fix: qdrant - Fallback to default filter policy when deserializing retrievers without the init parameter

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

@@ -108,7 +108,10 @@ def from_dict(cls, data: Dict[str, Any]) -> "QdrantEmbeddingRetriever":
         """
         document_store = QdrantDocumentStore.from_dict(data["init_parameters"]["document_store"])
         data["init_parameters"]["document_store"] = document_store
-        data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(data["init_parameters"]["filter_policy"])
+        # Pipelines serialized with old versions of the component might not
+        # have the filter_policy field.
+        if filter_policy := data["init_parameters"].get("filter_policy"):
+            data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(filter_policy)
         return default_from_dict(cls, data)
 
     @component.output_types(documents=List[Document])
@@ -249,7 +252,10 @@ def from_dict(cls, data: Dict[str, Any]) -> "QdrantSparseEmbeddingRetriever":
         """
         document_store = QdrantDocumentStore.from_dict(data["init_parameters"]["document_store"])
         data["init_parameters"]["document_store"] = document_store
-        data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(data["init_parameters"]["filter_policy"])
+        # Pipelines serialized with old versions of the component might not
+        # have the filter_policy field.
+        if filter_policy := data["init_parameters"].get("filter_policy"):
+            data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(filter_policy)
         return default_from_dict(cls, data)
 
     @component.output_types(documents=List[Document])
@@ -394,7 +400,10 @@ def from_dict(cls, data: Dict[str, Any]) -> "QdrantHybridRetriever":
         """
         document_store = QdrantDocumentStore.from_dict(data["init_parameters"]["document_store"])
         data["init_parameters"]["document_store"] = document_store
-        data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(data["init_parameters"]["filter_policy"])
+        # Pipelines serialized with old versions of the component might not
+        # have the filter_policy field.
+        if filter_policy := data["init_parameters"].get("filter_policy"):
+            data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(filter_policy)
         return default_from_dict(cls, data)
 
     @component.output_types(documents=List[Document])

integrations/qdrant/tests/test_retriever.py

@@ -296,6 +296,31 @@ def test_from_dict(self):
         assert retriever._return_embedding is True
         assert retriever._score_threshold is None
 
+    def test_from_dict_no_filter_policy(self):
+        data = {
+            "type": "haystack_integrations.components.retrievers.qdrant.retriever.QdrantSparseEmbeddingRetriever",
+            "init_parameters": {
+                "document_store": {
+                    "init_parameters": {"location": ":memory:", "index": "test"},
+                    "type": "haystack_integrations.document_stores.qdrant.document_store.QdrantDocumentStore",
+                },
+                "filters": None,
+                "top_k": 5,
+                "scale_score": False,
+                "return_embedding": True,
+                "score_threshold": None,
+            },
+        }
+        retriever = QdrantSparseEmbeddingRetriever.from_dict(data)
+        assert isinstance(retriever._document_store, QdrantDocumentStore)
+        assert retriever._document_store.index == "test"
+        assert retriever._filters is None
+        assert retriever._top_k == 5
+        assert retriever._filter_policy == FilterPolicy.REPLACE  # defaults to REPLACE
+        assert retriever._scale_score is False
+        assert retriever._return_embedding is True
+        assert retriever._score_threshold is None
+
     def test_run(self, filterable_docs: List[Document], generate_sparse_embedding):
         document_store = QdrantDocumentStore(location=":memory:", index="Boi", use_sparse_embeddings=True)