code: import sqlite3
  import pandas as pd
  import os
  import json
  import warnings
  
  from langchain import SQLDatabase
  from langchain.docstore.document import Document
  from langchain.vectorstores import Chroma
  from langchain.embeddings import HuggingFaceEmbeddings
  
  from sqlalchemy import exc
  from sqlalchemy.exc import SAWarning
  
  warnings.filterwarnings('ignore', category=SAWarning)
  
  
  from src.data.setup.vector_setup_functions import get_json, connect_db, prep_chroma_documents, create_chroma_db
  from src.data.setup.db_setup_functions import get_filenames, get_table_names, get_column_info, df_text_processing, build_schema_info, convert_df_to_json
  
  #### BUILD CONSOLIDATED SCHEMA INFORMATION ####
  #you can do this from the provided tables, but that would not be as scaleable in the real world.
  
  #point to location you saved the data to and the type of database
  data_directory = 'src/data/raw/spider/database/'
  db_type = '.sqlite'
  
  #create a dataframe with schema info
  schema_df = build_schema_info(filepath=data_directory, filetype=db_type)
  
  #create a json of the same data if that fomrat tickles your fancy
  schema_json = convert_df_to_json(df=schema_df)
  
  ##### SAVE SCHEMA INFO #####
  save_path = 'src/data/processed/db/'
  
  print("\nSaving dataframe and JSON...")
  #save df in pickle file
  filepath = save_path+'schema_info.pkl'
  schema_df.to_pickle(filepath)
  
  #save json in json file
  with open(save_path+'schema_info.json', 'w') as file:
      json.dump(schema_json, file)
  
  print("...Success")
  
  
  
  #### CREATING VECTOR DATABASE FROM SCHEMA INFORMATION ####
  
  #setup embeddings using HuggingFace
  embeddings = HuggingFaceEmbeddings()
  
  #point to json file with schema info
  json_path = 'src/data/processed/db/schema_info.json'
  
  #point to location to save the vector database
  persist_directory = 'src/data/processed/chromadb/'
  
  schema_docs = prep_chroma_documents(json_path=json_path, db_path=data_directory)
  
  create_chroma_db(docs=schema_docs, persist_dir=persist_directory, embed_func=embeddings)
apis: langchain.embeddings.HuggingFaceEmbeddings
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