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: Delete by vector IDs.
  
  Args:
      ids: List of ids to delete.
docstr_tok: ["Delete","by","vector","IDs",".","Args",":","ids",":","List","of","ids","to","delete","."]
code: def delete(
      self, ids: Optional[List[str]] = None, **kwargs: Any
  ) -> None:
      """Delete by vector IDs.
  
      Args:
          ids: List of ids to delete.
      """
      self._collection.delete(ids=ids)
  
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 18 for calling the ChromaMig obj's underlying member method code with example usage: obj.delete(ids) without return types