expose backend for ST diversity backend

haystack/components/embedders/backends/sentence_transformers_backend.py

@@ -28,7 +28,7 @@ def get_embedding_backend(  # pylint: disable=too-many-positional-arguments
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
-        backend: Optional[Literal["torch", "onnx", "openvino"]] = "torch",
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
         embedding_backend_id = f"{model}{device}{auth_token}{truncate_dim}{backend}"
 
@@ -64,15 +64,10 @@ def __init__(  # pylint: disable=too-many-positional-arguments
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
-        backend: Optional[Literal["torch", "onnx", "openvino"]] = "torch",
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
         sentence_transformers_import.check()
 
-        # this line is necessary to avoid an arg-type error for the type checker
-        # or we can make backend not optional
-        if backend is None:
-            backend = "torch"
-
         self.model = SentenceTransformer(
             model_name_or_path=model,
             device=device,

haystack/components/rankers/sentence_transformers_diversity.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from enum import Enum
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from haystack import Document, component, default_from_dict, default_to_dict, logging
 from haystack.lazy_imports import LazyImport
@@ -126,6 +126,7 @@ def __init__(
         embedding_separator: str = "\n",
         strategy: Union[str, DiversityRankingStrategy] = "greedy_diversity_order",
         lambda_threshold: float = 0.5,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):  # pylint: disable=too-many-positional-arguments
         """
         Initialize a SentenceTransformersDiversityRanker.
@@ -172,6 +173,7 @@ def __init__(
         self.strategy = DiversityRankingStrategy.from_str(strategy) if isinstance(strategy, str) else strategy
         self.lambda_threshold = lambda_threshold or 0.5
         self._check_lambda_threshold(self.lambda_threshold, self.strategy)
+        self.backend = backend
 
     def warm_up(self):
         """
@@ -182,6 +184,7 @@ def warm_up(self):
                 model_name_or_path=self.model_name_or_path,
                 device=self.device.to_torch_str(),
                 use_auth_token=self.token.resolve_value() if self.token else None,
+                backend=self.backend,
             )
 
     def to_dict(self) -> Dict[str, Any]:

test/components/embedders/test_sentence_transformers_document_embedder.py

@@ -362,7 +362,8 @@ def test_prefix_suffix(self):
         )
 
     @pytest.mark.integration
-    def test_model_onnx_quantization(self):
+    def test_model_onnx_quantization(self, monkeypatch):
+        monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         documents = [Document(content="document number 0"), Document(content="document number 1")]
         onnx_embedder = SentenceTransformersDocumentEmbedder(
             model="sentence-transformers/all-MiniLM-L6-v2",
@@ -383,7 +384,8 @@ def test_model_onnx_quantization(self):
     @pytest.mark.skip(
         reason="OpenVINO backend does not support our current transformers + sentence transformers dependencies versions"
     )
-    def test_model_openvino_quantization(self):
+    def test_model_openvino_quantization(self, monkeypatch):
+        monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         documents = [Document(content="document number 0"), Document(content="document number 1")]
         openvino_embedder = SentenceTransformersDocumentEmbedder(
             model="sentence-transformers/all-MiniLM-L6-v2",

test/components/embedders/test_sentence_transformers_text_embedder.py

@@ -319,7 +319,8 @@ def test_embed_encode_kwargs(self):
         )
 
     @pytest.mark.integration
-    def test_model_onnx_quantization(self):
+    def test_model_onnx_quantization(self, monkeypatch):
+        monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         text = "a nice text to embed"
 
         onnx_embedder_def = SentenceTransformersTextEmbedder(
@@ -340,7 +341,8 @@ def test_model_onnx_quantization(self):
     @pytest.mark.skip(
         reason="OpenVINO backend does not support our current transformers + sentence transformers dependencies versions"
     )
-    def test_model_openvino_quantization(self):
+    def test_model_openvino_quantization(self, monkeypatch):
+        monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         text = "a nice text to embed"
 
         openvino_embedder_def = SentenceTransformersTextEmbedder(

test/components/rankers/test_sentence_transformers_diversity.py

@@ -291,6 +291,7 @@ def test_warm_up(self, similarity, monkeypatch):
                 model_name_or_path="mock_model_name",
                 device=ComponentDevice.resolve_device(None).to_torch_str(),
                 use_auth_token=None,
+                backend="torch",
             )
             assert ranker.model == mock_model_instance
 
@@ -674,8 +675,11 @@ def test_run_real_world_use_case(self, similarity, monkeypatch):
         assert result_content == expected_content
 
     @pytest.mark.integration
-    @pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
-    def test_run_with_maximum_margin_relevance_strategy(self, similarity, monkeypatch):
+    @pytest.mark.parametrize(
+        "similarity,backend",
+        [("dot_product", "torch"), ("dot_product", "onnx"), ("cosine", "torch"), ("cosine", "onnx")],
+    )  # we don't use "openvino" due to dependency issues
+    def test_run_with_maximum_margin_relevance_strategy(self, similarity, backend, monkeypatch):
         monkeypatch.delenv("HF_API_TOKEN", raising=False)  # https://github.com/deepset-ai/haystack/issues/8811
         query = "renewable energy sources"
         docs = [
@@ -690,7 +694,10 @@ def test_run_with_maximum_margin_relevance_strategy(self, similarity, monkeypatc
         ]
 
         ranker = SentenceTransformersDiversityRanker(
-            model="sentence-transformers/all-MiniLM-L6-v2", similarity=similarity, strategy="maximum_margin_relevance"
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            similarity=similarity,
+            strategy="maximum_margin_relevance",
+            backend=backend,
         )
         ranker.warm_up()