mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-11 22:51:28 +00:00
test: update unit testing
This commit is contained in:
Krrish Dholakia
parent
f0ae2bef4f
commit
3c0df6a2da
1 changed files with 173 additions and 118 deletions
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@ -147,8 +147,7 @@ class TestBaseOpenAIPassThroughHandler:
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mock_user_api_key_dict = MagicMock()
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# Mock the endpoint function returned by create_pass_through_route
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mock_endpoint_func = MagicMock()
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mock_endpoint_func.return_value = {"result": "success"}
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mock_endpoint_func = AsyncMock(return_value={"result": "success"})
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mock_create_pass_through.return_value = mock_endpoint_func
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print("Testing standard endpoint pass-through...")
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@ -178,7 +177,9 @@ class TestBaseOpenAIPassThroughHandler:
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# Verify endpoint_func was called with correct parameters
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print("Verifying endpoint_func call parameters...")
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call_kwargs = mock_endpoint_func.call_args[1]
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mock_endpoint_func.assert_awaited_once()
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assert mock_endpoint_func.await_args is not None
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call_kwargs = mock_endpoint_func.await_args[1]
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print(f"stream parameter: {call_kwargs['stream']}")
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print(f"query_params: {call_kwargs['query_params']}")
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assert call_kwargs["stream"] is False
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@ -364,7 +365,6 @@ class TestVertexAIPassThroughHandler:
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custom_headers={"Authorization": f"Bearer {test_token}"},
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)
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@pytest.mark.parametrize(
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"initial_endpoint",
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[
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@ -581,10 +581,10 @@ class TestVertexAIPassThroughHandler:
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{
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"embedding": [0.11, 0.22, 0.33, 0.44, 0.55],
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"startOffsetSec": 0,
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"endOffsetSec": 5
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"endOffsetSec": 5,
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}
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]
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}
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},
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]
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}
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@ -600,28 +600,37 @@ class TestVertexAIPassThroughHandler:
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# Test URL with multimodal embedding model
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url_route = "/v1/projects/test-project/locations/us-central1/publishers/google/models/multimodalembedding@001:predict"
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start_time = datetime.datetime.now()
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end_time = datetime.datetime.now()
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with patch("litellm.llms.vertex_ai.multimodal_embeddings.transformation.VertexAIMultimodalEmbeddingConfig") as mock_multimodal_config:
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with patch(
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"litellm.llms.vertex_ai.multimodal_embeddings.transformation.VertexAIMultimodalEmbeddingConfig"
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) as mock_multimodal_config:
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# Mock the multimodal config instance and its methods
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mock_config_instance = Mock()
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mock_multimodal_config.return_value = mock_config_instance
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# Create a mock embedding response that would be returned by the transformation
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from litellm.types.utils import Embedding, EmbeddingResponse, Usage
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mock_embedding_response = EmbeddingResponse(
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object="list",
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data=[
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Embedding(embedding=[0.1, 0.2, 0.3, 0.4, 0.5], index=0, object="embedding"),
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Embedding(embedding=[0.6, 0.7, 0.8, 0.9, 1.0], index=1, object="embedding"),
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Embedding(
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embedding=[0.1, 0.2, 0.3, 0.4, 0.5], index=0, object="embedding"
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),
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Embedding(
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embedding=[0.6, 0.7, 0.8, 0.9, 1.0], index=1, object="embedding"
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),
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],
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model="multimodalembedding@001",
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usage=Usage(prompt_tokens=0, total_tokens=0, completion_tokens=0)
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usage=Usage(prompt_tokens=0, total_tokens=0, completion_tokens=0),
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)
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mock_config_instance.transform_embedding_response.return_value = mock_embedding_response
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mock_config_instance.transform_embedding_response.return_value = (
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mock_embedding_response
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)
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# Call the handler
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result = VertexPassthroughLoggingHandler.vertex_passthrough_handler(
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httpx_response=mock_httpx_response,
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@ -630,18 +639,18 @@ class TestVertexAIPassThroughHandler:
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result="test-result",
