Add truncate and normalize parameters to TEI Embedders (#7460)

haystack/components/embedders/hugging_face_tei_document_embedder.py

@@ -1,3 +1,4 @@
+import json
 from typing import Any, Dict, List, Optional
 from urllib.parse import urlparse
 
@@ -50,6 +51,8 @@ class HuggingFaceTEIDocumentEmbedder:
         token: Optional[Secret] = Secret.from_env_var("HF_API_TOKEN", strict=False),
         prefix: str = "",
         suffix: str = "",
+        truncate: bool = True,
+        normalize: bool = False,
         batch_size: int = 32,
         progress_bar: bool = True,
         meta_fields_to_embed: Optional[List[str]] = None,
@@ -70,6 +73,15 @@ class HuggingFaceTEIDocumentEmbedder:
             A string to add at the beginning of each text.
         :param suffix:
             A string to add at the end of each text.
+        :param truncate:
+            Truncate input text from the end to the maximum length supported by the model. This option is only available
+            for self-deployed Text Embedding Inference (TEI) endpoints and paid HF Inference Endpoints deployed with
+            TEI. It will be ignored when used with free HF Inference endpoints or paid HF Inference endpoints deployed
+            without TEI.
+        :param normalize:
+            Normalize the embeddings to unit length. This option is only available for self-deployed Text Embedding
+            Inference (TEI) endpoints and paid HF Inference Endpoints deployed with TEI. It will be ignored when used
+            with free HF Inference endpoints or paid HF Inference endpoints deployed without TEI.
         :param batch_size:
             Number of Documents to encode at once.
         :param progress_bar:
@@ -95,6 +107,8 @@ class HuggingFaceTEIDocumentEmbedder:
         self.client = InferenceClient(url or model, token=token.resolve_value() if token else None)
         self.prefix = prefix
         self.suffix = suffix
+        self.truncate = truncate
+        self.normalize = normalize
         self.batch_size = batch_size
         self.progress_bar = progress_bar
         self.meta_fields_to_embed = meta_fields_to_embed or []
@@ -113,6 +127,8 @@ class HuggingFaceTEIDocumentEmbedder:
             url=self.url,
             prefix=self.prefix,
             suffix=self.suffix,
+            truncate=self.truncate,
+            normalize=self.normalize,
             batch_size=self.batch_size,
             progress_bar=self.progress_bar,
             meta_fields_to_embed=self.meta_fields_to_embed,
@@ -167,8 +183,12 @@ class HuggingFaceTEIDocumentEmbedder:
             range(0, len(texts_to_embed), batch_size), disable=not self.progress_bar, desc="Calculating embeddings"
         ):
             batch = texts_to_embed[i : i + batch_size]
-            embeddings = self.client.feature_extraction(text=batch)
-            all_embeddings.extend(embeddings.tolist())
+            response = self.client.post(
+                json={"inputs": batch, "truncate": self.truncate, "normalize": self.normalize},
+                task="feature-extraction",
+            )
+            embeddings = json.loads(response.decode())
+            all_embeddings.extend(embeddings)
 
         return all_embeddings
 

haystack/components/embedders/hugging_face_tei_text_embedder.py

@@ -1,3 +1,4 @@
+import json
 from typing import Any, Dict, List, Optional
 from urllib.parse import urlparse
 
@@ -45,6 +46,8 @@ class HuggingFaceTEITextEmbedder:
         token: Optional[Secret] = Secret.from_env_var("HF_API_TOKEN", strict=False),
         prefix: str = "",
         suffix: str = "",
+        truncate: bool = True,
+        normalize: bool = False,
     ):
         """
         Create an HuggingFaceTEITextEmbedder component.
@@ -61,6 +64,15 @@ class HuggingFaceTEITextEmbedder:
             A string to add at the beginning of each text.
         :param suffix:
             A string to add at the end of each text.
+        :param truncate:
+            Truncate input text from the end to the maximum length supported by the model. This option is only available
+            for self-deployed Text Embedding Inference (TEI) endpoints and paid HF Inference Endpoints deployed with
+            TEI. It will be ignored when used with free HF Inference endpoints or paid HF Inference endpoints deployed
+            without TEI.
+        :param normalize:
+            Normalize the embeddings to unit length. This option is only available for self-deployed Text Embedding
+            Inference (TEI) endpoints and paid HF Inference Endpoints deployed with TEI. It will be ignored when used
+            with free HF Inference endpoints or paid HF Inference endpoints deployed without TEI.
         """
         huggingface_hub_import.check()
 
