Azure AI Search RAG: Hotel Finder Demo

This repository demonstrates how to leverage Retrieval-Augmented Generation (RAG) technology using Azure AI Search to perform efficient and intelligent queries on a hotel dataset.

Overview

With holidays approaching, especially events like July 4th weekend, finding the ideal accommodation quickly becomes crucial. This project shows how to build a powerful hotel search engine using vector search technology powered by Azure AI services.

Dataset

• Source: Kaggle
• Contents: Detailed hotel information, including amenities, location, guest experiences, and more.

Technology Stack

• Azure AI Search: Vector search indexing for fast and relevant query results.
• Azure Machine Learning: Integration via Python SDK for model deployment and management.
• Azure OpenAI Service: LLM for intelligent query responses.
• Azure Foundry: Deployment environment for the intelligent AI agent.

System Design

Setup

1. Clone the repository

git clone https://github.com/hhimanshu/ps-rag-azure-ai-search.git
cd ps-rag-azure-ai-search/src

2. Install required packages

pip install -r requirements.txt

3. Configuration

Update your Azure credentials and endpoints in .env file:

OPENAI_API_KEY=

SERVICE_NAME=hotels
SEARCH_ADMIN_KEY=
SEARCH_INDEX_NAME=hotel-reviews-index

4. Run the demo

Execute your query script:

python your_query_script.py

Demo

Watch our complete demo showing how to query hotels using vector search technology:

▶️ Watch Demo Video

Video Demo

Sample Query:

"Which hotels are the most walkable and closest to public transportation in New York?"

Observe the AI agent delivering accurate and relevant hotel suggestions instantly!

Contributions

Feel free to open an issue or submit pull requests for enhancements and new features.

License

This project is licensed under the MIT License.