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129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
"""Test Chroma functionality."""
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import pytest
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from langchain.docstore.document import Document
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from langchain.vectorstores import Chroma
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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def test_chroma() -> 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 = Chroma.from_texts(
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collection_name="test_collection", 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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@pytest.mark.asyncio
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async def test_chroma_async() -> 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 = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
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)
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output = await docsearch.asimilarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_chroma_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 = Chroma.from_texts(
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collection_name="test_collection",
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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_chroma_with_metadatas_with_scores() -> None:
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"""Test end to end construction and scored 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 = Chroma.from_texts(
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collection_name="test_collection",
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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_with_score("foo", k=1)
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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def test_chroma_search_filter() -> None:
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"""Test end to end construction and search with metadata filtering."""
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texts = ["far", "bar", "baz"]
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metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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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("far", k=1, filter={"first_letter": "f"})
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assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
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output = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
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assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
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def test_chroma_search_filter_with_scores() -> None:
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"""Test end to end construction and scored search with metadata filtering."""
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texts = ["far", "bar", "baz"]
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metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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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_with_score(
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"far", k=1, filter={"first_letter": "f"}
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)
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assert output == [
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(Document(page_content="far", metadata={"first_letter": "f"}), 0.0)
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]
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output = docsearch.similarity_search_with_score(
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"far", k=1, filter={"first_letter": "b"}
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)
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assert output == [
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(Document(page_content="bar", metadata={"first_letter": "b"}), 1.0)
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]
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def test_chroma_with_persistence() -> None:
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"""Test end to end construction and search, with persistence."""
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chroma_persist_dir = "./tests/persist_dir"
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collection_name = "test_collection"
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name=collection_name,
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texts=texts,
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embedding=FakeEmbeddings(),
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persist_directory=chroma_persist_dir,
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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 = Chroma(
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collection_name=collection_name,
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embedding_function=FakeEmbeddings(),
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persist_directory=chroma_persist_dir,
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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_collection()
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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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