forked from Archives/langchain
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
"""Test Deep Lake functionality."""
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from langchain.docstore.document import Document
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from langchain.vectorstores import DeepLake
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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def test_deeplake() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch = DeepLake.from_texts(
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dataset_path="mem://test_path", texts=texts, embedding=FakeEmbeddings()
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_deeplake_with_metadatas() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = DeepLake.from_texts(
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dataset_path="mem://test_path",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo", metadata={"page": "0"})]
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def test_deeplakewith_persistence() -> None:
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"""Test end to end construction and search, with persistence."""
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dataset_path = "./tests/persist_dir"
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texts = ["foo", "bar", "baz"]
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docsearch = DeepLake.from_texts(
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dataset_path=dataset_path,
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texts=texts,
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embedding=FakeEmbeddings(),
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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docsearch.persist()
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# Get a new VectorStore from the persisted directory
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docsearch = DeepLake(
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dataset_path=dataset_path,
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embedding_function=FakeEmbeddings(),
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)
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output = docsearch.similarity_search("foo", k=1)
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# Clean up
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docsearch.delete_dataset()
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# Persist doesn't need to be called again
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# Data will be automatically persisted on object deletion
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# Or on program exit
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