In today’s digital era, businesses across the globe are inundated with vast oceans of unstructured data. From emails and documents to social media posts and beyond, this data holds invaluable insights that can drive innovation, enhance customer satisfaction, and streamline operations. However, the sheer volume and complexity of unstructured data present significant challenges in terms of analysis and information retrieval. Traditional data processing tools often fall short when faced with the nuanced, irregular, and often unpredictable nature of this data.
Enter Google Gemini 1.0 Pro, a cutting-edge Generative AI Model. In this article I would like to propose an intriguing way of utilizing such models to navigate the labyrinth of unstructured data with unprecedented ease and efficiency. By leveraging the power of Gemini 1.0 Pro, businesses can transform their data analysis processes, uncovering the hidden gems of information that lie buried within the digital textual chaos.
In the ever-evolving landscape of technology, the synergy between serverless architectures, NoSQL databases, and Large Language Models (LLMs) is opening new frontiers in application development. This article delves into the integration of these cutting-edge technologies using Google’s PaLM 2 and the LangChain framework, demonstrated through the development of a ‘shopper’ chat-bot.
In this entry I will describe an example I am preparing to showcase the possibility of using ReAct (Reasoning & Acting) paradigm of Large Language Model and incorporate serverless apps into our GenAI-powered applications
So here it is – a shopper architecture. Fairly straight forward. We are going to utilize Firestore as our NoSQL database, 3 Cloud functions that can accept API calls to list or modify content of the database, and 3 python-developed tools that will be utilized by LangChain Agent, powered by PaLM 2 Large Language model. But I’m getting ahead of myself. Let’s start step by step.
In the ever-evolving landscape of AI, Google’s PaLM 2 has emerged as a revolutionary force, unlocking new potentials in natural language processing. Imagine harnessing this cutting-edge technology to create something as interactive and engaging as a chat application. In this blog entry, I’m thrilled to show you exactly how straightforward it can be to develop your very first Python application—a “chat application” that interacts with the remarkable chat-bison model from the PaLM 2 family.
Fair warning: a very basic understanding of Python is required, but you definitely do not need to be a pro!
We’ll dive into the world of Generative AI Studio, a remarkable tool (set of tools really) that provides us with a baseline of code. From there, I’ll guide you through tweaking and customizing this foundation to fit your unique vision for the app. This isn’t just about coding; it’s about creativity and bringing your ideas to life.