feat: SentenceTransformersDocumentEmbedder and SentenceTransformersTe…

…xtEmbedder can accept and pass any arguments to SentenceTransformer.encode (#8806)

* feat: SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder can accept and pass any arguments to SentenceTransformer.encode

* refactor: encode_kwargs parameter of SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder mae to be the last positional parameter for backward compatibility reasons

* docs: added explanation for encode_kwargs in SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder

* test: added tests for encode_kwargs in SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder

* doc: removed empty lines from docstrings of SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder

* refactor: encode_kwargs parameter of SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder mae to be the last positional parameter for backward compatibility (part II.)

haystack/components/embedders/sentence_transformers_document_embedder.py

@@ -56,6 +56,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
         precision: Literal["float32", "int8", "uint8", "binary", "ubinary"] = "float32",
+        encode_kwargs: Optional[Dict[str, Any]] = None,
     ):
         """
         Creates a SentenceTransformersDocumentEmbedder component.
@@ -104,6 +105,10 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
             All non-float32 precisions are quantized embeddings.
             Quantized embeddings are smaller and faster to compute, but may have a lower accuracy.
             They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks.
+        :param encode_kwargs:
+            Additional keyword arguments for `SentenceTransformer.encode` when embedding documents.
+            This parameter is provided for fine customization. Be careful not to clash with already set parameters and
+            avoid passing parameters that change the output type.
         """
 
         self.model = model
@@ -121,6 +126,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         self.model_kwargs = model_kwargs
         self.tokenizer_kwargs = tokenizer_kwargs
         self.config_kwargs = config_kwargs
+        self.encode_kwargs = encode_kwargs
         self.embedding_backend = None
         self.precision = precision
 
@@ -155,6 +161,7 @@ def to_dict(self) -> Dict[str, Any]:
             tokenizer_kwargs=self.tokenizer_kwargs,
             config_kwargs=self.config_kwargs,
             precision=self.precision,
+            encode_kwargs=self.encode_kwargs,
         )
         if serialization_dict["init_parameters"].get("model_kwargs") is not None:
             serialize_hf_model_kwargs(serialization_dict["init_parameters"]["model_kwargs"])
@@ -232,6 +239,7 @@ def run(self, documents: List[Document]):
             show_progress_bar=self.progress_bar,
             normalize_embeddings=self.normalize_embeddings,
             precision=self.precision,
+            **(self.encode_kwargs if self.encode_kwargs else {}),
         )
 
         for doc, emb in zip(documents, embeddings):

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -50,6 +50,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
         precision: Literal["float32", "int8", "uint8", "binary", "ubinary"] = "float32",
+        encode_kwargs: Optional[Dict[str, Any]] = None,
     ):
         """
         Create a SentenceTransformersTextEmbedder component.
@@ -94,6 +95,10 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
             All non-float32 precisions are quantized embeddings.
             Quantized embeddings are smaller in size and faster to compute, but may have a lower accuracy.
             They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks.
+        :param encode_kwargs:
+            Additional keyword arguments for `SentenceTransformer.encode` when embedding texts.
+            This parameter is provided for fine customization. Be careful not to clash with already set parameters and
+            avoid passing parameters that change the output type.
         """
 
         self.model = model
@@ -109,6 +114,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         self.model_kwargs = model_kwargs
         self.tokenizer_kwargs = tokenizer_kwargs
         self.config_kwargs = config_kwargs
+        self.encode_kwargs = encode_kwargs
         self.embedding_backend = None
         self.precision = precision
 
@@ -141,6 +147,7 @@ def to_dict(self) -> Dict[str, Any]:
             tokenizer_kwargs=self.tokenizer_kwargs,
             config_kwargs=self.config_kwargs,
             precision=self.precision,
+            encode_kwargs=self.encode_kwargs,
         )
         if serialization_dict["init_parameters"].get("model_kwargs") is not None:
             serialize_hf_model_kwargs(serialization_dict["init_parameters"]["model_kwargs"])
@@ -209,5 +216,6 @@ def run(self, text: str):
             show_progress_bar=self.progress_bar,
             normalize_embeddings=self.normalize_embeddings,
             precision=self.precision,
+            **(self.encode_kwargs if self.encode_kwargs else {}),
         )[0]
         return {"embedding": embedding}

releasenotes/notes/add-encode-kwargs-sentence-transformers-f4d885f6c5b1706f.yaml

@@ -0,0 +1,6 @@
+---
+enhancements:
+  - |
+    Enhanced `SentenceTransformersDocumentEmbedder` and `SentenceTransformersTextEmbedder` to accept
+    an additional parameter, which is passed directly to the underlying `SentenceTransformer.encode` method
+    for greater flexibility in embedding customization.

