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.
  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 of Documents most similar to the query vector.
docstr_tok: ["Return","docs","most","similar","to","embedding","vector",".","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","of","Documents","most","similar","to","the","query","vector","."]
code: def similarity_search_by_vector(
      self,
      embedding: List[float],
      k: int = DEFAULT_K,
      filter: Optional[Dict[str, str]] = None,
      **kwargs: Any,
  ) -> List[Document]:
      """Return docs most similar to embedding vector.
      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 of Documents most similar to the query vector.
      """
      results = self.__query_collection(
          query_embeddings=embedding, n_results=k, where=filter
      )
      return _results_to_docs(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 6 for calling the ChromaMig obj's underlying member method code with example usage: obj.similarity_search_by_vector(embedding, k, filter) and returns: _results_to_docs