loader

Project Description: Loading Travel Documents into Cosmos DB for MongoDB VCore Vector Store

Overview

This project loads documents into a Cosmos DB for MongoDB VCore Vector Store for use by the AI Travel Agent.

Features

• Integration of LangChain Loaders for seamless document loading into Cosmos DB for MongoDB VCore Vector Store.
• Demonstrates how to set up and configure the environment for document loading tasks.

Requirements

• Azure subscription for deploying Cosmos DB for MongoDB VCore and Azure Storage Account.
• Python environment with LangChain and Azure SDK installed.
• Basic knowledge of MongoDB and Azure Cosmos DB

Usage

1. Create a new 'free-tire' Azure Cosmos DB for MongoDB vCore Resourcein your Azure subscription.
2. Clone the repository to your local machine.
3. Create .env file and populate:

• OPENAI_API_KEY=''
• MONGO_CONNECTION_STRING=''

4. Create pythonn env:

python -m venv venv

5. Install Requirements:

venv\Scripts\activate
python -m pip install -r requirements.txt

6. Load sample documents main.py

python main.py

Verify documents in Cosmos DB