refactoring

integrations/qdrant/pyproject.toml

@@ -103,6 +103,8 @@ ignore = [
   "B027",
   # Allow boolean positional values in function calls, like `dict.get(... True)`
   "FBT003",
+  # Allow boolean arguments in function definition
+  "FBT001", "FBT002",
   # Ignore checks for possible passwords
   "S105",
   "S106",

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

@@ -33,8 +33,8 @@ def __init__(
         document_store: QdrantDocumentStore,
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 10,
-        scale_score: bool = True,  # noqa: FBT001, FBT002
-        return_embedding: bool = False,  # noqa: FBT001, FBT002
+        scale_score: bool = True,
+        return_embedding: bool = False,
     ):
         """
         Create a QdrantEmbeddingRetriever component.
@@ -137,7 +137,7 @@ class QdrantSparseRetriever:
     document_store = QdrantDocumentStore(
         ":memory:",
         recreate_index=True,
-        return_sparse_embedding=True,
+        return_embedding=True,
         wait_result_from_api=True,
     )
     retriever = QdrantSparseRetriever(document_store=document_store)
@@ -151,8 +151,8 @@ def __init__(
         document_store: QdrantDocumentStore,
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 10,
-        scale_score: bool = True,  # noqa: FBT001, FBT002
-        return_embedding: bool = False,  # noqa: FBT001, FBT002
+        scale_score: bool = True,
+        return_embedding: bool = False,
     ):
         """
         Create a QdrantSparseRetriever component.

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

@@ -11,88 +11,70 @@
 SPARSE_VECTORS_NAME = "text-sparse"
 
 
-class HaystackToQdrant:
-    """A converter from Haystack to Qdrant types."""
-
-    UUID_NAMESPACE = uuid.UUID("3896d314-1e95-4a3a-b45a-945f9f0b541d")
-
-    def documents_to_batch(
-        self,
-        documents: List[Document],
-        *,
-        embedding_field: str,
-        use_sparse_embeddings: bool,
-        sparse_embedding_field: str,
-    ) -> List[rest.PointStruct]:
-        points = []
-        for document in documents:
-            payload = document.to_dict(flatten=False)
-            if use_sparse_embeddings:
-                vector = {}
-
-                dense_vector = payload.pop(embedding_field, None)
-                if dense_vector is not None:
-                    vector[DENSE_VECTORS_NAME] = dense_vector
-
-                sparse_vector = payload.pop(sparse_embedding_field, None)
-                if sparse_vector is not None:
-                    sparse_vector_instance = rest.SparseVector(**sparse_vector)
-                    vector[SPARSE_VECTORS_NAME] = sparse_vector_instance
-
-            else:
-                vector = payload.pop(embedding_field) or {}
-            _id = self.convert_id(payload.get("id"))
-
-            point = rest.PointStruct(
-                payload=payload,
-                vector=vector,
-                id=_id,
-            )
-            points.append(point)
-        return points
-
-    def convert_id(self, _id: str) -> str:
-        """
-        Converts any string into a UUID-like format in a deterministic way.
-
-        Qdrant does not accept any string as an id, so an internal id has to be
-        generated for each point. This is a deterministic way of doing so.
-        """
-        return uuid.uuid5(self.UUID_NAMESPACE, _id).hex
+UUID_NAMESPACE = uuid.UUID("3896d314-1e95-4a3a-b45a-945f9f0b541d")
+
+
+def convert_haystack_documents_to_qdrant_points(
+    documents: List[Document],
+    *,
+    embedding_field: str,
+    use_sparse_embeddings: bool,
+) -> List[rest.PointStruct]:
+    points = []
+    for document in documents:
+        payload = document.to_dict(flatten=False)
+        if use_sparse_embeddings:
+            vector = {}
+
+            dense_vector = payload.pop(embedding_field, None)
+            if dense_vector is not None:
+                vector[DENSE_VECTORS_NAME] = dense_vector
+
+            sparse_vector = payload.pop("sparse_embedding", None)
+            if sparse_vector is not None:
+                sparse_vector_instance = rest.SparseVector(**sparse_vector)
+                vector[SPARSE_VECTORS_NAME] = sparse_vector_instance
+
+        else:
+            vector = payload.pop(embedding_field) or {}
+        _id = convert_id(payload.get("id"))
+
+        point = rest.PointStruct(
+            payload=payload,
+            vector=vector,
+            id=_id,
+        )
+        points.append(point)
+    return points
+
+
+def convert_id(_id: str) -> str:
+    """
+    Converts any string into a UUID-like format in a deterministic way.
+
+    Qdrant does not accept any string as an id, so an internal id has to be
+    generated for each point. This is a deterministic way of doing so.
+    """
+    return uuid.uuid5(UUID_NAMESPACE, _id).hex
 
 
 QdrantPoint = Union[rest.ScoredPoint, rest.Record]
 
 
-class QdrantToHaystack:
-    def __init__(
-        self,
-        content_field: str,
-        name_field: str,
-        embedding_field: str,
-        use_sparse_embeddings: bool,  # noqa: FBT001
-        sparse_embedding_field: str,
-    ):
-        self.content_field = content_field
-        self.name_field = name_field
-        self.embedding_field = embedding_field
-        self.use_sparse_embeddings = use_sparse_embeddings
-        self.sparse_embedding_field = sparse_embedding_field
-
-    def point_to_document(self, point: QdrantPoint) -> Document:
-        payload = {**point.payload}
-        payload["score"] = point.score if hasattr(point, "score") else None
-        if not self.use_sparse_embeddings:
-            payload["embedding"] = point.vector if hasattr(point, "vector") else None
-        else:
-            if hasattr(point, "vector") and point.vector is not None:
-                payload["embedding"] = point.vector.get(DENSE_VECTORS_NAME)
+def convert_qdrant_point_to_haystack_document(point: QdrantPoint, use_sparse_embeddings: bool) -> Document:
+    payload = {**point.payload}
+    payload["score"] = point.score if hasattr(point, "score") else None
+
+    if not use_sparse_embeddings:
+        payload["embedding"] = point.vector if hasattr(point, "vector") else None
+    elif hasattr(point, "vector") and point.vector is not None:
+        payload["embedding"] = point.vector.get(DENSE_VECTORS_NAME)
 
-            if hasattr(point, "vector") and point.vector is not None and SPARSE_VECTORS_NAME in point.vector:
-                parse_vector_dict = {
-                    "indices": point.vector[SPARSE_VECTORS_NAME].indices,
-                    "values": point.vector[SPARSE_VECTORS_NAME].values,
-                }
-                payload["sparse_embedding"] = parse_vector_dict
+        if SPARSE_VECTORS_NAME in point.vector:
+            parse_vector_dict = {
+                "indices": point.vector[SPARSE_VECTORS_NAME].indices,
+                "values": point.vector[SPARSE_VECTORS_NAME].values,
+            }
+            payload["sparse_embedding"] = parse_vector_dict
 
-        return Document.from_dict(payload)
+    return Document.from_dict(payload)