Fix HuggingFace embeddings using non-default input_type (#11452)

* fix(huggingface): use get() instead of pop() for input_type parameter

Fixes embedding generation for HuggingFace models where input_type override
is required (e.g. BAAI/bge-m3). The pop() method was mutating optional_params
and removing input_type before downstream functions could access it.

* Add unit tests to catch regression

* Move tests around

litellm/llms/huggingface/embedding/handler.py

@@ -342,7 +342,7 @@ class HuggingFaceEmbedding(BaseLLM):
             messages=[],
             litellm_params=litellm_params,
         )
-        task_type = optional_params.pop("input_type", None)
+        task_type = optional_params.get("input_type", None)
         task = get_hf_task_embedding_for_model(
             model=model, task_type=task_type, api_base=HF_HUB_URL
         )

tests/test_litellm/llms/huggingface/embedding/test_handler.py

@@ -0,0 +1,111 @@
+import json
+import os
+import sys
+from unittest.mock import patch, MagicMock, AsyncMock
+
+sys.path.insert(
+    0, os.path.abspath("../../../../..")
+)  # Adds the parent directory to the system path
+
+import litellm
+import pytest
+
+
+MOCK_EMBEDDING_RESPONSE = [[0.1, 0.2, 0.3, 0.4, 0.5]]
+
+
+@pytest.fixture
+def mock_embedding_http_handler():
+    """Fixture to mock the HTTP handler for embedding tests"""
+    with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
+        mock_response.status_code = 200
+        mock_response.json.return_value = MOCK_EMBEDDING_RESPONSE
+        mock_post.return_value = mock_response
+        yield mock_post
+
+
+@pytest.fixture
+def mock_embedding_async_http_handler():
+    """Fixture to mock the async HTTP handler for embedding tests"""
+    with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock) as mock_post:
+        mock_response = MagicMock()
+        mock_response.raise_for_status.return_value = None
+        mock_response.status_code = 200
+        mock_response.headers = {"content-type": "application/json"}
+        mock_response.json.return_value = MOCK_EMBEDDING_RESPONSE
+        mock_post.return_value = mock_response
+        yield mock_post
+
+class TestHuggingFaceEmbedding:
+    @pytest.fixture(autouse=True)
+    def setup(self, mock_embedding_http_handler, mock_embedding_async_http_handler):
+        self.mock_get_task_patcher = patch("litellm.llms.huggingface.embedding.handler.get_hf_task_embedding_for_model")
+        self.mock_get_task = self.mock_get_task_patcher.start()
+
+        def mock_get_task_side_effect(model, task_type, api_base):
+            if task_type is not None:
+                return task_type
+            return "sentence-similarity"
+
+        self.mock_get_task.side_effect = mock_get_task_side_effect
+
+        self.model = "huggingface/BAAI/bge-m3"
+        self.mock_http = mock_embedding_http_handler
+        self.mock_async_http = mock_embedding_async_http_handler
+        litellm.set_verbose = False
+
+        yield
+
+        self.mock_get_task_patcher.stop()
+
+    def test_input_type_preserved_in_optional_params(self):
+        input_text = ["hello world"]
+
+        response = litellm.embedding(
+            model=self.model,
+            input=input_text,
+            input_type="embed",
+        )
+
+        self.mock_http.assert_called_once()
+        post_call_args = self.mock_http.call_args
+        request_data = json.loads(post_call_args[1]["data"])
+
+        # When input_type="embed", it should use simple format {"inputs": [...]}
+        # NOT the sentence-similarity format which would require 2+ sentences
+        assert "inputs" in request_data
+        assert request_data["inputs"] == input_text
+
+        # Should NOT have sentence-similarity format
+        assert "source_sentence" not in str(request_data)
+        assert "sentences" not in str(request_data)
+
+    def test_embedding_with_sentence_similarity_task(self):
+        """Test embedding when task type is sentence-similarity (requires 2+ sentences)"""
+
+        similarity_response = {
+            "similarities": [[0, 0.9], [1, 0.8]]
+        }
+
+        self.mock_http.return_value.json.return_value = similarity_response
+
+        # Test with 2+ sentences (required for sentence-similarity)
+        input_text = ["This is the source sentence", "This is sentence one", "This is sentence two"]
+
+        response = litellm.embedding(
+            model=self.model,
+            input=input_text,
+            # Use the model's natural task type (sentence-similarity)
+        )
+
+        self.mock_http.assert_called_once()
+        post_call_args = self.mock_http.call_args
+        request_data = json.loads(post_call_args[1]["data"])
+
+        assert "inputs" in request_data
+        assert "source_sentence" in request_data["inputs"]
+        assert "sentences" in request_data["inputs"]
+        assert request_data["inputs"]["source_sentence"] == input_text[0]
+        assert request_data["inputs"]["sentences"] == input_text[1:]