qdrant: Add similarity_search_with_score_by_vector() function to the QdrantVectorStore (#29641)

Added `similarity_search_with_score_by_vector()` function to the
`QdrantVectorStore` class.

It is required when we want to query multiple time with the same
embeddings. It was present in the now deprecated original `Qdrant`
vectorstore implementation, but was absent from the new one. It is also
implemented in a number of others `VectorStore` implementations

I have added tests for this new function

Note that I also argued in this discussion that it should be part of the
general `VectorStore`
https://github.com/langchain-ai/langchain/discussions/29638

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/qdrant/langchain_qdrant/qdrant.py

@@ -568,7 +568,7 @@ class QdrantVectorStore(VectorStore):
             for result in results
         ]
 
-    def similarity_search_by_vector(
+    def similarity_search_with_score_by_vector(
         self,
         embedding: List[float],
         k: int = 4,
@@ -578,11 +578,11 @@ class QdrantVectorStore(VectorStore):
         score_threshold: Optional[float] = None,
         consistency: Optional[models.ReadConsistency] = None,
         **kwargs: Any,
-    ) -> List[Document]:
+    ) -> List[tuple[Document, float]]:
         """Return docs most similar to embedding vector.
 
         Returns:
-            List of Documents most similar to the query.
+            List of Documents most similar to the query and distance for each.
         """
         qdrant_filter = filter
 
@@ -609,15 +609,46 @@ class QdrantVectorStore(VectorStore):
         ).points
 
         return [
-            self._document_from_point(
-                result,
-                self.collection_name,
-                self.content_payload_key,
-                self.metadata_payload_key,
+            (
+                self._document_from_point(
+                    result,
+                    self.collection_name,
+                    self.content_payload_key,
+                    self.metadata_payload_key,
+                ),
+                result.score,
             )
             for result in results
         ]
 
+    def similarity_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[models.Filter] = None,
+        search_params: Optional[models.SearchParams] = None,
+        offset: int = 0,
+        score_threshold: Optional[float] = None,
+        consistency: Optional[models.ReadConsistency] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector.
+
+        Returns:
+            List of Documents most similar to the query.
+        """
+        results = self.similarity_search_with_score_by_vector(
+            embedding,
+            k,
+            filter=filter,
+            search_params=search_params,
+            offset=offset,
+            score_threshold=score_threshold,
+            consistency=consistency,
+            **kwargs,
+        )
+        return list(map(itemgetter(0), results))
+
     def max_marginal_relevance_search(
         self,
         query: str,

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py

@@ -66,6 +66,39 @@ def test_similarity_search_by_vector(
     assert_documents_equals(output, [Document(page_content="foo")])
 
 
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("batch_size", [1, 64])
+def test_similarity_search_with_score_by_vector(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    vector_name: str,
+    batch_size: int,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=location,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        batch_size=batch_size,
+        vector_name=vector_name,
+    )
+    embeddings = ConsistentFakeEmbeddings().embed_query("foo")
+    output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
+    assert len(output) == 1
+    document, score = output[0]
+    assert_documents_equals([document], [Document(page_content="foo")])
+    assert score >= 0
+
+
 @pytest.mark.parametrize("location", qdrant_locations())
 @pytest.mark.parametrize(
     "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]