MockVectorStore Class — langchain Architecture

Architecture documentation for the MockVectorStore class in test_time_weighted_retriever.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18["MockVectorStore"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|extends| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  3ccece1f_f844_33e7_bdea_1996eb777e35["test_time_weighted_retriever.py"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|defined in| 3ccece1f_f844_33e7_bdea_1996eb777e35
  82413698_b642_bf79_bef9_fc2b78aa8923["add_texts()"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|method| 82413698_b642_bf79_bef9_fc2b78aa8923
  ce1796f3_6f99_9517_3ede_8fba78f701dc["similarity_search()"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|method| ce1796f3_6f99_9517_3ede_8fba78f701dc
  2c1a291a_0dae_be57_519e_9029a7e446e9["from_texts()"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|method| 2c1a291a_0dae_be57_519e_9029a7e446e9
  ebf698ca_7e85_ba04_a601_be0149d84574["_similarity_search_with_relevance_scores()"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|method| ebf698ca_7e85_ba04_a601_be0149d84574
  0ef3a3ec_e971_5340_e773_d7478d9bfd57["_asimilarity_search_with_relevance_scores()"]
  7e77bbc5_6b03_10c7_3db9_fd34f9dd5b18 -->|method| 0ef3a3ec_e971_5340_e773_d7478d9bfd57

Relationship Graph

Source Code

libs/langchain/tests/unit_tests/retrievers/test_time_weighted_retriever.py lines 32–79

class MockVectorStore(VectorStore):
    """Mock invalid vector store."""

    @override
    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: list[dict] | None = None,
        **kwargs: Any,
    ) -> list[str]:
        return list(texts)

    @override
    def similarity_search(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> list[Document]:
        return []

    @classmethod
    @override
    def from_texts(
        cls: type["MockVectorStore"],
        texts: list[str],
        embedding: Embeddings,
        metadatas: list[dict] | None = None,
        **kwargs: Any,
    ) -> "MockVectorStore":
        return cls()

    @override
    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        return [(doc, 0.5) for doc in _get_example_memories()]

    async def _asimilarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        return self._similarity_search_with_relevance_scores(query, k, **kwargs)