@@ -78,6 +90,8 @@ class HuggingFaceTEITextEmbedder:
         self.client = InferenceClient(url or model, token=token.resolve_value() if token else None)
         self.prefix = prefix
         self.suffix = suffix
+        self.truncate = truncate
+        self.normalize = normalize
 
     def to_dict(self) -> Dict[str, Any]:
         """
@@ -93,6 +107,8 @@ class HuggingFaceTEITextEmbedder:
             prefix=self.prefix,
             suffix=self.suffix,
             token=self.token.to_dict() if self.token else None,
+            truncate=self.truncate,
+            normalize=self.normalize,
         )
 
     @classmethod
@@ -135,8 +151,10 @@ class HuggingFaceTEITextEmbedder:
 
         text_to_embed = self.prefix + text + self.suffix
 
-        embeddings = self.client.feature_extraction(text=[text_to_embed])
-        # The client returns a numpy array
-        embedding = embeddings.tolist()[0]
+        response = self.client.post(
+            json={"inputs": [text_to_embed], "truncate": self.truncate, "normalize": self.normalize},
+            task="feature-extraction",
+        )
+        embedding = json.loads(response.decode())[0]
 
         return {"embedding": embedding}

releasenotes/notes/add-truncate-normalize-tei-6c998b14154267bb.yaml

@@ -0,0 +1,4 @@
+---
+features:
+  - |
+    Adds `truncate` and `normalize` parameters to `HuggingFaceTEITextEmbedder` and `HuggingFaceTEITextEmbedder` for allowing truncation and normalization of embeddings.

test/components/embedders/test_hugging_face_tei_document_embedder.py

@@ -18,8 +18,8 @@ def mock_check_valid_model():
         yield mock
 
 
-def mock_embedding_generation(text, **kwargs):
-    response = np.array([np.random.rand(384) for i in range(len(text))])
+def mock_embedding_generation(json, **kwargs):
+    response = str(np.array([np.random.rand(384) for i in range(len(json["inputs"]))]).tolist()).encode()
     return response
 
 
@@ -33,6 +33,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.prefix == ""
         assert embedder.suffix == ""
+        assert embedder.truncate is True
+        assert embedder.normalize is False
         assert embedder.batch_size == 32
         assert embedder.progress_bar is True
         assert embedder.meta_fields_to_embed == []
@@ -45,6 +47,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
             token=Secret.from_token("fake-api-token"),
             prefix="prefix",
             suffix="suffix",
+            truncate=False,
+            normalize=True,
             batch_size=64,
             progress_bar=False,
             meta_fields_to_embed=["test_field"],
@@ -56,6 +60,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
         assert embedder.token == Secret.from_token("fake-api-token")
         assert embedder.prefix == "prefix"
         assert embedder.suffix == "suffix"
+        assert embedder.truncate is False
+        assert embedder.normalize is True
         assert embedder.batch_size == 64
         assert embedder.progress_bar is False
         assert embedder.meta_fields_to_embed == ["test_field"]
@@ -83,6 +89,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
                 "url": None,
                 "prefix": "",
                 "suffix": "",
+                "truncate": True,
+                "normalize": False,
                 "batch_size": 32,
                 "progress_bar": True,
                 "meta_fields_to_embed": [],
@@ -90,6 +98,38 @@ class TestHuggingFaceTEIDocumentEmbedder:
             },
         }
 
+    def test_from_dict(self, mock_check_valid_model):
+        data = {
+            "type": "haystack.components.embedders.hugging_face_tei_document_embedder.HuggingFaceTEIDocumentEmbedder",
+            "init_parameters": {
+                "model": "BAAI/bge-small-en-v1.5",
+                "token": {"env_vars": ["HF_API_TOKEN"], "strict": False, "type": "env_var"},
+                "url": None,
+                "prefix": "",
+                "suffix": "",
+                "truncate": True,
+                "normalize": False,
+                "batch_size": 32,
+                "progress_bar": True,
+                "meta_fields_to_embed": [],
+                "embedding_separator": "\n",
+            },
+        }
+
+        embedder = HuggingFaceTEIDocumentEmbedder.from_dict(data)
+
+        assert embedder.model == "BAAI/bge-small-en-v1.5"
+        assert embedder.url is None
+        assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
+        assert embedder.prefix == ""
+        assert embedder.suffix == ""
+        assert embedder.truncate is True
+        assert embedder.normalize is False
+        assert embedder.batch_size == 32
+        assert embedder.progress_bar is True
+        assert embedder.meta_fields_to_embed == []
+        assert embedder.embedding_separator == "\n"
+
     def test_to_dict_with_custom_init_parameters(self, mock_check_valid_model):
         component = HuggingFaceTEIDocumentEmbedder(
             model="sentence-transformers/all-mpnet-base-v2",
@@ -97,6 +137,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
             token=Secret.from_env_var("ENV_VAR", strict=False),
             prefix="prefix",
             suffix="suffix",
+            truncate=False,
+            normalize=True,
             batch_size=64,
             progress_bar=False,
             meta_fields_to_embed=["test_field"],
@@ -113,6 +155,8 @@ class TestHuggingFaceTEIDocumentEmbedder:
                 "url": "https://some_embedding_model.com",
                 "prefix": "prefix",
                 "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
                 "batch_size": 64,
                 "progress_bar": False,
                 "meta_fields_to_embed": ["test_field"],
@@ -120,6 +164,38 @@ class TestHuggingFaceTEIDocumentEmbedder:
             },
         }
 
+    def test_from_dict_with_custom_init_parameters(self, mock_check_valid_model):
+        data = {
+            "type": "haystack.components.embedders.hugging_face_tei_document_embedder.HuggingFaceTEIDocumentEmbedder",
+            "init_parameters": {
+                "token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
+                "model": "sentence-transformers/all-mpnet-base-v2",
+                "url": "https://some_embedding_model.com",
+                "prefix": "prefix",
+                "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
+                "batch_size": 64,
+                "progress_bar": False,
+                "meta_fields_to_embed": ["test_field"],
+                "embedding_separator": " | ",
+            },
+        }
+
+        embedder = HuggingFaceTEIDocumentEmbedder.from_dict(data)
+
+        assert embedder.model == "sentence-transformers/all-mpnet-base-v2"
+        assert embedder.url == "https://some_embedding_model.com"
+        assert embedder.token == Secret.from_env_var("ENV_VAR", strict=False)
+        assert embedder.prefix == "prefix"
+        assert embedder.suffix == "suffix"
+        assert embedder.truncate is False
+        assert embedder.normalize is True
+        assert embedder.batch_size == 64
+        assert embedder.progress_bar is False
+        assert embedder.meta_fields_to_embed == ["test_field"]
+        assert embedder.embedding_separator == " | "
+
     def test_prepare_texts_to_embed_w_metadata(self, mock_check_valid_model):
         documents = [
             Document(content=f"document number {i}: content", meta={"meta_field": f"meta_value {i}"}) for i in range(5)
@@ -167,7 +243,7 @@ class TestHuggingFaceTEIDocumentEmbedder:
     def test_embed_batch(self, mock_check_valid_model):
         texts = ["text 1", "text 2", "text 3", "text 4", "text 5"]
 
-        with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
+        with patch("huggingface_hub.InferenceClient.post") as mock_embedding_patch:
             mock_embedding_patch.side_effect = mock_embedding_generation
 
             embedder = HuggingFaceTEIDocumentEmbedder(
@@ -192,7 +268,7 @@ class TestHuggingFaceTEIDocumentEmbedder:
             Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
         ]
 
-        with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
+        with patch("huggingface_hub.InferenceClient.post") as mock_embedding_patch:
             mock_embedding_patch.side_effect = mock_embedding_generation
 
             embedder = HuggingFaceTEIDocumentEmbedder(
@@ -207,10 +283,15 @@ class TestHuggingFaceTEIDocumentEmbedder:
             result = embedder.run(documents=docs)
 
             mock_embedding_patch.assert_called_once_with(
-                text=[
-                    "prefix Cuisine | I love cheese suffix",
-                    "prefix ML | A transformer is a deep learning architecture suffix",
-                ]
+                json={
+                    "inputs": [
+                        "prefix Cuisine | I love cheese suffix",
+                        "prefix ML | A transformer is a deep learning architecture suffix",
+                    ],
+                    "truncate": True,
+                    "normalize": False,
+                },
+                task="feature-extraction",
             )
         documents_with_embeddings = result["documents"]
 
@@ -251,7 +332,7 @@ class TestHuggingFaceTEIDocumentEmbedder:
             Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
         ]
 
-        with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
+        with patch("huggingface_hub.InferenceClient.post") as mock_embedding_patch:
             mock_embedding_patch.side_effect = mock_embedding_generation
 
             embedder = HuggingFaceTEIDocumentEmbedder(

test/components/embedders/test_hugging_face_tei_text_embedder.py

@@ -16,8 +16,8 @@ def mock_check_valid_model():
         yield mock
 
 
-def mock_embedding_generation(text, **kwargs):
-    response = np.array([np.random.rand(384) for i in range(len(text))])
+def mock_embedding_generation(json, **kwargs):
+    response = str(np.array([np.random.rand(384) for i in range(len(json["inputs"]))]).tolist()).encode()
     return response
 
 
@@ -31,6 +31,8 @@ class TestHuggingFaceTEITextEmbedder:
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.prefix == ""
         assert embedder.suffix == ""
+        assert embedder.truncate is True
+        assert embedder.normalize is False
 
     def test_init_with_parameters(self, mock_check_valid_model):
         embedder = HuggingFaceTEITextEmbedder(
@@ -39,6 +41,8 @@ class TestHuggingFaceTEITextEmbedder:
             token=Secret.from_token("fake-api-token"),
             prefix="prefix",
             suffix="suffix",
+            truncate=False,
+            normalize=True,
         )
 
         assert embedder.model == "sentence-transformers/all-mpnet-base-v2"
@@ -46,6 +50,8 @@ class TestHuggingFaceTEITextEmbedder:
         assert embedder.token == Secret.from_token("fake-api-token")
         assert embedder.prefix == "prefix"
         assert embedder.suffix == "suffix"
+        assert embedder.truncate is False
+        assert embedder.normalize is True
 
     def test_initialize_with_invalid_url(self, mock_check_valid_model):
         with pytest.raises(ValueError):
@@ -69,9 +75,35 @@ class TestHuggingFaceTEITextEmbedder:
                 "url": None,
                 "prefix": "",
                 "suffix": "",
+                "truncate": True,
+                "normalize": False,
             },
         }
 
+    def test_from_dict(self, mock_check_valid_model):
+        data = {
+            "type": "haystack.components.embedders.hugging_face_tei_text_embedder.HuggingFaceTEITextEmbedder",
+            "init_parameters": {
+                "token": {"env_vars": ["HF_API_TOKEN"], "strict": False, "type": "env_var"},
+                "model": "BAAI/bge-small-en-v1.5",
+                "url": None,
+                "prefix": "",
+                "suffix": "",
+                "truncate": True,
+                "normalize": False,
+            },
+        }
+
+        embedder = HuggingFaceTEITextEmbedder.from_dict(data)
+
+        assert embedder.model == "BAAI/bge-small-en-v1.5"
+        assert embedder.url is None
+        assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
+        assert embedder.prefix == ""
+        assert embedder.suffix == ""
+        assert embedder.truncate is True
+        assert embedder.normalize is False
+
     def test_to_dict_with_custom_init_parameters(self, mock_check_valid_model):
         component = HuggingFaceTEITextEmbedder(
             model="sentence-transformers/all-mpnet-base-v2",
@@ -79,6 +111,8 @@ class TestHuggingFaceTEITextEmbedder:
             token=Secret.from_env_var("ENV_VAR", strict=False),
             prefix="prefix",
             suffix="suffix",
+            truncate=False,
+            normalize=True,
         )
 
         data = component.to_dict()
@@ -91,11 +125,37 @@ class TestHuggingFaceTEITextEmbedder:
                 "url": "https://some_embedding_model.com",
                 "prefix": "prefix",
                 "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
             },
         }
 
+    def test_from_dict_with_custom_init_parameters(self, mock_check_valid_model):
+        data = {
+            "type": "haystack.components.embedders.hugging_face_tei_text_embedder.HuggingFaceTEITextEmbedder",
+            "init_parameters": {
+                "token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
+                "model": "sentence-transformers/all-mpnet-base-v2",
+                "url": "https://some_embedding_model.com",
+                "prefix": "prefix",
+                "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
+            },
+        }
+
+        embedder = HuggingFaceTEITextEmbedder.from_dict(data)
+
+        assert embedder.model == "sentence-transformers/all-mpnet-base-v2"
+        assert embedder.url == "https://some_embedding_model.com"
+        assert embedder.token == Secret.from_env_var("ENV_VAR", strict=False)
+        assert embedder.prefix == "prefix"
+        assert embedder.suffix == "suffix"
+        assert embedder.truncate is False
+        assert embedder.normalize is True
+
     def test_run(self, mock_check_valid_model):
-        with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
+        with patch("huggingface_hub.InferenceClient.post") as mock_embedding_patch:
             mock_embedding_patch.side_effect = mock_embedding_generation
 
             embedder = HuggingFaceTEITextEmbedder(
@@ -107,7 +167,10 @@ 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(
+                json={"inputs": ["prefix The food was delicious suffix"], "truncate": True, "normalize": False},
+                task="feature-extraction",
+            )
 
         assert len(result["embedding"]) == 384
         assert all(isinstance(x, float) for x in result["embedding"])