Files
litellm/tests/router_unit_tests/test_router_embedding_headers.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

374 lines
13 KiB
Python

"""
Test suite for router embedding method header propagation.
This tests the fix for the issue where the embedding method was not
propagating proxy model configuration headers to the LLM API calls.
The fix ensures that router.embedding() calls _update_kwargs_before_fallbacks()
just like router.completion() does, which properly sets up metadata and allows
default_litellm_params (including headers) to be propagated.
"""
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 TestRouterEmbeddingHeaders:
"""Test that embedding methods properly propagate headers from router configuration."""
def test_embedding_calls_update_kwargs_before_fallbacks(self):
"""
Test that router.embedding() calls _update_kwargs_before_fallbacks.
This ensures that metadata is properly set up before the fallback mechanism,
which is necessary for header propagation to work correctly.
"""
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
router = Router(model_list=model_list)
# Mock the _update_kwargs_before_fallbacks method to verify it's called
with patch.object(
router,
"_update_kwargs_before_fallbacks",
wraps=router._update_kwargs_before_fallbacks,
) as mock_update:
with patch("litellm.embedding") as mock_litellm_embedding:
mock_litellm_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="text-embedding-3-small", input=["test input"])
# Verify _update_kwargs_before_fallbacks was called
mock_update.assert_called_once()
call_kwargs = mock_update.call_args[1]
assert call_kwargs["model"] == "text-embedding-3-small"
assert "kwargs" in call_kwargs
@pytest.mark.asyncio
async def test_aembedding_calls_update_kwargs_before_fallbacks(self):
"""
Test that router.aembedding() calls _update_kwargs_before_fallbacks.
This ensures consistency between sync and async embedding methods.
"""
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
router = Router(model_list=model_list)
# Mock the _update_kwargs_before_fallbacks method to verify it's called
with patch.object(
router,
"_update_kwargs_before_fallbacks",
wraps=router._update_kwargs_before_fallbacks,
) as mock_update:
with patch(
"litellm.aembedding", new_callable=AsyncMock
) as mock_litellm_aembedding:
mock_litellm_aembedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
await router.aembedding(
model="text-embedding-3-small", input=["test input"]
)
# Verify _update_kwargs_before_fallbacks was called
mock_update.assert_called_once()
call_kwargs = mock_update.call_args[1]
assert call_kwargs["model"] == "text-embedding-3-small"
assert "kwargs" in call_kwargs
def test_embedding_propagates_default_litellm_params(self):
"""
Test that embedding calls properly propagate default_litellm_params including headers.
This is the main fix - ensuring that headers set in default_litellm_params
are included in the embedding request.
"""
custom_headers = {"X-Custom-Header": "test-value", "X-API-Version": "v2"}
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
# Create router with default_litellm_params containing headers
router = Router(
model_list=model_list,
default_litellm_params={
"headers": custom_headers,
"metadata": {"test_key": "test_value"},
},
)
with patch("litellm.embedding") as mock_litellm_embedding:
mock_litellm_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="text-embedding-3-small", input=["test input"])
# Verify that litellm.embedding was called with the headers
mock_litellm_embedding.assert_called_once()
call_kwargs = mock_litellm_embedding.call_args[1]
# Check that headers were included
assert "headers" in call_kwargs
assert call_kwargs["headers"] == custom_headers
# Check that metadata was properly set up
assert "metadata" in call_kwargs
assert "model_group" in call_kwargs["metadata"]
assert call_kwargs["metadata"]["model_group"] == "text-embedding-3-small"
@pytest.mark.asyncio
async def test_aembedding_propagates_default_litellm_params(self):
"""
Test that async embedding calls properly propagate default_litellm_params including headers.
"""
custom_headers = {"X-Custom-Header": "test-value", "X-API-Version": "v2"}
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
# Create router with default_litellm_params containing headers
router = Router(
model_list=model_list,
default_litellm_params={
"headers": custom_headers,
"metadata": {"test_key": "test_value"},
},
)
with patch(
"litellm.aembedding", new_callable=AsyncMock
) as mock_litellm_aembedding:
mock_litellm_aembedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
await router.aembedding(
model="text-embedding-3-small", input=["test input"]
)
# Verify that litellm.aembedding was called with the headers
mock_litellm_aembedding.assert_called_once()
call_kwargs = mock_litellm_aembedding.call_args[1]
# Check that headers were included
assert "headers" in call_kwargs
assert call_kwargs["headers"] == custom_headers
# Check that metadata was properly set up
assert "metadata" in call_kwargs
assert "model_group" in call_kwargs["metadata"]
assert call_kwargs["metadata"]["model_group"] == "text-embedding-3-small"
def test_embedding_metadata_includes_model_group(self):
"""
Test that embedding calls include model_group in metadata.
The _update_kwargs_before_fallbacks method should set this up.
"""
model_list = [
{
"model_name": "test-embedding-model",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
router = Router(model_list=model_list)
with patch("litellm.embedding") as mock_litellm_embedding:
mock_litellm_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="test-embedding-model", input=["test input"])
call_kwargs = mock_litellm_embedding.call_args[1]
# Verify metadata contains model_group
assert "metadata" in call_kwargs
assert "model_group" in call_kwargs["metadata"]
assert call_kwargs["metadata"]["model_group"] == "test-embedding-model"
def test_embedding_sets_num_retries_from_router(self):
"""
Test that embedding calls inherit num_retries from router configuration.
This is set by _update_kwargs_before_fallbacks.
"""
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
# Create router with num_retries set
router = Router(model_list=model_list, num_retries=3)
with patch("litellm.embedding") as mock_litellm_embedding:
mock_litellm_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="text-embedding-3-small", input=["test input"])
# Verify num_retries was not set in the call (it's handled by function_with_fallbacks)
# The important thing is that it was set in kwargs before being passed to function_with_fallbacks
# We verify this indirectly by checking that _update_kwargs_before_fallbacks was called
mock_litellm_embedding.assert_called_once()
def test_embedding_sets_litellm_trace_id(self):
"""
Test that embedding calls include a litellm_trace_id.
This is generated and set by _update_kwargs_before_fallbacks.
"""
model_list = [
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
}
]
router = Router(model_list=model_list)
with patch("litellm.embedding") as mock_litellm_embedding:
mock_litellm_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="text-embedding-3-small", input=["test input"])
call_kwargs = mock_litellm_embedding.call_args[1]
# Verify litellm_trace_id was set
assert "litellm_trace_id" in call_kwargs
assert isinstance(call_kwargs["litellm_trace_id"], str)
assert len(call_kwargs["litellm_trace_id"]) > 0
def test_embedding_consistency_with_completion(self):
"""
Test that embedding and completion methods handle kwargs similarly.
Both should call _update_kwargs_before_fallbacks to ensure consistent behavior.
"""
custom_headers = {"X-Test": "value"}
model_list = [
{
"model_name": "gpt-5-mini",
"litellm_params": {
"model": "gpt-5-mini",
"api_key": "fake-key",
},
},
{
"model_name": "text-embedding-3-small",
"litellm_params": {
"model": "text-embedding-3-small",
"api_key": "fake-key",
},
},
]
router = Router(
model_list=model_list, default_litellm_params={"headers": custom_headers}
)
# Test completion
with patch("litellm.completion") as mock_completion:
mock_completion.return_value = MagicMock()
router.completion(
model="gpt-5-mini", messages=[{"role": "user", "content": "test"}]
)
completion_kwargs = mock_completion.call_args[1]
# Test embedding
with patch("litellm.embedding") as mock_embedding:
mock_embedding.return_value = MagicMock(
data=[{"embedding": [0.1, 0.2, 0.3]}]
)
router.embedding(model="text-embedding-3-small", input=["test input"])
embedding_kwargs = mock_embedding.call_args[1]
# Both should have headers from default_litellm_params
assert "headers" in completion_kwargs
assert "headers" in embedding_kwargs
assert completion_kwargs["headers"] == custom_headers
assert embedding_kwargs["headers"] == custom_headers
# Both should have metadata with model_group
assert "metadata" in completion_kwargs
assert "metadata" in embedding_kwargs
assert "model_group" in completion_kwargs["metadata"]
assert "model_group" in embedding_kwargs["metadata"]
# Both should have litellm_trace_id
assert "litellm_trace_id" in completion_kwargs
assert "litellm_trace_id" in embedding_kwargs
if __name__ == "__main__":
# Run a simple test
test = TestRouterEmbeddingHeaders()
test.test_embedding_calls_update_kwargs_before_fallbacks()
test.test_embedding_propagates_default_litellm_params()
test.test_embedding_metadata_includes_model_group()
test.test_embedding_sets_litellm_trace_id()
test.test_embedding_consistency_with_completion()
print("All tests passed!") # noqa: T201