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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Mateo WangandGitHub
|
2c733c00f5 |
chore(ci): modernize model references in tests and configs (#27856)
* test: modernize models used in CircleCI e2e test suites
Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.
- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
(also aligning oai_misc_config model_name with what
test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
-> claude-sonnet-4-5-20250929
* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5
Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.
* test: modernize models across remaining CI-mounted configs & tests
Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).
Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
text-embedding-ada-002 underlying to text-embedding-3-small. User-
facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.
Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
+ paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
and test_bedrock_anthropic_messages_test.py: bump router fixtures
using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.
* test: modernize placeholder model literals in router_unit_tests
Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.
Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
claude-sonnet-4-5-20250929 / claude-opus-4-7 /
claude-haiku-4-5-20251001 as appropriate
Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers
* test: modernize placeholder model literals across remaining CI suites
Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.
Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
/ gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro
Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
translation/transformation logic). Only the deprecated 20250514
references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
equivalent).
- Top-level tests calling the proxy through user-facing aliases
(gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
in proxy_server_config.yaml stay; only the underlying model was
bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
is model-name handling).
- Fake / mock / openai/fake identifiers.
Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
(bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.
* test: fix CI failures from model modernization sweep
CI surfaced 4 categories of regression from the bulk modernization:
1. Azure deployment names are customer-specific. Reverted:
- tests/litellm_utils_tests/test_health_check.py: azure/text-
embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
account does not have a text-embedding-3-small deployment).
- tests/logging_callback_tests/test_custom_callback_router.py:
same revert for two router fixtures driving aembedding.
2. gpt-5 family does not accept temperature != 1. Tests that pass a
custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
non-reasoning OpenAI mini that still accepts temperature/logprobs):
- tests/logging_callback_tests/test_datadog.py
- tests/logging_callback_tests/test_langsmith_unit_test.py
- tests/logging_callback_tests/test_otel_logging.py
3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
tests/test_openai_endpoints.py::test_chat_completion_streaming
exercises logprobs/top_logprobs through that alias. Bumped the
underlying model to gpt-4.1 (non-reasoning, still modern).
4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
hardcoded model="gpt-4o" and a model-specific spend value. Reverted
the litellm.acompletion calls in the test to model="gpt-4o" so the
fixture's exact-match assertions still hold.
5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
anthropic.messages.create routing to openai/gpt-5-mini returned an
empty content[0] with max_tokens=100 (reasoning-token consumption).
Swapped to openai/gpt-4.1-mini.
* test: fix Assistants API model + 2 cursor[bot] review nits
1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
isn't accepted by the /v1/assistants endpoint
("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
API-supported, non-reasoning).
2. example_config_yaml/pass_through_config.yaml: the previous sweep
bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
Sonnet tier intact. (Cursor bugbot review.)
3. example_config_yaml/simple_config.yaml: model_name was left as
gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
muddles the "simple" example. Make both sides gpt-5-mini so the
most basic example is a straight 1:1 mapping again. (Cursor bugbot
review.)
* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models
tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
|
||
|
Cursor AgentandMateo Wang
|
b637d9f64a |
test(vcr): classify cache verdicts, detect live calls, surface cost leaks
Convert the per-test VCR verdict line from a single 'NOOP / HIT / MISS /
PARTIAL' tag into a classified outcome that distinguishes the cases that
silently bill the live API on every CI run from the ones that don't:
HIT pure replay
PARTIAL mixed replay + new recordings
MISS:RECORDED new cassette saved to Redis (cached next run)
MISS:OVERFLOW cassette > MAX_EPISODES_PER_CASSETTE; persister
refused to save; re-bills every run
MISS:NOT_PERSISTED test failed; save_cassette skipped; re-bills
NOOP VCR-marked but no HTTP traffic (mocked elsewhere)
UNMARKED:LIVE_CALL test bypassed VCR AND opened a TCP connection
to a known LLM provider host -> wasted spend
UNMARKED:NO_TRAFFIC test bypassed VCR but didn't call out
The UNMARKED:LIVE_CALL signal is what converts 'this test probably hits
live' into 'this test connected to api.openai.com'. We install a
socket.connect / socket.create_connection wrapper for the duration of
each non-VCR-marked test and record any outbound TCP to a known LLM
provider hostname. The probe sits below the httpx layer so vcrpy and
respx (which both patch above the socket) are unaffected.