test/components/embedders/test_sentence_transformers_document_embedder.py

@@ -1,9 +1,9 @@
 # SPDX-FileCopyrightText: 2022-present deepset GmbH <[email protected]>
 #
 # SPDX-License-Identifier: Apache-2.0
+import random
 from unittest.mock import MagicMock, patch
 
-import random
 import pytest
 import torch
 
@@ -79,6 +79,7 @@ def test_to_dict(self):
                 "truncate_dim": None,
                 "model_kwargs": None,
                 "tokenizer_kwargs": None,
+                "encode_kwargs": None,
                 "config_kwargs": None,
                 "precision": "float32",
             },
@@ -102,6 +103,7 @@ def test_to_dict_with_custom_init_parameters(self):
             tokenizer_kwargs={"model_max_length": 512},
             config_kwargs={"use_memory_efficient_attention": True},
             precision="int8",
+            encode_kwargs={"task": "clustering"},
         )
         data = component.to_dict()
 
@@ -124,6 +126,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "tokenizer_kwargs": {"model_max_length": 512},
                 "config_kwargs": {"use_memory_efficient_attention": True},
                 "precision": "int8",
+                "encode_kwargs": {"task": "clustering"},
             },
         }
 
@@ -316,6 +319,20 @@ def test_embed_metadata(self):
             precision="float32",
         )
 
+    def test_embed_encode_kwargs(self):
+        embedder = SentenceTransformersDocumentEmbedder(model="model", encode_kwargs={"task": "retrieval.passage"})
+        embedder.embedding_backend = MagicMock()
+        documents = [Document(content=f"document number {i}") for i in range(5)]
+        embedder.run(documents=documents)
+        embedder.embedding_backend.embed.assert_called_once_with(
+            ["document number 0", "document number 1", "document number 2", "document number 3", "document number 4"],
+            batch_size=32,
+            show_progress_bar=True,
+            normalize_embeddings=False,
+            precision="float32",
+            task="retrieval.passage",
+        )
+
     def test_prefix_suffix(self):
         embedder = SentenceTransformersDocumentEmbedder(
             model="model",

test/components/embedders/test_sentence_transformers_text_embedder.py

@@ -1,11 +1,11 @@
 # SPDX-FileCopyrightText: 2022-present deepset GmbH <[email protected]>
 #
 # SPDX-License-Identifier: Apache-2.0
+import random
 from unittest.mock import MagicMock, patch
 
-import torch
-import random
 import pytest
+import torch
 
 from haystack.components.embedders.sentence_transformers_text_embedder import SentenceTransformersTextEmbedder
 from haystack.utils import ComponentDevice, Secret
@@ -70,6 +70,7 @@ def test_to_dict(self):
                 "truncate_dim": None,
                 "model_kwargs": None,
                 "tokenizer_kwargs": None,
+                "encode_kwargs": None,
                 "config_kwargs": None,
                 "precision": "float32",
             },
@@ -91,6 +92,7 @@ def test_to_dict_with_custom_init_parameters(self):
             tokenizer_kwargs={"model_max_length": 512},
             config_kwargs={"use_memory_efficient_attention": False},
             precision="int8",
+            encode_kwargs={"task": "clustering"},
         )
         data = component.to_dict()
         assert data == {
@@ -110,6 +112,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "tokenizer_kwargs": {"model_max_length": 512},
                 "config_kwargs": {"use_memory_efficient_attention": False},
                 "precision": "int8",
+                "encode_kwargs": {"task": "clustering"},
             },
         }
 
@@ -297,3 +300,17 @@ def test_run_quantization(self):
 
         assert len(embedding_def) == 768
         assert all(isinstance(el, int) for el in embedding_def)
+
+    def test_embed_encode_kwargs(self):
+        embedder = SentenceTransformersTextEmbedder(model="model", encode_kwargs={"task": "retrieval.query"})
+        embedder.embedding_backend = MagicMock()
+        text = "a nice text to embed"
+        embedder.run(text=text)
+        embedder.embedding_backend.embed.assert_called_once_with(
+            [text],
+            batch_size=32,
+            show_progress_bar=True,
+            normalize_embeddings=False,
+            precision="float32",
+            task="retrieval.query",
+        )