Author: Tomek

Unlocking Unstructured Data Potential with Google Gemini 1.0 Pro

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.

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Integrating Serverless Apps with NoSQL Database and LLMs: Building a ‘Shopper’ Chat-Bot with PaLM 2 and LangChain

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

Shopper architecture

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.

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Deploy your PaLM 2 Chat App on Google Colab!

In my previous entry I showed you step-by-step instruction of how to develop a simple code for general text-based chat app. If you haven’t seen it yet, give it a try: Create Your first Chat App with PaLM 2. In this short entry I want to show you how you can test/run/showcase such application using Google Colaboratory

Quick introduction to colab

Google Colaboratory, commonly referred to as “Colab,” is a free cloud service hosted by Google to encourage Machine Learning and Artificial Intelligence research. It provides a versatile environment that combines executable code, rich text, and graphics to help users create, collaborate on, and share documents. Colab is based on the Jupyter notebook environment and supports Python 3 interpreters. It offers free access to computing resources including GPUs and TPUs which can be particularly beneficial for resource-intensive tasks. This platform is widely used for educational purposes, data analysis, and prototyping, as it allows users to write and execute code, save and share their analyses, and access powerful computing resources, all through their browsers without the need for any setup.

Sounds perfect for our use case, doesn’t it?

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