feat: implement max marginal relevance for momento vector index (lang…

…chain-ai#13619)

**Description**

Implements `max_marginal_relevance_search` and
`max_marginal_relevance_search_by_vector` for the Momento Vector Index
vectorstore.

Additionally bumps the `momento` dependency in the lock file and adds
logging to the implementation.

**Dependencies**

✅ updates `momento` dependency in lock file

**Tag maintainer**

@baskaryan 

**Twitter handle**

Please tag @momentohq for Momento Vector Index and @mloml for the
contribution 🙇

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libs/langchain/langchain/vectorstores/momento_vector_index.py

@@ -1,3 +1,4 @@
+import logging
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -11,15 +12,17 @@
 )
 from uuid import uuid4
 
+import numpy as np
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VectorStore
 
 from langchain.utils import get_from_env
-from langchain.vectorstores.utils import DistanceStrategy
+from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance
 
 VST = TypeVar("VST", bound="VectorStore")
 
+logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     from momento import PreviewVectorIndexClient
@@ -75,9 +78,8 @@ def __init__(
             index_name (str, optional): The name of the index to store the documents in.
                 Defaults to "default".
             distance_strategy (DistanceStrategy, optional): The distance strategy to
-                use. Defaults to DistanceStrategy.COSINE. If you select
-                DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared
-                Euclidean distance.
+                use. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses
+                the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
             text_field (str, optional): The name of the metadata field to store the
                 original text in. Defaults to "text".
             ensure_index_exists (bool, optional): Whether to ensure that the index
@@ -125,6 +127,7 @@ def _create_index_if_not_exists(self, num_dimensions: int) -> bool:
         elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
             similarity_metric = SimilarityMetric.EUCLIDEAN_SIMILARITY
         else:
+            logger.error(f"Distance strategy {self.distance_strategy} not implemented.")
             raise ValueError(
                 f"Distance strategy {self.distance_strategy} not implemented."
             )
@@ -137,8 +140,10 @@ def _create_index_if_not_exists(self, num_dimensions: int) -> bool:
         elif isinstance(response, CreateIndex.IndexAlreadyExists):
             return False
         elif isinstance(response, CreateIndex.Error):
+            logger.error(f"Error creating index: {response.inner_exception}")
             raise response.inner_exception
         else:
+            logger.error(f"Unexpected response: {response}")
             raise Exception(f"Unexpected response: {response}")
 
     def add_texts(
@@ -331,6 +336,87 @@ def similarity_search_by_vector(
         )
         return [doc for doc, _ in results]
 
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        from momento.requests.vector_index import ALL_METADATA
+        from momento.responses.vector_index import SearchAndFetchVectors
+
+        response = self._client.search_and_fetch_vectors(
+            self.index_name, embedding, top_k=fetch_k, metadata_fields=ALL_METADATA
+        )
+
+        if isinstance(response, SearchAndFetchVectors.Success):
+            pass
+        elif isinstance(response, SearchAndFetchVectors.Error):
+            logger.error(f"Error searching and fetching vectors: {response}")
+            return []
+        else:
+            logger.error(f"Unexpected response: {response}")
+            raise Exception(f"Unexpected response: {response}")
+
+        mmr_selected = maximal_marginal_relevance(
+            query_embedding=np.array([embedding], dtype=np.float32),
+            embedding_list=[hit.vector for hit in response.hits],
+            lambda_mult=lambda_mult,
+            k=k,
+        )
+        selected = [response.hits[i].metadata for i in mmr_selected]
+        return [
+            Document(page_content=metadata.pop(self.text_field, ""), metadata=metadata)  # type: ignore  # noqa: E501
+            for metadata in selected
+        ]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        embedding = self._embedding.embed_query(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult, **kwargs
+        )
+
     @classmethod
     def from_texts(
         cls: Type[VST],

libs/langchain/tests/integration_tests/vectorstores/test_momento_vector_index.py

@@ -125,7 +125,7 @@ def test_from_texts_with_metadatas(
 
 
 def test_from_texts_with_scores(vector_store: MomentoVectorIndex) -> None:
-    #    """Test end to end construction and search with scores and IDs."""
+    """Test end to end construction and search with scores and IDs."""
     texts = ["apple", "orange", "hammer"]
     metadatas = [{"page": f"{i}"} for i in range(len(texts))]
 
@@ -162,3 +162,25 @@ def test_add_documents_with_ids(vector_store: MomentoVectorIndex) -> None:
     )
     assert isinstance(response, Search.Success)
     assert [hit.id for hit in response.hits] == ids
+
+
+def test_max_marginal_relevance_search(vector_store: MomentoVectorIndex) -> None:
+    """Test max marginal relevance search."""
+    pepperoni_pizza = "pepperoni pizza"
+    cheese_pizza = "cheese pizza"
+    hot_dog = "hot dog"
+
+    vector_store.add_texts([pepperoni_pizza, cheese_pizza, hot_dog])
+    wait()
+    search_results = vector_store.similarity_search("pizza", k=2)
+
+    assert search_results == [
+        Document(page_content=pepperoni_pizza, metadata={}),
+        Document(page_content=cheese_pizza, metadata={}),
+    ]
+
+    search_results = vector_store.max_marginal_relevance_search(query="pizza", k=2)
+    assert search_results == [
+        Document(page_content=pepperoni_pizza, metadata={}),
+        Document(page_content=hot_dog, metadata={}),
+    ]