Cohere

Cohere provides access to advanced Large Language Models and NLP tools through one easy-to-use API. They provide multiple models such as Generate and Classify

Tutorials

Boilerplates

Cohere is a company that specializes in artificial intelligence. Their mission is to help businesses harness the power of AI to improve their operations. Cohere offers a suite of AI-powered tools that can be used to automate tasks, improve customer service, and boost sales. Their products are designed to be easy to use and integrate with existing business systems. The Cohere natural language processing platform makes it easier for developers to build natural language processing models into applications and helps companies infuse natural language processing capabilities into their business using tools like chatbots, without requiring AI expertise of their own. Useful links: Cohere website, Co:here platform, Co:here Docs

Cohere Libraries

Discover Co:here API SDK's to help you get started

Cohere Node

This package provides functionality developed to simplify interfacing with the cohere.ai natural language API. This SDK provides support for both TypeScript and JavaScript Node.js projects.

JS/TS Cohere Go

This package provides functionality developed to simplify interfacing with the cohere.ai natural language API in Go.

Go Cohere Python

This package provides functionality developed to simplify interfacing with the Cohere API in Python 3.

Python Cohere Ruby

This package provides functionality developed to simplify interfacing with the cohere.ai NLP API in Ruby.

Ruby

Cohere Boilerplates

Boilerplates to help you get started

Nextjs Boilerplate

Article summarizer Boilerplate for Nextjs, Co:here, TailwindCSS, Vercel.

Javascript Streamlit Boilerplate

Hashtag generator Boilerplate for Co:here, Streamlit, Streamlit Cloud.

Python Replit Boilerplate

Generate an email on command with Co:here and Replit.

Python

Cohere Sandbox

Sandbox is a collection of experimental, open-source GitHub repositories that make building applications using large language models fast and easy with Cohere

Sandbox Introduction Blog Post

Sandbox aims to help build and strengthen language AI communities while enabling contributors to build more robust applications and services faster than ever.

Sandbox Conversant - Github repository

Conversational AI tooling & personas built on Cohere's LLMs

Python Sandbox Conversant - PyPi repository

Conversational AI tooling & personas built on Cohere's LLMs

Python Sandbox Conversant Streamlit Demo

Conversational Personas using Cohere and Streamlit

Python

Cohere Semantic Search

Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search.

Introductory guide

Article for building a simple semantic search engine.

Collab Notebook - Basic Semantic Search

In this notebook, we'll build a simple semantic search engine

Python Semantic Search with Cohere Sandbox

A demonstration of how a semantic search engine can be quickly built using Cohere's platform.

Python

Cohere Multilingual Model

Cohere's Multilingual Text Understanding Model is Now Available

Sandbox Multilingual

Github Repo for the multilingual sandbox from Cohere

Quick Youtube Tutorial

Giving computers many human languages with Cohere's multilingual embeddings

Demo

Check out this multilingual movies search and recommendation demo that is built with Streamlit and Coheres' multilingual model

Cohere Tutorials

Explore the coding tutorials and how-to guides available on our website to help you get started and learn to build with Cohere artificial intelligence technology

Cohere tutorial: Text embedder with Cohere

We will compare random forest with Cohere Classifier

How to use Cohere tutorial: How to create a Cohere powered summarization Chrome Extension

Find out how to create AI powered Chrome extension for you medium.com posts!

Cohere tutorial: Q&A chatbot with heart from Cohere

In this tutorial, we create a Q&A chatbot.

Cohere tutorial: Build a product description generator API with Cohere

In this tutorial you will build your own product description generator API with cohere.

Cohere tutorial: Text classifier with Cohere

This tutorial is describing how you can build a text classifier with Cohere

Cohere tutorial: How to use Cohere for content moderation

In this tutorial we will show you how to use Cohere to moderate content in your app

How to use Cohere: Cohere Playground

How to use Cohere's Playground

Cohere tutorial: Entity extraction

In this tutorial we will show you how to use Cohere AI to extract entities from text.

Chat/Conversation Summarizer with Cohere

How to create a chat summarizer with Cohere and Python

Cohere tutorial: How to create a dog's breed recognition API

In this tutorial you will find out how to combine the power of Cohere AI and FastAPI for creating a dog's breed recognition API.

Semantic Search with Cohere

How to create a semantic search engine with Cohere

Cohere tutorial: Answering questions based on given data

In this tutorial we will show you how to use Cohere AI to answer questions based on given data.

Cohere tutorial: How to use Cohere to retrieve data from a table

In this tutorial, we'll build a FastAPI application that uses Cohere to retrieve data from a table.

Cohere tutorial: How to use Coheres' new multilingual model to answer questions about your business more efficiently

In this tutorial we will show you how to use Coheres' new multilingual model to answer questions about your business more efficiently

Cohere tutorial: How to use Cohere to get in depth insights from customer reviews

In this tutorial, we'll use sentiment analysis to get in depth insights from customer reviews with the help of Cohere Generate

Qdrant Tutorial: Text Similarity Search

In this tutorial we will utilize Qdrant Vector Database to store Embeddings from Cohere's model and search using cosine similarity.

Cohere Hackathon projects

Solutions built with Cohere that have been created during our hackathons by the members of our community

Multilingual Book

The Multilingual Book-based Information Retrieval System is an AI-powered tool that allows users to extract relevant information from a given book in any language. The system works by first identifying the user's input keywords or sentences related to a specific topic. It then uses natural language processing and machine learning algorithms to search the relevant book for information on the given topic. The system also provides a translation feature that allows the user to translate the retrieved information and references into any language of their choice. This feature makes it easier for users who are not familiar with the language of the book to access the information they need. The Multilingual Book-based Information Retrieval System is beneficial for researchers, students, and anyone who needs to access information from books in different languages. With this system, users can easily retrieve relevant information from books without having to spend hours searching through pages. The system is designed to provide accurate and reliable information in a user-friendly interface, making it a valuable tool for anyone seeking knowledge on different topics.

Maverick AI

Maverick REACT offers artificial intelligence integration for emergency situations. Our service uses AI with the necessary event information provided by government officials and acts as an assistant to provide key protocols and information to citizens. The AI service is accessed via SMS or web portal, offering a solution without internet. How does our service work? When an emergency situation occurs, such as a flood, fire or earthquake, our service sends an SMS message or makes a voice call to numbers registered in a database or the citizen can contact a number provided by the authorities. The message or call contains information about the type and severity of the emergency, preventive measures that should be taken and resources available in the area. The user can respond to the message or call with specific questions about their personal situation or request additional help. Our service uses AI algorithms to process responses and offer personalized and updated advice. REACT has several advantages over traditional emergency alert and response systems. Firstly, it does not depend on the internet, which means it can function even when there are power outages or problems with mobile networks. Secondly, REACT service is interactive and adaptable to the individual needs of each user. Thirdly, it uses reliable and verified sources of information provided by the government or other authorized organizations. And finally REACT is fast and efficient in sending and receiving large-scale messages or calls. Our goal is to contribute to creating a safer and more resilient world in the face of emergency situations through innovative and intelligent use of technology. We believe that our service can save lives and reduce suffering caused by disasters. If you want to know more about our service or how to register for it, contact us. We are Maverick AI.

Twitacker

In a post truth world, the ability to quickly fact check any information against the available official public records and statements is a superpower that can hold public figures accountable, make gaslighting the public during election time more difficult and save democracies. It also cuts down distractions and time required to do research on public figures and reduces the entry barrier for political journalism and fact checking. Twitter is the internet’s town house and most of the public figures use it so the ability to fact check them in real time is critical. We aim to empower the general public with tools that make the world a better place by keeping people in power accountable.

Dripper News

a personalized news feed focused on the tech industry, powered by artificial intelligence (AI). Our news aggregator is specifically designed for busy CEOs, providing them with the latest and most relevant news in the tech sector. Through the use of AI, our platform curates and filters news articles from reputable sources, presenting only the most important and timely news stories to our users. This allows CEOs to stay informed on the latest trends, industry developments, and competitor updates in a quick and efficient manner. Additionally, our news aggregator provides a personalized experience for each user. By analyzing the user's reading habits and interests, our AI technology tailors the news feed to provide a custom selection of articles that are most relevant to their business and industry. Overall, our personalized news feed offers a comprehensive solution for CEOs who want to stay informed on the latest developments in the tech industry without the hassle of sorting through countless news sources. With our platform, CEOs can stay ahead of the curve and make informed decisions for their companies.

Legal ease

Legal-ease is a web-app hosting a suite of tools useful for simplifying legal documents for common use. In this way, Legal-ease offers tools to perform: 1. QnA over legal documents 2. Document summarization 3. Cross-lingual search This is great for people who dont have the time, are not native speakers of english or otherwise struggle with legal documents on a day to day basis. It is also useful for lawyers and researchers with access to a large collection of legal documents. Summarization is handled via cohere, QnA is handled via langchain, cohere's embeddings and LLM, and qdrant's excellent vector DB search.

Scoper

Scoper is a multilingual semantic AI tool for searching across internal documents. Although you may be able to search for exact keywords, we require an intelligent embedding of strings (in this case we use Cohere’s endpoint) to have similarity based on meaning, not just text. For example, the phrase “save cost by switching” may not show up directly in any internal documents, but there are mentions in several documents about benefits of switching programming languages or models of work.Additional features are documentation classification, where we label all internal documents both as the type of document (wiki, project proposal, or memo) but also with their completion status, i.e. finished or unfinished. We also support document generation, since in order to keep moving a company quickly, having AI help draft proposals or wiki’s goes a long way.

