Enhancing Conversational AI with Dialogflow and Vertex AI: A Deep Dive into Vertex AI Agent Builder
In today’s interconnected digital environment, effective engagement with users is critical for enterprises. This blog explores the transformative potential of Vertex AI Agent Builder, a sophisticated tool from Google Cloud that empowers developers to create dynamic conversational agents, even without extensive machine learning expertise.
Building a production grade Q&A System for Knowledge Graphs and PDFs with LangChain Agents, Neo4j and Large Language Models (PART 1)
Building a Q&A system is an intriguing challenge in many ways. Typically, a simple Q&A system involves building a knowledge base, retrieving relevant knowledge from the knowledge base and using that knowledge to answer the user’s query. In this blog, I will describe two such Q&A systems. Both use an LLM-powered reasoning agent to interact with the user but are differentiated by the knowledge base where all the relevant information is stored.
The first, which I will call KG Q&A, uses a Neo4j Knowledge Graph (KG) as its knowledge base and the second, which I will call PDF Q&A, uses a Milvus vector store as its knowledge base. Both Q&A systems have an agent that stores chat history for each user (identified by a unique User ID) separately. This allows the agent to hold separate conversations with each user.
A.I Exercise Posture Assistant using GPT-4 Vision with a FastAPI backend and a Streamlit frontend
In today’s digital age, as our lifestyles become increasingly sedentary, fueled by prolonged screen time and idle habits, the importance of physical activity for both our physical and mental well-being has become undeniable. In light of that, the significance of maintaining proper posture during exercise cannot be overstated, as it directly impacts both the effectiveness of the workout and the prevention of potential injuries. Incorrect posture can lead to muscle imbalances, joint strain, and decreased range of motion, hindering progress and increasing the risk of long-term complications. To address these issues, welcome to our latest innovation in fitness technology: the AI Exercise Posture Assistant.
Leveraging the power of the OpenAI Python library, we’ve developed a cutting-edge software solution designed to revolutionize your workout experience. Say goodbye to improper form and hello to optimized performance, as our AI system analyzes your movements in real time, providing personalized feedback and guidance to ensure you achieve the perfect posture for every exercise. Whether you’re a seasoned athlete or just beginning your fitness journey, our AI Exercise Posture Assistant is here to help you reach your goals safely and effectively. Let’s dive right in and get moving!
Harnessing the Power of Generative AI for Next-Gen Search Experiences using Google Vertex AI Search
In today’s data-driven world, the ability to access and understand information across a multitude of platforms is paramount. Organizations face the critical challenge of enhancing discoverability amidst scattered data — from documents to databases and web pages. This blog explores how generative AI can transform search services to meet the high demands of today’s enterprises.
Simplifying Marketing Analytics with Google Cloud: A Beginner’s Guide
In today’s digital world, leveraging marketing analytics can significantly enhance the effectiveness of your advertising efforts. Google Cloud offers an extensive suite of tools and services tailored for this purpose, known as the Marketing Analytics Jumpstart. This guide aims to demystify the process of setting up a robust marketing analytics framework using Google Cloud, ensuring clarity and simplicity for beginners.
Ronaldo/Messi Individual Highlights Maker using YOLOv8 Detection and Tracking in Streamlit
Lionel Messi and Cristiano Ronaldo, synonymous with football excellence, have collectively shaped an era where their individual brilliance on the pitch transcends mere competition. Their extraordinary skills, stunning goals, and unmatched athleticism have not only etched their names in the archives of football history but have also given rise to a captivating spectacle that transcends the sport itself. The individual highlights of Messi and Ronaldo, capturing their mesmerizing goals and unparalleled feats, stand as some of the most watched and celebrated moments in the world of sports. If it wasn’t already apparent, I am an avid football enthusiast with the added perk of working in the field of artificial intelligence. Consequently, I made the deliberate choice to merge these two passions, resulting in an immensely enjoyable and fascinating endeavor. This synergy not only offers immense personal satisfaction but also presents a myriad of opportunities and potential monetary value. Without any more delay, let’s jump right into it.
Building an LLM-Powered Question Answering system for private documents on Discord
In the realm of Natural Language Processing (NLP), constructing a Question and Answer (Q&A) system presents an exciting challenge. This tutorial will illustrate how to develop a robust Q&A system using open-source tools and components including LangChain, GPT-3 (ChatGPT), OpenAI Embeddings, and FAISS Vector Index, and integrate the system as a Bot within a Discord application. This system empowers users to query their own PDF documents and receive precise answers. Let’s delve into the steps to craft this potent tool.
Multi-Label Video Classification using PyTorch Lightning Flash
Pytorch Lightning Flash is an amazing framework that allows you to build models without being overwhelmed by all the details, and then seamlessly override and experiment with Lightning for full flexibility. However, for some tasks, we can take a deep dive into the details and unearth gems to produce some magic!
Using your own custom dataset, you can follow this comprehensive pipeline to achieve Multi-Label Video Classification.
Video Classification using PyTorch Lightning Flash and the X3D family of models
Video Classification is the machine learning task of assigning labels to actions identified in a given video. The main objective is to predict the specific class to which the video clips belong.
An easy, simple, and highly flexible approach to achieving this is by using the Pytorch Lightning Flash API. Built on top of Pytorch LightningAI, it constitutes a collection of tasks for fast prototyping, establishing baselines, and fine-tuning scalable Deep Learning models. Its primary advantage lies in the flexibility it offers. All data loading in Flash is executed via a from_* class method on a DataModule. Lightning DataModules are shareable and reusable objects that encapsulate all data-related code.
Flash enables quick loading of videos and labels from various formats such as config files or folders into DataModules.
Given that the task at hand is Video Classification, the Flash class VideoClassificationData will be employed to create the DataModule. To load the video model used for training, the VideoClassifier class permits access to models and their weights. Both of these classes rely on Pytorch Video.
Empowering Your Business with an IVR System: A Step-by-Step Guide to Amazon Lex and Amazon Connect Integration (Part 1/3)
Implementing an IVR system in your business can significantly empower your operations and enhance the customer experience. By integrating technologies like Amazon Lex and Amazon Connect, you can automate processes, provide self-service options, and improve efficiency in industries such as healthcare, banking, telecommunications, and e-commerce.
Resolution Enhancements – Super Resolution techniques using Deep Learning – Part III
Image enhancement, picture quality improvement and increasing resolution of images without a significant drop in quality have been one of the major application areas of Deep Learning based Artificial Intelligence techniques in recent years. These technologies are collectively called Super Resolution (SR). This is part III of series of blogs on this topic.
A gentle introduction to Super Resolution techniques using Deep Learning – Part II
Image enhancement, picture quality improvement and increasing resolution of images without a significant drop in quality have been one of the major application areas of Deep Learning based Artificial Intelligence techniques in recent years. These technologies are collectively called Super Resolution (SR). Several researchers have developed successful techniques that have pushed State-Of-The-Art and applied them to several different applications and use cases. Recently, these techniques have also been successfully extended to video processing. In last post, we introduced some basics and this is about some additional use cases.
A gentle introduction to Super Resolution techniques using Deep Learning – Part I
Image enhancement, picture quality improvement and increasing resolution of images without a significant drop in quality have been one of the major application areas of Deep Learning based Artificial Intelligence techniques in recent years. These technologies are collectively called Super Resolution (SR). Several researchers have developed successful techniques that have pushed State-Of-The-Art and applied them to several different applications and use cases. Recently, these techniques have also been successfully extended to video processing.