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start_time=start_time,
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end_time=end_time,
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cache_hit=False
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cache_hit=False,
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)
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# Verify multimodal embedding detection and processing
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assert result is not None
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assert "result" in result
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assert "kwargs" in result
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# Verify that the multimodal config was instantiated and used
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mock_multimodal_config.assert_called_once()
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mock_config_instance.transform_embedding_response.assert_called_once()
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# Verify the response is an EmbeddingResponse
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assert isinstance(result["result"], EmbeddingResponse)
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assert result["result"].model == "multimodalembedding@001"
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@ -657,23 +666,25 @@ class TestVertexAIPassThroughHandler:
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# Test case 1: Response with textEmbedding should be detected as multimodal
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response_with_text_embedding = {
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"predictions": [
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{
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"textEmbedding": [0.1, 0.2, 0.3]
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}
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]
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"predictions": [{"textEmbedding": [0.1, 0.2, 0.3]}]
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}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(response_with_text_embedding) is True
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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response_with_text_embedding
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)
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is True
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)
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# Test case 2: Response with imageEmbedding should be detected as multimodal
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response_with_image_embedding = {
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"predictions": [
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{
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"imageEmbedding": [0.4, 0.5, 0.6]
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}
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]
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"predictions": [{"imageEmbedding": [0.4, 0.5, 0.6]}]
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}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(response_with_image_embedding) is True
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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response_with_image_embedding
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)
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is True
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)
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# Test case 3: Response with videoEmbeddings should be detected as multimodal
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response_with_video_embeddings = {
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@ -683,41 +694,49 @@ class TestVertexAIPassThroughHandler:
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{
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"embedding": [0.7, 0.8, 0.9],
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"startOffsetSec": 0,
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"endOffsetSec": 5
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"endOffsetSec": 5,
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}
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]
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}
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]
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}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(response_with_video_embeddings) is True
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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response_with_video_embeddings
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)
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is True
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)
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# Test case 4: Regular text embedding response should NOT be detected as multimodal
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regular_embedding_response = {
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"predictions": [
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{
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"embeddings": {
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"values": [0.1, 0.2, 0.3]
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}
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}
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]
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"predictions": [{"embeddings": {"values": [0.1, 0.2, 0.3]}}]
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}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(regular_embedding_response) is False
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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regular_embedding_response
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)
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is False
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)
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# Test case 5: Non-embedding response should NOT be detected as multimodal
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non_embedding_response = {
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"candidates": [
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{
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"content": {
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"parts": [{"text": "Hello world"}]
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}
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}
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]
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"candidates": [{"content": {"parts": [{"text": "Hello world"}]}}]
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}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(non_embedding_response) is False
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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non_embedding_response
