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"""Test Chroma functionality."""
import pytest
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_chroma() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
@pytest.mark.asyncio
async def test_chroma_async() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_chroma_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 = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
def test_chroma_with_metadatas_with_scores() -> None:
"""Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search_with_score("foo", k=1)
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
def test_chroma_search_filter() -> None:
"""Test end to end construction and search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search("far", k=1, filter={"first_letter": "f"})
assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
output = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
def test_chroma_search_filter_with_scores() -> None:
"""Test end to end construction and scored search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search_with_score(
"far", k=1, filter={"first_letter": "f"}
)
assert output == [
(Document(page_content="far", metadata={"first_letter": "f"}), 0.0)
]
output = docsearch.similarity_search_with_score(
"far", k=1, filter={"first_letter": "b"}
)
assert output == [
(Document(page_content="bar", metadata={"first_letter": "b"}), 1.0)
]
def test_chroma_with_persistence() -> None:
"""Test end to end construction and search, with persistence."""
chroma_persist_dir = "./tests/persist_dir"
collection_name = "test_collection"
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name=collection_name,
texts=texts,
embedding=FakeEmbeddings(),
persist_directory=chroma_persist_dir,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
docsearch.persist()
# Get a new VectorStore from the persisted directory
docsearch = Chroma(
collection_name=collection_name,
embedding_function=FakeEmbeddings(),
persist_directory=chroma_persist_dir,
)
output = docsearch.similarity_search("foo", k=1)
# Clean up
docsearch.delete_collection()
# Persist doesn't need to be called again
# Data will be automatically persisted on object deletion
# Or on program exit
def test_chroma_mmr() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.max_marginal_relevance_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_chroma_mmr_by_vector() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
embeddings = FakeEmbeddings()
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=embeddings
)
embedded_query = embeddings.embed_query("foo")
output = docsearch.max_marginal_relevance_search_by_vector(embedded_query, k=1)
assert output == [Document(page_content="foo")]
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