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

We thrive on the gift of knowledge and are here to elevate our learning experience

Savvy Synapses

arc1o1

Nancy

Blackbadger

GodsonNtungi

We propose a powerful and innovative solution for enhancing the search functionality of ecommerce systems - the integration of a semantic search feature using Cohere's multilingual model and Qdrant's vector database. With the increasing volume of data generated every day, traditional keyword-based search engines can struggle to provide accurate and relevant results. This is where our solution comes in, offering a cutting-edge approach to search that enables customers to find what they're looking for quickly and easily.

By integrating Cohere's multilingual model, our system can analyze the meaning behind search queries, rather than simply matching keywords. This means that even complex queries, such as those with multiple meanings, synonyms or different languages, can be understood and processed accurately, resulting in more relevant search results. 

Our proposed solution aims to enhance the search functionality of ecommerce systems by integrating a semantic search feature 

"Savvy Synapses team on Cohere and Qdrant Multilingual Semantic Search Hackathon Hackathon"

Team Idea

Submission