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00635744ed
This PR adds a local embedding model option as an alternative to using our OpenAI embedding brick. This brick uses LangChain's HuggingFacEmbeddings.
24 lines
862 B
Python
24 lines
862 B
Python
from unstructured.documents.elements import Text
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from unstructured.embed.huggingface import HuggingFaceEmbeddingEncoder
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def test_embed_documents_does_not_break_element_to_dict(mocker):
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# Mocked client with the desired behavior for embed_documents
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mock_client = mocker.MagicMock()
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mock_client.embed_documents.return_value = [1, 2]
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# Mock get_openai_client to return our mock_client
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mocker.patch.object(
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HuggingFaceEmbeddingEncoder,
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"get_huggingface_client",
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return_value=mock_client,
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)
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encoder = HuggingFaceEmbeddingEncoder()
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elements = encoder.embed_documents(
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elements=[Text("This is sentence 1"), Text("This is sentence 2")],
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)
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assert len(elements) == 2
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assert elements[0].to_dict()["text"] == "This is sentence 1"
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assert elements[1].to_dict()["text"] == "This is sentence 2"
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