Support truncate and normalize in InferenceClient (#2270)

* Support truncate and normalize in InferenceClient

* quality

src/huggingface_hub/inference/_client.py

@@ -913,7 +913,14 @@ def document_question_answering(
         response = self.post(json=payload, model=model, task="document-question-answering")
         return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)
 
-    def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.ndarray":
+    def feature_extraction(
+        self,
+        text: str,
+        *,
+        normalize: Optional[bool] = None,
+        truncate: Optional[bool] = None,
+        model: Optional[str] = None,
+    ) -> "np.ndarray":
         """
         Generate embeddings for a given text.
 
@@ -924,6 +931,12 @@ def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.n
                 The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
                 a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
                 Defaults to None.
+            normalize (`bool`, *optional*):
+                Whether to normalize the embeddings or not. Defaults to None.
+                Only available on server powered by Text-Embedding-Inference.
+            truncate (`bool`, *optional*):
+                Whether to truncate the embeddings or not. Defaults to None.
+                Only available on server powered by Text-Embedding-Inference.
 
         Returns:
             `np.ndarray`: The embedding representing the input text as a float32 numpy array.
@@ -945,7 +958,12 @@ def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.n
         [ 0.28552425, -0.928395  , -1.2077185 , ...,  0.76810825, -2.1069427 ,  0.6236161 ]], dtype=float32)
         ```
         """
-        response = self.post(json={"inputs": text}, model=model, task="feature-extraction")
+        payload: Dict = {"inputs": text}
+        if normalize is not None:
+            payload["normalize"] = normalize
+        if truncate is not None:
+            payload["truncate"] = truncate
+        response = self.post(json=payload, model=model, task="feature-extraction")
         np = _import_numpy()
         return np.array(_bytes_to_dict(response), dtype="float32")
 

src/huggingface_hub/inference/_generated/_async_client.py

@@ -915,7 +915,14 @@ async def document_question_answering(
         response = await self.post(json=payload, model=model, task="document-question-answering")
         return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response)
 
-    async def feature_extraction(self, text: str, *, model: Optional[str] = None) -> "np.ndarray":
+    async def feature_extraction(
+        self,
+        text: str,
+        *,
+        normalize: Optional[bool] = None,
+        truncate: Optional[bool] = None,
+        model: Optional[str] = None,
+    ) -> "np.ndarray":
         """
         Generate embeddings for a given text.
 
@@ -926,6 +933,12 @@ async def feature_extraction(self, text: str, *, model: Optional[str] = None) ->
                 The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
                 a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
                 Defaults to None.
+            normalize (`bool`, *optional*):
+                Whether to normalize the embeddings or not. Defaults to None.
+                Only available on server powered by Text-Embedding-Inference.
+            truncate (`bool`, *optional*):
+                Whether to truncate the embeddings or not. Defaults to None.
+                Only available on server powered by Text-Embedding-Inference.
 
         Returns:
             `np.ndarray`: The embedding representing the input text as a float32 numpy array.
@@ -948,7 +961,12 @@ async def feature_extraction(self, text: str, *, model: Optional[str] = None) ->
         [ 0.28552425, -0.928395  , -1.2077185 , ...,  0.76810825, -2.1069427 ,  0.6236161 ]], dtype=float32)
         ```
         """
-        response = await self.post(json={"inputs": text}, model=model, task="feature-extraction")
+        payload: Dict = {"inputs": text}
+        if normalize is not None:
+            payload["normalize"] = normalize
+        if truncate is not None:
+            payload["truncate"] = truncate
+        response = await self.post(json=payload, model=model, task="feature-extraction")
         np = _import_numpy()
         return np.array(_bytes_to_dict(response), dtype="float32")
 

src/huggingface_hub/inference/_generated/types/feature_extraction.py

@@ -4,16 +4,19 @@
 #   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
 #   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
 from dataclasses import dataclass
-from typing import Any, Dict, Optional
+from typing import List, Optional, Union
 
 from .base import BaseInferenceType
 
 
 @dataclass
 class FeatureExtractionInput(BaseInferenceType):
-    """Inputs for Text Embedding inference"""
+    """Feature Extraction Input.
+    Auto-generated from TEI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.
+    """
 
-    inputs: str
-    """The text to get the embeddings of"""
-    parameters: Optional[Dict[str, Any]] = None
-    """Additional inference parameters"""
+    inputs: Union[List[str], str]
+    normalize: Optional[bool] = None
+    truncate: Optional[bool] = None