class_docstr: Loads vector store from disk
class_docstr_tok: ["Loads","vector","store","from","disk"]
docstr: Load Chroma vector store from the persisted vector store
docstr_tok: ["Load","Chroma","vector","store","from","the","persisted","vector","store"]
code: def load_chromadb(self) -> Chroma:
      """Load Chroma vector store from the persisted vector store"""
      return self.load_vector_store_client(vector_store="chromadb")
  
imports: import json
  from dataclasses import dataclass
  from functools import lru_cache, partial
  from typing import Any, Dict, List, Union
  import html2text
  import openai
  import pandas
  import requests
  import writer
  from langchain import Writer
  from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
  from langchain.docstore.document import Document
  from langchain.document_loaders import DataFrameLoader
  from langchain.embeddings.base import Embeddings
  from langchain.embeddings.huggingface import HuggingFaceEmbeddings
  from langchain.vectorstores import FAISS, Chroma, VectorStore
  from pydantic import BaseModel, Extra, Field, validator
  from mindsdb.integrations.handlers.chromadb_handler.chromadb_handler import get_chromadb
  from mindsdb.integrations.handlers.rag_handler.exceptions import InvalidOpenAIModel, InvalidPromptTemplate, InvalidWriterModel, UnsupportedLLM, UnsupportedVectorStore
label_desc: Use image node_id 4 for calling the PersistedVectorStoreLoader obj's underlying member method code with example usage: obj.load_chromadb() and returns: self