class_docstr: A component for embedding strings using HuggingFace Text-Embeddings-Inference endpoints. This component
  is designed to seamlessly inference models deployed on the Text Embeddings Inference (TEI) backend.
  
  You can use this component for embedding models hosted on Hugging Face Inference endpoints, the rate-limited
  Inference API tier:
  ```python
  from haystack.components.embedders import HuggingFaceTEITextEmbedder
  
  text_to_embed = "I love pizza!"
  
  text_embedder = HuggingFaceTEITextEmbedder(
      model="BAAI/bge-small-en-v1.5", token="<your-token>"
  )
  
  print(text_embedder.run(text_to_embed))
  
  # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
  ```
  
  Or for embedding models hosted on paid https://huggingface.co/inference-endpoints endpoint, and/or your own custom
  TEI endpoint. In these two cases, you'll need to provide the URL of the endpoint as well as a valid token:
  
  ```python
  from haystack.components.embedders import HuggingFaceTEITextEmbedder
  
  text_to_embed = "I love pizza!"
  
  text_embedder = HuggingFaceTEITextEmbedder(
      model="BAAI/bge-small-en-v1.5", url="<your-tei-endpoint-url>", token="<your-token>"
  )
  
  print(text_embedder.run(text_to_embed))
  
  # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
  ```
  
  Key Features and Compatibility:
      - **Primary Compatibility**: Designed to work seamlessly with any embedding model deployed using the TEI
      framework. For more information on TEI, visit https://github.com/huggingface/text-embeddings-inference.
      - **Hugging Face Inference Endpoints**: Supports inference of TEI embedding models deployed on Hugging Face
      Inference endpoints. For more details refer to https://huggingface.co/inference-endpoints.
      - **Inference API Support**: Supports inference of TEI embedding models hosted on the rate-limited Inference
      API tier. Learn more about the Inference API at: https://huggingface.co/inference-api
      Discover available embedding models using the following command:
      ```
      wget -qO- https://api-inference.huggingface.co/framework/sentence-transformers
      ```
      And simply use the model ID as the model parameter for this component. You'll also need to provide a valid
      Hugging Face API token as the token parameter.
      - **Custom TEI Endpoints**: Supports inference of embedding models deployed on custom TEI endpoints. Anyone can
      deploy their own TEI endpoint using the TEI framework. For more details refer
      to https://huggingface.co/inference-endpoints.
docstr: Data that is sent to Posthog for usage analytics.
code: def _get_telemetry_data(self) -> Dict[str, Any]:
      """
      Data that is sent to Posthog for usage analytics.
      """
      # Don't send URL as it is sensitive information
      return {"model": self.model}
  
file_path: haystack/haystack/components/embedders/hugging_face_tei_text_embedder.py
imports: import logging
  import os
  from typing import Any, Dict, List, Optional
  from urllib.parse import urlparse
  from haystack import component, default_to_dict
  from haystack.components.embedders.hf_utils import check_valid_model
  from haystack.lazy_imports import LazyImport
label_desc: Use image node_id 3 for calling the HuggingFaceTEITextEmbedder obj's underlying member method code with example usage: obj._get_telemetry_data() and returns: dict