move embedding backends (#6033)

haystack/preview/components/embedders/sentence_transformers_document_embedder.py

@@ -1,7 +1,7 @@
 from typing import List, Optional, Union, Dict, Any
 
 from haystack.preview import component, Document, default_to_dict, default_from_dict
-from haystack.preview.embedding_backends.sentence_transformers_backend import (
+from haystack.preview.components.embedders.backends.sentence_transformers_backend import (
     _SentenceTransformersEmbeddingBackendFactory,
 )
 
@@ -29,11 +29,13 @@ class SentenceTransformersDocumentEmbedder:
         """
         Create a SentenceTransformersDocumentEmbedder component.
 
-        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub, such as ``'sentence-transformers/all-mpnet-base-v2'``.
-        :param device: Device (like 'cuda' / 'cpu') that should be used for computation. If None, checks if a GPU can be used.
+        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub,
+            such as ``'sentence-transformers/all-mpnet-base-v2'``.
+        :param device: Device (like 'cuda' / 'cpu') that should be used for computation.
+            Defaults to CPU.
         :param use_auth_token: The API token used to download private models from Hugging Face.
-                        If this parameter is set to `True`, then the token generated when running
-                        `transformers-cli login` (stored in ~/.huggingface) will be used.
+            If this parameter is set to `True`, then the token generated when running
+            `transformers-cli login` (stored in ~/.huggingface) will be used.
         :param prefix: A string to add to the beginning of each Document text before embedding.
         :param suffix: A string to add to the end of each Document text before embedding.
         :param batch_size: Number of strings to encode at once.
@@ -95,7 +97,7 @@ class SentenceTransformersDocumentEmbedder:
         Embed a list of Documents.
         The embedding of each Document is stored in the `embedding` field of the Document.
         """
-        if not isinstance(documents, list) or not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
             raise TypeError(
                 "SentenceTransformersDocumentEmbedder expects a list of Documents as input."
                 "In case you want to embed a list of strings, please use the SentenceTransformersTextEmbedder."

haystack/preview/components/embedders/sentence_transformers_text_embedder.py

@@ -1,7 +1,7 @@
 from typing import List, Optional, Union, Dict, Any
 
 from haystack.preview import component, default_to_dict, default_from_dict
-from haystack.preview.embedding_backends.sentence_transformers_backend import (
+from haystack.preview.components.embedders.backends.sentence_transformers_backend import (
     _SentenceTransformersEmbeddingBackendFactory,
 )
 
@@ -26,11 +26,13 @@ class SentenceTransformersTextEmbedder:
         """
         Create a SentenceTransformersTextEmbedder component.
 
-        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub, such as ``'sentence-transformers/all-mpnet-base-v2'``.
-        :param device: Device (like 'cuda' / 'cpu') that should be used for computation. If None, checks if a GPU can be used.
+        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub,
+            such as ``'sentence-transformers/all-mpnet-base-v2'``.
+        :param device: Device (like 'cuda' / 'cpu') that should be used for computation.
+            Defaults to CPU.
         :param use_auth_token: The API token used to download private models from Hugging Face.
-                        If this parameter is set to `True`, then the token generated when running
-                        `transformers-cli login` (stored in ~/.huggingface) will be used.
+            If this parameter is set to `True`, then the token generated when running
+            `transformers-cli login` (stored in ~/.huggingface) will be used.
         :param prefix: A string to add to the beginning of each text.
         :param suffix: A string to add to the end of each text.
         :param batch_size: Number of strings to encode at once.

test/preview/components/embedders/test_sentence_transformers_embedding_backend.py

@@ -1,12 +1,12 @@
 from unittest.mock import patch
 import pytest
-from haystack.preview.embedding_backends.sentence_transformers_backend import (
+from haystack.preview.components.embedders.backends.sentence_transformers_backend import (
     _SentenceTransformersEmbeddingBackendFactory,
 )
 
 
 @pytest.mark.unit
-@patch("haystack.preview.embedding_backends.sentence_transformers_backend.SentenceTransformer")
+@patch("haystack.preview.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
 def test_factory_behavior(mock_sentence_transformer):
     embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
         model_name_or_path="my_model", device="cpu"
@@ -21,7 +21,7 @@ def test_factory_behavior(mock_sentence_transformer):
 
 
 @pytest.mark.unit
-@patch("haystack.preview.embedding_backends.sentence_transformers_backend.SentenceTransformer")
+@patch("haystack.preview.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
 def test_model_initialization(mock_sentence_transformer):
     _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
         model_name_or_path="model", device="cpu", use_auth_token="my_token"
@@ -32,7 +32,7 @@ def test_model_initialization(mock_sentence_transformer):
 
 
 @pytest.mark.unit
-@patch("haystack.preview.embedding_backends.sentence_transformers_backend.SentenceTransformer")
+@patch("haystack.preview.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
 def test_embedding_function_with_kwargs(mock_sentence_transformer):
     embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(model_name_or_path="model")