Replace the file-level _RESPX_CONFLICTING_FILES blacklists in the
llm_translation and local_testing conftests with per-item respx
detection in apply_vcr_auto_marker_to_items. A test now skips VCR when
it actually carries @pytest.mark.respx or has respx_mock in its fixture
chain - not just because some other test in the same file imports
MockRouter. Items skipped by skip_files are split into respx_conflict
(real conflict, the module wires up respx) vs file_opt_out (dead skip-
list entry whose module never touches respx) so the session summary
makes pruning obvious.
Stabilize the AWS SigV4 fingerprint: the Authorization header on
Bedrock requests rotates its Credential date and Signature on every
call, which previously pushed every Bedrock test past the 50-episode
overflow threshold. Extract the access-key id only
('aws-sigv4:AKIA...') so two requests with the same identity match.
Always emit verdict logging when VCR is active (set
LITELLM_VCR_VERBOSE=0 to opt back into the legacy quiet mode). Add a
session-end classification summary that lists overflow tests, unmarked
live-call tests, and the skip-reason breakdown.
Wire the live-call probe + summary hook into every test directory that
already uses the Redis-backed VCR cache (audio_tests, guardrails_tests,
image_gen_tests, litellm_utils_tests, llm_responses_api_testing,
llm_translation, local_testing, logging_callback_tests, ocr_tests,
pass_through_unit_tests, router_unit_tests, search_tests,
unified_google_tests).
Add tests/llm_translation/test_vcr_classification.py covering the
verdict classifier, skip-reason tagging, AWS SigV4 fingerprint stability,
live-host classification, and session summary rendering.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
|
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|
7e13256fee |
test: add 24hr Redis-backed VCR cache to additional test suites (#27159)
* test: add 24hr Redis-backed VCR cache to additional test suites Extracts the existing llm_translation VCR plumbing into a reusable helper (tests/_vcr_conftest_common.py) and wires it into the conftest.py files of the test directories listed in LIT-2787: audio_tests, batches_tests, guardrails_tests, image_gen_tests, litellm_utils_tests, local_testing, logging_callback_tests, pass_through_unit_tests, router_unit_tests, unified_google_tests The same helper is also adopted by the pre-existing llm_translation and llm_responses_api_testing conftests to remove the copy-pasted VCR setup. Each consuming conftest: - registers the Redis persister via pytest_recording_configure - auto-marks collected tests with pytest.mark.vcr (skipping respx-using files where applicable, since respx and vcrpy both patch httpx) - gates cassette writes on test success via _vcr_outcome_gate The cache is opt-in via CASSETTE_REDIS_URL; when unset, VCR is disabled and tests hit live providers as before. LITELLM_VCR_DISABLE=1 still forces a bypass for ad-hoc local runs. Test directories that run LiteLLM proxy in Docker (build_and_test, proxy_logging_guardrails_model_info_tests, proxy_store_model_in_db_tests) are intentionally not included: VCR.py patches the in-process httpx transport and cannot intercept calls made from inside a Docker container. The installing_litellm_on_python* jobs make no LLM calls and don't benefit from caching. https://linear.app/litellm-ai/issue/LIT-2787/add-24hr-caching-to-additional-test-suites * test(vcr): add safe-body matcher to handle JSONL and binary request bodies vcrpy's stock body matcher inspects Content-Type and unconditionally runs json.loads on application/json bodies. JSON Lines payloads (used by the Bedrock batch S3 PUT and other upload paths) crash that with json.JSONDecodeError: Extra data, before the matcher can return 'not a match'. This was the root cause of the batches_testing CI job failing on test_async_create_file once VCR auto-marking was applied to the batches_tests directory. Add a conservative byte-equality body matcher and use it in place of 'body' in the shared match_on tuple. The matcher is strictly more conservative than vcrpy's default — the only thing it gives up is 'different JSON key order is treated as the same body', which doesn't apply to deterministic litellm-built request payloads. It can never produce a false positive that the default would have rejected, so there is no cross-contamination risk. