is unreliable upstream and list is unsupported, so both are gated off (can_cancel=False, can_list=False) when that provider is enabled in the matrix. Bedrock file upload requires a model on the request (encoded / unified scenarios only); model_param and provider_fallback are omitted because POST /bedrock/v1/files has no model-less passthrough path.

Routing scenarios (per litellm/proxy/batches_endpoints/endpoints.py)

Each create-capable provider runs all four. The test asserts the returned file id and batch id carry the shape that scenario must produce (matches_id_shape):

Scenario How the batch is routed File id Batch id
encoded upload with ?model= -> model-encoded file id -> create with just that id model-encoded model-encoded
unified upload with target_model_names= -> unified managed file id -> create with that id managed managed
model_param raw file (provider-fallback upload) -> create with model in the body raw model-encoded
provider_fallback raw file -> POST /{provider}/v1/batches, env creds, no model raw raw (native provider shape)

"managed" ids base64-decode to a litellm_proxy marker; "model-encoded" ids keep the provider prefix and base64-encode litellm:<id>;model,<model>; "raw" ids are the provider's native ids. Asserting these catches a proxy that returns a raw id where it should manage it, or vice versa. On top of the id shape, a misroute to the wrong provider also fails create (the file id / model do not belong there), and the provider_fallback raw batch id is additionally checked against the provider's native shape (raw_id_matches_provider).

Key model restriction

test_batch_key_model_access_denied mints a key restricted to one model (resources.key(models=[...])) and proves the proxy returns 403 key_model_access_denied both when that key uploads a file for a disallowed model (files endpoint) and when it creates a batch for a disallowed model (batches endpoint).

Per-endpoint output assertions

Each endpoint's full response is validated, not just the id. File upload asserts object=="file", purpose=="batch", a positive bytes, a status, and a created-at. Batch create / retrieve assert object=="batch", endpoint=="/v1/chat/completions", completion_window=="24h", a non-empty input_file_id, and a created-at; retrieve additionally cross-checks that id and input_file_id match the created batch. Cancel asserts the same id, object=="batch", and a cancelling/cancelled status. List asserts the object=="list" envelope and that the created batch is present as a batch. File delete asserts object=="file" and deleted==True.

This suite's files

File Covers
batch_client.py typed file upload/download + batch create/retrieve/cancel/list/delete over the shared Gateway; runtime batch model registration via /model/new; denial helpers
capabilities.py the provider x scenario matrix + per-provider /model/new params + id-shape classifiers + per-provider raw-id assertion
conftest.py session-scoped batch deployment registration and teardown
test_batches_e2e.py parametrized lifecycle with per-endpoint output assertions, file upload/delete outputs, key-model-access denial

Out of scope (intentionally)

Driving a batch to completed, cost tracking on completion, and the DB write-back are not covered here; the 24h window makes them unfit for a synchronous gate. That logic belongs in a DI-stubbed proxy integration test under tests/test_litellm/proxy/ where the provider client is injected to return completed deterministically.