rename QdrantSparseRetriever to QdrantSparseEmbeddingRetriever

deepset-ai · Apr 23, 2024 · 7e29719 · 7e29719
1 parent 70df967
commit 7e29719
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Showing 3 changed files with 14 additions and 14 deletions.
diff --git a/integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/__init__.py b/integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/__init__.py
@@ -2,6 +2,6 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from .retriever import QdrantEmbeddingRetriever, QdrantSparseRetriever
+from .retriever import QdrantEmbeddingRetriever, QdrantSparseEmbeddingRetriever
 
-__all__ = ("QdrantEmbeddingRetriever", "QdrantSparseRetriever")
+__all__ = ("QdrantEmbeddingRetriever", "QdrantSparseEmbeddingRetriever")
diff --git a/integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/retriever.py b/integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/retriever.py
@@ -124,13 +124,13 @@ def run(
 
 
 @component
-class QdrantSparseRetriever:
+class QdrantSparseEmbeddingRetriever:
     """
     A component for retrieving documents from an QdrantDocumentStore using sparse vectors.
 
     Usage example:
     ```python
-    from haystack_integrations.components.retrievers.qdrant import QdrantSparseRetriever
+    from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
     from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
     from haystack.dataclasses.sparse_embedding import SparseEmbedding
 
@@ -140,7 +140,7 @@ class QdrantSparseRetriever:
         return_embedding=True,
         wait_result_from_api=True,
     )
-    retriever = QdrantSparseRetriever(document_store=document_store)
+    retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)
     sparse_embedding = SparseEmbedding(indices=[0, 1, 2, 3], values=[0.1, 0.8, 0.05, 0.33])
     retriever.run(query_sparse_embedding=sparse_embedding)
     ```
@@ -155,7 +155,7 @@ def __init__(
         return_embedding: bool = False,
     ):
         """
-        Create a QdrantSparseRetriever component.
+        Create a QdrantSparseEmbeddingRetriever component.
 
         :param document_store: An instance of QdrantDocumentStore.
         :param filters: A dictionary with filters to narrow down the search space. Default is None.

diff --git a/integrations/qdrant/tests/test_retriever.py b/integrations/qdrant/tests/test_retriever.py
@@ -8,7 +8,7 @@
 )
 from haystack_integrations.components.retrievers.qdrant import (
     QdrantEmbeddingRetriever,
-    QdrantSparseRetriever,
+    QdrantSparseEmbeddingRetriever,
 )
 from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
 
@@ -135,21 +135,21 @@ def test_run_with_sparse_activated(self, filterable_docs: List[Document]):
             assert document.embedding is None
 
 
-class TestQdrantSparseRetriever(FilterableDocsFixtureMixin):
+class TestQdrantSparseEmbeddingRetriever(FilterableDocsFixtureMixin):
     def test_init_default(self):
         document_store = QdrantDocumentStore(location=":memory:", index="test")
-        retriever = QdrantSparseRetriever(document_store=document_store)
+        retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)
         assert retriever._document_store == document_store
         assert retriever._filters is None
         assert retriever._top_k == 10
         assert retriever._return_embedding is False
 
     def test_to_dict(self):
         document_store = QdrantDocumentStore(location=":memory:", index="test")
-        retriever = QdrantSparseRetriever(document_store=document_store)
+        retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)
         res = retriever.to_dict()
         assert res == {
-            "type": "haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseRetriever",
+            "type": "haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseEmbeddingRetriever",
             "init_parameters": {
                 "document_store": {
                     "type": "haystack_integrations.document_stores.qdrant.document_store.QdrantDocumentStore",
@@ -202,7 +202,7 @@ def test_to_dict(self):
 
     def test_from_dict(self):
         data = {
-            "type": "haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseRetriever",
+            "type": "haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseEmbeddingRetriever",
             "init_parameters": {
                 "document_store": {
                     "init_parameters": {"location": ":memory:", "index": "test"},
@@ -214,7 +214,7 @@ def test_from_dict(self):
                 "return_embedding": True,
             },
         }
-        retriever = QdrantSparseRetriever.from_dict(data)
+        retriever = QdrantSparseEmbeddingRetriever.from_dict(data)
         assert isinstance(retriever._document_store, QdrantDocumentStore)
         assert retriever._document_store.index == "test"
         assert retriever._filters is None
@@ -241,7 +241,7 @@ def test_run(self, filterable_docs: List[Document]):
             doc.sparse_embedding = SparseEmbedding.from_dict(self._generate_mocked_sparse_embedding(1)[0])
 
         document_store.write_documents(filterable_docs)
-        retriever = QdrantSparseRetriever(document_store=document_store)
+        retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)
         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)["documents"]