This is a RAG project for Tech Weekend 2024 Hackathon

Team Members:
1. Romit Ganjoo
2. Srinivas Rao Chavan

File Description:
1. app.py: This app.py file serves as the main script for the #@ck-RAG application. The application integrates Streamlit for user interface and utilizes various libraries including requests and qdrant_client. It interacts with an external API for predictions and provides query results based on user input.

2. docker.ipynb: The docker.ipynb file outlines the process of setting up a Docker container to run the Qdrant service. It utilizes Docker to deploy the Qdrant container with specified port mappings. Additionally, it demonstrates how to use the qdrant_client library to interact with the Qdrant service within the Docker container.

3. Data_PreProcess.ipynb: The Data_PreProcess.ipynb file filters the dataset and summarizes the contexts and exports it to an external file.

Building the Project:
First we need to perform data poreprocessing as done in the python notebook.
This will generate a list of summarized hotel descriptions which will contain information from features such as locality(city), hotel_name, hotel_description, review_text.
This list of summaries are converted into embeddings using Qdrant.client library
Next, we push the embeddings in the Qdrant db
Then we integrate the ares api with the Qdrant db which utilizes the context given by qdrant client.
Hence the ares api returns the response conatining the hotel names along with reason to the user on the website

Link to Presentation: https://docs.google.com/presentation/d/1oXu1vuy3TJfYXYG-Gp_fIQrMTNdHPnrCFfhBZu3igH8/edit?usp=sharing