litellm/tests/e2e/coverage_registry
devin-ai-integration[bot] c9e8a04139
feat(vertex): native batch JSONL passthrough with cost tracking (#42810)
* feat(vertex): native batch JSONL passthrough with cost tracking

Add a per-request `passthrough=true` multipart field on `POST /v1/files`
(and the same kwarg on `litellm.create_file`) that uploads a native
Vertex AI batch JSONL to the deployment's GCS bucket unchanged, so rows
using `googleSearch` and other Gemini-only features run as written and
the output, `groundingMetadata` included, comes back untouched.

Passthrough is sticky through the GCS object path
(`litellm-vertex-files/passthrough/...`), so batch create and output
retrieval inherit it without new state. Native output rows are costed
from their `usageMetadata` with the deployment's model and model_info,
in the polling and retrieve paths and for the existing global
`disable_vertex_batch_output_transformation` flag, which billed $0
before.

The proxy requires the target to resolve to vertex_ai deployments only,
refuses `passthrough` with a non-batch purpose, a non-default
`target_storage`, or pre-call guardrails, and validates native rows on
`request` instead of the OpenAI batch keys.

* refactor(vertex): keep native batch row pricing inside the Vertex adapter

Moves native Vertex batch row detection, response parsing, and per-row
pricing from litellm/batches/batch_utils.py into
litellm/llms/vertex_ai/batches/transformation.py, so batch_utils only
aggregates the rows it gets back. Adds tests/test_litellm/files to the
misc unit shard so the new test directory is claimed by a shard.

* fix(files): say what a passthrough batch upload takes when a row is not native

The missing-key 400 listed bare key names, so an OpenAI-shaped row under
passthrough=true read "Each line must be a JSON object with keys request".
The batch line shape now carries its own hint, and the passthrough one says
a passthrough upload takes native Vertex batch rows with a request key

* fix(batches): bill native Vertex embedding batch rows on the native cost path

A native Vertex output row whose response holds an embedding was validated as a
generateContent response, so the documented tokenCount-only shape counted as a failed
row. Price embedding rows from their own usage (promptTokenCount, else tokenCount) with
the helper the transformed embeddings path already used, and drop the prompt-details
helper nothing calls anymore.

* fix(batches): keep modality batch rates on native Vertex embedding rows

An embedding row that carries usageMetadata was billed from promptTokenCount alone, so
its promptTokensDetails no longer reached the audio, image, and video batch rates the
way it did before the native cost path. Run every row with usageMetadata through the
Gemini usage parser and keep the flat tokenCount fallback for embedding rows without it.

* fix(batches): price native Vertex batch rows by modelVersion under a wildcard deployment

A `vertex_ai/*` deployment hands the batch cost path `*` as the deployment model, which
no cost map resolves, so every native (passthrough or flag-on) row was billed at $0. A
wildcard deployment model now defers to the row's own `modelVersion`, the way the
transformed path already prices by the row's `model`.

Also moves the native passthrough tests under tests/test_litellm, the tree codecov
reads, and covers the raw upload chunking, the embedding output translation, the
unpriceable-row path, and the flag-on dispatch.

* fix(batches): keep explicit deployment prices for native Vertex rows without a modelVersion

Under a wildcard deployment a native batch row that carries no modelVersion (an embedding
row, or a generateContent row Vertex returned without one) was billed at $0 even when the
deployment's model_info sets explicit batch prices, because the cost calculator was never
called. The row now falls back to the wildcard name, which the cost calculator prices from
the explicit model_info, and only a row with neither a modelVersion nor a deployment model
is billed at $0 with the warning

---------

Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
2026-09-24 12:35:34 -07:00
..
__init__.py chore: consolidate CLAUDE.md into AGENTS.md 2026-09-19 02:30:35 +00:00
collector.py fix(e2e-changed): keep the gate off suites the stack cannot run 2026-09-05 21:03:50 -07:00
guardrail.yaml feat(proxy): opt-in include_guardrail_response returns guardrail_information in the response (#42327) 2026-09-22 12:43:30 -07:00
llm_claude_code_compat.yaml test(e2e): replay a real tool-search assistant turn back to Bedrock Invoke (#36856) 2026-08-17 11:59:26 -07:00
llm_conversational.yaml test(e2e): add conversational matrix across chat, messages and responses (#42359) 2026-09-21 22:32:56 -07:00
llm_nonconversational.yaml feat(vertex): native batch JSONL passthrough with cost tracking (#42810) 2026-09-24 12:35:34 -07:00
logging.yaml fix(langsmith): json.dumps with default=str so non-serializable metadata does not crash batch flush (#42424) 2026-09-22 12:21:11 -07:00
management_cases.py test: enforce isolated actors and stop OIDC process groups 2026-09-12 13:49:49 -07:00
mcp.yaml test(e2e): restore LIT-3467 implementation for rework 2026-09-19 16:21:53 -07:00
mgmt.yaml fix(proxy): write key deleted audit logs for cascade and alias key deletions (#42446) 2026-09-22 11:52:08 -07:00
other.yaml fix(proxy): list key and team model aliases in GET /v1/models (#42908) 2026-09-24 06:41:13 -07:00
quota_management.yaml feat(logging): add normalized_error cluster key to error_information (#41715) 2026-09-22 15:56:50 -07:00
README.md chore: consolidate CLAUDE.md into AGENTS.md 2026-09-19 02:30:35 +00:00
registry.py ci: gate tests/e2e on zero basedpyright errors in pre-commit and lint CI 2026-07-11 10:25:22 -07:00
reliability.yaml test(e2e): hold every worker under an idle RSS budget before any traffic (#42552) 2026-09-22 14:47:14 -07:00
schema.py feat(vertex): native batch JSONL passthrough with cost tracking (#42810) 2026-09-24 12:35:34 -07:00
test_collector.py fix(e2e): exclude skipped tests from coverage-registry numerator 2026-07-30 22:19:30 -07:00

e2e coverage registry

This directory is the denominator for e2e test coverage: the set of behaviors we want covered, one row per behavior, checked into the repo so coverage is a number we can track instead of a guess. It implements the plan in the "E2E Coverage Tracking" note; the naming grammar lives in tests/e2e/AGENTS.md.