Joan Holloway

Currently, the most popular corporate knowledge management system is Confluence by Alatasian. It is known for a lack of search capabilities and makes most corporate knowledge inaccessible, especially in fast-growing companies where regular structure and responsibilities change. Some independent vendors fill this gap by offering carefully tuned solar-based search engines for Confluence, but not real semantic search. Confluence is a proprietary cloud-based solution, and it would be difficult to MVP a search extension in a hackathon. The most advanced open-source alternative is wiki.js, which already supports external search engines. So the current goal is to implement an external search engine for wiki.js using Cohere's LLM-powered Multilingual Text Understanding model and Qdrant's vector search engine. At the second stage of the project (most likely outside the hackathon scope), we plan to add the capability to upload and index videos in our knowledge management system. Recordings of presentations and meetings are the richest source of knowledge, but they were left outside knowledge management due to technical difficulties. Simple transcription and semantic search of that content could significantly boost corporate knowledge accessibility.

hexium

Introducing a unique and immersive gaming experience that takes players on a hero's journey through the many aspects of human life. This game is designed to connect players with each other through a stunning astrological geometric universe, inspired by the 64 hexagrams of the i-ching. Players will be able to explore twelve distinct areas of human life, including health and fitness, intellectual pursuits, emotional well-being, character development, spirituality, love and relationships, parenting, social life, financial stability, career advancement, quality of life, and life vision. By playing this game, players will have the opportunity to discover their compatibility with others and make meaningful connections with like-minded individuals. The game's unique algorithm calculates compatibility based on the 64 hexagrams, providing players with a personalized and accurate understanding of their compatibility with others. Whether you are seeking a partner, a friend, or simply looking to connect with others who share your interests and passions, this game offers a fun and engaging way to do so. Join us on this epic journey through the human experience and discover what lies ahead!

Semantic Press

Problem: Government agencies such as the Department of Justice regularly release press statements to inform the public about their activities and decisions. These press releases contain important information that can be difficult to find and understand due to their volume and complexity. Currently, there is no easy way for individuals to search through these press releases and find relevant information. Traditional keyword-based search engines often return irrelevant or outdated results, making it difficult for users to find what they’re looking for. Solution: To address this problem, we propose the development of an app that provides semantic search results for queries on press releases from the government of justice. This app will use advanced natural language processing techniques to understand the meaning behind user queries and provide accurate and relevant search results. The major idea is that implementing the semantic search on public press releases will lead to a faster access to relevant information, Improved accuracy of the information, Increased transparency, and better data analysis. It can reduce the risk of misinformation by providing more accurate and relevant information to journalists and the public.

Language Matchmaker

Language Matchmaker is an app that aims to help language learners find conversation partners who share their interests and language proficiency levels. The app will use Cohere's multilingual semantic search technology to analyze user profiles and match them with other users who have similar interests and language skills. Users will create a profile on the app where they can specify their native language, target language(s), and interests. The app will then use Cohere's technology to identify commonalities between users and present them with a list of potential conversation partners. The Qdrant technology will be used to rank the matches based on similarity and provide recommendations for the best matches. Once a match is made, users can schedule a virtual conversation through the app and practice their language skills with their partner. The app will also provide conversation prompts based on the users' interests to facilitate the conversation. We highlighted the unique features of the app, such as the use of Cohere and Qdrant technologies to match users based on shared interests and language proficiency, and the ability to schedule virtual conversations through the app. You can also discuss the potential use cases of the app, such as for language learning, cultural exchange, or making new friends from different parts of the world. For the app development, we will use programming languages like Python that integrates the Cohere and Qdrant APIs.

LegalFruit

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.

EaseFind

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.

MedAI

"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.

Heuristic AI

Heuristic AI brings browsing your Slack chat histories into a new dimension. Fueled by Qdrant vector search engine and the Generative model of Cohere, Heuristic.ai extracts the context from your question and matches it with your chat messages to elaborate the answer. Forget keywords and chats scrolling. We give you the answer and the source message in seconds! Vision: to enable people to find answers to any questions in their digital experience. Mission: to bring browsing chat histories to a new dimension How it works: 1. The user write normal query with the structure we have “hai, setup” or “hai, question” 2. Ngrok forward queries from slack_api to the Amazon server 3. Here, we evaluate the query to take action: - Setup from the sentence <hai, setup> or a sentence which contains hai and setup - Search: from the sentence that contains only the word hai - None, if the message sent in slack is a normal message 4. here we have two scenarios: - in the case of the setup action, we retrieve all the messages from all the channels, then encode them using co.embed prepare to be ready to store in Qdrant vector database - in the case of the search action, we encode the user query to retrieve the first 5 relevant messages from the conversations, then extract the answer to the user query from the first message retrieved using co.generate 5. Qdrant is the vectors search engine that allows us to store our vectors and to search on them. 6. Then lastly, the extracted answer is sent to the user.

LawSuite

Imagine a world where legal consultations are not only accurate and efficient but also intuitive and accessible. A world where the power of cutting-edge technology meets the intricacies of law to provide you with the best legal counsel, right at your fingertips. Welcome to the world of Cohere and Qdrant - a revolutionary website that leverages the power of semantic search to provide unparalleled legal consultation services. Cohere and Qdrant is the embodiment of modern legal expertise. Built with a focus on providing customized and highly efficient solutions, this website uses state-of-the-art algorithms to understand and analyze complex legal matters. Through its innovative approach, Cohere and Qdrant brings together the best of legal expertise and technology to help individuals and businesses navigate the intricacies of law.

Logos

We created Logos with the goal of enhancing people’s experiences in their relationship with their sources of knowledge: books and articles they read, podcasts and songs they listen, videos they watch and spaces where they discuss. Logos achieves this with the use of cutting-edge technologies to index and search for relevant content, assemble and interpret it in a semantic meaningful way. The multi-language feature of our solution grants super powers to our users, and our specially created expert agents are capable of answering very complex questions. We believe Logos will help heighten people’s passion for knowledge and thought, and that it will be a great tool for students, professionals and curious people alike!

Semantic recommendations

Recommendations cold-start problem is not actually a problem, if you leverage content and item metadata to build your recommendations. To showcase this idea we build a movie recommender, so you can visually see the difference between collaborative-filtering and content recommendations. We made two PRs to an existing open-source project Metarank: * support semantic recommendations with cohere-ai and sentence-transformers embeddings * use qdrant as a vector search engine to quickly perform vector similarity search With these two PRs merged building such a recommender is just a matter of a few lines of YAML code. But the semantic-similarity approach is not only about movies, but can be applied more generically in traditional places like e-commerce. For example, in fashion with high inventory churn, being able to recommend something for new clothes having zero feedback is really valuable.

R paperPAL

The dataset we used is Arxiv’s Open access dataset and we used 15000 records from it which contains ML and AI-oriented papers. We utilized Abstract to create embeddings for each paper. Cohere’s small model was used for creating embeddings which produced 1024 embeddings for each records. Then the 15000 X 1024 Vectors are uploaded to the created collections in qdrant. Finally, the embeddings are generated for the abstract of chosen articles or the given prompt, and the Qdrant searches for similar texts in the collection and outputs the indices of it. The distance metric used to measure the similar vectors is Cosine distance. Besides the recommendation for articles we provided features such as research paper summarization and Translating contents of given paragraphs from English to 8 different languages such as Tamil, Nepali, Indonesia, Thai, Spanish, Russian , Turkish, and French. The model we used for Language Translation is MBART Large-50-one-to-many for multilingual machine translation. The Text Summarization part is done using cohere’s API

mindmate

In today's fast-paced work environment, information overload can make it challenging to find the information you need quickly. Research shows that knowledge workers spend 1 to 3 hours per day looking for information and documents. A big chunk of that time is spent understanding their organization's internal knowledge base. This process can be time-consuming, frustrating, and inefficient. That's why I created mindmate, an AI-powered assistant that helps users make sense of their company's internal knowledge base. With mindmate, users can easily search their company's internal knowledge base and receive answers to their questions in plain English using a simple chatbot interface. I built a proof of concept using GitLab's employee handbook during the hackathon. I created a simple yet powerful tool that allows users to ask questions and receive natural language answers by processing the handbook's 3,000 pages, creating embeddings with Cohere, storing them with Qdrant, and leveraging Cohere's text generation capabilities. mindmate is easy to use and provides quick access to information related to a variety of topics, including company policies, benefits, and more. By tailoring search results to each user's specific needs, mindmate helps knowledge workers save time and stay focused on their core responsibilities.

I AM AI personalized ChatGPT

I-AM-AI is your personalized chatbot companion that can be integrated with Telegram and Discord. Trained exclusively on your chosen content and offering secure and private access, I-AM-AI provides tailored conversations, personalized insights, and relevant recommendations based on your interests and preferences. With its powerful AI capabilities, I-AM-AI makes it easy to access and organize information, learn new things, and stay engaged with the world around you. I-AM-AI now offers integration with Telegram and Discord chatbots, making it even more accessible and convenient. Whether on the go or at your desk, you can access I-AM-AI from your favorite messaging platform and get instant access to the information you need. With the ability to pre-train with your latest data, I-AM-AI can create a customized knowledge base optimized for your specific needs and provide fast and accurate answers to your queries. From onboarding to customer support, I-AM-AI can be used for various business cases, including documentation updates, research article summaries, product recommendations, marketing campaign assistance, second brain, financial advice, employee training, HR support, sales support, news overview, and market overview. Experience the power of I-AM-AI today and see how it can transform how you work and learn.

Psy Q Bot

Psy-Q Bot is an AI-powered chatbot that provides psychology-related information and resources to users. The development of the bot was guided by the goals of creating a product that is both useful and easy to use, and leveraging the capabilities of Cohere's API as much as possible: we use both generate and embed. The system is composed of a web client built using React and a server that provides centralized access to Cohere. The key component of the application is Cohere, which is used to analyze user questions, match them with relevant sources in the dataset, and generate responses. The Embed endpoint of Cohere is utilized to analyze and compare questions, while the Generate endpoint is used to generate responses and improve the custom Cohere model. The dataset used by the bot has also been partially augmented using Cohere's Generate endpoint, allowing for continuous training and refinement of the model.

