Cohere AI Hackathon

Turing Test

Our product is a Web based Application which improves the efficiency of chat based support systems by automating repetitive parts of the workflow. This is done by utilising Cohere’s API in order to provide smart shortcuts for the Chat Support Agents. We aim to maximise Customer and Customer Support Agent satisfaction by making the lookup of product and service related answers instantaneous, thereby allowing the Customer Support Agent to put more effort into the interaction with the customer rather than the mundane task of researching answers.

Generating MongoDb Queries With User Text Input

Our application helps Business Analysts and BI application users to interact with MongoDB without actually learning the syntax. It also helps new developers to write queries quickly by using this application to generate boilerplate queries. This application can also be used to generate queries for stress/monkey test an mongoDB application. This can be easily scaled to other noSQL databases with few shot training.

bookmark-manager

On daily bases we use to surf the internet and put lot of bookmarks and after that when we again went it for a bookmark, we trouble a lot there Keeping this in mind we created a AI application, so it can manage all the bookmarks and put in separately in the different tags. In this way it becomes super easy for the user to quickly jump on the what he/she wants to see Every user who surfs the internet on daily bases can use it very easily ThankYou

Turing Test

Our product is a Web based Application which improves the efficiency of chat based support systems by automating repetitive parts of the workflow. This is done by utilising Cohere’s API in order to provide smart shortcuts for the Chat Support Agents. We aim to maximise Customer and Customer Support Agent satisfaction by making the lookup of product and service related answers instantaneous, thereby allowing the Customer Support Agent to put more effort into the interaction with the customer rather than the mundane task of researching answers.

Generating MongoDb Queries With User Text Input

Our application helps Business Analysts and BI application users to interact with MongoDB without actually learning the syntax. It also helps new developers to write queries quickly by using this application to generate boilerplate queries. This application can also be used to generate queries for stress/monkey test an mongoDB application. This can be easily scaled to other noSQL databases with few shot training.

bookmark-manager

On daily bases we use to surf the internet and put lot of bookmarks and after that when we again went it for a bookmark, we trouble a lot there Keeping this in mind we created a AI application, so it can manage all the bookmarks and put in separately in the different tags. In this way it becomes super easy for the user to quickly jump on the what he/she wants to see Every user who surfs the internet on daily bases can use it very easily ThankYou

Imagine Art

Imagine Art is an inspiration hub for all aspiring & existing artists. Using Co:Here's API playground, Imagine Art can generate a set of images that gives artists a starting point to their blank canvas. Type simple or complex English sentences, and Imagine Art will use AI to provide you similar prompts, which you can then select and land on the final set of pictures

Simplified Universe

Often we come across documents, or text, in general which contain words and phrases that might be difficult for a normal guy to understand. For example - legal documents (docs published by Public Administration, contracts), and medical reports. We have developed an end to end web application, that takes such texts and tries to provide a lexically and syntactically simpler version of the provided text. These changes can be of the form - replacing complex words and phrases, breaking sentences, etc. We finetuned the small and medium models upon publically available datasets - Asset Data, MedWiki, and SimPA Corpus. We made use of the 'generate' API to get the simplified text. Also we used the 'embed' API to filter out noisy examples from the data, and also to choose the best possible response from the mulitple generations.

DocuCenter

DocuCenter untagles all your document related frustrations. A powerful yet simple, interactive system for storing, tracking, sharing and managing documents. Different document types have different needs. For example: all your documents related to immigration have to be time-tracked. DocuCenter extracts information from all the uploaded documents and embeds them into its renderable knowledge graph to provide assistance such as smart notifications, AI powered communication assist and prompt question answering.

ML Queries App

The app lets you check if the queries you have have already been asked on MachineLearning subreddit! It shows 10 related matches to your query along with the relatedness distance, url and date when it was asked. [Cohere was used to generate about 17k embeddings from the queries mined on reddit. It is also used real time to generate the embeddings for the user's query to find related matches. (Though the ~17k embeddings took a lot of time to collect due to rate limit owing to free tier, the api had no downtime and embeddings were seamlessly stored over a period of 6 hours without throwing any exception.) ]

Get Help

A telegram bot that tries to detect your disease based on your symptoms.

whatisit.app

Simple to use, cross-platform application, which helps to * Remember the stories from the childhood which name u can't remember * Searching the word, which describes something, that u can explain * Describe the movie sense to increase the phrase composition skill if you learning English

Mind-ai-full

It's a simple web application which allows you to choose which types of emails are important for you. Based on that it will display only those that could be potentially important for you in the future. Furthermore it only shows you the summaries.

WordGsr

WordGsr is a cooperative party minigame played on a single device within the Python console. It can be played in either of two modes - the telephone mode, or the point mode. Players take turns inputting words into the program, which then using the Cohere Generate api, outputs a realistic sentence for other players to guess the original words. The telephone version has the players pass their guesses to the next sentence, with the goal of keeping as close to the original words as possible. The point version is similar, except the players choose a new set of words each turn, and other players can earn points by guessing the words correctly. The program begins within the Main.py file, where the player can pick their desired mode. Then, based on their choice, they are directed to either the pointsVer.py or telephoneVer.py file, which runs the game. Players also get to choose their difficulty level, which changes the complexity of the sentence produced by the AI. This is achieved through changing the complexity the example dataset itself used to train it, located in the Prompt.txt files.