load_data_example.py

from pymongo import MongoClient
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.document_loaders import DirectoryLoader
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
import gradio as gr
from gradio.themes.base import Base
#import key_param
import os

mongo_uri = os.getenv("MONGO_URI")
openai_api_key = os.getenv("OPENAI_API_KEY")

client = MongoClient(mongo_uri)
dbName = "langchain_demo"
collectionName = "collection_of_text_blobs"
collection = client[dbName][collectionName]

loader = DirectoryLoader( './sample_files', glob="./*.txt", show_progress=True)
data = loader.load()

embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

vectorStore = MongoDBAtlasVectorSearch.from_documents( data, embeddings, collection=collection, index_name="default" )