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27d1af3068
* feat!: Use `Secret` for passing authentication secrets to components * Add comment to clarify type ignore
126 lines
4.9 KiB
Python
126 lines
4.9 KiB
Python
from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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from huggingface_hub.utils import RepositoryNotFoundError
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from haystack.utils.auth import Secret
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from haystack.components.embedders.hugging_face_tei_text_embedder import HuggingFaceTEITextEmbedder
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@pytest.fixture
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def mock_check_valid_model():
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with patch(
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"haystack.components.embedders.hugging_face_tei_text_embedder.check_valid_model", MagicMock(return_value=None)
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) as mock:
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yield mock
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def mock_embedding_generation(text, **kwargs):
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response = np.random.rand(384)
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return response
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class TestHuggingFaceTEITextEmbedder:
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def test_init_default(self, monkeypatch, mock_check_valid_model):
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monkeypatch.setenv("HF_API_TOKEN", "fake-api-token")
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embedder = HuggingFaceTEITextEmbedder()
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assert embedder.model == "BAAI/bge-small-en-v1.5"
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assert embedder.url is None
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assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
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assert embedder.prefix == ""
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assert embedder.suffix == ""
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def test_init_with_parameters(self, mock_check_valid_model):
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embedder = HuggingFaceTEITextEmbedder(
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model="sentence-transformers/all-mpnet-base-v2",
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url="https://some_embedding_model.com",
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token=Secret.from_token("fake-api-token"),
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prefix="prefix",
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suffix="suffix",
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)
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assert embedder.model == "sentence-transformers/all-mpnet-base-v2"
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assert embedder.url == "https://some_embedding_model.com"
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assert embedder.token == Secret.from_token("fake-api-token")
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assert embedder.prefix == "prefix"
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assert embedder.suffix == "suffix"
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def test_initialize_with_invalid_url(self, mock_check_valid_model):
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with pytest.raises(ValueError):
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HuggingFaceTEITextEmbedder(model="sentence-transformers/all-mpnet-base-v2", url="invalid_url")
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def test_initialize_with_url_but_invalid_model(self, mock_check_valid_model):
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# When custom TEI endpoint is used via URL, model must be provided and valid HuggingFace Hub model id
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mock_check_valid_model.side_effect = RepositoryNotFoundError("Invalid model id")
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with pytest.raises(RepositoryNotFoundError):
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HuggingFaceTEITextEmbedder(model="invalid_model_id", url="https://some_embedding_model.com")
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def test_to_dict(self, mock_check_valid_model):
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component = HuggingFaceTEITextEmbedder()
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.hugging_face_tei_text_embedder.HuggingFaceTEITextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["HF_API_TOKEN"], "strict": False, "type": "env_var"},
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"model": "BAAI/bge-small-en-v1.5",
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"url": None,
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"prefix": "",
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"suffix": "",
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},
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}
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def test_to_dict_with_custom_init_parameters(self, mock_check_valid_model):
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component = HuggingFaceTEITextEmbedder(
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model="sentence-transformers/all-mpnet-base-v2",
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url="https://some_embedding_model.com",
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token=Secret.from_env_var("ENV_VAR", strict=False),
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prefix="prefix",
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suffix="suffix",
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)
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.hugging_face_tei_text_embedder.HuggingFaceTEITextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
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"model": "sentence-transformers/all-mpnet-base-v2",
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"url": "https://some_embedding_model.com",
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"prefix": "prefix",
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"suffix": "suffix",
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},
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}
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def test_run(self, mock_check_valid_model):
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with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
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mock_embedding_patch.side_effect = mock_embedding_generation
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embedder = HuggingFaceTEITextEmbedder(
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model="BAAI/bge-small-en-v1.5",
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token=Secret.from_token("fake-api-token"),
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prefix="prefix ",
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suffix=" suffix",
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)
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result = embedder.run(text="The food was delicious")
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mock_embedding_patch.assert_called_once_with(text="prefix The food was delicious suffix")
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assert len(result["embedding"]) == 384
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assert all(isinstance(x, float) for x in result["embedding"])
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def test_run_wrong_input_format(self, mock_check_valid_model):
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embedder = HuggingFaceTEITextEmbedder(
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model="BAAI/bge-small-en-v1.5",
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url="https://some_embedding_model.com",
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token=Secret.from_token("fake-api-token"),
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)
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list_integers_input = [1, 2, 3]
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with pytest.raises(TypeError, match="HuggingFaceTEITextEmbedder expects a string as an input"):
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embedder.run(text=list_integers_input)
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