test: added tests for encode_kwargs in SentenceTransformersTextEmbedd…

…er and SentenceTransformersDocumentEmbedder

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
 
@@ -319,6 +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
@@ -300,3 +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",
+        )