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): exclude tests that VCR replay actively breaks A few tests are incompatible with cassette replay and were failing on the latest CI run after VCR auto-marking was extended to local_testing and logging_callback_tests: - test_amazing_s3_logs.py (logging_callback_tests): the test asserts on a per-run response_id that should round-trip through a real S3 PUT/LIST. vcrpy's boto3 stub intercepts the PUT and the LIST replays stale keys, so the freshly-generated id is never found. - test_async_embedding_azure (logging_callback_tests) and test_amazing_sync_embedding (local_testing): the failure branches deliberately pass api_key='my-bad-key' to assert that the failure callback fires. We scrub auth headers from cassettes (so the bad-key request matches the prior good-key request), and vcrpy replays the recorded 200 — the failure callback never fires. - test_assistants.py (local_testing): the OpenAI Assistants polling APIs mint fresh thread/run IDs every recording session and then poll until status=='completed'. Replays of those polled GETs can never match a freshly-generated run id, so every CI run effectively re-records and the suite blows past the 15m no_output_timeout. Skip these from VCR auto-marking so they continue to hit live providers as they did before this change. The remaining tests in each directory still get cached. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): expand skip lists for second batch of incompatible tests Followup to the previous commit. After re-running CI on the rebuilt branch, three more tests surfaced as VCR-replay-incompatible: - litellm_utils_testing :: test_get_valid_models_from_dynamic_api_key Calls GET /v1/models with api_key='123' to assert the result is empty. We scrub auth headers, so the bad-key request matches the prior good-key cassette and replays the recorded model list. - litellm_utils_testing :: test_litellm_overhead.py Measures litellm_overhead_time_ms as a percentage of total wall-clock time. With cached responses the upstream 'network' time collapses to microseconds, blowing past the 40%% threshold the test asserts on. Skip the whole file (every parametrization is at risk). - local_testing_part1 :: test_async_custom_handler_completion and test_async_custom_handler_embedding Same bad-key failure-callback pattern as the already-skipped test_amazing_sync_embedding. - litellm_router_testing :: test_router_caching.py Asserts on litellm's own router-level response cache by comparing response1.id to response2.id across repeat upstream calls (test bypasses litellm cache via ttl=0 and expects upstream to return a *new* id). With VCR replay both upstream calls return the same cassette body, so the ids are identical. Skip the whole file. - logging_callback_tests :: test_async_chat_azure (preemptive) Same shape as already-skipped test_async_embedding_azure; was masked by upstream OpenAI rate-limit failures on baseline. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): use item.path and tighten matcher docstring - Replace pytest's deprecated item.fspath with item.path in apply_vcr_auto_marker_to_items so we don't emit deprecation warnings under pytest 8. - Clarify _safe_body_matcher docstring to reflect actual behavior (direct == first, then UTF-8 bytes comparison, no repr fallback). Addresses Greptile review feedback on PR #27159. * test(vcr): swallow all RedisError on cassette save/load Cassette persistence is strictly best-effort: any Redis-side failure (connection blip, timeout, OutOfMemoryError when the maxmemory cap is hit, READONLY replicas, etc.) should degrade to 'test passed but cassette not cached' rather than fail the test on teardown. Previously the persister only caught ConnectionError and TimeoutError, so OutOfMemoryError — which Redis Cloud raises when the cassette cache hits its memory cap and there are no evictable keys — propagated out of vcrpy's autouse fixture and ERRORed otherwise-passing tests on teardown. This caused the litellm_utils_testing CircleCI job to fail on the latest commit's run, even though the underlying test was a unit test that used mock_response and produced no real upstream traffic (the cassette was dirtied by a background langfuse callback). The rerun only succeeded because Redis evictions happened to free enough room before the SET — i.e. it was timing-dependent flakiness. Catch redis.exceptions.RedisError (the common base of all server- and client-side Redis exceptions) on both save and load, and parametrize the regression tests across ConnectionError, TimeoutError, and OutOfMemoryError to pin the new behavior. * test(vcr): surface cassette-cache failures with warnings + session banner When the persister silently swallows a Redis OOM (or any RedisError) on save/load there is otherwise no visible signal that the cache is degraded — tests pass, the cassette just isn't persisted, and the next session still hits the same Redis at the same near-cap memory. Add three layers of observability so that failure mode is loud: 1. Per-process health counters ("save_failures", "load_failures", and the last error string for each), exposed via cassette_cache_health() and reset via reset_cassette_cache_health(). The persister increments these in addition to logging. 