Cohere Cohere Generate Top Builders
Explore the top contributors showcasing the highest number of Cohere Cohere Generate app submissions within our community.
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.
|Type||Autoregressive, Transformer, Language model|
Start building with Cohere Generate
To see what others are building with Cohere Generate, check out the community built Cohere Use Cases and Applications.
Cohere Generate Tutorials
👉 Discover more Cohere Generate Tutorials on lablab.ai
Discover Co:here API SDK's to help you get started
- Cohere GoThis package provides functionality developed to simplify interfacing with the cohere.ai natural language API in Go.
- Cohere PythonThis package provides functionality developed to simplify interfacing with the Cohere API in Python 3.
- Cohere RubyThis package provides functionality developed to simplify interfacing with the cohere.ai NLP API in Ruby.
Boilerplates to help you get started
- Nextjs Boilerplate Article summarizer Boilerplate for Nextjs, Co:here, TailwindCSS, Vercel.
- Streamlit Boilerplate Hashtag generator Boilerplate for Co:here, Streamlit, Streamlit Cloud.
- Replit Boilerplate Generate an email on command with Co:here and Replit.
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 Generate Hackathon projects
Discover innovative solutions crafted with Cohere Cohere Generate, 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
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.
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.