test_chroma_with_metadatas_with_scores_using_vector() — langchain Function Reference

Source Code

libs/partners/chroma/tests/integration_tests/test_vectorstores.py lines 203–225

def test_chroma_with_metadatas_with_scores_using_vector() -> None:
    """Test end to end construction and scored search, using embedding vector."""
    texts = ["foo", "bar", "baz"]
    metadatas = [{"page": str(i)} for i in range(len(texts))]
    ids = [f"id_{i}" for i in range(len(texts))]
    embeddings = FakeEmbeddings()

    docsearch = Chroma.from_texts(
        collection_name="test_collection",
        texts=texts,
        embedding=embeddings,
        metadatas=metadatas,
        ids=ids,
    )
    embedded_query = embeddings.embed_query("foo")
    output = docsearch.similarity_search_by_vector_with_relevance_scores(
        embedding=embedded_query,
        k=1,
    )
    docsearch.delete_collection()
    assert output == [
        (Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0),
    ]