CinematixAI

With an automatic script breakdown interface and a script AI assistant, our platform empowers producers and writers throughout the pre-production workflow, enabling them to focus on creating amazing content. Script breakdown is an important part of the filmmaking pre-production stages for scheduling and budgeting. We will automatically create the script breakdown with all the categories you need: Cast, Props, Locations, Makeup, etc. Taking to few producers from Netflix and huge production houses, we realised traditional script breakdown software is an absolute chore, and don't even get us started on the script breakdown sheet templates that line producers and assistant directors have to print and fill out by hand. Breaking down a screenplay doesn't have to be a bummer. Script breakdown, understanding, and scheduling should become more accessible, accurate, and intuitive with cutting-edge AI solutions, saving numerous man-hours and introducing structure and best practices to the pre-production workflow.

Smart Study

Smart Study is a cutting-edge web application designed for students, teachers, and anyone looking to enhance their learning experience. The app provides a unique and convenient way to test and evaluate your understanding of a specific topic or YouTube video. By generating multiple choice quizzes, users can quickly and easily assess their knowledge, retain information better, and identify areas that require further study. The app's interface is user-friendly and intuitive, making it easy for anyone to get started. To generate a quiz, simply input the text content or provide a link to the desired YouTube video. Smart Study will then extract the most important information and create a comprehensive quiz, complete with multiple choice questions and answers. This makes studying for exams, completing homework assignments, or just keeping your knowledge up-to-date more efficient and enjoyable. One of the most significant benefits of Smart Study is its ability to personalize the learning experience for each user. With the app's adaptive learning technology, the difficulty level of the quizzes adjusts based on the user's performance, ensuring that the user is always challenged and engaged. As a result, users can make faster progress and retain information better.

AI Job Interview Assistant

This project aims to provide users with a realistic interview experience, thereby boosting their confidence. By simulating real-life interview scenarios, users will get the opportunity to practice and perfect their interview skills, including answering questions, body language, and overall demeanor. Additionally, the project will also help users overcome their anxiety and nervousness, allowing them to perform at their best during actual interviews. This project will be beneficial for job seekers, students, and anyone looking to improve their interview skills. With a focus on helping users achieve their goals, this project will provide an engaging and supportive environment that will encourage users to achieve their full potential. By the end of the project, users will leave with a renewed sense of confidence and the skills they need to succeed in any interview setting.

Project Peace

Project Peace is a Multilingual Text Detoxifier. It is an innovative solution to identify and neutralize toxic or harmful language in written text. It utilizes advanced AI algorithms powered by Cohere’s multilingual models to understand and analyze text across multiple languages, and flag potentially toxic language, including the ability to convert that toxic language into neutral and non-toxic one. Project Peace’s ability to process text in multiple languages, allows it to address the problem of toxic language on a global scale. Project Peace can be integrated into online platforms, such as social media websites, online forums, and online communities, to help prevent the spread of toxic language and promote a safer online environment. It can be used by businesses and organizations to monitor and control the language used on their website and even in their customer care services. It can also be used by governments and public institutions to monitor and control the language used in online communication channels and to promote social harmony and inclusion. It can be used by educators and schools to help prevent bullying and toxic language in online learning environments, ensuring that students have a safe and supportive learning environment. private individuals as well who want to promote a safer and more inclusive online environment, or who want to ensure that the language they use online is respectful and non-toxic. Project Peace has an appealing future by its scalability and customization. By integrating it with the existing social platforms, it can be made accessible to a wide range of users. Moreover, it has the potential to become an industry standard for detecting and detoxifying toxic texts. The goal of the project remains to create a safer online community by reducing the spread of hate speech, cyberbullying, and other forms of harmful language.

notifAI

notifAI, a note-taking assistant powered by Artificial Intelligence, is a practical tool for students, teachers, or anyone that needs to read and understand large amounts of text. It can be challenging to process complex paragraphs of information, which is why we made notifAI. Using it is as simple as opening the app and taking a picture of the text, and the user will have access to an easy-to-read summary and main ideas. This is made possible with Cohere's large language model for summarizations as well as the Google Cloud Vision API to translate images to text. With this tool, we hope to help others understand the world around them efficiently and effectively.

Ask Quran

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/

FINXAI

Usually firms have excess liquidity from their operations and people have savings that want to invest so that they can protect themselves from inflation or to generate a passive income. The problem is that in order to invest in the financial markets they either have to hire an specialist or they need to manage their investments themselves and this can expose them to high risks since they aren't usually experts on the field. There have been cases of companies even going into bankruptcy for preciselly investing in the financial markets with poor management or little understanding of the complexity of the markets. Here's where we come in, our solution powered by AI enalbes firms and people to invest in the financial markets without having to hire an expensive investment manager or having to learn themselves. It is a virtual portfolio manager. First the user has to provide financial information and a news or article related to him, with this an ML classifier using the Cohere API determines the risk profile of the user. With this information the platform generates a tailored portfolio, out of a selected financial asset universe specific to each risk profile. The available assets vary between different types of US stocks, FOREX pairs and even cryptocurrencies for the more risk taker profiles. Once the portfolio has been created it reports an overview of its composition as well as a backtest of its performance on the market. At this point the user has the option to decide whether to pursue a passive portfolio management strategy or an active one with just the click of a button. If he prefers a passive strategy the platform will take positions for a classic buy and hold strategy of the selected assets. If, in contrast, the user selects the use of trading bots then he will opt for an active portfolio management and trained DL bots will be buying and selling the assets at convenience for a better portfolio performance.

OutTHERe

We track emotional states within text, then we provide a recommendation or simply show an ad that is suitable with what the user really feels. This project is using many machine learning algorithms to train a classifier. We deployed cohere embedding system API. The backend service was built using python django framework. The motivation behind this work is that a big share of people spends a long time online, and although they express their negative emotions freely, they don’t actively take action to improve them. Even when they have positive emotions, they don’t make use of their excitement and energy. On the other hand, many local activities and services are available out there, but not everyone knows about them. Besides, the truth is that a single type of activity isn’t suitable for all. So, a strategy must be followed to recommend a specific activity to people with a specific type of emotions. Many companies and organizations will be interested in advertising, especially the ones that offer life coaching services, trip organizers, fitness and massage centers, comedy shows, therapists, and many other activity organizers and human well-being services.

Docs Buddy

Docs Buddy is a cutting-edge medical semantic search engine that combines the power of coherence embeddings with the reach of Google search. It offers healthcare professionals and pharmaceutical associates an efficient way to access relevant information about symptoms, medications, and treatment procedures. By simply inputting symptoms, users can retrieve a comprehensive list of documents that match their search criteria. Additionally, Docs Buddy also allows users to search for specific medications, providing images, prices, and even the option to buy online. Say goodbye to endless scrolling and hello to fast and accurate results with Docs Buddy. 🚀 Upgrade your medical search game today! 💻

CYCLOPS

Cyclops is a smart assistant device that takes input from an inbuilt camera and microphone and gives response through inbuilt speaker it understands and responds to human commands using both visual and audio cues. The camera captures live video while the microphone picks up spoken commands. By integrating both visual and audio input, Cyclops smart assistant is able to provide a more intuitive and natural user experience. The device uses facial recognition to identify the user and then use voice commands to perform specific actions. This is useful for tasks like home automation, where visual instructions are necessary. Cyclops improve its understanding of the user's commands over time, continually learning from its interactions, provides a unique and versatile user experience, offering a new level of convenience and accessibility. We have built cyclops using an IP camera and used raspberry pi model zero w as its computing unit, video images captured by the inbuilt camera are converted into a string by means of a TensorFlow image processing code while voice to is turned into a string via speech to text this is then sent to cohere for processing via the API

Flash Learning

Flash Learning is a flashcard application that helps the user to retain any material, like terms, definitions, laws, etc, through active recall. Unlike the classic flashcards, where the process of evaluation is ambiguous because the users say their answers verbally, our application offers the opportunity to get AI-based self-assessment scores that quantify their level of knowledge of the studied card. These scores can serve as indicators on how well the users know the material and if they are ready to learn something else or end their learning session. It can be used for both formal and informal education, where the instructor can prepare public flashcards with the curriculum needed for tests and exams. The Leitner System - a widely used method of efficiently using flashcards - is now implemented digitally in our application. It is a simple implementation of the principle of spaced repetition, where cards are reviewed at increasing intervals to enhance the process of learning and store the information in the long-term memory. The users can choose to know the card at the GOOD (>80%) or EXCELLENT (>90%) level based on their necessity. If their score is not excellent, they will even get explanations with GenerativeAI to help them improve their score.

News Media Monitoring

News monitoring news allow observing and collecting key customer data directly from mentions and discussions about your company (or your client’s company). These discussions take place in social media comments, product review websites, blogs, podcasts, discussion forums, and media websites.

Search Light

A semantic search system that enables you to search your codebase using natural language. Project repo https://github.com/silvererudite/code_search

Podsearch

Podcasts are an excellent source of knowledge. But they can be too long and hard to pay attention to it the entire time. What if there is a more intuitive way to search for podcasts and also for info within podcasts? This is where our product comes into play. Key highlights 1. Searching for podcasts suited to your taste 2. Searching for answers within a podcast itself by asking it queries and without listening 3. Marking exactly where the answer is and summarising it. 4. Telling user what queries this podcast answers Major Uplifts: 1. Generating queries for dialogues in transcript using the prompt - "Generate 5 questions for the following passage {passage}" 2. Training a classifier using cohere api using the generated queries and dialogues 3. Highly scalable architecture 4. Podcast is just an example. Most documentation (python libraries, eth doc) have only keyword search. It is possible to scrape the documentation and build an index for a search engine using our architecture easily.