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)
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is False
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)
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# Test case 6: Empty response should NOT be detected as multimodal
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empty_response = {}
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assert VertexPassthroughLoggingHandler._is_multimodal_embedding_response(empty_response) is False
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assert (
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VertexPassthroughLoggingHandler._is_multimodal_embedding_response(
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empty_response
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)
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is False
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)
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def test_vertex_passthrough_handler_predict_cost_tracking(self):
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"""
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@ -739,9 +758,7 @@ class TestVertexAIPassThroughHandler:
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{
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"embeddings": {
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"values": [0.1, 0.2, 0.3, 0.4, 0.5],
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"statistics": {
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"token_count": 10
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}
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"statistics": {"token_count": 10},
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}
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}
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]
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@ -759,14 +776,14 @@ class TestVertexAIPassThroughHandler:
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# Test URL with /predict endpoint
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url_route = "/v1/projects/test-project/locations/us-central1/publishers/google/models/textembedding-gecko@001:predict"
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start_time = datetime.datetime.now()
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end_time = datetime.datetime.now()
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with patch("litellm.completion_cost") as mock_completion_cost:
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# Mock the completion cost calculation
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mock_completion_cost.return_value = 0.0001
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# Call the handler
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result = VertexPassthroughLoggingHandler.vertex_passthrough_handler(
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httpx_response=mock_httpx_response,
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@ -775,25 +792,25 @@ class TestVertexAIPassThroughHandler:
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result="test-result",
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start_time=start_time,
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end_time=end_time,
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cache_hit=False
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cache_hit=False,
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)
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# Verify cost tracking was implemented
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assert result is not None
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assert "result" in result
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assert "kwargs" in result
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# Verify cost calculation was called
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mock_completion_cost.assert_called_once()
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# Verify cost is set in kwargs
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assert "response_cost" in result["kwargs"]
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assert result["kwargs"]["response_cost"] == 0.0001
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# Verify cost is set in logging object
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assert "response_cost" in mock_logging_obj.model_call_details
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assert mock_logging_obj.model_call_details["response_cost"] == 0.0001
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# Verify model is set in kwargs
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assert "model" in result["kwargs"]
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assert result["kwargs"]["model"] == "textembedding-gecko@001"
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@ -830,13 +847,13 @@ class TestVertexAIDiscoveryPassThroughHandler:
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pass_through_router,
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)
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endpoint = f"/v1/projects/{vertex_project}/locations/{vertex_location}/dataStores/default/servingConfigs/default:search"
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endpoint = f"v1/projects/{vertex_project}/locations/{vertex_location}/dataStores/default/servingConfigs/default:search"
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# Mock request
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mock_request = Mock()
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mock_request.method = "POST"
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mock_request.headers = {
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"Authorization": "Bearer test-creds",
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"Authorization": "Bearer test-key",
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"Content-Type": "application/json",
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}
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mock_request.url = Mock()
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@ -848,44 +865,59 @@ class TestVertexAIDiscoveryPassThroughHandler:
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# Mock vertex credentials
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test_project = vertex_project
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test_location = vertex_location
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test_token = vertex_credentials
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test_token = "test-auth-token"
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with mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.vertex_llm_base._ensure_access_token_async"
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) as mock_ensure_token, mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.vertex_llm_base._get_token_and_url"
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) as mock_get_token, mock.patch(
