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.

Open Interpreter is an incredibly versatile and innovative tool that bridges the gap between natural language understanding and computer programming. It represents a groundbreaking open-source project that brings the power of language models to your local machine. This tool essentially functions as a locally hosted implementation of OpenAI's renowned Code Interpreter, revolutionizing the way you interact with and manipulate code through a user-friendly and intuitive natural-language interface in your terminal. The scope of possibilities that Open Interpreter unlocks is nothing short of remarkable, with three major and impactful use cases standing out prominently. First and foremost, it opens the doors to a realm of image manipulation that is limited only by your imagination. You can effortlessly create, edit, and transform images with a simple conversational command, turning your computer into a dynamic canvas for your creative expression. Beyond the realm of visual artistry, Open Interpreter becomes an indispensable ally in the domain of presentations. Crafting engaging and informative presentations becomes a breeze as you seamlessly communicate your ideas, and Open Interpreter translates them into captivating slides and visual aids. This feature is particularly valuable for professionals and educators, streamlining the process of conveying complex information in a comprehensible and visually appealing manner. Moreover, Open Interpreter's capabilities extend into the realm of finance, specifically stock price analysis. It offers you the unique ability to delve into the world of financial data and perform comprehensive stock market analyses using only a single prompt. This empowers investors, traders, and financial enthusiasts with a robust tool for making data-driven decisions, tracking market trends, and assessing investment opportunities with ease and precision.

Mistral LLM is a powerful language model that can be used for various applications such as text classification, sentiment analysis, and question-answering. However, the full potential of Mistral LLM can only be realized when it is integrated with a vector database like Chroma Vector Database to create a RAG (Recallable AI Guide) application. In this proposal, we will discuss the problem statement, solution overview, benefits of the solution, our proposal, implementation plan, and pricing and packages for integrating Mistral LLM with Chroma Vector Database. Our solution involves creating a script that connects Mistral LLM with Chroma Vector Database to create a RAG application. The Streamlit application that we created does 3 things Create a normal chat interface to converse with Mistral 7B Create a RAG application to let you chat with your documents and save them to a Chroma DB Make a connection with an existing Chroma DB and chat with this

Imagine instantly accessing and understanding complex legal documents without having to read through pages of dense text or rely on outside experts. With our AI application, powered by LangChain, Mistral 7b, and Chroma Vector Database, that's exactly what you can do. Using advanced natural language processing and machine learning techniques, the platform analyzes legal documents and extracts key information which are stored in the Chroma Vector Database for quick retrieval. Whether you need to find a specific clause or understand the overall context of a document or legal situation, our AI application can help. Upload new documents to your locally stored vector database and ask questions without a heavy bill. - Legal professionals can quickly review and analyze large volumes of legal documents, saving them valuable time and increasing their productivity. - Students and researchers can access a wealth of legal information to inform their studies and research, without having to sift through irrelevant data. - Lawyers can easily find the information they need to build strong cases and defend their clients. - Non-profits and advocacy groups can quickly analyze legal documents to better understand the impact of laws on their communities. - Businesses can understand their rights and responsibilities under the law, helping them navigate complex legal issues and avoid costly mistakes.