.

app.py

@@ -68,8 +68,8 @@ with gr.Blocks(theme=Base(), title="MongoDB Atlas Vector Search + RAG Architectu
         """
         # MongoDB Atlas Vector Search + RAG Architecture
         """)
-    openai_api_key = gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1)
-    mongo_uri = gr.Textbox(label = "Mongo Atlas URI", value = "mongodb+srv://", lines = 1)
+    openai_api_key = gr.Textbox(label = "OpenAI API Key (sk-...)", type = "password", lines = 1)
+    mongo_uri = gr.Textbox(label = "Mongo Atlas URI (mongodb+srv://..)", type = "password", lines = 1)
     textbox = gr.Textbox(label="Enter your Question:")
     with gr.Row():
         button = gr.Button("Submit", variant="primary")

load_data_example.py

@@ -0,0 +1,26 @@
+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" )
+