test_similarity_search_filters_with_qdrant_filters() — langchain Function Reference

Source Code

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py lines 260–311

def test_similarity_search_filters_with_qdrant_filters(
    location: str,
    content_payload_key: str,
    metadata_payload_key: str,
    retrieval_mode: RetrievalMode,
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    metadatas = [
        {"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
        for i in range(len(texts))
    ]
    docsearch = QdrantVectorStore.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=location,
        metadatas=metadatas,
        content_payload_key=content_payload_key,
        metadata_payload_key=metadata_payload_key,
        retrieval_mode=retrieval_mode,
        sparse_embedding=ConsistentFakeSparseEmbeddings(),
    )

    qdrant_filter = models.Filter(
        must=[
            models.FieldCondition(
                key=content_payload_key, match=models.MatchValue(value="bar")
            ),
            models.FieldCondition(
                key=f"{metadata_payload_key}.page",
                match=models.MatchValue(value=1),
            ),
            models.FieldCondition(
                key=f"{metadata_payload_key}.details.page",
                match=models.MatchValue(value=2),
            ),
            models.FieldCondition(
                key=f"{metadata_payload_key}.details.pages",
                match=models.MatchAny(any=[3]),
            ),
        ]
    )
    output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
    assert_documents_equals(
        actual=output,
        expected=[
            Document(
                page_content="bar",
                metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
            )
        ],
    )