Project Eval

Eval aims to address the problem of subjectively evaluating test answers. Traditionally, this task has been carried out manually by human graders, which can be time-consuming and prone to bias. To address this issue, the project utilizes Cohere powered APIs to automate the evaluation process. The use of Cohere APIs allows for the integration of advanced natural language processing techniques, enabling the system to accurately understand and analyze the content of test answers. The custom model built upon these APIs then scores the answers based on suitable metrics, which can be tailored to the specific requirements of the test or assessment. One potential application of this technology is in the field of education, where it could be used to grade assignments or exams in a more efficient and unbiased manner. It could also be utilized in professional settings for evaluating job applications or performance evaluations. In addition to increasing efficiency and reducing bias, the use of automated evaluation techniques has the potential to provide more consistent and reliable scoring. This can help to ensure that test-takers receive fair and accurate assessments of their knowledge and skills. The model for the same was evaluated based on 4 major metrics: - Semantic Search: this is the primary scoring strategy of Eval. It is used to semantically understand the answer given and evaluate based on content rather than simply scoring based on textual similarities. Cohere Embed was used to generate embeddings for 5 suggested answers for the question and the answer to be checked. Then we find the distance from the nearest neighbor out of the 5 suggestions and the answer. This distance is used to grade the answer. - Duplication Check: partially correct answers with duplication of text tended to get higher similarity scores compared to the ones without duplication. To stop students from using this exploit to gain extra marks, a duplication checker was implemented based on Jaccard-Similarity between sentences within the answer. - Grammar Check: this strategy aims to check the grammar of the answer and assign a score based on the number of grammatical errors. We used Cohere Generate endpoint to generate a grammatically correct version of the answer, then check for cosine similarity of the generated version with original version to check if the original version was grammatically correct. - Toxicity Check: this aims to detect for toxic content in the answer and penalize an answer if it is toxic. We trained a custom classification model on Cohere using the Social Media Toxicity Dataset by SurgeAI which gave a 98% precision on the test split. We also implemented a Custom Checks which allows users to give different weights to each of the three different metrics based on how important they are for the evaluation of the answer. This allows for a more personalized evaluation of the answer. We built our custom model into a Flask-based REST API server deployed on Replit to streamline usage and allow people to access the full-functionality of the model. We also built a highly interactive UI that allows for users to easily interact with the API and evaluate their answers as well as submit questions.

YTBrief

AI solution that will make your YouTube experience easier by getting answers from videos in seconds.

Genesis

It is an search app to look up meaning, hope, faith, etc in the Bible based on the user situation (happiness, sadness, depression, motivation, ...)

Libris

Libris is a tool for making knowledge accessible to all. You just describe an important topic that you want to investigate and Libris will bring to light the relevant excerpts from the most brilliant minds of all time. You can also filter by specific authors and titles to search in a specific subset of the bibliography that is specially relevant to your research. The magical thing about Libris is that it doesn't matter the language you speak or the language the text was written on, the experience is seamless: you write the concept in your own language and get results in your own language. You can even access the original work for further reference!

ezInvest

ezInvest is an application, that helps user in making a decision to get a Stock based on News headlines Sentiments. Investment in Stocks is very Risky. Many Cases like Political, Inflation, economics, investors behavior Impacts Stock Prices. Identifying Stocks to Invest is Challenging. Existing Applications are very Expensive. ezInvest will address all these issues.

CoHotel

Recommend and book the best matching hotel for travelers to the US based on Semantically Searching Hotel Reviews in the city they are traveling to. Integrates with MyCroft AI to provide text and voice conversational interfaces with feedback loops that increase the accuracy of choice.

Citation needed

Citation Needed is a fact verification assistant that allows you to find relevant sources and citations to a given claim. Specifically, we begin with a information database, where each claim or fact is substantiated with a reference. From there, a query claim can be used with semantic search to find relevant sentences, and hence the citations of them. This application can be useful to a variety of audiences, from researchers looking to more efficiently browse through papers, teachers looking to better fact check essays, to even students looking to find more reading material. We believe that Citation Needed can be a powerful tool if scaled up fully. The current demo has a "Narrow" mode, which only contains topics related to neural networks, and a "General" mode, which scrapes for additional information on the fly, after extracting important subjects through the use of the cohere text generation API.

Found In Translation

Our solution is a smart Slack bot that implements multilingual semantic search and sentiment analysis to facilitate multi-language messaging platforms by improving the chat search function. The bot allows users to search using any language for messages in any language. It also contains a sentiment analysis that gives moderators a sentiment report on the messages of specific users or the chat as a whole.

AlphaBot

Around 42% of start-ups fail due to a lack of market-related knowledge. Our tool helps start-ups or small businesses or even people with great ideas, to understand the market, the market need, and the financial aspect of the industry that they want to venture in.

Semantic search models

Semantic search has been used in daily life applications, especially for the elderly people in speech-recognition, because they are not able to use touch device due to their bad eyesight. Speech recognition is one of the applications of semantic search, and for this there have been used NLP and Preprocessing. As a result, the information is gathered as a cluster, which then embedded into a system. Moreover, the politics has also been analyzed through preprocessing which is branch of NLP that is being used in semantic search.

SemantoTube

Our application aims to make it easier for users to find and access the information they need within the specified video. By using the Cohere API, we are able to perform advanced semantic searches that go beyond simple keyword matching, allowing users to find content relevant to their query in the specified video. Our application utilizes the Cohere API to locate and highlight the specific transcript within the video that matches their search. This makes it easier for users to quickly find and access the information they need, without having to manually search through the entire video. Overall, our application is a game-changer for anyone looking to find and access specific information within YouTube videos. Whether you're a student looking for a specific lecture, a researcher trying to locate a specific quote, or just someone looking for information on a particular topic, our application makes it easier than ever to find what you need.

sundarEduAI

sundarEduAI is an advanced AI learning tool which helps you to find answers of questions of given topics/chapters within few seconds. It also increase accessibility for users who want find solutions quickly. It's quite fast, easy-to-use & accurate. It'll be next-generation of edtech.

Resume AI

Generate personalized resume customized to the job description from a master resume.

whereDoYouMean

When students are revising their work, they face a few hurdles - (1) finding content from a big repository of school materials is time-consuming, especially when the student does not know specifically where to look for the information; (2) results searched online might not be exactly the same as what the school is teaching in terms of scope. Hence, we decided to create an application where teachers can transcribe their videos and generate the embeddings. Users can then ask a question and the top few links to the relevant videos will be given to the student (with the time-stamp attached). The context is also provided to the student so that they can make a judgement as to which video is worth exploring. After which, students can also vote on the relevance of the videos, which will be used to affect future training. We hope that this project can help students learn better with their school's resources.

LinguaScope

LinguaScope helps refugees query official immigation documentation in their own language for urgent questions when they seek asylum. Users can query for childcare, getting heathcare etc. Often it is not possible to get translators and this app will help them navigate resources for critical information.

Coherent Speech

Multilingual voice content generator, powered by co;her(💜+🤖) Generate model. Using the power of whiper, co;her(💜+🤖) and google to create speech content generator. This Application helps in generating audio content using speech as a prompt.

Quick Learners

Our education platform aims to revolutionize the way students learn by providing a unique and personalized learning experience. Through the use of semantic search, we enable users to easily find specific moments in a video that discuss a particular concept, making it easier for them to focus on and understand the material. Additionally, we recommend people with similar interests to connect with each other, fostering a sense of community and collaboration among learners. Lastly, we recommend posts to users based on their interests, ensuring that they are constantly exposed to relevant and engaging content of the user with same interests.

AI Generation

In my Project you can get blogpost content for posting you blogs and you can have the trending hashtags and you can also generate mails

Cohire

Cohire is an AI powered job portal that allows top employers to find the best talents to hire. Contrary to AI taking over our jobs, this project tries to take a different narrative where AI will help us find jobs, not replace us.

Cofinder

Built by the Community, for the Community A semantic search tool enabling Cohere users to find relevant content in one place based on their personal goal. The aim is to breakdown barriers for users such as developers, entrepreneurs and Data Scientists, making it easier to find the information they need, to remove the effort of obtaining information and focus on building amazing applications using Cohere's language models.

ZenWork

ZenWork is an MVP semantic search engine for finding your dream company that shares your vision.

Summit

Summit helps you search and summarise research papers to quickly get an overview of what the research says, saving you time and effort in your quest for knowledge.

Profile Picker

For startups and new businesses, it's hard to get budget, manpower and time to put on resume sorting kind of works. When large number of applicants apply for a job role then it's really difficult to choose the best ones by going through each resume. Our product "Profile Picker" provides businesses ability to sort best resumes based on the job description. Our ML model understands the meaning of the words that are in the resumes and in the job descriptions and it also gives multilingual support which means job description and resumes can be written in different languages.

FilmyCode

We made an app to search movies. We have used cohere and pinecone. Pinecone is being used for vector database and cohere is for embeddings. Our app gets movies based on user inputted query. So if a user search "Alien invasion movie", the app outputs "Edge of tomorrow, etc". It is mostly google like search but for movies and also we are using NLP (cohere-large) model.

