AssemblyAI’s Post

🚀 Excited to share my latest project: a PDF Chatbot web application built with Streamlit by Langchain and Gemini! 📚💬 ℹ️ This application allows you to upload PDF files and interactively search for information within them using natural language queries. Whether you're studying for exams, conducting research, or simply exploring new topics, this tool makes it easy to extract and comprehend information from PDF documents. 🔍 Key Features: - PDF Upload: Seamlessly upload multiple PDF files. - Interactive Chatbot: Ask questions about the content of your PDF documents and receive detailed answers. Retrieval-Augmented Generation (RAG): Enhances the chatbot's responses by retrieving relevant information from the PDF documents. Gemini Integration: Empowers the chatbot with Gemini's conversational capabilities, providing more engaging and informative interactions. - Streamlit Web Interface: Clean and intuitive user interface powered by Streamlit. 📌 Give it a try and discover a new way to interact with your PDF documents: https://lnkd.in/gXzYsqXv 🔧 Built with Python, Streamlit, and PyPDF2. 👨💻 Developed by Sambit Feedback and suggestions are always welcome! Feel free to reach out and let me know what you think. Happy exploring! 🎉 #gemini #generativeai #langchain #RAG #PDF #AI

In case you're wondering where we are right now with regard to AI technology's ability to generate code, please check out this video I just published tonight. Thanks to Anthropic's recent Claude 3.5 LLM upgrade, an open source AI coding assistant called aider was able to generate a full stack app using multiple technologies. I never touched a line of code. To ensure it wasn't a fluke, I tore the whole stack down 5 times and rebuilt both apps, using only prompts. Except for one incorrect line of code in a unit test on one attempt, it worked flawlessly every try. To top it off, I reviewed the code it generated. Contrary to popular belief, the code I'm seeing so far looks quite clean and maintainable. We haven't reached a point at which AI is able to generate complex apps in one shot. I think that day is far off. But it's clear to me that, when it comes to coding, it's rapidly improving and can no longer be ignored as a must have tool in the developer's toolbox. I'd love to hear your thoughts and experiences if you've been using AI coding assistants. https://lnkd.in/e7TRiXnW

🚀 Ready to ensure your LLM app runs smoothly? Check out our latest post by Daniel Baptista Dias on trace-based testing for LLM apps! 🔍 We’ve created a step-by-step guide to help you identify and fix performance bottlenecks with Tracetest. Here’s what you’ll find in the post: 🔹 What is trace-based testing and how it applies to LLM apps 🔹 A detailed step-by-step tutorial to troubleshoot and resolve issues 🔹 Best practices for using Tracetest with large language models 🔹 How to improve LLM app performance by leveraging distributed tracing 🛠️ Dive into the tutorial now: https://lnkd.in/e9d8uHM3

Excited to share my latest project: the Gemini Pro LLM Application! In this project, I've leveraged the power of Streamlit and Google's Generative AI to create an interactive application that seamlessly interacts with Gemini Pro Models. Here's a brief overview of how it works: 🔹 Models in Action :   - Gemini Pro Model : Natural Language Understanding    Users can input prompts/questions to receive intelligible responses generated by the Gemini Pro Model. An ideal tool for natural language interaction!   - Gemini Pro Vision Model : Image Processing    This model not only processes textual prompts but also understands visual data. Upload an image, ask a question, and watch as it provides insightful illustrated responses. 🔹 Efficiency with Streamlit :   The user-friendly interface is built using Streamlit, allowing for a smooth and intuitive experience. Streamlit's simplicity and elegance make it a perfect match for quickly deploying data apps without compromising on functionality. 🔹 Caching for Performance:   To enhance performance, I've implemented caching using Streamlit's cache_data. This ensures that repeated queries don't recompute unnecessarily, resulting in faster response times. 🔹 Behind the Scenes :   - The application utilizes the `google-generative-ai` library, configured with a Google API key for access.   - The models, powered by Gemini, showcase the capabilities of state-of-the-art natural language understanding and image processing. 🔹 How to Interact :   - Choose the desired model in the sidebar.   - For Gemini Pro Model : Enter a prompt/question and click "Ask the Question."   - For Gemini Pro Vision Model : Enter a prompt/question, upload an image, and click "Illustrate the Image." 🔗 Github Link: https://lnkd.in/dHWQdXB2 Feel free to explore and experience the intersection of language and vision in a whole new way! Your feedback is valuable, and I'd love to hear your thoughts. #AI #Streamlit #GoogleGenerativeAI #GeminiPro #DataScience #NaturalLanguageUnderstanding #ImageProcessing

