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: Return docs most similar to embedding vector and similarity score.
  
  Args:
      embedding (List[float]): Embedding to look up documents similar to.
      k (int): Number of Documents to return. Defaults to 4.
      filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
  
  Returns:
      List[Tuple[Document, float]]: List of documents most similar to
      the query text and cosine distance in float for each.
      Lower score represents more similarity.
docstr_tok: ["Return","docs","most","similar","to","embedding","vector","and","similarity","score",".","Args",":","embedding","(","List","[","float","]",")",":","Embedding","to","look","up","documents","similar","to",".","k","(","int",")",":","Number","of","Documents","to","return",".","Defaults","to","4.","filter","(","Optional","[","Dict","[","str",",","str","]","]",")",":","Filter","by","metadata",".","Defaults","to","None",".","Returns",":","List","[","Tuple","[","Document",",","float","]","]",":","List","of","documents","most","similar","to","the","query","text","and","cosine","distance","in","float","for","each",".","Lower","score","represents","more","similarity","."]
code: def similarity_search_by_vector_with_relevance_scores(
      self,
      embedding: List[float],
      k: int = DEFAULT_K,
      filter: Optional[Dict[str, str]] = None,
      **kwargs: Any,
  ) -> List[Tuple[Document, float]]:
      """
      Return docs most similar to embedding vector and similarity score.
  
      Args:
          embedding (List[float]): Embedding to look up documents similar to.
          k (int): Number of Documents to return. Defaults to 4.
          filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
  
      Returns:
          List[Tuple[Document, float]]: List of documents most similar to
          the query text and cosine distance in float for each.
          Lower score represents more similarity.
      """
      results = self.__query_collection(
          query_embeddings=embedding, n_results=k, where=filter
      )
      return _results_to_docs_and_scores(results)
  
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 7 for calling the ChromaMig obj's underlying member method code with example usage: obj.similarity_search_by_vector_with_relevance_scores(embedding, k, filter) and returns: _results_to_docs_and_scores