The 6th incarnation of Elmer's blog
In this example, we’ll imagine that our chatbot needs to answer questions about the content of a website. To do that, we’ll need a way to store and access that information when the chatbot generates its response.
Source: Building a Multi-User Chatbot with Langchain and Pinecone in Next.JS
Mountpoint for Amazon S3 is an open source file client that makes it easy for your file-aware Linux applications to connect directly to Amazon Simple Storage Service (Amazon S3) buckets. Announced earlier this year as an alpha release, it is now generally available and ready for production use on your large-scale read-heavy applications: data lakes, machine learning training, image rendering, autonomous vehicle simulation, ETL, and more. It supports file-based workloads that perform sequential and random reads, sequential (append only) writes, and that don’t need full POSIX semantics.
— Mountpoint for Amazon S3 – Generally Available and Ready for Production Workloads
AudioCraft is a simple framework that generates high-quality, realistic audio and music from text-based user inputs after training on raw audio signals as opposed to MIDI or piano rolls.
Source: AudioCraft: A simple one-stop shop for audio modeling
Enhance your academic content’s credibility and make navigation of your articles easier on WordPress with these essential plugins.
Source: Meet Academic Standards With These Essential WordPress Plugins For Scholarly Content
In general the plan is to scrape the site to a vector database, enable embeddings of the vector db in Llama 2, provide API endpoints to search/find things.
The challenge is to wire together these technologies and then figure out how to get it to play nice with Drupal. One possibility is just to build this with an API and then use the API to interact with Drupal. That approach also offers the possibility of allowing the membership to interact with the API too.
In the world of data, textual data stands out as being particularly complex. It doesn’t fall into neat rows and columns like numerical data does. As a side project, I’m in the process of developing my own personal AI assistant. The objective is to use the data within my notes and documents to answer my questions. The important benefit is all data processing will occure locally on my computer, ensuring that no documents are uploaded to the cloud, and my documents will remain private.
Demystifying Text Data with the unstructured Python Library — https://saeedesmaili.com/demystifying-text-data-with-the-unstructured-python-library/
To handle such unstructured data, I’ve found the unstructured Python library to be extremely useful. It’s a flexible tool that works with various document formats, including Markdown, , XML, and HTML documents.
What I’m reading today.
Here’s a great quick start guide to getting Jupyter Notebook and Lab up and running with the Miniconda environment in WSL2 running Ubuntu. When you’re finished walking through the steps you’ll have a great data science space up and running on your Windows machine.
I am going to explain how to configure Windows 10 and Miniconda to work with Notebooks using WSL2
The longer holiday weekend edition.
What I’m reading today.
