retrievers: MultiVectorRetriever similarity_score_threshold search type (#23539)

Description: support MultiVectorRetriever similarity_score_threshold
search type.

Issue: #23387 #19404

---------

Co-authored-by: gongwn1 <gongwn1@lenovo.com>

libs/langchain/langchain/retrievers/multi_vector.py

@@ -19,6 +19,8 @@ class SearchType(str, Enum):
 
     similarity = "similarity"
     """Similarity search."""
+    similarity_score_threshold = "similarity_score_threshold"
+    """Similarity search with a score threshold."""
     mmr = "mmr"
     """Maximal Marginal Relevance reranking of similarity search."""
 
@@ -64,6 +66,13 @@ class MultiVectorRetriever(BaseRetriever):
             sub_docs = self.vectorstore.max_marginal_relevance_search(
                 query, **self.search_kwargs
             )
+        elif self.search_type == SearchType.similarity_score_threshold:
+            sub_docs_and_similarities = (
+                self.vectorstore.similarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            sub_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
         else:
             sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
 
@@ -89,6 +98,13 @@ class MultiVectorRetriever(BaseRetriever):
             sub_docs = await self.vectorstore.amax_marginal_relevance_search(
                 query, **self.search_kwargs
             )
+        elif self.search_type == SearchType.similarity_score_threshold:
+            sub_docs_and_similarities = (
+                await self.vectorstore.asimilarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            sub_docs = [sub_doc for sub_doc, _ in sub_docs_and_similarities]
         else:
             sub_docs = await self.vectorstore.asimilarity_search(
                 query, **self.search_kwargs

libs/langchain/tests/unit_tests/retrievers/test_multi_vector.py

@@ -1,13 +1,20 @@
-from typing import Any, List
+from typing import Any, Callable, List, Tuple
 
 from langchain_core.documents import Document
 
-from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
 from langchain.storage import InMemoryStore
 from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
 
 
 class InMemoryVectorstoreWithSearch(InMemoryVectorStore):
+    @staticmethod
+    def _identity_fn(score: float) -> float:
+        return score
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        return self._identity_fn
+
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
@@ -16,6 +23,14 @@ class InMemoryVectorstoreWithSearch(InMemoryVectorStore):
             return []
         return [res]
 
+    def similarity_search_with_score(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        res = self.store.get(query)
+        if res is None:
+            return []
+        return [(res, 0.8)]
+
 
 def test_multi_vector_retriever_initialization() -> None:
     vectorstore = InMemoryVectorstoreWithSearch()
@@ -41,3 +56,65 @@ async def test_multi_vector_retriever_initialization_async() -> None:
     results = await retriever.ainvoke("1")
     assert len(results) > 0
     assert results[0].page_content == "test document"
+
+
+def test_multi_vector_retriever_similarity_search_with_score() -> None:
+    documents = [Document(page_content="test document", metadata={"doc_id": "1"})]
+    vectorstore = InMemoryVectorstoreWithSearch()
+    vectorstore.add_documents(documents, ids=["1"])
+
+    # score_threshold = 0.5
+    retriever = MultiVectorRetriever(  # type: ignore[call-arg]
+        vectorstore=vectorstore,
+        docstore=InMemoryStore(),
+        doc_id="doc_id",
+        search_kwargs={"score_threshold": 0.5},
+        search_type=SearchType.similarity_score_threshold,
+    )
+    retriever.docstore.mset(list(zip(["1"], documents)))
+    results = retriever.invoke("1")
+    assert len(results) == 1
+    assert results[0].page_content == "test document"
+
+    # score_threshold = 0.9
+    retriever = MultiVectorRetriever(  # type: ignore[call-arg]
+        vectorstore=vectorstore,
+        docstore=InMemoryStore(),
+        doc_id="doc_id",
+        search_kwargs={"score_threshold": 0.9},
+        search_type=SearchType.similarity_score_threshold,
+    )
+    retriever.docstore.mset(list(zip(["1"], documents)))
+    results = retriever.invoke("1")
+    assert len(results) == 0
+
+
+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"])
+
+    # score_threshold = 0.5
+    retriever = MultiVectorRetriever(  # type: ignore[call-arg]
+        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)))
+    results = retriever.invoke("1")
+    assert len(results) == 1
+    assert results[0].page_content == "test document"
+
+    # score_threshold = 0.9
+    retriever = MultiVectorRetriever(  # type: ignore[call-arg]
+        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)))
+    results = retriever.invoke("1")
+    assert len(results) == 0