fixes #1214 (#3003)

### Background

Continuing to implement all the interface methods defined by the
`VectorStore` class. This PR pertains to implementation of the
`max_marginal_relevance_search_by_vector` method.

### Changes

- a `max_marginal_relevance_search_by_vector` method implementation has
been added in `weaviate.py`
- tests have been added to the the new method
- vcr cassettes have been added for the weaviate tests

### Test Plan

Added tests for the `max_marginal_relevance_search_by_vector`
implementation

### Change Safety

- [x] I have added tests to cover my changes

langchain/vectorstores/annoy.py

@@ -201,7 +201,12 @@ class Annoy(VectorStore):
         return [doc for doc, _ in docs_and_scores]
 
     def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -212,6 +217,10 @@ class Annoy(VectorStore):
             embedding: Embedding to look up documents similar to.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
             k: Number of Documents to return. Defaults to 4.
+            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.
@@ -221,7 +230,10 @@ class Annoy(VectorStore):
         )
         embeddings = [self.index.get_item_vector(i) for i in idxs]
         mmr_selected = maximal_marginal_relevance(
-            np.array([embedding], dtype=np.float32), embeddings, k=k
+            np.array([embedding], dtype=np.float32),
+            embeddings,
+            k=k,
+            lambda_mult=lambda_mult,
         )
         # ignore the -1's if not enough docs are returned/indexed
         selected_indices = [idxs[i] for i in mmr_selected if i != -1]
@@ -236,7 +248,12 @@ class Annoy(VectorStore):
         return docs
 
     def max_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -247,12 +264,17 @@ class Annoy(VectorStore):
             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_function(query)
-        docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
+        docs = self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult=lambda_mult
+        )
         return docs
 
     @classmethod

langchain/vectorstores/base.py

@@ -153,7 +153,12 @@ class VectorStore(ABC):
         return await asyncio.get_event_loop().run_in_executor(None, func)
 
     def max_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -164,25 +169,40 @@ class VectorStore(ABC):
             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.
         """
         raise NotImplementedError
 
     async def amax_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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."""
 
         # This is a temporary workaround to make the similarity search
         # asynchronous. The proper solution is to make the similarity search
         # asynchronous in the vector store implementations.
-        func = partial(self.max_marginal_relevance_search, query, k, fetch_k, **kwargs)
+        func = partial(
+            self.max_marginal_relevance_search, query, k, fetch_k, lambda_mult, **kwargs
+        )
         return await asyncio.get_event_loop().run_in_executor(None, func)
 
     def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -193,14 +213,22 @@ class VectorStore(ABC):
             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.
         """
         raise NotImplementedError
 
     async def amax_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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."""
         raise NotImplementedError

langchain/vectorstores/chroma.py

@@ -198,6 +198,7 @@ class Chroma(VectorStore):
         embedding: List[float],
         k: int = 4,
         fetch_k: int = 20,
+        lambda_mult: float = 0.5,
         filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Document]:
@@ -208,6 +209,10 @@ class Chroma(VectorStore):
             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.
             filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
         Returns:
             List of Documents selected by maximal marginal relevance.
@@ -220,7 +225,10 @@ class Chroma(VectorStore):
             include=["metadatas", "documents", "distances", "embeddings"],
         )
         mmr_selected = maximal_marginal_relevance(
-            np.array(embedding, dtype=np.float32), results["embeddings"][0], k=k
+            np.array(embedding, dtype=np.float32),
+            results["embeddings"][0],
+            k=k,
+            lambda_mult=lambda_mult,
         )
 
         candidates = _results_to_docs(results)
@@ -233,6 +241,7 @@ class Chroma(VectorStore):
         query: str,
         k: int = 4,
         fetch_k: int = 20,
+        lambda_mult: float = 0.5,
         filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Document]:
@@ -243,6 +252,10 @@ class Chroma(VectorStore):
             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.
             filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
         Returns:
             List of Documents selected by maximal marginal relevance.
@@ -254,7 +267,7 @@ class Chroma(VectorStore):
 
         embedding = self._embedding_function.embed_query(query)
         docs = self.max_marginal_relevance_search_by_vector(
-            embedding, k, fetch_k, filter
+            embedding, k, fetch_k, lambda_mul=lambda_mult, filter=filter
         )
         return docs
 

langchain/vectorstores/deeplake.py

@@ -315,8 +315,12 @@ class DeepLake(VectorStore):
 
             view = view[indices]
             if use_maximal_marginal_relevance:
+                lambda_mult = kwargs.get("lambda_mult", 0.5)
                 indices = maximal_marginal_relevance(
-                    query_emb, embeddings[indices], k=min(k, len(indices))
+                    query_emb,
+                    embeddings[indices],
+                    k=min(k, len(indices)),
+                    lambda_mult=lambda_mult,
                 )
                 view = view[indices]
                 scores = [scores[i] for i in indices]
@@ -406,7 +410,12 @@ class DeepLake(VectorStore):
         )
 
     def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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
@@ -415,6 +424,10 @@ class DeepLake(VectorStore):
             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.
         """
@@ -423,10 +436,16 @@ class DeepLake(VectorStore):
             k=k,
             fetch_k=fetch_k,
             use_maximal_marginal_relevance=True,
+            lambda_mult=lambda_mult,
         )
 
     def max_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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
@@ -435,6 +454,10 @@ class DeepLake(VectorStore):
             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.
         """
@@ -443,7 +466,11 @@ class DeepLake(VectorStore):
                 "For MMR search, you must specify an embedding function on" "creation."
             )
         return self.search(
-            query=query, k=k, fetch_k=fetch_k, use_maximal_marginal_relevance=True
+            query=query,
+            k=k,
+            fetch_k=fetch_k,
+            use_maximal_marginal_relevance=True,
+            lambda_mult=lambda_mult,
         )
 
     @classmethod

langchain/vectorstores/faiss.py

@@ -227,7 +227,12 @@ class FAISS(VectorStore):
         return [doc for doc, _ in docs_and_scores]
 
     def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -238,7 +243,10 @@ class FAISS(VectorStore):
             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.
         """
@@ -246,7 +254,10 @@ class FAISS(VectorStore):
         # -1 happens when not enough docs are returned.
         embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
         mmr_selected = maximal_marginal_relevance(
-            np.array([embedding], dtype=np.float32), embeddings, k=k
+            np.array([embedding], dtype=np.float32),
+            embeddings,
+            k=k,
+            lambda_mult=lambda_mult,
         )
         selected_indices = [indices[0][i] for i in mmr_selected]
         docs = []
@@ -266,6 +277,7 @@ class FAISS(VectorStore):
         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.
@@ -277,12 +289,17 @@ class FAISS(VectorStore):
             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_function(query)
-        docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
+        docs = self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult=lambda_mult
+        )
         return docs
 
     def merge_from(self, target: FAISS) -> None:

langchain/vectorstores/milvus.py

@@ -619,6 +619,7 @@ class Milvus(VectorStore):
         query: str,
         k: int = 4,
         fetch_k: int = 20,
+        lambda_mult: float = 0.5,
         param: Optional[dict] = None,
         expr: Optional[str] = None,
         timeout: Optional[int] = None,
@@ -631,6 +632,10 @@ class Milvus(VectorStore):
             k (int, optional): How many results to give. Defaults to 4.
             fetch_k (int, optional): Total results to select k from.
                 Defaults to 20.
+            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
             param (dict, optional): The search params for the specified index.
                 Defaults to None.
             expr (str, optional): Filtering expression. Defaults to None.
@@ -652,6 +657,7 @@ class Milvus(VectorStore):
             embedding=embedding,
             k=k,
             fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
             param=param,
             expr=expr,
             timeout=timeout,
@@ -663,6 +669,7 @@ class Milvus(VectorStore):
         embedding: list[float],
         k: int = 4,
         fetch_k: int = 20,
+        lambda_mult: float = 0.5,
         param: Optional[dict] = None,
         expr: Optional[str] = None,
         timeout: Optional[int] = None,
@@ -675,6 +682,10 @@ class Milvus(VectorStore):
             k (int, optional): How many results to give. Defaults to 4.
             fetch_k (int, optional): Total results to select k from.
                 Defaults to 20.
+            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
             param (dict, optional): The search params for the specified index.
                 Defaults to None.
             expr (str, optional): Filtering expression. Defaults to None.
@@ -730,7 +741,7 @@ class Milvus(VectorStore):
 
         # Get the new order of results.
         new_ordering = maximal_marginal_relevance(
-            np.array(embedding), ordered_result_embeddings, k=k
+            np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult
         )
 
         # Reorder the values and return.

langchain/vectorstores/qdrant.py

@@ -151,6 +151,7 @@ class Qdrant(VectorStore):
         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.
@@ -163,7 +164,10 @@ class Qdrant(VectorStore):
             k: Number of Documents to return. Defaults to 4.
             fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                      Defaults to 20.
-
+            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.
         """
@@ -176,7 +180,9 @@ class Qdrant(VectorStore):
             limit=fetch_k,
         )
         embeddings = [result.vector for result in results]
-        mmr_selected = maximal_marginal_relevance(embedding, embeddings, k=k)
+        mmr_selected = maximal_marginal_relevance(
+            embedding, embeddings, k=k, lambda_mult=lambda_mult
+        )
         return [
             self._document_from_scored_point(
                 results[i], self.content_payload_key, self.metadata_payload_key

langchain/vectorstores/supabase.py

@@ -236,6 +236,7 @@ class SupabaseVectorStore(VectorStore):
         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.
@@ -247,7 +248,10 @@ class SupabaseVectorStore(VectorStore):
             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.
         """
@@ -259,7 +263,10 @@ class SupabaseVectorStore(VectorStore):
         matched_embeddings = [doc_tuple[2] for doc_tuple in result]
 
         mmr_selected = maximal_marginal_relevance(
-            np.array([embedding], dtype=np.float32), matched_embeddings, k=k
+            np.array([embedding], dtype=np.float32),
+            matched_embeddings,
+            k=k,
+            lambda_mult=lambda_mult,
         )
 
         filtered_documents = [matched_documents[i] for i in mmr_selected]
@@ -271,6 +278,7 @@ class SupabaseVectorStore(VectorStore):
         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.
@@ -282,7 +290,10 @@ class SupabaseVectorStore(VectorStore):
             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.
 
@@ -318,5 +329,7 @@ class SupabaseVectorStore(VectorStore):
         $$;```
         """
         embedding = self._embedding.embed_documents([query])
-        docs = self.max_marginal_relevance_search_by_vector(embedding[0], k, fetch_k)
+        docs = self.max_marginal_relevance_search_by_vector(
+            embedding[0], k, fetch_k, lambda_mult=lambda_mult
+        )
         return docs

langchain/vectorstores/weaviate.py

@@ -135,7 +135,12 @@ class Weaviate(VectorStore):
         return docs
 
     def max_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
+        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.
 
@@ -146,12 +151,14 @@ class Weaviate(VectorStore):
             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.
         """
-        lambda_mult = kwargs.get("lambda_mult", 0.5)
-
         if self._embedding is not None:
             embedding = self._embedding.embed_query(query)
         else:
@@ -159,6 +166,35 @@ class Weaviate(VectorStore):
                 "max_marginal_relevance_search requires a suitable Embeddings object"
             )
 
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
+        )
+
+    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.
+        """
         vector = {"vector": embedding}
         query_obj = self._client.query.get(self._index_name, self._query_attrs)
         results = (
@@ -180,6 +216,7 @@ class Weaviate(VectorStore):
             payload[idx].pop("_additional")
             meta = payload[idx]
             docs.append(Document(page_content=text, metadata=meta))
+
         return docs
 
     @classmethod

tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml

@@ -121,231 +121,231 @@ interactions:
         M0fjN39o9dRD5r2dU9//+PS4PfsM2yiIR+/1pKvepC2Vd3VY8vBwDcH+FDlzv/VJ54OQUDjfR3Q4
         KswcG29nwPV2ebAw9Z7ix79hu4y2hLinh8kf65EiuNtHtqW5aopLPgt/+vDHJ8w/vt7OghPz6/cs
         pXrvFZDLbEMs36clLZywBksjFsN41pQjAxNgdRzvLPjqcfb1Y+GBu4SCHa4Rjx9XCa2ViyA/P2Dq
-        sVmsxnS2Jp7tP83JfkkKIsZYEvs1Pctvvk+wLZYGfr8r7I+owSEsQrr7PwAAAP//pJ1Lr6PAlqXn
-        9StKd0pfgTEQQc14m5cJXsa21GoBtjFgjA1EACHVfy/hc7vVgxp1DzOVx2nHY6+1vr3N2fKhQWu/
-        IjEMJr5BakdqnQ7hIPz6LQHLR3gghmYy0I0tgMyXbw879DmUcONVAafVdrN8yv7y868bT4zBysiL
-        BRVm8JEN1jRZwbfrpSI68sSR1aSZb+mhArLCPYif1BMdN734W393Bm5B6bs04IV9nX79rGbjcV9A
-        7UVCbtOp4M9/+ZGn477OEaBDfQmgtKxn5EnuJ6GHY1BBRs4CdHQvaJibNa+gvPgqcjceh51ZvsMI
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tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search_by_vector.yaml

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tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata.yaml

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     status:
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       message: OK

tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml

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tests/integration_tests/vectorstores/test_weaviate.py

@@ -88,3 +88,32 @@ class TestWeaviate:
             Document(page_content="foo", metadata={"page": 0}),
             Document(page_content="bar", metadata={"page": 1}),
         ]
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_max_marginal_relevance_search_by_vector(
+        self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
+    ) -> None:
+        """Test end to end construction and MRR search by vector."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+
+        docsearch = Weaviate.from_texts(
+            texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
+        )
+        foo_embedding = embedding_openai.embed_query("foo")
+
+        # if lambda=1 the algorithm should be equivalent to standard ranking
+        standard_ranking = docsearch.similarity_search("foo", k=2)
+        output = docsearch.max_marginal_relevance_search_by_vector(
+            foo_embedding, k=2, fetch_k=3, lambda_mult=1.0
+        )
+        assert output == standard_ranking
+
+        # if lambda=0 the algorithm should favour maximal diversity
+        output = docsearch.max_marginal_relevance_search_by_vector(
+            foo_embedding, k=2, fetch_k=3, lambda_mult=0.0
+        )
+        assert output == [
+            Document(page_content="foo", metadata={"page": 0}),
+            Document(page_content="bar", metadata={"page": 1}),
+        ]