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"litellm.llms.vertex_ai.vertex_llm_base.VertexBase.load_auth"
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) as mock_load_auth, mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.create_pass_through_route"
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) as mock_create_route, mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_litellm_virtual_key"
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) as mock_get_virtual_key, mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.user_api_key_auth"
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) as mock_user_auth:
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) as mock_user_auth, mock.patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints.get_vertex_pass_through_handler"
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) as mock_get_handler:
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# Mock credentials object with necessary attributes
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mock_credentials = Mock()
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mock_credentials.token = test_token
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# Setup mocks
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mock_ensure_token.return_value = ("test-auth-header", test_project)
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mock_get_token.return_value = (test_token, "")
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mock_load_auth.return_value = (mock_credentials, test_project)
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mock_get_virtual_key.return_value = "Bearer test-key"
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mock_user_auth.return_value = {"api_key": "test-key"}
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# Mock the discovery handler
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mock_handler = Mock()
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mock_handler.get_default_base_target_url.return_value = (
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"https://discoveryengine.googleapis.com"
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)
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mock_handler.update_base_target_url_with_credential_location = Mock(
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return_value="https://discoveryengine.googleapis.com"
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)
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mock_get_handler.return_value = mock_handler
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# Mock create_pass_through_route to return a function that returns a mock response
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mock_endpoint_func = AsyncMock(return_value={"status": "success"})
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mock_create_route.return_value = mock_endpoint_func
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# Call the route
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try:
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result = await vertex_discovery_proxy_route(
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endpoint=endpoint,
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request=mock_request,
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fastapi_response=mock_response,
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)
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except Exception as e:
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print(f"Error: {e}")
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result = await vertex_discovery_proxy_route(
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endpoint=endpoint,
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request=mock_request,
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fastapi_response=mock_response,
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)
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# Verify create_pass_through_route was called with correct arguments
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mock_create_route.assert_called_once_with(
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endpoint=endpoint,
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target=f"https://discoveryengine.googleapis.com/v1/projects/{test_project}/locations/{test_location}/dataStores/default/servingConfigs/default:search",
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custom_headers={"Authorization": f"Bearer {test_token}"},
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mock_create_route.assert_called_once()
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call_args = mock_create_route.call_args
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assert call_args[1]["endpoint"] == endpoint
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assert test_project in call_args[1]["target"]
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assert test_location in call_args[1]["target"]
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assert "Authorization" in call_args[1]["custom_headers"]
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assert (
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call_args[1]["custom_headers"]["Authorization"]
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== f"Bearer {test_token}"
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)
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@pytest.mark.asyncio
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@ -936,6 +968,7 @@ async def test_is_streaming_request_fn():
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mock_request.form = AsyncMock(return_value={"stream": "true"})
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assert await is_streaming_request_fn(mock_request) is True
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class TestBedrockLLMProxyRoute:
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@pytest.mark.asyncio
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async def test_bedrock_llm_proxy_route_application_inference_profile(self):
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@ -945,28 +978,40 @@ class TestBedrockLLMProxyRoute:
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mock_user_api_key_dict = Mock()
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mock_request_body = {"messages": [{"role": "user", "content": "test"}]}
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mock_processor = Mock()
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mock_processor.base_passthrough_process_llm_request = AsyncMock(return_value="success")
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with patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints._read_request_body", return_value=mock_request_body), \
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patch("litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing", return_value=mock_processor):
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mock_processor.base_passthrough_process_llm_request = AsyncMock(
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return_value="success"
