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Hasnaat hussain 2026-09-16 09:04:01 +08:00 committed by GitHub
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2 changed files with 79 additions and 1 deletions

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@ -3523,6 +3523,9 @@ def get_optional_params_embeddings(
special_params: Final = passed_params.pop("kwargs")
drop_params = normalize_drop_params(passed_params.pop("drop_params", None))
resolved_drop_params = drop_params
if resolved_drop_params is None:
resolved_drop_params = normalize_drop_params(litellm.drop_params)
additional_drop_params = passed_params.pop("additional_drop_params", None)
allowed_openai_params = passed_params.pop("allowed_openai_params", None) or []
# Remove function objects from passed_params to avoid JSON serialization errors
@ -3568,7 +3571,7 @@ def get_optional_params_embeddings(
non_default_params=non_default_params,
optional_params={},
model=model,
drop_params=drop_params if drop_params is not None else False,
drop_params=resolved_drop_params if resolved_drop_params is not None else False,
)
# Provider-only params (e.g. Cohere input_type) are not in
# OPENAI_EMBEDDING_PARAMS, so embedding_pre_process drops them from
@ -3842,6 +3845,14 @@ def get_optional_params_embeddings(
optional_params = non_default_params
else:
optional_params = non_default_params
if (
resolved_drop_params is True
and (custom_llm_provider == "azure" or custom_llm_provider in litellm.openai_compatible_providers)
and "text-embedding-3" not in model
and "dimensions" in optional_params
and "dimensions" not in (allowed_openai_params or ())
):
optional_params.pop("dimensions", None)
final_params = add_provider_specific_params_to_optional_params(
optional_params=optional_params,

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@ -0,0 +1,67 @@
import pytest
import litellm
@pytest.mark.parametrize("provider", ["azure", "together_ai"])
def test_embedding_dimensions_drop_params_for_openai_compatible_provider(provider, monkeypatch):
monkeypatch.setattr(litellm, "drop_params", False)
dropped = litellm.utils.get_optional_params_embeddings(
model=f"{provider}/dummy-model",
custom_llm_provider=provider,
dimensions=512,
drop_params=True,
)
assert "dimensions" not in dropped
monkeypatch.setattr(litellm, "drop_params", True)
dropped_globally = litellm.utils.get_optional_params_embeddings(
model=f"{provider}/dummy-model",
custom_llm_provider=provider,
dimensions=512,
)
assert "dimensions" not in dropped_globally
monkeypatch.setattr(litellm, "drop_params", False)
preserved = litellm.utils.get_optional_params_embeddings(
model=f"{provider}/dummy-model",
custom_llm_provider=provider,
dimensions=512,
)
assert preserved["dimensions"] == 512
monkeypatch.setattr(litellm, "drop_params", True)
model_supported = litellm.utils.get_optional_params_embeddings(
model=f"{provider}/text-embedding-3-small",
custom_llm_provider=provider,
dimensions=512,
)
assert model_supported["dimensions"] == 512
explicitly_allowed = litellm.utils.get_optional_params_embeddings(
model=f"{provider}/legacy-model",
custom_llm_provider=provider,
dimensions=512,
allowed_openai_params=["dimensions"],
)
assert explicitly_allowed["dimensions"] == 512
@pytest.mark.parametrize(
("provider", "model"),
[
("nvidia_nim", "nvidia_nim/nv-embedqa-e5-v5"),
("fireworks_ai", "fireworks_ai/nomic-ai/nomic-embed-text-v1.5"),
("dashscope", "dashscope/text-embedding-v3"),
("hosted_vllm", "hosted_vllm/Qwen/Qwen3-Embedding-0.6B"),
],
)
def test_embedding_dimensions_preserved_for_provider_mappings(provider, model):
optional_params = litellm.utils.get_optional_params_embeddings(
model=model,
custom_llm_provider=provider,
dimensions=128,
drop_params=True,
)
assert optional_params["dimensions"] == 128