Added search parameters to qdrant max_marginal_relevance_search (#7745)

Adds the qdrant search filter/params to the
`max_marginal_relevance_search` method, which is present on others. I
did not add `offset` for pagination, because it's behavior would be
ambiguous in this setting (since we fetch extra and down-select).

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Kacper Łukawski <lukawski.kacper@gmail.com>

libs/langchain/langchain/vectorstores/qdrant.py

@@ -265,6 +265,8 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
 
         Returns:
             List of Documents most similar to the query.
@@ -339,6 +341,8 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
 
         Returns:
             List of documents most similar to the query text and distance for each.
@@ -394,6 +398,9 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to
+                QdrantClient.async_grpc_points.Search().
 
         Returns:
             List of documents most similar to the query text and distance for each.
@@ -448,6 +455,8 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
 
         Returns:
             List of Documents most similar to the query.
@@ -504,6 +513,9 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to
+                QdrantClient.async_grpc_points.Search().
 
         Returns:
             List of Documents most similar to the query.
@@ -559,6 +571,8 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
 
         Returns:
             List of documents most similar to the query text and distance for each.
@@ -601,6 +615,56 @@ class Qdrant(VectorStore):
             for result in results
         ]
 
+    async def _asearch_with_score_by_vector(
+        self,
+        embedding: List[float],
+        *,
+        k: int = 4,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        offset: int = 0,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
+        with_vectors: bool = False,
+        **kwargs: Any,
+    ) -> Any:
+        """Return results most similar to embedding vector."""
+        from qdrant_client import grpc  # noqa
+        from qdrant_client.conversions.conversion import RestToGrpc
+        from qdrant_client.http import models as rest
+
+        if filter is not None and isinstance(filter, dict):
+            warnings.warn(
+                "Using dict as a `filter` is deprecated. Please use qdrant-client "
+                "filters directly: "
+                "https://qdrant.tech/documentation/concepts/filtering/",
+                DeprecationWarning,
+            )
+            qdrant_filter = self._qdrant_filter_from_dict(filter)
+        else:
+            qdrant_filter = filter
+
+        if qdrant_filter is not None and isinstance(qdrant_filter, rest.Filter):
+            qdrant_filter = RestToGrpc.convert_filter(qdrant_filter)
+
+        response = await self.client.async_grpc_points.Search(
+            grpc.SearchPoints(
+                collection_name=self.collection_name,
+                vector_name=self.vector_name,
+                vector=embedding,
+                filter=qdrant_filter,
+                params=search_params,
+                limit=k,
+                offset=offset,
+                with_payload=grpc.WithPayloadSelector(enable=True),
+                with_vectors=grpc.WithVectorsSelector(enable=with_vectors),
+                score_threshold=score_threshold,
+                read_consistency=consistency,
+                **kwargs,
+            )
+        )
+        return response
+
     @sync_call_fallback
     async def asimilarity_search_with_score_by_vector(
         self,
@@ -641,44 +705,23 @@ class Qdrant(VectorStore):
                 - 'quorum' - query the majority of replicas, return values present in
                              all of them
                 - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to
+                QdrantClient.async_grpc_points.Search().
 
         Returns:
             List of documents most similar to the query text and distance for each.
         """
-        from qdrant_client import grpc  # noqa
-        from qdrant_client.conversions.conversion import RestToGrpc
-        from qdrant_client.http import models as rest
-
-        if filter is not None and isinstance(filter, dict):
-            warnings.warn(
-                "Using dict as a `filter` is deprecated. Please use qdrant-client "
-                "filters directly: "
-                "https://qdrant.tech/documentation/concepts/filtering/",
-                DeprecationWarning,
-            )
-            qdrant_filter = self._qdrant_filter_from_dict(filter)
-        else:
-            qdrant_filter = filter
-
-        if qdrant_filter is not None and isinstance(qdrant_filter, rest.Filter):
-            qdrant_filter = RestToGrpc.convert_filter(qdrant_filter)
-
-        response = await self.client.async_grpc_points.Search(
-            grpc.SearchPoints(
-                collection_name=self.collection_name,
-                vector_name=self.vector_name,
-                vector=embedding,
-                filter=qdrant_filter,
-                params=search_params,
-                limit=k,
+        response = await self._asearch_with_score_by_vector(
+            embedding,
+            k=k,
+            filter=filter,
+            search_params=search_params,
             offset=offset,
-                with_payload=grpc.WithPayloadSelector(enable=True),
-                with_vectors=grpc.WithVectorsSelector(enable=False),
             score_threshold=score_threshold,
-                read_consistency=consistency,
+            consistency=consistency,
             **kwargs,
         )
-        )
 
         return [
             (
@@ -696,6 +739,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -712,12 +759,41 @@ class Qdrant(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
+            filter: Filter by metadata. Defaults to None.
+            search_params: Additional search params
+            score_threshold:
+                Define a minimal score threshold for the result.
+                If defined, less similar results will not be returned.
+                Score of the returned result might be higher or smaller than the
+                threshold depending on the Distance function used.
+                E.g. for cosine similarity only higher scores will be returned.
+            consistency:
+                Read consistency of the search. Defines how many replicas should be
+                queried before returning the result.
+                Values:
+                - int - number of replicas to query, values should present in all
+                        queried replicas
+                - 'majority' - query all replicas, but return values present in the
+                               majority of replicas
+                - 'quorum' - query the majority of replicas, return values present in
+                             all of them
+                - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
         query_embedding = self._embed_query(query)
         return self.max_marginal_relevance_search_by_vector(
-            query_embedding, k, fetch_k, lambda_mult, **kwargs
+            query_embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            search_params=search_params,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
         )
 
     @sync_call_fallback
@@ -727,6 +803,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -743,12 +823,42 @@ class Qdrant(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
+            filter: Filter by metadata. Defaults to None.
+            search_params: Additional search params
+            score_threshold:
+                Define a minimal score threshold for the result.
+                If defined, less similar results will not be returned.
+                Score of the returned result might be higher or smaller than the
+                threshold depending on the Distance function used.
+                E.g. for cosine similarity only higher scores will be returned.
+            consistency:
+                Read consistency of the search. Defines how many replicas should be
+                queried before returning the result.
+                Values:
+                - int - number of replicas to query, values should present in all
+                        queried replicas
+                - 'majority' - query all replicas, but return values present in the
+                               majority of replicas
+                - 'quorum' - query the majority of replicas, return values present in
+                             all of them
+                - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to
+                QdrantClient.async_grpc_points.Search().
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
         query_embedding = self._embed_query(query)
         return await self.amax_marginal_relevance_search_by_vector(
-            query_embedding, k, fetch_k, lambda_mult, **kwargs
+            query_embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            search_params=search_params,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
         )
 
     def max_marginal_relevance_search_by_vector(
@@ -757,6 +867,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -772,11 +886,40 @@ class Qdrant(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
+            filter: Filter by metadata. Defaults to None.
+            search_params: Additional search params
+            score_threshold:
+                Define a minimal score threshold for the result.
+                If defined, less similar results will not be returned.
+                Score of the returned result might be higher or smaller than the
+                threshold depending on the Distance function used.
+                E.g. for cosine similarity only higher scores will be returned.
+            consistency:
+                Read consistency of the search. Defines how many replicas should be
+                queried before returning the result.
+                Values:
+                - int - number of replicas to query, values should present in all
+                        queried replicas
+                - 'majority' - query all replicas, but return values present in the
+                               majority of replicas
+                - 'quorum' - query the majority of replicas, return values present in
+                             all of them
+                - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
         results = self.max_marginal_relevance_search_with_score_by_vector(
-            embedding=embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
+            embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            search_params=search_params,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
         )
         return list(map(itemgetter(0), results))
 
@@ -787,6 +930,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -801,12 +948,42 @@ class Qdrant(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
+            filter: Filter by metadata. Defaults to None.
+            search_params: Additional search params
+            score_threshold:
+                Define a minimal score threshold for the result.
+                If defined, less similar results will not be returned.
+                Score of the returned result might be higher or smaller than the
+                threshold depending on the Distance function used.
+                E.g. for cosine similarity only higher scores will be returned.
+            consistency:
+                Read consistency of the search. Defines how many replicas should be
+                queried before returning the result.
+                Values:
+                - int - number of replicas to query, values should present in all
+                        queried replicas
+                - 'majority' - query all replicas, but return values present in the
+                               majority of replicas
+                - 'quorum' - query the majority of replicas, return values present in
+                             all of them
+                - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to
+                QdrantClient.async_grpc_points.Search().
         Returns:
             List of Documents selected by maximal marginal relevance and distance for
             each.
         """
         results = await self.amax_marginal_relevance_search_with_score_by_vector(
-            embedding, k, fetch_k, lambda_mult, **kwargs
+            embedding,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            filter=filter,
+            search_params=search_params,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
         )
         return list(map(itemgetter(0), results))
 
@@ -816,6 +993,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs selected using the maximal marginal relevance.
@@ -830,6 +1011,27 @@ class Qdrant(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
+            filter: Filter by metadata. Defaults to None.
+            search_params: Additional search params
+            score_threshold:
+                Define a minimal score threshold for the result.
+                If defined, less similar results will not be returned.
+                Score of the returned result might be higher or smaller than the
+                threshold depending on the Distance function used.
+                E.g. for cosine similarity only higher scores will be returned.
+            consistency:
+                Read consistency of the search. Defines how many replicas should be
+                queried before returning the result.
+                Values:
+                - int - number of replicas to query, values should present in all
+                        queried replicas
+                - 'majority' - query all replicas, but return values present in the
+                               majority of replicas
+                - 'quorum' - query the majority of replicas, return values present in
+                             all of them
+                - 'all' - query all replicas, and return values present in all replicas
+            **kwargs:
+                Any other named arguments to pass through to QdrantClient.search()
         Returns:
             List of Documents selected by maximal marginal relevance and distance for
             each.
@@ -841,9 +1043,14 @@ class Qdrant(VectorStore):
         results = self.client.search(
             collection_name=self.collection_name,
             query_vector=query_vector,
+            query_filter=filter,
+            search_params=search_params,
+            limit=fetch_k,
             with_payload=True,
             with_vectors=True,
-            limit=fetch_k,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
         )
         embeddings = [
             result.vector.get(self.vector_name)  # type: ignore[index, union-attr]
@@ -871,6 +1078,10 @@ class Qdrant(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        filter: Optional[MetadataFilter] = None,
+        search_params: Optional[common_types.SearchParams] = None,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[common_types.ReadConsistency] = None,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs selected using the maximal marginal relevance.
@@ -889,18 +1100,17 @@ class Qdrant(VectorStore):
             List of Documents selected by maximal marginal relevance and distance for
             each.
         """
-        from qdrant_client import grpc  # noqa
         from qdrant_client.conversions.conversion import GrpcToRest
 
-        response = await self.client.async_grpc_points.Search(
-            grpc.SearchPoints(
-                collection_name=self.collection_name,
-                vector_name=self.vector_name,
-                vector=embedding,
-                with_payload=grpc.WithPayloadSelector(enable=True),
-                with_vectors=grpc.WithVectorsSelector(enable=True),
-                limit=fetch_k,
-            )
+        response = await self._asearch_with_score_by_vector(
+            embedding,
+            k=fetch_k,
+            filter=filter,
+            search_params=search_params,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            with_vectors=True,
+            **kwargs,
         )
         results = [
             GrpcToRest.convert_vectors(result.vectors) for result in response.result

libs/langchain/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
 import pytest
+from qdrant_client import models
 
 from langchain.schema import Document
 from langchain.vectorstores import Qdrant
@@ -20,6 +21,17 @@ def test_qdrant_max_marginal_relevance_search(
     vector_name: Optional[str],
 ) -> None:
     """Test end to end construction and MRR search."""
+    filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page",
+                match=models.MatchValue(
+                    value=2,
+                ),
+            ),
+        ],
+    )
+
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = Qdrant.from_texts(
@@ -40,3 +52,10 @@ def test_qdrant_max_marginal_relevance_search(
         Document(page_content="foo", metadata={"page": 0}),
         Document(page_content="baz", metadata={"page": 2}),
     ]
+
+    output = docsearch.max_marginal_relevance_search(
+        "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter
+    )
+    assert output == [
+        Document(page_content="baz", metadata={"page": 2}),
+    ]