2. VCRCassetteCacheWarning (UserWarning subclass) emitted via warnings.warn() inside the persister's except block. Pytest's built-in warnings summary at session end automatically lists every such warning, so the failure is visible in CI logs without any conftest-level wiring. 3. Session-end banner via emit_cassette_cache_session_banner() and a stderr-fallback atexit handler registered from register_persister_if_enabled(). Two states: - red "VCR CASSETTE CACHE DEGRADED" when save_failures or load_failures > 0 - yellow "VCR CASSETTE CACHE NEAR CAPACITY" (no failures, but used_memory >= 85% of maxmemory) so the next session knows the Redis is approaching OOM before any SET actually fails Capacity comes from a best-effort INFO memory probe (cassette_cache_capacity_snapshot) that returns None on any failure or when maxmemory is uncapped. The atexit handler skips xdist workers so only the controller emits. Tests: parametrize the existing save/load swallow-error tests across ConnectionError/TimeoutError/OutOfMemoryError, add direct tests for the health counters and warning emission, and a new test_vcr_conftest_common_banner.py covering banner output for every state (silent/red/yellow/disabled/xdist-worker). * test(vcr): bucket cassettes by API key fingerprint, drop bad-key skips Tests that deliberately call an LLM API with a bad key (e.g. to assert that the failure callback fires, or that check_valid_key returns False) were being silently served the prior good-key cassette: we scrub the real Authorization / x-api-key header from the cassette before storing it, so a follow-up bad-key call is byte-identical to the good-key call under the existing match_on tuple. Add a 'key_fingerprint' custom matcher that distinguishes requests by the SHA-256 of their API-key headers. The fingerprint is stamped into a synthetic 'x-litellm-key-fp' header by a new before_record_request hook, which then strips the real auth headers (we have to do the scrubbing here instead of via vcrpy's filter_headers knob, because filter_headers runs *first* and would erase the value we want to hash). Bad-key requests now get a different cassette bucket than good-key requests, so vcrpy will not replay a recorded 200 in place of the expected 401. The fingerprint is a one-way hash of the secret, so cassettes never contain the key. This permanently removes the 'bad-key' category of skips: - tests/local_testing: dropped ::test_amazing_sync_embedding, ::test_async_custom_handler_completion, ::test_async_custom_handler_embedding - tests/logging_callback_tests: dropped ::test_async_chat_azure, ::test_async_embedding_azure - tests/litellm_utils_tests: dropped ::test_get_valid_models_from_dynamic_api_key Coverage: 7 new unit tests in tests/test_litellm/test_vcr_safe_body_matcher.py covering header stripping, fingerprint determinism, no-auth bucketing, good-vs-bad key discrimination, x-api-key (Anthropic/Azure) discrimination, and idempotence under replay. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): drop redundant comments and docstrings Trim narration of code that is already self-evident from function and variable names. Keep the two genuinely non-obvious bits: - ordering constraint between filter_headers and before_record_request, which would invite a maintainer to re-introduce the bug if removed - the per-directory _VCR_INCOMPATIBLE_FILES rationale, since 'why exactly is this skipped' is not knowable from the test name alone Also drop the 40-line commented-out drop-in conftest snippet at the bottom of _vcr_conftest_common.py — the consuming conftests are the canonical reference. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): make _before_record_request idempotent vcrpy invokes before_record_request more than once per request: can_play_response_for calls it, then __contains__ / _responses (reached via play_response) call it again on the result. The second invocation sees a request whose auth headers we already stripped, so a naive recompute yields "no-key" and overwrites the real fingerprint stored in the header. This makes can_play_response_for and play_response disagree on matchability — the former says "yes, we have a stored response for this" (matching no-key to no-key) and the latter throws UnhandledHTTPRequestError because it computes a fresh real fingerprint that doesn't match the stored no-key. In CI this manifested as ~30 failing tests across guardrails_testing, audio_testing, batches_testing, image_gen_testing, llm_responses_api, litellm_router_unit_testing, etc. Skip the recompute when the header is already set, so re-applying the hook is a no-op. Adds a regression test that fires the hook twice on the same dict and asserts the fingerprint stays put. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> * test(vcr): drop more redundant docstrings and headers * test(vcr): enable 24hr cache for ocr_tests and search_tests These two directories were the only non-dockerized test suites in the build_and_test workflow that make live LLM/provider API calls but were not VCR-enabled by this PR. Together they account for 96 tests: - tests/ocr_tests/ (31): Mistral OCR, Azure AI OCR, Azure Document Intelligence, Vertex AI OCR. Pure-unit tests inside the same files (e.g. TestAzureDocumentIntelligencePagesParam) make no HTTP calls and become benign VCR NOOPs. - tests/search_tests/ (65): Brave, DataForSEO, DuckDuckGo, Exa, Firecrawl, Google PSE, Linkup, Parallel.ai, Perplexity, SearchAPI, Searxng, Serper, Tavily. Both directories use the canonical minimal conftest pattern from tests/audio_tests/conftest.py with no skip lists. None of the test files use respx, none assert on per-call upstream non-determinism (no response1.id != response2.id, no overhead-as-fraction-of-total, no live polling), so the default match_on tuple should cache cleanly. If a flake surfaces during the first cassette-recording CI run, we can add a targeted skip the same way we did for the other dirs. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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Ishaan Jaffer
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e8461b5b97 | style: run black formatter on files from main merge | ||
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Krrish Dholakia
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1fb677702d | test: update to new vertex ai keys | ||
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yuneng-jiangandGitHub
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96b2d134a7 | Revert "[Infra] Changing Google Tests to use Gemini 3 Flash Preview" | ||
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yuneng-jiang
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0dc92a6979 | changing to gemini 3 flash preview | ||
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Ishaan JaffandGitHub
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0f63cbea59 |
[Feat] Interactions API - allow using all litellm providers (interactions -> responses api bridge) (#18373)
* add BaseInteractionsTest * add interactions_api_handler * init bridge * init LiteLLMResponsesInteractionsConfig * LiteLLMResponsesInteractionsHandler * mv test * fixes api spec * docs * fix transform+iterators * docs fix * fix iterator |
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Ishaan Jaffer
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00f44861ea | fix: gooogle GenAI route tests | ||
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Ishaan JaffandGitHub
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b9132968b2 |
[Perf] Improvements for Async Success Handler (Logging Callbacks) - Approx +130 RPS (#13905)
* [Performance] Reduce Significant CPU overhead from litellm_logging.py (#13895) * fix: litellm.configured_cold_storage_logger * fix Session Management - Non-OpenAI Models docs * ruff fix * test fix * create LoggingWorker * add GLOBAL_LOGGING_WORKER for async task handling * fix logging tests * add conftest * fix conftest * test fix location of encode bedrock runtime modelid arn * fix conftest.py * tuning LoggingWorker * conftest.py * fix conftest batches/ * test_async_chat_azure * event_loop * test_bedrock_streaming_passthrough_test2 * fix GLOBAL_LOGGING_WORKER * logging worker * add flush for global logging worker * Revert "fix GLOBAL_LOGGING_WORKER" This reverts commit d254f508f48935652f054777652938ad71976cce. * fix conftest clear_queue * fix conftest clear_queue * setup_and_teardown for llm translation * docs AWS_REGION * test_async_chat_azure * change test DIR * run ci/cd again * use 1 job for litellm_router_unit_testing * fix space * fix litellm_router_unit_testing * test_aaarouter_dynamic_cooldown_message_retry_time * litellm_router_unit_testing * conftest.py clearing qu * fixes litellm_router_unit_testing * fixes clear_queue * fix router_unit_tests * remove conftest * add back conftest for router * fix event loop test * test fix * fixes for LoggingWorker * ruff fix |
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Ishaan Jaff
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eeed03a78f | test fix: gcp deprecated gemini-1.5-flash | ||
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Ishaan JaffandGitHub
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cbb922b1bb |
[Bug Fix] Gemini-CLI Integration - ensure tool calling works as expected on generateContent (#13189)
* transform_generate_content_request * add tools in GenerateContentRequestDict * add generate_content_handler tool calling * google_generate_content_endpoint_testing * test_mock_stream_generate_content_with_tools * test_validate_post_request_parameters * fixes for generate_content_handler * fix VertexAIGoogleGenAIConfig * fixes veretx ai * google_generate_content_endpoint_testing * test_async_streaming_with_logging * load_vertex_ai_credentials * test_vertex_anthropic.py |