Patent Search and Generation

This is a service assists patent attorneys/professionals/applicants/to trawl through patent databases and return the most semantically relevant patents instead of simply returning patents that correspond to lexical/keywords matching AND generate a draft patent application for the inventor. As such, we are able to reduce the time taken for the patent application process,resulting in greater revenues for patent attorneys/professionals while extending the useful patent life ofthe inventor's patent, as well as expedite his patent application process

CoDoctor

CoDoctor is a healthcare assistance tool that helps users find answers to their questions about different health conditions. It uses a multilingual model called Co:here, which uses a semantic search algorithm to search through a dataset and provide accurate and relevant information based on users query. CoDoctor is a useful resource for anyone looking for reliable information about various health conditions and how to manage them. Whether you have a specific question or just want to learn more about different health conditions in general, CoDoctor can help you find the information you need.

Emoji Character Voice Recog

A voice recognition way of creating emojis and special characters. 10 emojis and 13 special characters are initialized. When we speak with initialized key(there names in code), they will be displayed.

Product Description Generator

This web app let the user to fill in the name of product and its properties as keywords. Then, uses cohere-ai (via Node) to generate the description of the product related to the given keywords. Can be used for advertisement, Digital Marketing,& SEO related purposes.

Indic News search engine

🌍 The Solution uses a multilingual semantic model from COhere to 🚀 revolutionize the media and news industry in multilingual markets like India(We have used telugu , hindi ,bengali,English news dataset for this use case), allowing anyone to track 📰 regional news in real-time without the need for translation or understanding of other regional languages. 🙌

Embetter support for Cohere

By supporting Cohere via embetter, we gain scikit-learn compatibility. That means it's easy to: 1. Build active learning pipelines via partial_fit 2. Use it for bulk labelling via bulk 3. Use it to find bad labels via doubtlab 4. Run quick retreival projects via simsity And more!

I Rene

I-Rene Provides CBT for the lovely user using Cohere Conversant AI tool. The NFT is minted for free by the lovely user to track the CBT sessions and be proud of their healing process. It is a free, open-source, specific & effective mental health therapy needed by everyone, anywhere at anytime. The mental health AI chatbot will be developed as a standalone application that users can download and install on their mobile devices or access through a web-based platform. The chatbot will collect and use user feedback to improve its performance and effectiveness. This will involve monitoring user interactions and responses, and using machine learning algorithms to continuously adapt and improve the chatbot's responses and support. The mental health AI chatbot will use sentiment analysis to understand the emotional state of users and react accordingly. For example, if a user is feeling sad or anxious, the chatbot can provide appropriate support and resources to help the user manage their emotions and feelings. The mental health AI chatbot will use entity extraction to provide context-dependent answers and support, rather than just generic responses. This will involve analyzing the user's messages and extracting relevant entities and information, such as the user's goals, concerns, and challenges. The chatbot can then use this information to provide personalized and tailored support. The mental health AI chatbot will be integrated with a decentralized autonomous organization (DAO) and the Metaverse, which is a virtual shared space for communities and organizations. This will enable the chatbot to access and use decentralized resources and data to provide more accurate, relevant, and engaging support to users.

Project chAI

An AI-based chatbot

DiscernAI

DiscernAI is designed to tell you whether a text is misinformation or benign

Code Generation

We first tried to push the dataset to the API and train the model through Python script but couldn't find any documentations on that. So trained on web platform and also provided dataset as prompts.

Virtual AI Patient

"Virtual AI Patient" is a new way dedicated to medical students in order to help them practice on their virtual patient so that they can act more professional and much faster when they face a similar cases in the reality.

Baskful

Baskful is a tool to ease mass asset liquidation. The tool is meant to document items that are to be sold on sites such as Amazon and Ebay. Target users include estate sales, moving companies, and company closings. AI is used to generate copy for the products.

StoryTimeAI

storytime.ai is a innovative, creative application of stable diffusion model (dreamstudio.ai) paired with transformer model(cohere.ai). We leverage powerful features of those models in attempt to solve vital problem: storytelling. It's important to recognize crucial drive of humans to create stories. Now, with help of artificial intelligence our stories could get even more creative and picturesque. Think of all the possible aplications of on-demand stories generator. Batteries (and stylish ilustrations) included! Our solution allows parents to tell brand-new fairy tales to their children. Writters, marketing specialists, basically any proffesion or bussines that deals with creating stories would also benefit from storytime.ai

Regenerate

This application can convert any given text into a visualization. We use LLMs to convert the text into a structured format, and then we use existing visualization libraries to generate infographics from the structured data. Right now in the demo we have built this end to end workflow for Graphs and Timelines but potentially this same tech can be used to power any infographic generation from raw text.

vidSummarizer

vidSummarizer helps you to analyze sentiment & summarize of your audio & video files into simple text within few seconds. It also increase accessibility for users who are deaf.

Zodiac

Zo:diac is an interactive web app that provides unique, personalized horoscopes on-demand to users. It prompts the user for their information, including their birth date, and then returns a horoscope specific to their astrological information. It then allows the user to specify a topic, which zo:diac will then return additional horoscope content for. This process can be repeated, allowing the user to delve deeper into their astrology by generating new content. Both the initial horoscope reading and followup topic-based horoscopes are created using co:here’s generate endpoint, by modifying the input prefix to get the appropriate output. The application is created using react, and hosted by cloudflare. It can be accessed either on a desktop or mobile browser. The app is available at: https://zodiac.tarinyoom.io/

Prompt Tune Generation

Prompt Tune Generation is a prototype model which has ability to generate music using Cohere prompt by understanding the user input. Using Prompt Tune user can play with different set of Genre as input prompt to the model which will generate music notes followed by a short music clip tuned using custom virtual Keyboard which has single octave. This is one step closer diffusion based model where Generative AI plays a vital role in generating contents on its own.

Smarty personal assistant

Smarty quiets the noise around you so you can work peacefully to get things done. It is a personalized task planner that evaluates the importance and urgency of your tasks, then prioritizes and batches them rigorously so you can focus on accomplishing what matters the most.

MongoDb Quesries

To get MongoDB queries (output) as a response to the UI. In other words, MongoDB Query is a way to get the data from the MongoDB database. MongoDB queries provide the simplicity in process of fetching data from the database, it’s similar to SQL queries in SQL Database language.

Osusume

Osusume is a Text-Based Recommendation Engine Powered by Cohere. We are attempting to create a sentiment-based recommendation system using Cohere's Text Embedding and use Text Generation to Explain why it is recommended. We implement Osusume as a framework for people to use their data for more comprehensive implementation for Cohere to be the core processor.

Selfwriter

The solution is based on helping the content writers to easily generate content for their materials. Presently, content creators have to deeply think to write their content which is very time consuming and full of mental stress. This service can tremendously make content creator’s job easy. They can purchase monthly subscription to use tool.

AI chat bot

It’s an AI based chatbot, the customers will put their queries and the system will analyze and then provide the best possible answer according to the query.

MeetMate

We created an application to ease the lives of team leaders, by creating a fast and effortless pipeline to propagate the needed information to their teams, other than storing trivial information in pills for a more concise backtracking experience. Starting with OpenAI Whisper, we generate a transcription of the meeting, then by crossing the work of Cohere Generate and Classify, we managed to extract the needed information, then we organize them into a comfortable format to make sure they are easily findable. We developed a platform designed that recaps full business meetings and provides concise summaries including all the relevant information, a list of tasks mentioned during the meeting and the questions asked, with the relative answers. We thought this information was the most useful for a team's member, so the leader can send them the information fast, in order to fill a lack of knowledge and consequent slowdowns in the team's productivity.

GenerativEDU

Using Cohere's API, we developed a solution to building more dynamic lessons by making lesson content creation easier and more efficient. By creating a more sustainable workflow for teachers, we hope this idea helps to step away from repetitive tasks to maximize class time with unique learning experiences and engage in more meaningful social interactions in the class room. The spark for this project was focused to build a culture of collaboration amongst fellow teachers to design more cross-curricular activities as well as interdisciplinary / multidisciplinary lessons to go beyond the textbook material in order for students to be exposed to more real-world connections for deeper understanding. With the numerous benefits of collaboration amongst peers, working together can exponentially improve educational experiences for teachers similarly to the idea that teachers encourage for their students. This project holds great meaning for us as we are passionate learners ourselves and we believe working together truly produces amazing results. We hope you enjoy it!

AGI Chat

A ChatGPT alternative based on Cohere’s LLM. In the future, intelligent assistants that are smarter than humans will be a part of everyone's lives. They will assist us in our work, help us have fun, and improve our lives overall. And this is already happening with OpenAI's ChatGPT, which received amazing feedback over the last few days. Some even call it a Google killer! However, we want the future of AI assistants to be open and not solely belong to one company. AGI.chat is an initiative to create an open platform for these intelligent assistants. Currently, only Cohere models are supported, but we plan to add customization, switching between different language models, and more. We see AGI.chat as WordPress for AI-powered assistants.

Klurdy XR

This is a demonstration of using NLP to create voice-activated experiences for interacting with products using 3D technologies. It uses NLP to generate product descriptions and intent recognition to create actions from voice. It leverages Web Speech API for text-to-speech and ThreeJS for rendering 3D, currently works on Chrome.

Sincerely Mr Darcy

Travel back in time and meet your oldest new friend - Mr. Darcy! Our project is a unique pen pal experience; customers subscribe to snail mail one letter every week. We scan and process the letter, using a combination of Google Cloud OCR and Cohere's LLM. This allows Cohere to generate a response in the style of Mr. Darcy, from Pride and Prejudice. To complete the 18th century feel, we send the result to HandWrytten, a robot handwriting service that mails cards with authentic-looking handwriting. Our users are able to experience the joys of a true 18th century friendship, as the Mr. Darcy bot evolves to match the customer's whims through a long history of conversation. Even in the digital age, sometimes we all need a brief return to the analog systems of the past.

