Nils Jennissen@nilsjenn665

This is a repository that was made for a Hackathon orginized by Lablab.AI. The challenge was to create different types of agents that will carry our several tasks. Use the power of LLMs with LangChain and OpenAI to scan through your documents. Find information and insight's with lightning speed. 🚀 Create new content with the support of state of the art language models and and voice command your way through your documents. 🎙️""") st.write("We wills how you 5 different agents that we build\n" "1. **AssemblyAI Agent**\n" "2. **PandasAI Agent**\n" "3. **Presentation Agent**\n" "4. **README Agent**\n" "5. **Webscraping generator Agent**\n

This project revolves around the development of a research assistant using the Google Vertex AI Palm2 platform. The aim is to streamline the process of searching for and accessing academic papers from Google Scholar, providing researchers with a user-friendly and efficient tool. The research assistant is implemented as a Streamlit application, allowing users to input their search specifications and navigate through Google Scholar seamlessly. One of the key features of the research assistant is its automatic scraping functionality. Once the user provides their search criteria, the application scours Google Scholar across multiple pages, retrieving relevant papers. The scraped papers are then organized into a comprehensive dataframe, providing researchers with a structured overview of the available literature. Additionally, the application also selects and provides downloadable PDF versions of the papers, making it convenient for users to access and read the full content. To further enhance the capabilities of the research assistant, it integrates with Google Vertex AI and Langchain. Google Vertex AI is a powerful machine learning platform that enables users to leverage advanced AI models and tools. By integrating with Vertex AI, the research assistant allows researchers to create a knowledge base from the downloaded papers, enabling them to extract insights and answer questions related to the content. Langchain, another crucial component, provides additional functionality for knowledge extraction. It offers a range of AI models and tools specifically designed for language processing and analysis. Integrating Langchain with the research assistant expands its capabilities, allowing researchers to delve deeper into the papers and extract valuable information.

Our team harnessed the power of OpenAI's shap-e and gpt4all technologies to transform mere text into tangible 3D objects, all within a tight timeframe. But what sets our project apart is our commitment to sustainability and resourcefulness. We utilized recycled plastic filament as our raw material and self-assembled 3D printers for production. This project is not just about technological innovation. It's about envisioning a future where personalized consumer goods, from furniture to fashion items, can be produced on demand using sustainable materials. Join us as we delve deeper into this exciting journey of combining AI, 3D printing, and sustainability to revolutionize the manufacturing landscape.

🗓️ This will be a week of hacking and fun from 24 February to 3 March 💻 Create innovative new apps with OpenAI's latest AI tools 💡 Learn from top AI professionals ⚒️ Combine GPT-3, Codex, Dalle-2, and Whisper to build your AI app 🐱‍💻 Now is the time to register and let's get started!

🛠️ 7 days of Learning and Building 🤝 Come with your team or find one during the Hackathon 🦾 Free access to Generative AI models and Tech Tutorials 🏅 Win a place in the Early Stage StartUp Accelerator program 🚀 Join community of AI experts

🔥 48-hours AI Challenge 🦾 Build your AI agent, or agent simulation, in 1 day 🚀 Use the LangChain framework to power up your coding! 🤝 You can find co-founders and mentors here