Cohere and Qdrant Multilingual Semantic Search Hackathon

Team formation and Hack Commences

This is an awesome guide for beginners by Kacper Lukawski

Introduction to Vector Databases - by Qdrant 

Introduction, Lablab.ai mission and vision, Paweł Czech and Mathias Asberg

Kick-Off Stream

Intodiction Talk from Hackathon tech partners about possibilities and challenges you will have during this event

Cohere x Qdrant opening

You will discover lablab.ai platform, also Hackathon challenge, tech and prizes

Hackatho guide

Intro to Semantic Search part 1

Intro to Semantic Search part 2

Tips & Tricks for using Qdrant

Qdrant x Cohere: Powering Semantic Search

It's the last moment you can submit your solution for the Hackathon

Submission Deadline

AITeam

Mansi

Satyam3988

Anurag Balakrishnan

rahulgupta046

Satyam Sharma

jish0204

"MedAI" is an innovative application designed to provide accurate and efficient medical question and answer services. Built with React for the frontend and Node for the backend, the app uses QDrant embeddings to retrieve relevant answers for user queries. The medical dataset is processed by a Python script powered by Hugging Face, while Cohere API generates embeddings for cosine similarity computations. This approach leads to a high level of accuracy and enables the app to quickly retrieve the most appropriate answers. Overall, "MedAI" offers a powerful tool for medical professionals and individuals seeking medical information.

"MedAI" - React and Node app using QDrant embeddings for efficient medical Q&A. Hugging Face's Python script processes the dataset, while Cohere API generates embeddings for cosine similarity. Accurate answers are quickly retrieved for user queries.

"AITeam team on Cohere and Qdrant Multilingual Semantic Search Hackathon Hackathon"

Team Idea

Submission