test_similarity_search_with_score_by_vector() — langchain Function Reference

Source Code

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py lines 76–99

def test_similarity_search_with_score_by_vector(
    location: str,
    content_payload_key: str,
    metadata_payload_key: str,
    vector_name: str,
    batch_size: int,
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = QdrantVectorStore.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=location,
        content_payload_key=content_payload_key,
        metadata_payload_key=metadata_payload_key,
        batch_size=batch_size,
        vector_name=vector_name,
    )
    embeddings = ConsistentFakeEmbeddings().embed_query("foo")
    output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
    assert len(output) == 1
    document, score = output[0]
    assert_documents_equals([document], [Document(page_content="foo")])
    assert score >= 0