Twitter Helper

Twitter Helper uses cohere's Api to helps you increase your tweets engagement with your audience by generating hashtags or mention accounts relevant to your tweets. It can also summarize your long tweets to overcome twitter's 280 characters limitation

Image to Emote

The system generates emotions from images, primarily intended for game NPCs so that they would react emotionally to the game environment. The sentiment analysis model is an LSTM-based Dense Neural Network that are fed Word2Vec embeddings. The model was trained using generated data from cohere.ai using the prompt: "I felt <emotion> when I saw <img2text>" System Flow: img2txt -> cohere.ai generator -> text2emote -> LSTM+DenseNN The images are passed to a CLIP Interrogator (BLIP + CLIP (ViT-32-B)) to generate text descriptions. Such text descriptions are elaborated by cohere.ai generator to generate emotional responses using the prompts: "When I saw <img2txt output>, I felt emotions such as"

Cover Letter Generator

Generates the Cover letter Based on prompt with the help of cohere playground API. Feather is AI based model that automatically generates magnificent coverletter within a minute based on prompts feather helps you create eloquent cover letter for your dream.

Virtual Story

Today the number of literature readers is declining worldwide, especially among younger generations, which is affecting overall education level very negatively. This is mostly due to increasing popularity of online social media and gaming activities. However, technological development shouldn't mean for important knowledge to fade away. It is important to digitalize books and cognitive literature and adapt it to modern norms. That why we created Virtual Story app, which is hybrid of books and video games, based on various literature and with help of refined audio-visual and gaming elements, it will make reading process educational like books and engaging, fun and interactive like video games.

Baith al suroor

Baith al suroor بَیتُ الْسرور (house of happiness in Arabic) 🏡🤖 is deeptech solution the power of artificial intelligence to transform your space. With the Cohere language Command model, it can generate descriptions of your desired design, and the Stable Diffusion generative model creates relevant images and transitional videos to bring your vision to your thoughts.

AI Chatbot

Chatbots in the healthcare field are providing patient assistance and care. AI-powered medical assistant can book appointment, monitor a patient health status and perform other time-intensive responsibilities such as inventory, billing and claims management . There are three key limitations:1)Explainability 2)Datarequirement 3)Transferability STATEMENT: To be able to enter a prescription with structured data in a software system, within a comparable time to hand written prescription.IDEA:1) Automation of handwritten and digital prescriptions to reduce entry time. 2) Improve the effectiveness of customer service terms. 3) Reduce the potential for human error. 4)Collect candid and meaningful customer feedback. 5) Guide customers along the path to purchase.6) Build stronger customer relationships Chatbots reside in the most commonly used apps in the form of assistants on various websites where they can converse with the users.With advanced machine learning algorithms and natural language processing methods enabled , these chatbots can create maps linking symptoms and diseases.Chatbots in the industry for medical care ,ask some standard questions and help create a profile based on age ,sex, and medical history . They can record the users history and analyze symptoms based on users inputs.They can also use image and voice processing to record and match symptoms against the database using the gathered information it automatically print handwritten and digital prescriptions. Natural language processingDeep learningContext aware processingIntelligent robotsNeural networksFuzzy logicSupport vector machinesGenetic algorithmsHybrid system

TheraLove

Matching effective mental health therapy that suits you the most, precisely using Cohere Classify artificial intelligence tool. According to TheraLove Bot's assessment of the lovely kind soul's personality, preferences, and current state of mind, TheraLove recommends the appropriate type of mental health therapy. Effective mental health therapy will be recommended after eliminating therapies that are unlikely to suit the unique and lovely individual. While this model had been tested and built at its Alpha Stage by a biomedical engineer, please take the recommendation as a complimentary identification method and seek help from social worker and mental health professionals as everyone’s mental health state varies. Self-diagnosis are not encouraged in any forms. The mission of this project is to bring you the best mental health technology, so please feel free to contact the team as seen on Github for any comments. If in pain or with suicidal attempts, please call your local lifeline from The List of International Suicide Hotlines or any of the lifelines as shown on Github.

Vessel

AI is really easy good at answering our questions, creating videos and even creating voice models. So many ground breaking tools but they can not understand each other? We are building a AI mediator that can answer your questions and knows how to talk to translate it to other AI tools. Our first use case is a text-To-Education model where you can input a question that runs through vessel generate an answer and translate this to other Ai tools. Vessel is backbone of this new age of AI media.

NLPNER

We have tried to build various models such as rnn, gru, lstm and bert transformer for our project and tried to build the app.

vacancy finder

We decided to extract vacancy finder website through image text cohere ai, and then use non-code app in order to solve registration problems of the vacancies.

FAQ Generator for Chatbots

Creating content is tough, time-consuming, and requires experts. This is no different for content for chatbots, e.g., when creating a customer support chatbot that typically needs a knowledge base of FAQs (frequently asked questions) and their answers. This is typically done manually in practice and better tools are needed inside the customer experience (CX) platforms. We aim to provide a one-click tool for non-experts to create FAQs and responses for common use cases such as customer support. *Input*: the user can select relevant offline and online documents, such as a product description webpage, internal posts and emails. *Output*: the tool generates a list of FAQs and their answer in a number of uniform styles, e.g., formal, friendly, fun-with-emojis. *Deploy:* with built-in integrations the generated content can be published on a website or chatbot to serve automated answers. In this hackathon we focus on a proof-of-concept service that is able to extract text from a landing page and generate the company name, sector, and FAQs with appropriate answers. In particular, we demonstrate how zero-shot text generation works i) in a simple web application on Streamlit, and ii) on a web app on our systems (Helvia.io) that also puts the generated content into a running chatbot.

revise

Writing is hard. Editing is harder. Why not make it easier for ourselves? Presenting re:vise, a contextualized writing feedback generation bot. Our bot aims to provide the benefits of a peer review without the peer! Imagine having a friend by your side that you can bounce ideas off of or get constructive criticism whenever you need it. Does that sound appealing? Try re:vise today. Just call 1-800-IRE-VISE! Just call 1-800-IRE-VISE! On a more serious note, re:vise aims to help people efficiently write clearly and concisely. This bot is designed to give high-quality feedback about the user's ideas. This type of feedback is more valuable than grammatical fixes because it makes ideas better which in turn improves the writing. On top of that, the bot will always be there when you need it. This is not the case for peer reviews when you could have delays for the review or just bad reviews. At the end of the day, I would rather have written a paper that had an amazing idea with a few grammatical mistakes than a paper with a flat idea and no grammatical mistakes. Which would you prefer?

Working with cohere chat

Traditional customer service web chat is dead. Customers have a poor experience while they wait for inexperienced customer service agents to respond to queries and work through their systems. It's not surprising that most customers come away feeling frustrated. Businesses must take the time to train staff but this comes at a high cost. Introducing co:here chat. Not only does this address the business challenges but it uses most of the components from the co:here stack. With co:here chat customer service web chat might not be dead after all.

Sally

Celebrate thanksgiving beyond having a feast with our present loved ones! Meet Sally, who will help us reconnect with our lost loved ones. Sally is a bot powered by co:here Generate and co:here Classify, that will help us reconnect with our lost loved ones through chat. We can use Sally with just 2 simple steps: 1. First, add Sally on Telegram (https://t.me/ReconnectWithSallybot). 2. Second, Upload a chat archive with a lost loved one. Checkout the pitch: https://youtu.be/COjzdB5uyPs Checkout the live technical demo: https://youtu.be/Xc0aRSRrQcw What happens under the hood? When a user send a message to our Telegram bot, a webhook is send to our service deployed on EC2. Our server then call Cohere generate API to generate the appropriate response. But before we forward it to the user, we call Cohere classify API to determine whether the text is safe to send to our user or not! If not, we regenerate it again! Why? There are about 500 people grieving a lost one every minute, with 20% of them experiencing a complicated grief. Although we can not fix their grief, Sally is here to help the cope. Any undelivered thank yous, I love yous, and goodbyes. Say them through Sally. Because our lost loved ones are not truly lost, as long as we keep them in our memory. <3

Blue Fractal

Given a room full of strangers, blue fractal helps users find the most compatible people in the room. The host creates a room on blue fractal, the participants sign up and provide details like Casual interests, Professional interests, Things they can talk about for hours and a message to their match. Blue Fractal then pairs them up with each other based on these fields. It then sends out emails introducing them to each other. It also tells them why they were matched to help start the conversation.

Health E AI assistant

By combining a custom conversant persona and grounded QA, Health-E is able to aid in processing patients, extracting relevant information to fill out forms, and providing advice common knowledge advice on applicable scenarios if prompted with a question. We hope this technology helps cut down healthcare costs by reducing queue times, paperwork, and increasing efficiency overall. Moreover, we envision applications of Health-E's underlying technology beyond the healthcare industry. In particular, regional economics and local communities in developing countries, that now have wide internet access, could greatly benefit from virtual advisors or teachers to supplement the lack of labor supply or poor level of education. We are really proud of what we built and we hope you enjoy both our presentation as well as experimenting with Health-E.

VerifAI

VerifAI is an AI-powered browser extension that helps you verify news from news websites. VerifAi - Detects the claim being made by a news - Checks if the claim has been verified by fact-checkers - Verifies the credibility of the website and author

Perfect Present

This app utilises the power of Large Language Models to provide gift suggestions for a person given their age, gender and interest.

Drunk driver identification

Thanksgiving is a time for getting together with family and friends. There is typically an overabundance of food but also the temptation to overindulge in alcohol. This makes it one of the most dangerous times of the year in terms of drunk driving car accidents. That's why we came up with this solutuion, an Artificial intelligence based (Deep learning and comuter vision) drunk driver detection system in cars will have a great impact in reducing accidents and increasing the safety for everyone.

