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🤝 Top Collabolators
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🤓 Submissions
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Podsearch
Podcasts are an excellent source of knowledge. But they can be too long and hard to pay attention to it the entire time. What if there is a more intuitive way to search for podcasts and also for info within podcasts? This is where our product comes into play. Key highlights 1. Searching for podcasts suited to your taste 2. Searching for answers within a podcast itself by asking it queries and without listening 3. Marking exactly where the answer is and summarising it. 4. Telling user what queries this podcast answers Major Uplifts: 1. Generating queries for dialogues in transcript using the prompt - "Generate 5 questions for the following passage {passage}" 2. Training a classifier using cohere api using the generated queries and dialogues 3. Highly scalable architecture 4. Podcast is just an example. Most documentation (python libraries, eth doc) have only keyword search. It is possible to scrape the documentation and build an index for a search engine using our architecture easily.
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.
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.
