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2c733c00f5
* 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.
150 lines
5.5 KiB
Python
150 lines
5.5 KiB
Python
"""
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Regression tests for Router._pre_call_checks() performance optimization.
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Background:
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_pre_call_checks() runs on EVERY request to filter deployments based on
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context window size, rate limits, region constraints, and supported parameters.
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Optimization:
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Changed from copy.deepcopy(healthy_deployments) to list(healthy_deployments).
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This is ~1400x faster while maintaining correctness because the function only
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removes items from the list, never modifies the deployment objects themselves.
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Critical Requirement:
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The input healthy_deployments list must NEVER be mutated. Callers depend on
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this for retries, fallbacks, and logging.
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"""
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import copy
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import pytest
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from litellm import Router
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class TestPreCallChecksOptimization:
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"""
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Verify that using list() instead of deepcopy() doesn't break behavior.
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If these tests fail, the optimization should be reverted.
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"""
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def test_no_mutation_of_input_list(self):
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"""
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Verify the input list is never modified by _pre_call_checks.
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The function uses list() instead of deepcopy for performance.
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This is safe because it only filters items, never modifies them.
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-5-mini",
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"litellm_params": {"model": "gpt-5-mini", "api_key": "sk-test"},
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"model_info": {"id": "test-1"},
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},
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{
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"model_name": "gpt-5-mini",
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"litellm_params": {"model": "gpt-5.5", "api_key": "sk-test2"},
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"model_info": {"id": "test-2"},
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},
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],
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set_verbose=False,
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enable_pre_call_checks=True,
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)
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deployments = router.get_model_list(model_name="gpt-5-mini")
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assert deployments is not None
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# Capture the original state
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original_length = len(deployments)
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original_deployment_ids = [id(d) for d in deployments]
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original_litellm_params_ids = [id(d["litellm_params"]) for d in deployments]
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snapshot = copy.deepcopy(deployments)
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# Call the function under test
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router._pre_call_checks(
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model="gpt-5-mini",
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healthy_deployments=deployments,
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messages=[{"role": "user", "content": "test"}],
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)
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# Verify nothing changed:
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# 1. Same number of items
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assert len(deployments) == original_length, "List length changed!"
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# 2. Same deployment objects (not replaced with copies)
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assert [
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id(d) for d in deployments
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] == original_deployment_ids, "Deployment dicts replaced!"
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# 3. Same nested objects (not replaced with copies)
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assert [
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id(d["litellm_params"]) for d in deployments
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] == original_litellm_params_ids, "Nested dicts replaced!"
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# 4. Same values (catches any mutation)
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assert deployments == snapshot, "Values were mutated!"
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def test_filtering_still_works(self):
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"""
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Verify that filtering works correctly while preserving the original list.
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Scenario: Send a message too long for one deployment but fine for another.
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Expected: Filtered result excludes the small deployment, but original list is unchanged.
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"""
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router = Router(
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model_list=[
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{
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"model_name": "test",
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"litellm_params": {"model": "gpt-5-mini", "api_key": "sk-test"},
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"model_info": {"id": "small", "max_input_tokens": 50},
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},
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{
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"model_name": "test",
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"litellm_params": {"model": "gpt-5.5", "api_key": "sk-test"},
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"model_info": {"id": "large", "max_input_tokens": 10000},
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},
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],
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set_verbose=False,
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enable_pre_call_checks=True,
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)
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deployments = router.get_model_list(model_name="test")
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assert deployments is not None
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# Save references to the original deployment objects
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original_small_deployment = deployments[0] # max_input_tokens=50
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original_large_deployment = deployments[1] # max_input_tokens=10000
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# Send a long message (100 words) that exceeds 50 tokens but fits in 10000 tokens
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filtered = router._pre_call_checks(
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model="test",
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healthy_deployments=deployments,
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messages=[{"role": "user", "content": " ".join(["word"] * 100)}],
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)
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# Verify the filtered result only contains the large deployment
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assert (
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len(filtered) == 1
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), f"Expected 1 deployment after filtering, got {len(filtered)}"
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assert (
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filtered[0]["model_info"]["id"] == "large"
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), "Wrong deployment kept after filtering"
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# Verify the original list still has both deployments
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assert (
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len(deployments) == 2
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), f"Original list was modified! Expected 2, got {len(deployments)}"
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assert (
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deployments[0] is original_small_deployment
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), "First deployment object replaced!"
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assert (
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deployments[1] is original_large_deployment
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), "Second deployment object replaced!"
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assert (
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deployments[0].get("model_info", {}).get("id") == "small"
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), "First deployment ID changed!"
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assert (
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deployments[1].get("model_info", {}).get("id") == "large"
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), "Second deployment ID changed!"
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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