In Part 1, we built a basic RAG-based chatbot using OpenAI and FAISS to answer questions from a custom knowledge base. In Part 2, we added conversational memory to make the chatbot aware of previous messages.
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In Part 1, I built a lightweight Retrieval-Augmented Generation (RAG) chatbot using Python, FAISS, and OpenAI — trained on articles from my site, PointsCrowd. The result: a functional chatbot that could answer domain-specific questions based on a custom knowledge base.
Learn More AI can sometimes feel like an intimidating “black box,” but my recent project helped me understand that it doesn’t have to be. With some curiosity and hands-on experimentation, I built a GPT-4-powered chatbot trained on a specific knowledge base. In this article, I’ll walk you through the process and share what I learned along the way.
Learn More At AWS Re:Invent 2023, the preview of Amazon Aurora Limitless Database was announced. This groundbreaking serverless offering permits access to a single Aurora database, capable of immense write throughput and storage capacity. Traditional Aurora setups needed multiple writers and a complex scaling solution across instances. Presently, the service is by invitation, operating in Aurora PostgreSQL version 15 across various AWS regions.
Learn More Amazon Web Services (AWS) offers a wide range of database services to its customers, including Aurora Serverless, which is a serverless relational database service that provides on-demand auto-scaling and pay-per-use billing. Recently, AWS announced a new version of Aurora Serverless, called Aurora Serverless v2, which promises to deliver faster performance and lower costs compared to the previous version, Aurora Serverless v1. In this article, we will take a closer look at Aurora Serverless v2 and compare it to Aurora Serverless v1 to see if it is worth upgrading.
Learn More AWS Lambda is a great serverless solution by itself and together with Serverless, it is easy to develop and deploy cloud applications. Lambda is great but not perfect as it comes with its own set of limitations. One of which is size – the total size of the deployed unzipped package cannot exceed 250MB.
Databases fulfill a vital role in our businesses. They allow us to maintain, update, and edit our stored information quickly and securely. We use databases for a wide variety of data storage needs like inventory tracking, customer relationship management, payroll, data analysis, networking, and much more. Doing this efficiently means we can support the wider business functions and help the company excel at its goals.
Learn More Running your code serverless in Lambda is great: it doesn’t require resource provisioning, managing a server and you pay as you go. Is it perfect? Not quite. Among the other issues natural to any serverless implementation there is one with… drivers, of course. With traditional implementations drivers are installed across the entire system, which is no problem as they are centrally registered and referenced. With serverless deployment, however, the references are broken which inevitably leads to some noticeable pain in the neck. Something I have experienced firsthand not so long ago while trying to deploy Python Lambda with MySQL ODBC connection (using pyodbc).
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