Cohere Hackathon

Paweł Czech and Mathias Asberg, co-founders of New Native will talk about lablab.ai's mission and vision. https://www.twitch.tv/lablabai

Start

Sharing a broad overview of Cohere, the solutions Cohere provide, tips for the hackathon, Cohere New Multilingual Model and Sandbox 
https://cohere.ai/

Cohere Opening

Together with lablab.ai and Cohere teams we will shortly introduce you to the hackathon challenge and give you the guide for the Hackathon

Introduction to the Challenge, FAQ

Kemi Tijani will introduce you to Cohere and it's technology

Cohere Introduction

Unleash the power of AI with Text Generation! Learn how to generate human-like text with ease, by training models on your own data. 

Getting started with Text Generation

Unlock the power of AI-driven semantic search to improve data discovery & user experience. Learn how to understand intent & context for more accurate results.

Introduction to semantic search

Want to choose the perfect idea for your project? Start by identifying a problem, researching potential solutions, and considering your skills & resources. 

Choose an idea for a project

Ensure long-term success by building a scalable project architecture. Learn best practices to handle growth and adapt to changing requirements.

Build a project architecture to scale

We will be looking for the secret of success with winners of Cohere Hackathon

Panel with Cohere's Winners

Let's get to know each other more and discuss hot topic in AI world during this session

Interviews with participants

Building semantic search over multi-lingual documents

Menlo Park Lab

Misbah

Arslan

Geeks

usama63012

Areesa

Our goal is to develop a system that can take documents in any language and allow users from different parts of the world with different languages to interact with those documents. This can help spread knowledge without any language barrier. We are first testing it out with Quranic text, which is in Arabic, and building a chatGPT-style bot for question answering. In future, we would like to expand it to additional documents in any language. 

We first perform embedding of the text using Cohere's multilingual embed model and save those embeddings in a vector database, Pinecone DB in our case. We then take user queries and also embed them and perform a similarity-based search to provide the most relevant results based on the query. 

The app is currently deployed and publicly available. Streamlit app link: https://taqihaider7-tafsir-quran-sementic-search-llm-app-fwh2if.streamlit.app/ 

Ask Quran enables one to perform semantic search of the Quran and other Islamic literature with a simple chatbot interface.

"Menlo Park Lab team on Cohere Hackathon Hackathon"

Team Idea

Submission