Cohere Cohere Embed Top Builders
Explore the top contributors showcasing the highest number of Cohere Cohere Embed app submissions within our community.
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.
|Relese date||November 15, 2021|
|Type||Autoregressive, 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.
- Nextjs Boilerplate Article summarizer Boilerplate for Nextjs, Cohere, TailwindCSS, Vercel.
- Streamlit Boilerplate Hashtag generator Boilerplate for Cohere, Streamlit, Streamlit Cloud.
- Replit Boilerplate Generate an email on command with Co:here and Replit.
Awesome Cohere Embed Libraries
A curated list of libraries and technologies to help you build great projects with Cohere.
- 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 Playground Interact with Cohere API through their playground
- Langchain Toolset for building applications powered by LLM
- Cohere Embed Documentation
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.
- Sandbox Conversant - PyPi repository Conversational AI tooling & personas built on Cohere's LLMs.
- Sandbox Conversant Streamlit Demo Conversational Personas using Cohere and Streamlit.
Cohere Cohere Embed Hackathon projects
Discover innovative solutions crafted with Cohere Cohere Embed, developed by our community members during our engaging hackathons.
Enhancing Readability with Whisper and ChatGPT Whisper is an incredibly powerful transcription model, which we utilized to convert video content into text format. However, the resulting transcript was a dense wall of text, making it difficult to digest. To improve readability, we employed ChatGPT to introduce structure, including paragraph breaks and headers. The text is now significantly more reader-friendly. Integrating Slides and Transcripts for Seamless Presentations During presentations, speakers often refer to slides, which are absent from the transcript. To address this issue, we have synchronized the text with the video in our wiki. This feature allows users to click on the text and instantly view the corresponding slide. Alternatively, users can play the video without audio and follow along with the highlighted text, creating a more integrated and accessible experience. And everything is backed by our semantic search we introduced at the previous hackathon
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.
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.
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.
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.
Your personal health assistant. SmartHealth is capable of helping users quickly and easily access personalized health advice and guidance. By offering range of services such as symptom checker and personalized health tips to help users stay on top of their health and well-being. Who are We? SmartHealth is composed of passionate and talented team of Computer Science, Medical, Arts and Business individuals. Josh, Lizzie, Jason and Raj continue to apply the latest technology to improve Healthcare. How Do We Do It? We leverage the power of GPT3, Redis Vectorized DB, Python and React to provide you with an interactive, personalized health assistant. We put together a database of health conditions and their symptoms, causes, and treatments. By pairing our secure and personalized database with GPT3 we are able to help you understand your health without having to visit a doctor in person. Our Technologies We chose to use a future proof tech stack to get our ideas to become a reality, below are a few of the technologies we used.