Cohere Cohere Embed AI technology Top Builders
Explore the top contributors showcasing the highest number of Cohere Cohere Embed AI technology 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 AI technology Hackathon projects
Discover innovative solutions crafted with Cohere Cohere Embed AI technology, developed by our community members during our engaging hackathons.
"Insight" is an innovative app that revolutionizes the research process by leveraging cutting-edge technologies like LLM, RAG, Weaviate, and Langchain. It simplifies knowledge discovery and empowers researchers. The primary objective of Research Insight is to enhance the research exploration process by enabling users to ask questions about their research documents and receive insightful, context-aware responses. This project aims to bridge the gap between users and their research content, making information retrieval more intuitive and interactive. Key Components: Cohere: Research Insight integrates Cohere, a powerful natural language processing platform. Cohere enables the project to understand and process user queries effectively. Its advanced language models contribute to accurate comprehension and interpretation of user questions. LangChain: LangChain, a sophisticated language processing framework, forms the core of Research Insight. It orchestrates the flow of information, handling tasks such as text splitting, embeddings generation, and conversational retrieval. LangChain ensures a seamless and efficient interaction between users and the research data. Weaviate: Weaviate, a semantic vector database, is employed to store and retrieve information. It plays a crucial role in organizing and indexing the research content, making it easily accessible for queries. Weaviate's capabilities contribute to efficient and fast retrieval of relevant information.
BYTE - AI-Based Nutrition App
Transform Your Diet with Our AI-Powered Nutrition App: Discover personalized dietary guidance with our innovative app, combining AI technology and personalized nutrition. Tailored to enhance your health and wellness, our app caters to your unique dietary needs and goals. Key Features: Personal Health Profiling: Input your health details, including allergies, weight goals, and exercise habits. Upload medical documents for an in-depth analysis. Instant Nutritional Label Analysis: Scan food labels to understand their nutritional content instantly. Customized AI Dietary Advice: Using Weaviate's vector database, our AI algorithm provides personalized dietary recommendations based on your health data and food label information. Personalized Internet-Enabled Suggestions: Coral, our intelligent chatbot, uses co.chat's RAG connector to retrieve tailored nutritional information and user data, ensuring responses that meet your specific dietary needs. Healthier Product Alternatives: The app employs co.chat's RAG search-query-generator and Google's Custom Search API to find better-suited food products on Amazon, aligning with your health profile. Data Security and Privacy: Weaviate's multi-tenancy feature ensures the protection of your personal information through secure session tokens. Streamlined User Interface: Our Streamlit-powered interface provides an intuitive and seamless user experience. Robust Heroku Backend: Hosted on Heroku, the app guarantees reliable and scalable performance. Purpose: This app is your pocket-sized dietitian, simplifying nutrition labels into actionable advice. Whether managing allergies, pursuing weight goals, or enhancing athletic performance, our app offers a spectrum of dietary solutions through cutting-edge AI and comprehensive data analysis.
Chat with Your Football Scouter
We use 2022-2023 Football Player Stats from Kaggle. The data encompasses nearly 2500 players across Premier League, Ligue 1, Bundesliga, Serie A, and La Liga. Covering 125 metrics, ranging from basic player information such as name, age, and nation, to performance statistics like goals and pass completion rates, our dataset is extensive and diverse. To harness and organize this wealth of information, we leverage Cohere Embedding and Weaviate Cloud Service (WCS), employing vector transformation, storage, and indexing. The focal points of our application are the Chat and Compare Player features, each powered by advanced language models, including Cohere and ChatCohere. Both functionalities employ Retrieval-augmented Generation (RAG) techniques, albeit with distinct details and components. For the Chat feature, we've constructed a compressor retriever using Cohere Rag Retriever, incorporating a web-search connector and CohereRerank as a compressor. Within the ConversationBufferMemory chain, this chain processes chat history (a list of messages) and new questions, ultimately delivering a response. The algorithm in this chain comprises three key steps: first, the creation of a "standalone question" using chat history and the new question; second, passing this question to the retriever to fetch relevant documents; and finally, utilizing the retrieved documents in conjunction with either the new question or the original question and chat history to generate a comprehensive response. Conversely, the Player Comparison feature utilizes the Weaviate Hybrid Search Retriever to extract statistical data of players by their names from WCS. Through an LLM chain, we then summarize this data and generate a comprehensive report based on the retrieved documents. Our approach ensures a robust and dynamic platform for users seeking nuanced insights into player performances across top football leagues.
It's no secret that when working with a new library, SDK, or API, software developers often waste hours and hours hopelessly poring over a sea of scattered documentation pages to find the one syntax example or function parameter datatype they needed. With the arrival of AI tools such as ChatGPT, sometimes developers can get lucky and get the exact code they need simply by asking the LLM. However, traditional LLM’s knowledge pools are limited to their training data, so when they are asked about perhaps newer tech, they may be rendered useless, or even worse, hallucinate and spew nonsense, wasting even more of a developer’s time. Pylibrarian is a special chatbot that solves all of these headaches by granting LLM access to complete documentation for Python’s most popular libraries using RAG architecture. Pylibrarian was built by processing, embedding (using cohere.embed), and storing documentation pages into Weaviate’s vector database. Upon a user query, we can semantically search for the most relevant pages of documentation to that query. Using Cohere’s chat endpoint’s document mode, the chatbot synthesizes a response citing the documents, leading to far more consistent, grounded responses.
AI Research Copilot
Welcome to AI Research Copilot, your indispensable tool for navigating the vast world of academia with unparalleled ease and efficiency. Imagine a virtual companion that not only simplifies the complexities of research but enhances your entire academic journey. 1. Intelligent Paper Conversations: Engage in insightful conversations with research papers through our unique chat feature. No more deciphering dense paragraphs—our AI transforms the experience into a dynamic dialogue, making knowledge extraction an interactive and engaging process. 2. Swift Literature Search: Say goodbye to hours spent scouring databases. Our advanced search functionality, powered by Cohere LLMs and Semantic Scholar connector(based on semantic scholar API), revolutionizes literature searches. Experience the speed and precision of AI-driven discovery, ensuring you find relevant academic information in record time. 4. Cohere LLM Integration: Harness the power of Cohere LLMs to enhance your understanding of research papers. Our integration brings cutting-edge language models into play, providing you with valuable insights and context that transcend traditional comprehension. 5. Academic Discovery Simplified: AI Research Copilot is not just an app; it's your academic ally. We understand the challenges of staying current and navigating the ever-expanding landscape of scholarly information. Let us guide you through the vast sea of knowledge, ensuring you stay on the forefront of your field. Embark on a new era of academic exploration with AI Research Copilot. Revolutionize the way you engage with research papers, discover knowledge, and stay ahead in your field. Your academic success is just a conversation away—let AI Research Copilot be your guide to a world of limitless possibilities.
OrbChat - Always-On Support for your Website
Our platform enables you to create highly intelligent chatbots, tailor-made to your unique needs using your own documents and URLs. OrbChat bots are designed to provide instant, accurate responses, elevating the user experience on your website. 1. AI-Powered Responses backed by your data Utilise your existing files and website to train chatbots that understand and respond accurately to user queries. 2. Seamless Integration with your website Embed OrbChat effortlessly into your website, harmonising with your design and brand aesthetics. 3. Human Handover Capability Switch seamlessly between AI and human support to ensure complex inquiries receive the personal touch they deserve. 4. Fully Customisable Modify the look, feel, and functionality of your chatbots to match your website's unique style and requirements. 5. Invite Your Team Bring your entire team into OrbChat with unlimited seating, forever. Only pay for message credit limits and features that suit your needs.