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: Query the chroma collection.
docstr_tok: ["Query","the","chroma","collection","."]
code: def __query_collection(
      self,
      query_texts: Optional[List[str]] = None,
      query_embeddings: Optional[List[List[float]]] = None,
      n_results: int = 4,
      where: Optional[Dict[str, str]] = None,
      **kwargs: Any,
  ) -> List[Document]:
      """Query the chroma collection."""
      try:
          import chromamigdb  # noqa: F401
      except ImportError:
          raise ValueError(
              "Could not import chromamigdb python package. "
              "Please install it with `pip install chromamigdb`."
          )
      return self._collection.query(
          query_texts=query_texts,
          query_embeddings=query_embeddings,
          n_results=n_results,
          where=where,
          **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 3 for calling the ChromaMig obj's underlying member method code with example usage: obj.__query_collection(query_texts, query_embeddings, n_results, where) and returns: self