TALDERA

Messenger apps like Discord & Slack have "channels" where multiple parties can send hundreds of texts daily. This can get out of hand pretty quickly if you've been offline, and we've sometimes had to scroll through hundreds of messages to understand what we've missed. The issue is that there isn't a quick & accessible way to figure things out. Tal;dera is a Discord bot that provides an easy-to-read, semantically rich summary of a channel's Discord activity in a particular time range. The summary is split into parts, and each part has a timestamp/link to its original messages. Users can call Tal;dera to extract message history and generate summaries (tl;dr's) of previous message chunks, given parameters such as number of messages, start time/date, output format, and chunk sizes. Summaries are given in an organized, embedded format, and are linked to where the conversations took place in the chats. Even though this was a challenging project for us, we were happy with what we were able to get done, and believe that this project has potential for B2B SaaS applications to team managers, employees, and chat service providers. We hope to continue this project by running pilot tests with smaller communities so that they can give feedback on Tal;dera, the summarizer bot for missed activity in Discord channels.

Zipit

Using Cohere's API, we have built a solution that aims to significantly increase the productivity of every single busy entrepreneur through the automated processing of emails. Think of this application like a personal AI assistant that can help you with your emails.

ShubhMitra

Shubh-Mitra(शुभ-मित्रा), or a well-wishing friend. AI assistance for positivity. Positively reframe your thoughts and develop gratitude, thankfulness and other qualities.

Cohere Call Center

Automated call manager for companies. Uses voice detection and text classification. It's a microservices approach with python as code language.

Poison

This project extracts text from images of product labels and uses GPT-3 to predict whether the substance is toxic to dogs. There are a number of potentially toxic substances to dogs, including food, rodenticides, plants, drugs (prescription and other), and mycotoxins (e.g., moldy grains). Since ingredients are important in determining the toxicity of a substance, the user can upload a photograph of the ingredient label or manually enter the ingredients. This can also be combined with external applications, e.g. for plant and mushroom identification. Disclaimer: this is a weekend hackathon project. If you think your pet may have been exposed to poison, please contact a professional and not GPT-3.

mEYE Buddy App

mEYE Buddy app is an application whose main role is to play part as your very own PERSONAL assistant who will make the world a little bit more accessible for anyone who is in any way visually impaired and requires assistance. The BEST SIDES OF THIS APP are that IT is highly affordable, with a premium version and is based on a loyalty program (business model). With mEYE buddy, not only would the life of blind people be much easier, but they could have the privilege of INDEPENDENCE - they would not need to rely on their caretaker or service dog to help them withq everyday chores.The state of the art tool for assistance to visually impaired persons! This state of the art AI technology will assist you in your everyday life. How app works: For first login you would have to register, and you'd do that using your fingerprint. Everytime the app is opened, the voice will tell you where everything is located on the screen. The design was made simple and the buttons were made big for easy access. The user can always press the icon in the middle of the screen for a reminder of the locations of the buttons. The voice assistant is the main function of the app. It will activate the AI, which will use a connected camera to describe the surroundings and warn the user of hazards. The key places tab will take the user to a tab where they can activate a guide to registered locations that are important to the user. (workplace, supermarket, hospital, home, coffee shop, etc.) They can also register new locations and remove old ones. The hazards that were noted by the ai could also be registered here. The devices tab is simple. It is used to connect the device to an external camera, preferably one on the glasses of the user or attached to their body. The user can also connect to their smart watch for easy access. The settings tab is pretty self-explanatory. However, besides the settings, it will also contain emergency information about the user, in case they need help from someone. The “mEYE Buddy” app will be connected to a camera on the glasses or on your body, and will describe everything important going in front of you - every peculiar movement that triggers its sensors or warn you against any potential hazards that may come in your way. The app can also be asked to describe something specific in more detail. Our Buddy also stores any hazard or newly recognized item in its already vast and enriched database. The app can also be told where certain points of interest are located (workplace, supermarket, hospital, home, coffee shop, etc.) for easier access to it later. The app comes with a simple UI with big buttons for input, and can be instructed through the AI voice as well. Also comes with a 3x3 keyboard for simpler accessibility.

Brain Tumor Classification

We have developed a Brain tumor classification app that helps doctors with the diagnosis. With this tool insurance and public health systems can launch massive brain tumor detection campaigns and save much time with the diagnosis, which will help in the early detection of this deadly disease.

AioMedica

Our Service is an aided diagnosis tool, that aids the workflow of doctors and also allows people to self-diagnose, both by means of AI

Andjelika The Tourist Guide

Imagine you're in some foreign city or country, this web app would help you navigate, socialize with locals, suggests you places to visit and make your stay way better.

RecluAI

RecluAI is an api create with Django Rest Framework that uses the Co:Here api to generate Summaries and extract Information from interviews to help recruiter

Vacancy finder

Nowadays there are so many students and mothers who are willing to improve, but with no money (obviously with no work), are being unable to do so. Our team’s solution is this: Use the vacancy finder website while sorting it into the "knowledge" and directing it (via summarization through GPT-3) to the resources to learn from.

Research Paper Clustering

The goal is to provide an interactive tool for visualizing research papers in a 2D map where the papers are placed on the map based on some vector embedding of the summary. The papers that are similar to each other will cluster together. This will allow researchers to identify papers that have similar concepts and even allow users to look at concepts from different fields that are applicable in their own field.

Fake News Detector

The proposed solution alongside my teammates is to build a Fake News Detection Application meant to limit the propaganda effect. With the use of Node.js and GPT-3 trained model Davinci, we gave birth to our CrewBot .

Student chatbot

This application is a chatbot for students, to reply for common asked questions in a certain material, we use cohere's model to answer students questions in a specific topic, the data used is WIKIFAQ and ideally emails from teachers of frequent questions and answers, the added value is that it's course specific and not like asking a broad question to google. I haven't advanced much in the implementation of the web interface and the model, but I wanted to submit the idea at least.

Auto Message Processing System

Hello! We created this bot drawing inspiration from the COVID-19 pandemic in Singapore. Back in 2021, Singapore faced a surge of COVID-19 cases that overwhelmed the Ministry of Health in Singapore. Most people called the MOH hotline when they were unsure what to do with regard to COVID-19, but this flooded their hotlines and they were not able to answer most of their concerns. This was because they had no way to filter the urgency level of incoming messages and therefore had to answer to them one by one manually. We created this telegram bot to overcome this challenge. We made use of the text classification ability of GPT-3 to classify incoming messages to the bot based on their urgency levels, and the right level of assistance would then be provided.

ChatBot

Aisistant for online learning

During the covid pandemic, a big lesson learned was that students in k-12 education did not do well with online learning. Teachers were given mere weeks to prepare lessons, and they struggled to make engaging content. This project uses the CoHere API and live transcription, done entirely in the browser, to create engaging messages for the students. The AI will "listen" to a presenter's words, find keywords, search wikipedia, find a closely matching topic, rephrase the topic in a "fun" way, and then display this to the user.

Debait

an AI-system capable of debating complex topics.

BridgeDoc

https://krusnabalar-bridgedoc-frontendsrcmydoc-jwe73t.streamlitapp.com/ INSPIRATION: When was the last time you had an uncomfortable sensation that you didn’t know how to describe? You look it up on google, WebMD and ask onreddit, but find yourself just as uninformed and even more stressed than before. You end up calling a clinic or hospital to speak with a doctor, and find yourself having to wait a week until the next available appointment. The general public is not trained to be aware of and describe the symptoms they might be going through. The way we describe our sensations can vary enormously, often we use idioms and other figures of speech. The stress and struggle of being unable to understand our body’s pain can be frustrating. Even in speaking with doctors, there’s often misunderstandings, and a lot of back and forth, until the doctor can finally understand the patient’s symptoms. In the field of medicine and healthcare, that kind of subjectivity and unpredictability can be dangerous, inefficient, and costly. That got our team to wonder: What if there’s a way to effectively predict a patient’s symptoms based on their own description in a fraction of the time at no cost to the patient? BMJ Journal published a study which performed research on clinical text to extract mental health symptoms and using a classification NLP model, citing the automatability of the symptom detection process as being a credible way to approach this issue. SOLUTION: Introducting BridgeDoc, the tool that doctors and the general public can use to understand and identify their symptoms and diseases. BridgeDoc will use classification tools provided by co:here to detect and identify the specific symptoms, for the knowledge of the doctor, and possible disease diagnoses, for the knowledge of the doctor and the patient. It will allow an ease of communication between a doctor, clinic, or hospital with the patients by using a model trained with colloquial descriptions of symptoms to identify the likelihood of the patient’s symptoms. COMPETITIVE ADVANTAGE: Companies like WebMD, Mayo Clinic, DearDoc, Mercury Healthcare all lack a way to enhance user inquiry and streamline the communication between doctors and patients. BridgeDoc equips users and medical businesses to prevent the struggle and misunderstandings involved in translating a patient’s description of their issue and the doctor’s knowledge of what exactly those symptoms are, helping them efficiently pin point the most likely solutions. This has not been used in a professional medical sense for patients and can help edge over competitors in a significant way with a high quality symptom prediction model. REVENUE AND EXPENSES: There are two B2B (doctors, hospitals) and B2C (online website clients) solution that BridgeDoc provides. B2B solutions will be provided initially per user at a contract price based on the user report and needs of the doctor/hospital. B2C users will have free access. Expenses will be website hosting when moved to a different website that is more customized, as well as to access data that is more reliable by finding methods to get a secure access to it. Research expenses for improving model prediction will also be there. KEY METRICS: We will track the following key metrics Growth: number of doctor contracts acquired per month, number of users accessing website Engagement: use tools like Hotjar to track website interaction (user remains anonymous), track number of searches made per user Marketing: to measure success for our marketing efforts, we will evaluate cost per acquisition, cost per clicks, and understanding the trends in impression to generate better marketing assets iteratively. We will also research marketing tacics of companies like Zocdoc who created an industry tool used by doctors. Product: we will track and receive product feedback dynamically using tools like hotjar to understand user engagement with the product (what parts of the website are being used, how long they spend on the website, etc. HOW WE BUILT IT: We built everything with python. Using jupyter notebooks we tested out co:here’s endpoints, integrated them into the boilerplate provided by LabLab and co:here. The app is deployed using streamlit. Data was collected from various tools such as reddit and google search scrapers, forums. To reach co:here’s requirement for a minimum of 250 examples to train the classify model, due to scarcity of data, we used co:here’s generate tool to build more examples by fine-tuning the model and using specific input phrases that generate reliable results. WHAT'S NEXT FOR BRIDGEDOC We would love to see BridgeDoc to be a standalone tool that can be integrated with online tools for clinics and doctors with private practices, as well as hospital which often deal with issues of patient capacity limits, to automate the report creation by listing possible symptoms and diagnoses automatically. This would require adding a co:here-trained chat-bot that can extract the information from the user in a friendly, secure, and reliable way, similar to how a doctor might on the phone, and produce a report based on the user’s profile (gender, age, previous conditions, etc.) to improve the symptom and disease prediction. Additionally, we leveraged insights from our mentor Ervin, and the “How to get funding from your startup” workshop by Pawel Czech and Mathias Asberg to understand business needs and identify the gaps in what’s currently being offered in our product. It’s important to focus our efforts to specific medical domains to improve accuracy, user retention, and help market and get clients. And the user base can expand by changing the architecture of our model to have a bigger tool that operates like the following: Take input from user and classify what medical domain the query is in. Based on the prediction confidence levels, use the top medical domains and send the user input to specialized classify models trained for the specific domain. Get top predictions from those results of the symptoms as well as the disease. This does not narrow down the user base, and more importantly, it provides improved symptom prediction and more reliable results.

