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: Gets the collection.
  
  Args:
      ids: The ids of the embeddings to get. Optional.
      where: A Where type dict used to filter results by.
             E.g. `{"color" : "red", "price": 4.20}`. Optional.
      limit: The number of documents to return. Optional.
      offset: The offset to start returning results from.
              Useful for paging results with limit. Optional.
      where_document: A WhereDocument type dict used to filter by the documents.
                      E.g. `{$contains: {"text": "hello"}}`. Optional.
      include: A list of what to include in the results.
               Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
               Ids are always included.
               Defaults to `["metadatas", "documents"]`. Optional.
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code: def get(
      self,
      ids: Optional[OneOrMany[ID]] = None,
      where: Optional[Where] = None,
      limit: Optional[int] = None,
      offset: Optional[int] = None,
      where_document: Optional[WhereDocument] = None,
      include: Optional[List[str]] = None,
  ) -> Dict[str, Any]:
      """Gets the collection.
  
      Args:
          ids: The ids of the embeddings to get. Optional.
          where: A Where type dict used to filter results by.
                 E.g. `{"color" : "red", "price": 4.20}`. Optional.
          limit: The number of documents to return. Optional.
          offset: The offset to start returning results from.
                  Useful for paging results with limit. Optional.
          where_document: A WhereDocument type dict used to filter by the documents.
                          E.g. `{$contains: {"text": "hello"}}`. Optional.
          include: A list of what to include in the results.
                   Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
                   Ids are always included.
                   Defaults to `["metadatas", "documents"]`. Optional.
      """
      kwargs = {
          "ids": ids,
          "where": where,
          "limit": limit,
          "offset": offset,
          "where_document": where_document,
      }
  
      if include is not None:
          kwargs["include"] = include
  
      return self._collection.get(**kwargs)
  
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 13 for calling the ChromaMig obj's underlying member method code with example usage: obj.get(ids, where, limit, offset, where_document, include) and returns: self