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test(e2e/batches): cover bedrock model-scoped-file create_batch

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
Mubashir Osmani 2026-07-20 18:47:11 +00:00
parent ce88999ed1
commit 6baa264ac0
2 changed files with 8 additions and 3 deletions
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9
tests/e2e/batches/COVERAGE.md
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@ -17,13 +17,18 @@ failures are hard test failures (see `tests/e2e/CLAUDE.md`).
| OpenAI | yes | yes | yes | yes | OpenAI Files |
| Azure | yes | yes | yes | yes | Azure Files |
| Vertex AI | yes | yes | yes | yes | GCS (`gcs_bucket_name` / `GCS_BUCKET_NAME` on model) |
| Bedrock | yes (unified only) | yes | no (limited upstream) | no | S3 (`s3_bucket_name` + `aws_*` + `AWS_BATCH_ROLE_ARN` on model) |
| Bedrock | yes (`encoded` + `unified`) | yes | no (limited upstream) | no | S3 (`s3_bucket_name` + `aws_*` + `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.
model-less passthrough path, and Bedrock `create_batch` itself has no model-less path:
without a model it raises `LiteLLM doesn't support custom_llm_provider=bedrock for
'create_batch'`, since the model is what resolves the region, batch config, and IAM role
ARN. The `encoded` and `unified` scenarios both recover that model at create time (from the
model-scoped file id, or the managed file's `target_model_names`), so both route into the
Bedrock job-creation path; a raw file created model-less cannot.
## Routing scenarios (per `litellm/proxy/batches_endpoints/endpoints.py`)

2
tests/e2e/batches/capabilities.py
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@ -145,7 +145,7 @@ def _model_for(provider_name: str) -> str:
OPENAI_BATCH_MODEL = _model_for("openai")
AZURE_BATCH_MODEL = _model_for("azure")
BEDROCK_SCENARIOS: tuple[Scenario, ...] = ("unified",)
BEDROCK_SCENARIOS: tuple[Scenario, ...] = ("encoded", "unified")
def scenarios_for_provider(provider: Provider) -> tuple[Scenario, ...]: