feat(Qdrant): SparseEmbedding instead of Dict

Author: lambda-science

integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/retriever.py

@@ -132,6 +132,7 @@ class QdrantSparseRetriever:
     ```python
     from haystack_integrations.components.retrievers.qdrant import QdrantSparseRetriever
     from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
+    from haystack.dataclasses.sparse_embedding import SparseEmbedding
 
     document_store = QdrantDocumentStore(
         ":memory:",
@@ -140,8 +141,8 @@ class QdrantSparseRetriever:
         wait_result_from_api=True,
     )
     retriever = QdrantSparseRetriever(document_store=document_store)
-
-    retriever.run(query_sparse_embedding={"indices":[0, 1, 2, 3], "values":[0.1, 0.8, 0.05, 0.33]})
+    sparse_embedding = SparseEmbedding(indices=[0, 1, 2, 3], values=[0.1, 0.8, 0.05, 0.33])
+    retriever.run(query_sparse_embedding=sparse_embedding)
     ```
     """
 

integrations/qdrant/src/haystack_integrations/document_stores/qdrant/document_store.py

@@ -310,9 +310,8 @@ def query_by_sparse(
         return_embedding: bool = False,  # noqa: FBT001, FBT002
     ) -> List[Document]:
         qdrant_filters = self.qdrant_filter_converter.convert(filters)
-
-        query_indices = query_sparse_embedding["indices"]
-        query_values = query_sparse_embedding["values"]
+        query_indices = query_sparse_embedding.indices
+        query_values = query_sparse_embedding.values
 
         points = self.client.search(
             collection_name=self.index,

integrations/qdrant/tests/test_retriever.py

@@ -227,14 +227,12 @@ def test_run(self, filterable_docs: List[Document]):
         document_store.write_documents(filterable_docs)
 
         retriever = QdrantSparseRetriever(document_store=document_store)
-
-        results: List[Document] = retriever.run(query_sparse_embedding=self._generate_mocked_sparse_embedding(1)[0])
+        sparse_embedding = SparseEmbedding(indices=[0, 1, 2, 3], values=[0.1, 0.8, 0.05, 0.33])
+        results: List[Document] = retriever.run(query_sparse_embedding=sparse_embedding)
 
         assert len(results["documents"]) == 10  # type: ignore
 
-        results = retriever.run(
-            query_sparse_embedding=self._generate_mocked_sparse_embedding(1)[0], top_k=5, return_embedding=False
-        )
+        results = retriever.run(query_sparse_embedding=sparse_embedding, top_k=5, return_embedding=False)
 
         assert len(results["documents"]) == 5  # type: ignore