Context: Snowflake, container services, GenAI, code documentation How to include mermaid.js in streamlit app

Want to learn how to build an AI-powered app using HTML, TailwindCSS and JavaScript? I wrote an article about how I created a contributing guide generator app. Read it here: https://lnkd.in/gt6-Y8Sa

🚀 Excited to share my latest tutorial on building a Video Analyzer App using Streamlit, OpenAI GPT-4, Whisper API, and MoviePy! 🎥🤖 In this video, I guide you step-by-step through the process of creating a powerful app that: Extracts frames from videos using OpenCV 📸 Transcribes audio with OpenAI’s Whisper API 🎙️ Summarizes videos using GPT-4 for detailed insights 🔍 Whether you're working on AI-powered video processing or looking to integrate AI into your Python projects, this tutorial has everything you need to get started! 🚀 📺 Watch now on YouTube: [https://lnkd.in/d6meGyhC] 👉 Learn how to: Use Streamlit for interactive video apps Work with video frames & audio transcriptions Build insightful video summaries with GPT-4 Don’t forget to like, share, and subscribe for more AI and Python content! 💡 #AI #VideoProcessing #Python #GPT4 #Streamlit #OpenAI #WhisperAPI #MachineLearning #Automation #MoviePy #OpenCV #AIDevelopment #TechTutorial Anisha Udayakumar Joel Nadar Harpreet Sahota 🥑 Dragos Stan Vasagiri S. Jon Nordmark Jonathan Adu-Sarkodie Félix Vásquez Minaya Muhammad Rizwan Munawar Aygun A.

In case you're wondering where we are right now with regard to AI technology's ability to generate code, please check out this video I just published tonight. Thanks to Anthropic's recent Claude 3.5 LLM upgrade, an open source AI coding assistant called aider was able to generate a full stack app using multiple technologies. I never touched a line of code. To ensure it wasn't a fluke, I tore the whole stack down 5 times and rebuilt both apps, using only prompts. Except for one incorrect line of code in a unit test on one attempt, it worked flawlessly every try. To top it off, I reviewed the code it generated. Contrary to popular belief, the code I'm seeing so far looks quite clean and maintainable. We haven't reached a point at which AI is able to generate complex apps in one shot. I think that day is far off. But it's clear to me that, when it comes to coding, it's rapidly improving and can no longer be ignored as a must have tool in the developer's toolbox. I'd love to hear your thoughts and experiences if you've been using AI coding assistants.

🚀 Exciting News! 🚀 I’m thrilled to share that I’ve completed the "Build LLM Apps with LangChain.js" course from DeepLearning.ai! This deep dive into Language Learning Models has been eye-opening, and I can't wait to leverage these cutting-edge technologies in my upcoming projects. Exciting times ahead—stay tuned for updates! #LangChainJS #LLM #AI #MachineLearning

Introducing Wikipedia-based knowledge retrieval in my AI-powered app! 🤖 uBot is an AI-powered app I created for the sole purpose of implementing techniques to improve the quality of language models by giving them relevant context. Current context retrieval sources available: 📹 YouTube Chat: Upload YouTube videos, get concise summaries, and query the content. 🆕 Wiki Chat: Ask highly complex questions and get a fully research-based answer with citations included. Check out the demo for Wiki Chat below! 👇 Try out the new feature here: ubot.netlify.app