Better tags

Better tags for short story recommendations using cohere's generate feature! Take in the URL of a story you enjoyed and receive a list of three other stories that you may like to read in return!

Icebreaker Question Generation

Icebreaker questions are often hard to answer, but it is (arguably) even harder to come up with good ones. This app both generates a list of icebreakers given a type (e.g., creative, funny, deep), then judges the difficulty of those questions using a classifier. Another use case of this app could be to better understand potential users of a product. The users' answers could help a company with asking more targeted survey questions and the like. The question generation uses LM prompting on the `xlarge` Cohere pretrained LM. The difficulty classifier is an SVM that inputs embeddings generated through Cohere's API. The code for querying the model APIs and serving the web app is only 135 lines long—showing that anyone could build it, irrespective of coding ability.

NozApp App For Knowing You

Solution is the the NozApp -- the App that knows everything about everyone! It analyzes tons of information from Open Sources and Media, so that it can collect and summarize all info about everyone! So when we first open the app, we are asked to enter the username of a twitter user. Once we enter the username, the app checks if the username is valid. This is done so the app can collect information of a valid user based on their Tweets. The app takes sometime to process and collect the data, while sending a request to analyze all the contents. It may take from seconds to minutes, depending on the amount of Tweets the user has. Once it's done anlyzing, the app gets the results requested and shows it as a list of user data, including generated hashtags, the contents summary, the user's recent mood and matching emojis. Just paste identification info about them (nickname or real name) -- and NozApp will collect and summarize all info about the subject: Summary about the person Hashtags, describing this person All URLs of that person Current mood and more other info! Stay tuned! This app will turn the game!

Wonhiderland

Wohinderland is a powerful NLP application to find vacation destinations, powered by co:here classify. Users simply need to type in their vacation ideas, such as about the weather or about the activities, and the app will give recommendations on places to match the ideas. For example, if the input is “wine, beach, and party”, current prototype will suggest “Bali island” as the place to go. We use classify to try few-shot learning and fine-tuning on collected travel text data. For the prototype, we only limit to seven regions globally and this can be further improved with more data to fine-tune the model. The application has business values in travel industry especially in this post-pandemic era!

Perfect Prompt

Perfect Prompt is a one stop shop for prompt engineering and image generation. Currently, generating images with prompts for free can be costly with respect to time and money. Perfect Prompt allows you to experiment with your prompt before submitting it several times to a image generation model. Just type in a prompt, and Perfect Prompt will generate an image as well 10 variations of the input prompt with a given art style. First, Perfect Prompt identifies the art style using cohere classify, and matches it to one of five. Then, this style is added to an internal prompt, which asks cohere generate to create variations of the input prompt. These prompts are then ranked again by the classifier, and only the top ten are returned. The user may use any details from these to create their next prompt, and generate another image!

Recipe Generator

The idea behind this project is to generate recipes from ingredients to avoid wastage of the Food. A great and easier look through to understand the app better just refer to the presentation. In the workflow plan we have two categories to understand. The very first one is "Data". In here the app first scrape about 100 recipes. For each recipe, it gets the list of ingredients as list without quantities. Then it cluster(/embed) ingredients lists and make categories based on embedding clusters. For the second part we have "Usage" where we first take as user input list of ingredients and classify ingredients list according to closest cluster. Then we build prompt from cluster and ingredients list. For the Uses and Scope : (1) It is a resourceful app that can contribute to Sustainable development. (2) Can be a time saver for people who loves to create new recipes especially chefs. (3) Can come out as a treasure for travellers who love to try new cuisines and recipes. (4) Can also become a great online platform for foodies and help them in becoming pro chef who can cook different cuisines.

READefine

The idea is to be able to create an app that lets users add documents they would like to read which are then vectorized to create a search functionality. The app supports inbuilt features like clarifying hard-to-understand phrases in a certain context, generating keywords that would help the user remember better and generating queries that would test the understanding of the reader. The app also creates a network of all the documents that are added to it (using the embeddings).

Refrai

Recommendation letter in a simpler and quicker way. Сreating a praise letter about another person based on uploaded information from their personal pages and questionary, using common points of intersection between the two people in their studies, work or business.

Chatty Businessmen

Chatty Businessmen is a chat support app, which uses Classify by Cohere to automate appropriate responses to the customers. This app will mainly be used by small businesses, and the main aim of the project is to make the owners of small business workload less.

SuperTransformer

AI Assisted Intelligent Inbox solution to automatically categorize your emails based on your feedback and preferences, saving you hours of manual triaging, and making you more productive, efficient and effective

SimpliFY

The app lets you classify your text into Elementary, Intermediate and Advanced according to the level of complexity. Simplify functionality lets you generate an alternate simplified version of it.

TarinyoomBot

Tarinyoom is a Discord chat bot that allows users to search for messages using a fuzzy search to better retrieve past messages. The bot uses the co:here API "Embed" tool to map discord messages onto their corresponding embedding vectors. These vectors are then stored in a back-end pinecone.io instance, so that their cosine similarities can be evaluated. The bot has two main commands: "/tsleuth" and "/tsearch <string>". "/tsleuth" instructs the bot to crawl the server that the command was issued in, storing the vector embeddings of any messages found as entries in the pinecone.io instance. "/tsearch <string>" instructs the bot to find the message whose vector embedding most closely resembles the vector embedding of the passed parameter, as evaluated by pinecone.io. Using these two commands, users can search for a past conversation with only a high-level recollection of the topics discussed, instead of having to exactly match to text in that conversation, as required by the current Discord search feature.

Learn visually

From a topic, generate a list of subtopics and explore them with a visual image for each. The subtopics are presented in a manageable way by showing only the most related subtopics at any given time. This naturally leads to exploring associatively like our brains are structured, from broad to deep. Each topic has a handy explanation in text from cohere generate, too!

intelliChat

IntelliChat is an app built specifically for Chat based and Conversational AI projects. *Who is this for?* - Businesses and Enterprises who are looking to optimize and enhance their Conversational AI Workflow.

Wayfinder

A travel recommendation for users of any kind. Users are asked a series of questions related to their personality. Then, based on those responses, the site returns a location most relevant to their personal interests.

Cohere Analyze

We all understand what Cohere Generate and Classify do. But embed ...?! Introducing ... Cohere Analyze. You can upload any csv file. You then have 3 options. EDA gives you an overview of the data and you can get some general exploratory data analysis. Cluster, allows you to do some group analysis, with keywords generated from the body of the text and using the titles. And finally, Search, allows you to query the data and retrieve the closest match. This has significant business value because now you can gain business insights using cluster that are non-trivial. You can search a knowledge-base or other internal data sources for information. Cohere Analyze is what we have all been waiting for, to better utilize Embed.

All in one AI app

This app is the combination of mutiples work that can be done using LLM. We tried to make the llm to do things by changing the prompt . There is a chatbot. We have text summarizer, that can make good summarization. We also implemented machine translation, we have 6 languages that you can choose from. There other things we have are code explanation and story generation. It is created using streamlit library. A free python library to run ml apps as web service.

IT Services and Solutions

Constantly changing and evolving solutions, services and products available in the telecom, mobility, and IT management industry create sourcing, evaluation, and selection complexities that SMBs (small and mid-sized businesses) are not always equipped to address due to limited time or resources. How do SMBs more easily and efficiently sort out the thousands of options available to them? Our solution is an IT product Service Recommender System which will help SMBs find verified solutions to simplify the sourcing, evaluation, and selection of telecom, mobility, and IT services and solutions. Input: SLA agreements Output: Classify the SLA agreements by Subscription and One-off

Tip of my Tongue by PowerPuff

Web application that allows user to find and browse books based on short prompts, keywords or short story plots.

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.

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.

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.

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

Get Help

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

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.

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

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.) ]

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.

Genie (Quester)

A python Application built to make,the researcher's life easy.

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