Cohere Top Builders
Explore the top contributors showcasing the highest number of Cohere app submissions within our community.
Cohere provides access to advanced Large Language Models and NLP tools through one easy-to-use API. They provide multiple models such as Generate, Embed, Semantic Search or Classify
Cohere is a Canadian 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.
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Start building with Cohere
Cohere has an amazing potential and extraordinary potential usage - you can incorporate it into many of your app ideas and it will solve many of the problems you faced perfectly.
Useful Cohere links
For ML teams looking to build their own text analysis applications, Embed offers high-performance, accurate embeddings in English and 100+ languages.
You can easily use Cohere Embed for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our Cohere Embed tech page.
Generate produces unique content for emails, landing pages, product descriptions, and more.
You can easily use Cohere Generate for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our Cohere Generate tech page.
Classify organizes information for more effective content moderation, analysis, and chatbot experiences.
You can easily use Cohere Classify for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our Cohere Classify tech page.
Cohere Neural Search
Neural Search provides powerful semantic search capabilities that find text, documents, and articles based on meaning, not just keywords.
You can easily use Cohere Neural Search for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our Cohere Neural Search tech page.
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.
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.
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 Hackathon projects
Discover innovative solutions crafted with Cohere, developed by our community members during our engaging hackathons.
Apollo ai for writers
We are developing a project that aims to help writers to mix written content by generating engaging images and optimizing placement within the text. Basically, We are building a web application that generates images based on user input. The user will enter some text and might specify the number of images they want to generate. Our application will analyze the text, identify entities within it, devide text into paragraphs and use that information to generate images using a generative AI model. The user will have the option to modify the generated images and generate new ones. The idea is that Ai will analyses the text and decides the best place to put images (where the reader becomes bored) and generate images according to the content. We included more detailed information in our presentation
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
WeCare Caretaker Assistant
We have built a solution for agencies which provide the caretaker services for parents who are in search of babysitters for their child. When users call the agency after business hours or when agents are not available for assistance, we are routing them to leave a voicemail with their babysitter requirement and contact number. With this solution, agents can focus on more complex tasks rather than manually retrieving voicemails, analysing them and coming up with a resolution. When the caller dials the agency phone number during office closed hours or peak hours when agents are not available to serve them, we route the caller to the voicemail menu where we ask them to leave a voicemail with babysitting requirements and their contact details, etc. Once the voicemail is available, we extract it and convert this speech to text using OpenAI’s whisper API which gives us the voicemail transcription. After that, we meticulously perform the prompt engineering for ChatGPT API to provide us all the required information from voicemail like intent, sentiment, babysitting date and time, etc in JSON format. Using this information, we query the EmployeeSchedule table which is in the H2 database. Once we have the information about availability of babysitters, we query RedisJSON to get the employee profile information like employee name, contact details, date of birth, languages spoken, image, etc. We then build a PDF document using itext library. This PDF containing available babysitter information will be sent on the caller’s WhatsApp. After this, we send an SMS to the agency as an alert notification about the customer enquiry and ask them to get in touch with the customer. Github link - https://github.com/technocouple/technocouple-caretaker-assistant Video link - https://drive.google.com/drive/folders/1NBew2U0Xgtm04ubQszjLvZV92fowR6-D?usp=sharing Presentation - https://drive.google.com/file/d/1TBMSU5Ohyn1v2P2u_RqbZOpuCvWv1Crq/view?usp=share_link DEMO is at the end of the video.
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.
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.