langchain Architecture Docs
Home / Function/ test_multi_vector_retriever_similarity_search_with_score_async() — langchain Function Reference

test_multi_vector_retriever_similarity_search_with_score_async() — langchain Function Reference

Architecture documentation for the test_multi_vector_retriever_similarity_search_with_score_async() function in test_multi_vector.py from the langchain codebase.

Function python LangChainCore Runnables

Entity Profile

Dependency Diagram

graph TD
  c073fef1_9a34_2420_7aa9_5e51f2c76174["test_multi_vector_retriever_similarity_search_with_score_async()"]
  ec1b85d3_6e5d_33cc_5e60_268fb7b7b716["test_multi_vector.py"]
  c073fef1_9a34_2420_7aa9_5e51f2c76174 -->|defined in| ec1b85d3_6e5d_33cc_5e60_268fb7b7b716
  style c073fef1_9a34_2420_7aa9_5e51f2c76174 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py lines 106–134

async def test_multi_vector_retriever_similarity_search_with_score_async() -> None:
    documents = [Document(page_content="test document", metadata={"doc_id": "1"})]
    vectorstore = InMemoryVectorstoreWithSearch()
    await vectorstore.aadd_documents(documents, ids=["1"])

    # test with score_threshold = 0.5
    retriever = MultiVectorRetriever(
        vectorstore=vectorstore,
        docstore=InMemoryStore(),
        doc_id="doc_id",
        search_kwargs={"score_threshold": 0.5},
        search_type=SearchType.similarity_score_threshold,
    )
    await retriever.docstore.amset(list(zip(["1"], documents, strict=False)))
    results = retriever.invoke("1")
    assert len(results) == 1
    assert results[0].page_content == "test document"

    # test with score_threshold = 0.9
    retriever = MultiVectorRetriever(
        vectorstore=vectorstore,
        docstore=InMemoryStore(),
        doc_id="doc_id",
        search_kwargs={"score_threshold": 0.9},
        search_type=SearchType.similarity_score_threshold,
    )
    await retriever.docstore.amset(list(zip(["1"], documents, strict=False)))
    results = retriever.invoke("1")
    assert len(results) == 0

Domain

Subdomains

Defined In

Source

Frequently Asked Questions

What does test_multi_vector_retriever_similarity_search_with_score_async() do?
test_multi_vector_retriever_similarity_search_with_score_async() is a function in the langchain codebase, defined in libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py.
Where is test_multi_vector_retriever_similarity_search_with_score_async() defined?
test_multi_vector_retriever_similarity_search_with_score_async() is defined in libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py at line 106.

Analyze Your Own Codebase

Get architecture documentation, dependency graphs, and domain analysis for your codebase in minutes.

Try Supermodel Free