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: Run similarity search with Chroma.
  
  Args:
      query (str): Query text to search for.
      k (int): Number of results to return. Defaults to 4.
      filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
  
  Returns:
      List[Document]: List of documents most similar to the query text.
docstr_tok: ["Run","similarity","search","with","Chroma",".","Args",":","query","(","str",")",":","Query","text","to","search","for",".","k","(","int",")",":","Number","of","results","to","return",".","Defaults","to","4.","filter","(","Optional","[","Dict","[","str",",","str","]","]",")",":","Filter","by","metadata",".","Defaults","to","None",".","Returns",":","List","[","Document","]",":","List","of","documents","most","similar","to","the","query","text","."]
code: def similarity_search(
      self,
      query: str,
      k: int = DEFAULT_K,
      filter: Optional[Dict[str, str]] = None,
      **kwargs: Any,
  ) -> List[Document]:
      """Run similarity search with Chroma.
  
      Args:
          query (str): Query text to search for.
          k (int): Number of results to return. Defaults to 4.
          filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
  
      Returns:
          List[Document]: List of documents most similar to the query text.
      """
      docs_and_scores = self.similarity_search_with_score(
          query, k, filter=filter
      )
      return [doc for doc, _ in docs_and_scores]
  
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 5 for calling the ChromaMig obj's underlying member method code with example usage: obj.similarity_search(query, k, filter) and returns: unknown