feat: SentenceTransformersTextEmbedder supports config_kwargs

haystack/components/embedders/backends/sentence_transformers_backend.py

@@ -29,6 +29,7 @@ def get_embedding_backend(
         truncate_dim: Optional[int] = None,
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        config_kwargs: Optional[Dict[str, Any]] = None,
     ):
         embedding_backend_id = f"{model}{device}{auth_token}{truncate_dim}"
 
@@ -42,6 +43,7 @@ def get_embedding_backend(
             truncate_dim=truncate_dim,
             model_kwargs=model_kwargs,
             tokenizer_kwargs=tokenizer_kwargs,
+            config_kwargs=config_kwargs,
         )
         _SentenceTransformersEmbeddingBackendFactory._instances[embedding_backend_id] = embedding_backend
         return embedding_backend
@@ -61,6 +63,7 @@ def __init__(
         truncate_dim: Optional[int] = None,
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        config_kwargs: Optional[Dict[str, Any]] = None,
     ):
         sentence_transformers_import.check()
         self.model = SentenceTransformer(
@@ -71,6 +74,7 @@ def __init__(
             truncate_dim=truncate_dim,
             model_kwargs=model_kwargs,
             tokenizer_kwargs=tokenizer_kwargs,
+            config_kwargs=config_kwargs,
         )
 
     def embed(self, data: List[str], **kwargs) -> List[List[float]]:

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -48,6 +48,7 @@ def __init__(
         truncate_dim: Optional[int] = None,
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        config_kwargs: Optional[Dict[str, Any]] = None,
         precision: Literal["float32", "int8", "uint8", "binary", "ubinary"] = "float32",
     ):
         """
@@ -86,6 +87,8 @@ def __init__(
         :param tokenizer_kwargs:
             Additional keyword arguments for `AutoTokenizer.from_pretrained` when loading the tokenizer.
             Refer to specific model documentation for available kwargs.
+        :param config_kwargs:
+            Additional keyword arguments for model configuration parameters
         :param precision:
             The precision to use for the embeddings.
             All non-float32 precisions are quantized embeddings.
@@ -105,6 +108,7 @@ def __init__(
         self.truncate_dim = truncate_dim
         self.model_kwargs = model_kwargs
         self.tokenizer_kwargs = tokenizer_kwargs
+        self.config_kwargs = config_kwargs
         self.embedding_backend = None
         self.precision = precision
 
@@ -135,6 +139,7 @@ def to_dict(self) -> Dict[str, Any]:
             truncate_dim=self.truncate_dim,
             model_kwargs=self.model_kwargs,
             tokenizer_kwargs=self.tokenizer_kwargs,
+            config_kwargs=self.config_kwargs,
             precision=self.precision,
         )
         if serialization_dict["init_parameters"].get("model_kwargs") is not None:
@@ -172,6 +177,7 @@ def warm_up(self):
                 truncate_dim=self.truncate_dim,
                 model_kwargs=self.model_kwargs,
                 tokenizer_kwargs=self.tokenizer_kwargs,
+                config_kwargs=self.config_kwargs,
             )
             if self.tokenizer_kwargs and self.tokenizer_kwargs.get("model_max_length"):
                 self.embedding_backend.model.max_seq_length = self.tokenizer_kwargs["model_max_length"]

pyproject.toml

@@ -327,6 +327,7 @@ ignore = [
   "SIM108",  # if-else-block-instead-of-if-exp
   "SIM115",  # open-file-with-context-handler
   "SIM118",  # in-dict-keys
+  "PLR0913", # Too many arguments in function definition
 ]
 
 [tool.ruff.lint.mccabe]

test/components/embedders/test_sentence_transformers_embedding_backend.py

@@ -42,6 +42,7 @@ def test_model_initialization(mock_sentence_transformer):
         truncate_dim=256,
         model_kwargs=None,
         tokenizer_kwargs=None,
+        config_kwargs=None,
     )
 
 

test/components/embedders/test_sentence_transformers_text_embedder.py

@@ -70,6 +70,7 @@ def test_to_dict(self):
                 "truncate_dim": None,
                 "model_kwargs": None,
                 "tokenizer_kwargs": None,
+                "config_kwargs": None,
                 "precision": "float32",
             },
         }
@@ -88,6 +89,7 @@ def test_to_dict_with_custom_init_parameters(self):
             truncate_dim=256,
             model_kwargs={"torch_dtype": torch.float32},
             tokenizer_kwargs={"model_max_length": 512},
+            config_kwargs={"use_memory_efficient_attention": False},
             precision="int8",
         )
         data = component.to_dict()
@@ -106,6 +108,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "truncate_dim": 256,
                 "model_kwargs": {"torch_dtype": "torch.float32"},
                 "tokenizer_kwargs": {"model_max_length": 512},
+                "config_kwargs": {"use_memory_efficient_attention": False},
                 "precision": "int8",
             },
         }
@@ -131,6 +134,7 @@ def test_from_dict(self):
                 "truncate_dim": None,
                 "model_kwargs": {"torch_dtype": "torch.float32"},
                 "tokenizer_kwargs": {"model_max_length": 512},
+                "config_kwargs": {"use_memory_efficient_attention": False},
                 "precision": "float32",
             },
         }
@@ -147,6 +151,7 @@ def test_from_dict(self):
         assert component.truncate_dim is None
         assert component.model_kwargs == {"torch_dtype": torch.float32}
         assert component.tokenizer_kwargs == {"model_max_length": 512}
+        assert component.config_kwargs == {"use_memory_efficient_attention": False}
         assert component.precision == "float32"
 
     def test_from_dict_no_default_parameters(self):
@@ -218,6 +223,7 @@ def test_warmup(self, mocked_factory):
             truncate_dim=None,
             model_kwargs=None,
             tokenizer_kwargs={"model_max_length": 512},
+            config_kwargs=None,
         )
 
     @patch(