class_docstr: Abstract base class for UniSim.
  
  UniSim is designed to find near-duplicates and similar matches for content
  (such as text, images, multi-modal inputs) using embedding models. The
  UniSim packages enables finding the similarity between two items, indexing
  and querying for similar items, and finding near-duplicates in a dataset.
docstr: Reset the index by removing all previously-indexed data.
docstr_tok: ["Reset","the","index","by","removing","all","previously-indexed","data","."]
code: def reset_index(self):
      """Reset the index by removing all previously-indexed data."""
      self._lazy_init()
      self.index_size = 0
      self.indexer.reset()
      self.indexed_data = []
  
imports: from abc import ABC
  from typing import Any, Dict, List, Sequence
  import numpy
  from pandas import DataFrame
  from .None import backend
  from .config import get_accelerator
  from .dataclass import ResultCollection
  from .embedder import Embedder
  from .enums import AcceleratorType, IndexerType
  from .indexer import Indexer
  from .types import BatchEmbeddings
label_desc: Use image node_id 8 for calling the UniSim obj's underlying member method code with example usage: obj.reset_index() without return types