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)
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with patch(
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"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints._read_request_body",
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return_value=mock_request_body,
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), patch(
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"litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing",
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return_value=mock_processor,
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):
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# Test application-inference-profile endpoint
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endpoint = "model/arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/r742sbn2zckd/converse"
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result = await bedrock_llm_proxy_route(
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endpoint=endpoint,
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request=mock_request,
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fastapi_response=mock_response,
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user_api_key_dict=mock_user_api_key_dict,
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)
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mock_processor.base_passthrough_process_llm_request.assert_called_once()
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call_kwargs = mock_processor.base_passthrough_process_llm_request.call_args.kwargs
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call_kwargs = (
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mock_processor.base_passthrough_process_llm_request.call_args.kwargs
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)
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# For application-inference-profile, model should be "arn:aws:bedrock:us-east-1:026090525607:application-inference-profile/r742sbn2zckd"
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assert call_kwargs["model"] == "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/r742sbn2zckd"
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assert (
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call_kwargs["model"]
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== "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/r742sbn2zckd"
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)
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assert result == "success"
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@pytest.mark.asyncio
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async def test_bedrock_llm_proxy_route_regular_model(self):
|
||||
mock_request = Mock()
|
||||
|
|
@ -975,14 +1020,21 @@ class TestBedrockLLMProxyRoute:
|
|||
mock_user_api_key_dict = Mock()
|
||||
mock_request_body = {"messages": [{"role": "user", "content": "test"}]}
|
||||
mock_processor = Mock()
|
||||
mock_processor.base_passthrough_process_llm_request = AsyncMock(return_value="success")
|
||||
|
||||
with patch("litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints._read_request_body", return_value=mock_request_body), \
|
||||
patch("litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing", return_value=mock_processor):
|
||||
|
||||
mock_processor.base_passthrough_process_llm_request = AsyncMock(
|
||||
return_value="success"
|
||||
)
|
||||
|
||||
with patch(
|
||||
"litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints._read_request_body",
|
||||
return_value=mock_request_body,
|
||||
), patch(
|
||||
"litellm.proxy.common_request_processing.ProxyBaseLLMRequestProcessing",
|
||||
return_value=mock_processor,
|
||||
):
|
||||
|
||||
# Test regular model endpoint
|
||||
endpoint = "model/anthropic.claude-3-sonnet-20240229-v1:0/converse"
|
||||
|
||||
|
||||
result = await bedrock_llm_proxy_route(
|
||||
endpoint=endpoint,
|
||||
request=mock_request,
|
||||
|
|
@ -990,8 +1042,10 @@ class TestBedrockLLMProxyRoute:
|
|||
user_api_key_dict=mock_user_api_key_dict,
|
||||
)
|
||||
mock_processor.base_passthrough_process_llm_request.assert_called_once()
|
||||
call_kwargs = mock_processor.base_passthrough_process_llm_request.call_args.kwargs
|
||||
|
||||
call_kwargs = (
|
||||
mock_processor.base_passthrough_process_llm_request.call_args.kwargs
|
||||
)
|
||||
|
||||
# For regular models, model should be just the model ID
|
||||
assert call_kwargs["model"] == "anthropic.claude-3-sonnet-20240229-v1:0"
|
||||
assert result == "success"
|
||||
|
|
@ -1007,41 +1061,38 @@ class TestBedrockLLMProxyRoute:
|
|||
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
|
||||
handle_bedrock_passthrough_router_model,
|
||||
)
|
||||
|
||||
|
||||
mock_request = Mock()
|
||||
mock_request.method = "POST"
|
||||
mock_request.headers = {"content-type": "application/json"}
|
||||
mock_request.query_params = {}
|
||||
|
||||
|
||||
mock_request_body = {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [{"textaaa": "Hello"}]
|
||||
}
|
||||
]
|
||||
"messages": [{"role": "user", "content": [{"textaaa": "Hello"}]}]
|
||||
}
|
||||
|
||||
|
||||
bedrock_error_message = '{"message":"ContentBlock object at messages.0.content.0 must set one of the following keys: text, image, toolUse, toolResult, document, video."}'
|
||||
|
||||
|
||||
# Create a mock httpx.Response for the error
|
||||
mock_error_response = Mock(spec=httpx.Response)
|
||||
mock_error_response.status_code = 400
|
||||
mock_error_response.aread = AsyncMock(return_value=bedrock_error_message.encode('utf-8'))
|
||||
|
||||
mock_error_response.aread = AsyncMock(
|
||||
return_value=bedrock_error_message.encode("utf-8")
|
||||
)
|
||||
|
||||
# Create the HTTPStatusError
|
||||
mock_http_error = httpx.HTTPStatusError(
|
||||
message="Bad Request",
|
||||
request=Mock(spec=httpx.Request),
|
||||
response=mock_error_response,
|
||||
)
|
||||
|
||||
|
||||
mock_llm_router = Mock()
|
||||
mock_llm_router.allm_passthrough_route = AsyncMock(side_effect=mock_http_error)
|
||||
|
||||
|
||||
endpoint = "model/test-model/converse"
|
||||
model = "test-model"
|
||||
|
||||
|
||||
with pytest.raises(HTTPException) as exc_info:
|
||||
await handle_bedrock_passthrough_router_model(
|
||||
model=model,
|
||||
|
|
@ -1050,15 +1101,19 @@ class TestBedrockLLMProxyRoute:
|
|||
request_body=mock_request_body,
|
||||
llm_router=mock_llm_router,
|
||||
)
|
||||
|
||||
|
||||
assert exc_info.value.status_code == 400
|
||||
assert "ContentBlock object at messages.0.content.0 must set one of the following keys" in str(exc_info.value.detail)
|
||||
assert (
|
||||
"ContentBlock object at messages.0.content.0 must set one of the following keys"
|
||||
in str(exc_info.value.detail)
|
||||
)
|
||||
|
||||
|
||||
class TestLLMPassthroughFactoryProxyRoute:
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_passthrough_factory_proxy_route_success(self):
|
||||
from litellm.types.utils import LlmProviders
|
||||
|
||||
mock_request = MagicMock(spec=Request)
|
||||
mock_request.method = "POST"
|
||||
mock_request.json = AsyncMock(return_value={"stream": False})
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue