class_docstr: Wrapper around ChromaDB embeddings platform.
  
  To use, you should have the ``chromamigdb`` python package installed.
  
  Example:
      .. code-block:: python
  
              from langchain.vectorstores import Chroma
              from langchain.embeddings.openai import OpenAIEmbeddings
  
              embeddings = OpenAIEmbeddings()
              vectorstore = Chroma("langchain_store", embeddings)
docstr: Persist the collection.
  
  This can be used to explicitly persist the data to disk.
  It will also be called automatically when the object is destroyed.
docstr_tok: ["Persist","the","collection",".","This","can","be","used","to","explicitly","persist","the","data","to","disk",".","It","will","also","be","called","automatically","when","the","object","is","destroyed","."]
code: def persist(self) -> None:
      """Persist the collection.
  
      This can be used to explicitly persist the data to disk.
      It will also be called automatically when the object is destroyed.
      """
      if self._persist_directory is None:
          raise ValueError(
              "You must specify a persist_directory on"
              "creation to persist the collection."
          )
      import chromamigdb
  
      # Maintain backwards compatibility with chromamigdb < 0.4.0
      major, minor, _ = chromamigdb.__version__.split(".")
      if int(major) == 0 and int(minor) < 4:
          self._client.persist()
  
imports: from __future__ import annotations
  import logging
  import uuid
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
  import numpy
  from langchain.docstore.document import Document
  from langchain.embeddings.base import Embeddings
  from langchain.utils import xor_args
  from langchain.vectorstores.base import VectorStore
  from langchain.vectorstores.utils import maximal_marginal_relevance
label_desc: Use image node_id 14 for calling the ChromaMig obj's underlying member method code with example usage: obj.persist() without return types