Cohere Cohere Generate Applications

Browse applications built on Cohere Cohere Generate technology. Explore PoC and MVP applications created by our community and discover innovative use cases for Cohere Cohere Generate technology.

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.

TechnoCouple
ChatGPTWhisperDALL-E-2Cohere GenerateCohere ClassifyRedis

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.

Fetch
CohereCohere GenerateCohere EmbedQdrant

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.

The Meowsterminds
Streamlit
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CohereCohere GenerateCohere EmbedQdrant

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.

Heuristic AI
QdrantCohereCohere Generate

The Future of AI podcast

An artificial intelligence podcast that is written by ChatGPT, GPT-3.5, Open-AI davinci, and human assistance. The art is generated by Stable Diffusion, Open Journey, and Dall-E 2. It is read by Natural Readers text-to-speech and Lifelike Speech Synthesis Google Cloud. The platform used is Anchor.fm and the availability of the podcast are in Google Podcasts, Apple Podcasts, Amazon Music, Spotify, Castbox, Pocket Casts, RadioPublic, and Stitcher. The podcast description is: "Join us as we explore the rapidly advancing world of artificial intelligence, and what it means for our future. In each episode, we'll discuss the latest AI research and developments, and how they are poised to impact various industries and aspects of our daily lives. From self-driving cars to intelligent virtual assistants, we'll delve into the potential and the challenges of this rapidly evolving technology. Tune in to stay up-to-date on the future of AI and its impact on society." Created and written by Artificial Intelligences and Cyber World. Currently the podcast has 12 episode in season 1 which has one episode for introduction and special and it has 5 episode currently for season 2. AI has come a long way since its inception and has been widely used in various fields such as healthcare, finance, and transportation. AI-powered machines and systems have the ability to learn and adapt to new situations without the need for human intervention. This ability of AI has made it an integral part of various industries and has brought about significant changes in the way we work and live. The current state of the AI industry is quite promising. The AI market is expected to grow from $9.5 billion in 2018 to $118.6 billion by 2025. The adoption of AI is increasing at a rapid pace and is being used in a variety of applications such as image recognition, speech recognition, and natural language processing. The use of AI in healthcare has also shown promising results, with AI-powered systems.

The Future of AI
OpenAI gymChatGPTReinforcement LearningStable DiffusionRedisCohere Generate

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.

Project Peace
Streamlit
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CohereCohere ClassifyCohere Generate