The model

A cell is one customer-noticeable behavior a single e2e test can assert pass/fail on, for example llm.chat_completions.bedrock_converse.tool_use.stream.works. Cells are grouped module > feature > test, with LLM cells split into Core LLMs and Non-Core LLMs for dashboarding. Each cell carries a tier (P0/P1/P2), a source, and a fail_before_fix flag.

The rows live in per-prefix YAML files (llm_*.yaml, mgmt.yaml, mcp.yaml, reliability.yaml, quota_management.yaml, logging.yaml, guardrail.yaml, other.yaml) and validate against the discriminated union in schema.py, so an LLM row cannot carry a guardrail field and vice versa. llm rows with subject_endpoint of chat_completions, messages, or responses roll up to Core LLMs; all other LLM endpoints roll up to Non-Core LLMs. LLM endpoint, route, and capability values are typed in schema.py, so new taxonomy values require an explicit schema change. logging and guardrail are two id-prefixes that roll up into the single Logging & Guardrails dashboard module.

A test declares what it covers with a marker:

@pytest.mark.covers("llm.chat_completions.openai.tool_use.stream.works")
def test_openai_streaming_tool_calls(self) -> None:
    ...

The number

collector.py diffs the registry against those markers and reports coverage per module. It is static: a collect-only pass reads the markers, so it runs no test and needs no live proxy. Whether a covered cell currently passes or fails is a separate, live concern.

A skipped test asserts nothing, so its markers do not count. A cell is covered only when at least one test pytest would actually run declares it; a cell claimed by both a live test and a skipped one stays covered. Skip state comes from pytest's own evaluator, so skip and skipif resolve exactly as they do in the e2e run, which also means a skipif on an absent credential makes that cell uncovered in the environments where the test cannot run. Cells left uncovered this way are listed under the headline (and counted by litellm_e2e_coverage_skipped_markers) so an unskipped-pending gap is visible rather than inflating the number. The one skip the collector cannot see is pytest.skip() called from inside a test body, since it does not exist until the test runs.

cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector

Use --format loki after the e2e pytest run in the same Kubernetes job/pod to print structured stdout lines for Loki:

cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector --format loki --strict

This emits exactly one COVERAGE_TOTAL line and one COVERAGE_MODULE line per module in MODULE_ORDER, in that order. Loki uses log-safe module= labels from LOKI_MODULE_LABELS (core_llms, management_ui, etc.) so existing JSON and Prometheus consumers keep their human-readable module names unchanged.

The headline is overall coverage. The collector also lists markers that point at ids not in the registry, so a typo or an unenumerated behavior surfaces instead of being silently dropped.

Use strict mode in CI once existing draft markers are reconciled:

cd tests/e2e && PYTHONPATH=. python -m coverage_registry.collector --strict

Strict mode exits non-zero on @pytest.mark.covers(...) ids that are not checked into the registry. Add --fail-on-collection-errors when the job should also fail on pytest collection errors.

Provider x feature matrix: customer-run Bedrock combinations

The provider and feature combinations customers actually run get explicit cells, expanded here as incidents surface new ones. The current Bedrock set, seeded from a customer's production shape (regional us.anthropic.* inference-profile ids over both chat routes, provider response headers for AWS-side correlation, and the Test Connection probe for a responses-mode Bedrock Mantle deployment):

Cell Feature Covering test
llm.chat_completions.bedrock_converse.basic.nonstream.works regional us. id, Converse llm_translation/test_chat_completions_regression_e2e.py
llm.chat_completions.bedrock_converse.basic.stream.works regional us. id, Converse stream llm_translation/test_chat_completions_regression_e2e.py
llm.chat_completions.bedrock_invoke.basic.nonstream.works regional us. id, Invoke llm_translation/test_bedrock_provider_matrix_e2e.py
llm.chat_completions.bedrock_invoke.basic.stream.works regional us. id, Invoke stream llm_translation/test_bedrock_provider_matrix_e2e.py
llm.chat_completions.bedrock_converse.response_headers.nonstream.works llm_provider-* headers llm_translation/test_bedrock_provider_matrix_e2e.py
llm.chat_completions.bedrock_converse.response_headers.stream.works llm_provider-* headers, stream llm_translation/test_bedrock_provider_matrix_e2e.py
mgmt.model.test_connection.happy_path Test Connection, Bedrock Mantle management/test_model_test_connection_e2e.py

Status: this is a draft for review

The cells were enumerated from the codebase and the tiers are a first proposal. Known things to settle before treating the set as final:

  • tiers are proposed, not signed off; 125 P0 is a lot to prove fail-before-fix, so P0 may want tightening
  • a few cells need a support check or a prune (for example llm.embeddings.anthropic.* and reliability.perf.throughput.under_slo)
  • auth is covered in two places (other.auth.* and the mgmt authz assertions); the boundary needs a decision, and the auth cluster may deserve promotion to its own module
  • the P2 "niche" cells each stand in for a large tail of integrations/providers by design, so the denominator is deliberately P0-weighted rather than a full inventory