fix(embeddings): accept encoding_format='float' for vertex_ai/gemini embeddings (#33617)

OpenAI SDKs (and litellm's own client since ~1.84) send
encoding_format='float' by default, but the vertex embedding config only
supports ['dimensions'], so get_optional_params_embeddings raised
UnsupportedParamsError at the provider default value. Any
OpenAI-compatible client talking to a litellm proxy with vertex
embedding models got a 400 unless the operator set proxy-wide
drop_params: true.

Float lists are exactly what the vertex API returns, so the param is a
no-op: pop it before validation. Other values (e.g. 'base64') keep the
existing unsupported-param behavior (dropped with drop_params, raise
otherwise).

Fixes #33173

Co-authored-by: Mihidum Hettiyahandi <55163074+mihidumh@users.noreply.github.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/utils.py

@@ -3197,6 +3197,12 @@ def get_optional_params_embeddings(
             non_default_params=non_default_params, optional_params={}, kwargs=kwargs
         )
     elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "gemini":
+        # OpenAI SDKs (and litellm's own client) send encoding_format="float"
+        # by default; float lists are exactly what the vertex API returns, so
+        # the param is a no-op — don't reject the provider default. Other
+        # values (e.g. "base64") stay on the unsupported-param path below.
+        if non_default_params.get("encoding_format") == "float":
+            non_default_params.pop("encoding_format")
         supported_params = get_supported_openai_params(
             model=model,
             custom_llm_provider="vertex_ai",

tests/test_litellm/test_utils.py

@@ -4714,7 +4714,6 @@ class TestValidateEnvironmentTencent:
         assert "TENCENT_API_KEY" in result["missing_keys"]
 
 
-
 @pytest.mark.parametrize(
     "model",
     [
@@ -4741,3 +4740,53 @@ def test_gemini_image_models_do_not_support_reasoning(
         f"{model} incorrectly classified as reasoning-capable. "
         "Add 'supports_reasoning: false' to its model_cost entry."
     )
+
+
+class TestVertexEmbeddingEncodingFormat:
+    """vertex_ai/gemini embeddings must accept encoding_format="float" — it's
+    the OpenAI SDK default and float lists are exactly what the vertex API
+    returns. Other values keep the unsupported-param behavior (drop with
+    drop_params, raise otherwise). Issue #33173."""
+
+    def test_encoding_format_float_is_accepted_and_dropped(self):
+        optional_params = litellm.utils.get_optional_params_embeddings(
+            model="gemini-embedding-001",
+            encoding_format="float",
+            custom_llm_provider="vertex_ai",
+        )
+        assert "encoding_format" not in optional_params
+
+    def test_encoding_format_float_accepted_for_gemini_provider(self):
+        optional_params = litellm.utils.get_optional_params_embeddings(
+            model="gemini-embedding-001",
+            encoding_format="float",
+            custom_llm_provider="gemini",
+        )
+        assert "encoding_format" not in optional_params
+
+    def test_encoding_format_base64_still_rejected_without_drop_params(self):
+        with pytest.raises(Exception) as excinfo:
+            litellm.utils.get_optional_params_embeddings(
+                model="gemini-embedding-001",
+                encoding_format="base64",
+                custom_llm_provider="vertex_ai",
+            )
+        assert "encoding_format" in str(excinfo.value)
+
+    def test_encoding_format_base64_dropped_with_drop_params(self):
+        optional_params = litellm.utils.get_optional_params_embeddings(
+            model="gemini-embedding-001",
+            encoding_format="base64",
+            custom_llm_provider="vertex_ai",
+            drop_params=True,
+        )
+        assert "encoding_format" not in optional_params
+
+    def test_dimensions_still_mapped(self):
+        optional_params = litellm.utils.get_optional_params_embeddings(
+            model="gemini-embedding-001",
+            encoding_format="float",
+            dimensions=256,
+            custom_llm_provider="vertex_ai",
+        )
+        assert optional_params.get("outputDimensionality") == 256