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Ishaan JaffandGitHub
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3387029723 |
[Bug Fix] Using gemini-cli with Vertex Anthropic Models (#12246)
* add is_vertex_partner_model * fixes for get_provider_google_genai_generate_content_config * bug fix use vertex anthropic models with gemini cli * vertex_anthropic_mock_response * test streaming vertex generateContent * add unified_google_tests * fix is_vertex_partner_model * add load_vertex_ai_credentials * test fix |
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Ishaan JaffandGitHub
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5b8e300150 |
[Feat] gemini-cli integration - Add Logging + Cost tracking for stream + non-stream Vertex / Google AI Studio routes (#12058)
* add google generate content to call types * Revert "add google generate content to call types" This reverts commit 6f57dde293e50674c8fca0feac6e01c27d9e1c96. * add CallTypesLiteral for gemini * allow passing model to vertexpass through logging handler * update logging handler * fix checking if stream * add async streaming logging for vtx * refactor _transform_google_generate_content_to_openai_model_response * fix logging_obj * fixes _handle_non_streaming_google_genai_generate_content_response_logging * logging callback tests * ruff check fixes * test _is_streaming_request * test_ensure_initialize_azure_sdk_client_always_used * fix BaseGoogleGenAIGenerateContentStreamingIterator * fix - linting errors * req - add google-genai |
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Ishaan JaffandGitHub
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14d05fe422 |
[Feat] Add gemini-cli support - call VertexAI models through LiteLLM Native gemini routes (#12053)
* init litellm google gen ai methods * feat init structure of functions for generate content * add init * add BaseGoogleGenAIGenerateContentConfig * add generate_content_handler * add get_provider_google_genai_generate_content_config * fixes for generate content * add get_vertex_ai_project etc to base * use VertexBase * fixes for BaseGoogleGenAIGenerateContentConfig * working validate env for google gemini * feat - add transform google response * fixes for transform_generate_content_request * fix get_supported_generate_content_optional_params * add BaseGoogleGenAITest * working e2e test * fixes init config * use correct types * fix test for google gen ai * fix types * add sync_get_auth_token_and_url * fixes for transform * add llm http handler for google * working non-streaming google endpoints * add BaseGoogleGenAIGenerateContentStreamingIterator * add GoogleGenAIGenerateContentStreamingIterator * fix working sync stream * fixes for litellm logging obj * working async streaming * add google gen ai types * fix - required imports * fix readme * fix deps * fix deps * fix ruff code QA checks * fix linting * fixes TYPE_CHECKING * fixes for typing * add google gemini methods to litellm router * [Feat] Add initial endpoints for using Gemini SDK (gemini-cli) with LiteLLM (#12040) * init with google endpoints * add Depends * feat - add gemini endpoints * google_generate_content * fix init * fixes import * fixes for streaming * fixes for sync/async * working streaming with google gemini cli * add google endpoints to llm api routes * add VertexAIGoogleGenAIConfig * use aiter_bytes * use common request for streaming data * re-use logic for anthropic streaming * add GoogleAIStudioDataGenerator |
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Ishaan JaffandGitHub
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35e46784d3 |
[Feat] Add Support for calling Gemini/Vertex models in their native format (#12046)
* init litellm google gen ai methods * feat init structure of functions for generate content * add init * add BaseGoogleGenAIGenerateContentConfig * add generate_content_handler * add get_provider_google_genai_generate_content_config * fixes for generate content * add get_vertex_ai_project etc to base * use VertexBase * fixes for BaseGoogleGenAIGenerateContentConfig * working validate env for google gemini * feat - add transform google response * fixes for transform_generate_content_request * fix get_supported_generate_content_optional_params * add BaseGoogleGenAITest * working e2e test * fixes init config * use correct types * fix test for google gen ai * fix types * add sync_get_auth_token_and_url * fixes for transform * add llm http handler for google * working non-streaming google endpoints * add BaseGoogleGenAIGenerateContentStreamingIterator * add GoogleGenAIGenerateContentStreamingIterator * fix working sync stream * fixes for litellm logging obj * working async streaming * add google gen ai types * fix - required imports * fix readme * fix deps * fix deps * fix ruff code QA checks * fix linting * fixes TYPE_CHECKING * fixes for typing |