fix: HuggingFaceTEITextEmbedder returning embedding of incorrect shape when used with Docker endpoint (#7319)

* Fix HuggingFaceTEITextEmbedder

* Update haystack/components/embedders/hugging_face_tei_text_embedder.py

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Improve imports; Add additional tests

---------

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

haystack/components/embedders/hugging_face_tei_document_embedder.py

@@ -9,7 +9,7 @@ from haystack.lazy_imports import LazyImport
 from haystack.utils import Secret, deserialize_secrets_inplace
 from haystack.utils.hf import HFModelType, check_valid_model
 
-with LazyImport(message="Run 'pip install transformers'") as transformers_import:
+with LazyImport(message="Run 'pip install huggingface_hub'") as huggingface_hub_import:
     from huggingface_hub import InferenceClient
 
 logger = logging.getLogger(__name__)
@@ -79,7 +79,7 @@ class HuggingFaceTEIDocumentEmbedder:
         :param embedding_separator:
             Separator used to concatenate the meta fields to the Document text.
         """
-        transformers_import.check()
+        huggingface_hub_import.check()
 
         if url:
             r = urlparse(url)

haystack/components/embedders/hugging_face_tei_text_embedder.py

@@ -6,7 +6,7 @@ from haystack.lazy_imports import LazyImport
 from haystack.utils import Secret, deserialize_secrets_inplace
 from haystack.utils.hf import HFModelType, check_valid_model
 
-with LazyImport(message="Run 'pip install transformers'") as transformers_import:
+with LazyImport(message="Run 'pip install huggingface_hub'") as huggingface_hub_import:
     from huggingface_hub import InferenceClient
 
 logger = logging.getLogger(__name__)
@@ -62,7 +62,7 @@ class HuggingFaceTEITextEmbedder:
         :param suffix:
             A string to add at the end of each text.
         """
-        transformers_import.check()
+        huggingface_hub_import.check()
 
         if url:
             r = urlparse(url)
@@ -135,8 +135,8 @@ class HuggingFaceTEITextEmbedder:
 
         text_to_embed = self.prefix + text + self.suffix
 
-        embedding = self.client.feature_extraction(text=text_to_embed)
+        embeddings = self.client.feature_extraction(text=[text_to_embed])
         # The client returns a numpy array
-        embedding = embedding.tolist()
+        embedding = embeddings.tolist()[0]
 
         return {"embedding": embedding}

releasenotes/notes/hf-tei-bug-fix-07732c672600aadd.yaml

@@ -0,0 +1,5 @@
+---
+fixes:
+  - |
+    Fixes `HuggingFaceTEITextEmbedder` returning an embedding of incorrect shape when used with a
+    Text-Embedding-Inference endpoint deployed using Docker.

test/components/embedders/test_hugging_face_tei_document_embedder.py

@@ -3,10 +3,10 @@ from unittest.mock import MagicMock, patch
 import numpy as np
 import pytest
 from huggingface_hub.utils import RepositoryNotFoundError
-from haystack.utils.auth import Secret
 
 from haystack.components.embedders.hugging_face_tei_document_embedder import HuggingFaceTEIDocumentEmbedder
 from haystack.dataclasses import Document
+from haystack.utils.auth import Secret
 
 
 @pytest.fixture
@@ -222,6 +222,29 @@ class TestHuggingFaceTEIDocumentEmbedder:
             assert len(doc.embedding) == 384
             assert all(isinstance(x, float) for x in doc.embedding)
 
+    @pytest.mark.flaky(reruns=5, reruns_delay=5)
+    @pytest.mark.integration
+    def test_run_inference_api_endpoint(self):
+        docs = [
+            Document(content="I love cheese", meta={"topic": "Cuisine"}),
+            Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
+        ]
+
+        embedder = HuggingFaceTEIDocumentEmbedder(
+            model="sentence-transformers/all-MiniLM-L6-v2", meta_fields_to_embed=["topic"], embedding_separator=" | "
+        )
+
+        result = embedder.run(documents=docs)
+        documents_with_embeddings = result["documents"]
+
+        assert isinstance(documents_with_embeddings, list)
+        assert len(documents_with_embeddings) == len(docs)
+        for doc in documents_with_embeddings:
+            assert isinstance(doc, Document)
+            assert isinstance(doc.embedding, list)
+            assert len(doc.embedding) == 384
+            assert all(isinstance(x, float) for x in doc.embedding)
+
     def test_run_custom_batch_size(self, mock_check_valid_model):
         docs = [
             Document(content="I love cheese", meta={"topic": "Cuisine"}),

test/components/embedders/test_hugging_face_tei_text_embedder.py

@@ -3,9 +3,9 @@ from unittest.mock import MagicMock, patch
 import numpy as np
 import pytest
 from huggingface_hub.utils import RepositoryNotFoundError
-from haystack.utils.auth import Secret
 
 from haystack.components.embedders.hugging_face_tei_text_embedder import HuggingFaceTEITextEmbedder
+from haystack.utils.auth import Secret
 
 
 @pytest.fixture
@@ -17,7 +17,7 @@ def mock_check_valid_model():
 
 
 def mock_embedding_generation(text, **kwargs):
-    response = np.random.rand(384)
+    response = np.array([np.random.rand(384) for i in range(len(text))])
     return response
 
 
@@ -107,7 +107,16 @@ class TestHuggingFaceTEITextEmbedder:
 
             result = embedder.run(text="The food was delicious")
 
-            mock_embedding_patch.assert_called_once_with(text="prefix The food was delicious suffix")
+            mock_embedding_patch.assert_called_once_with(text=["prefix The food was delicious suffix"])
+
+        assert len(result["embedding"]) == 384
+        assert all(isinstance(x, float) for x in result["embedding"])
+
+    @pytest.mark.flaky(reruns=5, reruns_delay=5)
+    @pytest.mark.integration
+    def test_run_inference_api_endpoint(self):
+        embedder = HuggingFaceTEITextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+        result = embedder.run(text="The food was delicious")
 
         assert len(result["embedding"]) == 384
         assert all(isinstance(x, float) for x in result["embedding"])