test_relevance_search_with_threshold() — langchain Function Reference

Source Code

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py lines 178–200

def test_relevance_search_with_threshold(
    location: str,
    vector_name: str,
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    metadatas = [
        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
        for i in range(len(texts))
    ]
    docsearch = QdrantVectorStore.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        metadatas=metadatas,
        location=location,
        vector_name=vector_name,
    )

    score_threshold = 0.99
    kwargs = {"score_threshold": score_threshold}
    output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
    assert len(output) == 1
    assert all(score >= score_threshold for _, score in output)