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5db6b796cf
Thanks @amogkam for the addition! Refactored slightly --------- Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""Test huggingface embeddings."""
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from langchain.embeddings.huggingface import (
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HuggingFaceEmbeddings,
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HuggingFaceInstructEmbeddings,
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)
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def test_huggingface_embedding_documents() -> None:
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"""Test huggingface embeddings."""
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documents = ["foo bar"]
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embedding = HuggingFaceEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 768
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def test_huggingface_embedding_query() -> None:
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"""Test huggingface embeddings."""
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document = "foo bar"
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embedding = HuggingFaceEmbeddings(encode_kwargs={"batch_size": 16})
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output = embedding.embed_query(document)
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assert len(output) == 768
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def test_huggingface_instructor_embedding_documents() -> None:
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"""Test huggingface embeddings."""
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documents = ["foo bar"]
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embedding = HuggingFaceInstructEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 768
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def test_huggingface_instructor_embedding_query() -> None:
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"""Test huggingface embeddings."""
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query = "foo bar"
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embedding = HuggingFaceInstructEmbeddings()
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output = embedding.embed_query(query)
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assert len(output) == 768
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