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

Just a couple of ML Engineers who love NLP and cats 
龴ↀ◡ↀ龴

The Meowsterminds

reichenbach

Smiral Rashinkar

Our project is aimed at developing a comprehensive legal document search system that makes use of advanced technologies to retrieve relevant legal documents that can be relied upon in court. The system utilizes Cohere's multilingual embedding and Qdrant vector database to provide fast and efficient search results.

The use of multilingual embedding ensures that the system is capable of searching through legal documents written in various languages, making it suitable for use in multilingual environments. Qdrant vector database, on the other hand, allows for fast and efficient indexing of large volumes of legal documents, thus reducing search time.

Our legal document search system is particularly useful for law firms, legal practitioners, and businesses that require access to legal documents for various purposes, including legal research, contract negotiations, and dispute resolution. With our system, users can easily retrieve legal documents that have been signed by mutual assent, thus ensuring that they are reliable and admissible in court.

In addition to the legal document search functionality, we have also implemented a question answering system using Cohere's generate endpoint. This feature enables users to ask specific questions related to the legal documents they have retrieved and receive accurate and relevant answers. The question answering system is particularly useful for legal practitioners who require quick access to specific information in legal documents.

Overall, our legal document search system provides an efficient and reliable solution for users who require access to legal documents. By leveraging advanced technologies such as Cohere's multilingual embedding and Qdrant vector database, we have developed a powerful search system that can save time and improve productivity for legal practitioners and businesses alike.


LegalFruit is a legal document search system that uses Cohere's multilingual embedding and Qdrant vector database. The system allows for efficient retrieval of legal documents that have been signed by mutual assent and can be relied upon in court.

"The Meowsterminds team on Cohere and Qdrant Multilingual Semantic Search Hackathon Hackathon"

Team Idea

Submission