mirror of
https://github.com/hwchase17/langchain
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39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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# flake8: noqa
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"""Test sentence_transformer embeddings."""
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from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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from langchain.vectorstores import Chroma
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def test_sentence_transformer_embedding_documents() -> None:
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"""Test sentence_transformer embeddings."""
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embedding = SentenceTransformerEmbeddings()
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documents = ["foo bar"]
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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]) == 384
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def test_sentence_transformer_embedding_query() -> None:
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"""Test sentence_transformer embeddings."""
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embedding = SentenceTransformerEmbeddings()
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query = "what the foo is a bar?"
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query_vector = embedding.embed_query(query)
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assert len(query_vector) == 384
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def test_sentence_transformer_db_query() -> None:
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"""Test sentence_transformer similarity search."""
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embedding = SentenceTransformerEmbeddings()
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texts = [
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"we will foo your bar until you can't foo any more",
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"the quick brown fox jumped over the lazy dog",
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]
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query = "what the foo is a bar?"
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query_vector = embedding.embed_query(query)
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assert len(query_vector) == 384
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db = Chroma(embedding_function=embedding)
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db.add_texts(texts)
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docs = db.similarity_search_by_vector(query_vector, k=2)
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assert docs[0].page_content == "we will foo your bar until you can't foo any more"
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