Cohere Cohere Embed Top Builders

Explore the top contributors showcasing the highest number of Cohere Cohere Embed app submissions within our community.

Cohere Embed

Gain more in-depth insights into language through numerical representation. Cohere Embed categorizes and evaluates text algorithmically to quickly extract meaning. Use Cohere Embed for semantic search, topic modeling, recommendations and multilingual embedding.

With Cohere Embed, you can embed content in more than 100 languages with high performance and accuracy.

General
Relese dateNovember 15, 2021
AuthorCohere
Documentationhttps://docs.cohere.ai/reference/classify
TypeAutoregressive, Transformer, Language model

Start building with Cohere Embed

To see what others are building with Cohere Embed, check out the community built Cohere Use Cases and Applications.

Cohere Embed Tutorials

    👉 Discover more Cohere Embed Tutorials on lablab.ai


    Awesome Cohere Embed Boilerplates

    Kickstart your development with a Cohere based boilerplate. Boilerplates is a great way to headstart when building your next project with Cohere.


    Awesome Cohere Embed Libraries

    A curated list of libraries and technologies to help you build great projects with Cohere.

    • 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.
    • Cohere Go This package provides functionality developed to simplify interfacing with the cohere.ai natural language API in Go.
    • Cohere Python This package provides functionality developed to simplify interfacing with the Cohere API in Python 3.
    • Cohere Ruby This package provides functionality developed to simplify interfacing with the cohere.ai NLP API in Ruby.

    Awesome Cohere Embed resources

    Complimentary resources that will help you build even better applications


    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.


    Cohere Cohere Embed Hackathon projects

    Discover innovative solutions crafted with Cohere Cohere Embed, developed by our community members during our engaging hackathons.

    Fetcher the work sidekick

    Fetcher the work sidekick

    In today's increasingly remote working style, organization’s messaging system, whether it's email or chat, contains lots of invaluable institutional knowledge. However, because these data are often unstructured and scattered, they are usually buried in the organization’s data ecosystem and are hard to search and extract value. Fetcher is a chatbot that integrates into popular chat platforms such as Discord and Slack to seamlessly help users find relevant people and documents to save them from endless frustrating search. It does this by semantically searching chat messages to find the most relevant results and help to deliver actions that leads to a peace of mind. Fetcher differs from traditional keyword search engines in that it searches by the meaning of the query, not just by keywords. It also enables multi lingual search, so that global teams can more quickly find important information even when language is a barrier. Since Fetcher searches in the embedding space, this search engine can extend to multi modal modes that includes audio and images. Fetcher works by collecting a chat channel’s history and embedding them using Cohere’s Embed API, then saving the embeddings to Qdrant’s vector search engine. When a new query comes in, Fetcher embeds the query and searches against the vector database to find the most relevant results, which can then feed into Cohere’s Generate API to summarize the message thread to kick start new conversations. Fetcher offers 3 commands, /fetch, using vector similarities search to find relevant chat messages. /discuss, summarize a message thread, and kick start a conversation with a channel number. /revise, a sentence correction tool similar to Grammarly, allows user to send professional sounding messages.

    LegalFruit

    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.

    Fudl app

    Fudl app

    Are you tired of overspending on groceries every month and wasting your time reviewing supermarkets' promo materials? Fudl is the answer to all your problems! Our revolutionary AI-powered app is designed to help you save money on your grocery bill without compromising on quality. With Fudl, you can plan your purchases, analyze discounts and special offers, discover analogs of products you need for less price, find more savings with value-sized items, and send your orders directly to the delivery service. Let me explain all the features in a more detailed way: By using Fudl's personalized recommendations, you can use your grocery budget to find the best deals on the products you need. Fudl's AI technology analyzes your shopping list, gives you recommendations based on your individual preferences, and suggests alternative products that are just as good, if not better, at a lower cost. Fudl's innovative technology also allows you to split one order into several from different grocery chains, which can save you up to 50% on your grocery bill. This means you can buy in for the next week or plan your purchases for the weeks ahead, without worrying about overspending. Using Fudl to split your order, you can save money and collect additional points from grocery loyalty programs while still getting the needed products. Fudl uses databases from online stores to provide you with the best possible recommendations. For this example, we tested our algorithms on three major chains in Slovenia - Mercator, Spar, and Tus and got phenomenal savings from 10 to 40% on single bills. To find the most successful alternative for each product, we utilize the power of AI to determine its coordinates in a multidimensional space. By doing so, we can identify products that are similar in quality, volume, and other characteristics. Our intelligent algorithms then display the closest analogs to the original product, giving you the information you need to make an informed purchase decision.