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langchain/tests/integration_tests/vectorstores/test_deeplake.py

59 lines
1.9 KiB
Python

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