Files
litellm/tests/router_unit_tests/test_router_embedding_integration.py
T
Mateo Wang 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.
2026-05-15 15:44:28 -07:00

357 lines
12 KiB
Python

"""
Integration tests for router embedding method with various configurations.
These tests simulate real-world scenarios where headers and configuration
need to be properly propagated through the router to the LLM API.
"""
import os
import sys
from unittest.mock import MagicMock, patch, AsyncMock
import pytest
sys.path.insert(0, os.path.abspath("../.."))
from litellm import Router
class TestRouterEmbeddingIntegration:
"""Integration tests for embedding with router configuration."""
def test_embedding_with_deployment_specific_headers(self):
"""
Test that deployment-specific headers are propagated.
This simulates a scenario where different deployments have
different header requirements (e.g., different API versions).
"""
model_list = [
{
"model_name": "embedding-deployment-1",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "key-1",
"headers": {"X-Deployment": "deployment-1"},
},
},
{
"model_name": "embedding-deployment-2",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "key-2",
"headers": {"X-Deployment": "deployment-2"},
},
},
]
router = Router(model_list=model_list)
# Test first deployment
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(model="embedding-deployment-1", input=["test"])
call_kwargs = mock_embedding.call_args[1]
assert call_kwargs["api_key"] == "key-1"
# Test second deployment
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(model="embedding-deployment-2", input=["test"])
call_kwargs = mock_embedding.call_args[1]
assert call_kwargs["api_key"] == "key-2"
def test_embedding_with_router_and_deployment_headers_merge(self):
"""
Test that router-level headers are propagated.
When no request headers are provided, router default headers should be used.
"""
model_list = [
{
"model_name": "test-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "test-key",
},
}
]
router = Router(
model_list=model_list,
default_litellm_params={
"headers": {
"X-Router-Header": "router-value",
"X-Common-Header": "router-common",
}
},
)
# Test: No request headers - router headers should be used
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(
model="test-embedding",
input=["test"],
)
call_kwargs = mock_embedding.call_args[1]
# Router headers should be present
assert "headers" in call_kwargs
assert call_kwargs["headers"]["X-Router-Header"] == "router-value"
assert call_kwargs["headers"]["X-Common-Header"] == "router-common"
def test_embedding_metadata_propagation(self):
"""
Test that metadata is properly set up and propagated.
This is important for logging, tracking, and debugging.
"""
model_list = [
{
"model_name": "test-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "test-key",
},
}
]
router = Router(
model_list=model_list,
default_litellm_params={
"metadata": {"environment": "test", "service": "embedding-service"}
},
)
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(
model="test-embedding",
input=["test"],
metadata={"request_id": "req-123"}, # Additional metadata from request
)
call_kwargs = mock_embedding.call_args[1]
# Check metadata contains all expected fields
assert "metadata" in call_kwargs
metadata = call_kwargs["metadata"]
# From _update_kwargs_before_fallbacks
assert "model_group" in metadata
assert metadata["model_group"] == "test-embedding"
# From default_litellm_params
assert "environment" in metadata
assert metadata["environment"] == "test"
assert "service" in metadata
assert metadata["service"] == "embedding-service"
# From request
assert "request_id" in metadata
assert metadata["request_id"] == "req-123"
@pytest.mark.asyncio
async def test_async_embedding_with_multiple_retries(self):
"""
Test that async embedding properly uses num_retries from router config.
This ensures the fix works with the retry mechanism.
"""
model_list = [
{
"model_name": "test-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "test-key",
},
}
]
router = Router(model_list=model_list, num_retries=2)
with patch("litellm.aembedding", new_callable=AsyncMock) as mock_aembedding:
mock_aembedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
await router.aembedding(model="test-embedding", input=["test"])
# The call should succeed
mock_aembedding.assert_called_once()
def test_embedding_with_timeout_from_router(self):
"""
Test that timeout settings from router config are propagated.
"""
model_list = [
{
"model_name": "test-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "test-key",
},
}
]
router = Router(model_list=model_list, timeout=30.0)
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(model="test-embedding", input=["test"])
call_kwargs = mock_embedding.call_args[1]
# Timeout should be set from router config
assert "timeout" in call_kwargs
assert call_kwargs["timeout"] == 30.0
def test_embedding_with_multiple_deployments_load_balancing(self):
"""
Test that headers are correctly propagated when router load balances
between multiple deployments.
"""
model_list = [
{
"model_name": "shared-embedding-model",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "key-1",
},
},
{
"model_name": "shared-embedding-model",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "key-2",
},
},
]
router = Router(
model_list=model_list,
default_litellm_params={"headers": {"X-Shared-Header": "shared-value"}},
)
# Make multiple calls and verify headers are always present
for i in range(5):
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2]}]
)
router.embedding(model="shared-embedding-model", input=[f"test {i}"])
call_kwargs = mock_embedding.call_args[1]
# Headers should always be present regardless of which deployment is chosen
assert "headers" in call_kwargs
assert call_kwargs["headers"]["X-Shared-Header"] == "shared-value"
@pytest.mark.asyncio
async def test_embedding_with_fallback_configuration(self):
"""
Test that headers are propagated correctly when using fallback models.
"""
model_list = [
{
"model_name": "primary-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "primary-key",
},
},
{
"model_name": "fallback-embedding",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fallback-key",
},
},
]
router = Router(
model_list=model_list,
fallbacks=[{"primary-embedding": ["fallback-embedding"]}],
default_litellm_params={"headers": {"X-Fallback-Test": "test-value"}},
)
# Simulate primary failing, fallback succeeding
with patch("litellm.aembedding", new_callable=AsyncMock) as mock_aembedding:
call_count = 0
async def side_effect(*args, **kwargs):
nonlocal call_count
call_count += 1
if call_count == 1:
# First call (primary) fails
raise Exception("Primary failed")
else:
# Second call (fallback) succeeds
return MagicMock(data=[{"embedding": [0.1, 0.2]}])
mock_aembedding.side_effect = side_effect
await router.aembedding(model="primary-embedding", input=["test"])
# Both calls should have headers
assert mock_aembedding.call_count == 2
# Check that both calls had headers
for call_obj in mock_aembedding.call_args_list:
call_kwargs = call_obj[1]
assert "headers" in call_kwargs
assert call_kwargs["headers"]["X-Fallback-Test"] == "test-value"
def test_embedding_with_custom_provider_headers(self):
"""
Test that provider-specific headers are correctly propagated.
Some providers require specific headers for API versioning, features, etc.
"""
model_list = [
{
"model_name": "azure-embedding",
"litellm_params": {
"model": "azure/text-embedding-3-small",
"api_key": "azure-key",
"api_base": "https://example.openai.azure.com",
"api_version": "2024-02-01",
},
}
]
router = Router(
model_list=model_list,
default_litellm_params={
"headers": {"X-Custom-Azure-Header": "azure-value"}
},
)
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
router.embedding(model="azure-embedding", input=["test"])
call_kwargs = mock_embedding.call_args[1]
# Verify Azure-specific params are present
assert call_kwargs["api_base"] == "https://example.openai.azure.com"
assert call_kwargs["api_version"] == "2024-02-01"
# Verify custom headers are present
assert "headers" in call_kwargs
assert call_kwargs["headers"]["X-Custom-Azure-Header"] == "azure-value"
if __name__ == "__main__":
# Run tests
pytest.main([__file__, "-v"])