Category/Tag: Software Development

Explaining DeepSeek R1 (and How to Use It)

Explaining DeepSeek R1 (and How to Use It)

China's new AI, DeepSeek R1, is causing all sorts of upset in the AI market right now, and so we've decided to break down exactly what it is, why it's causing such an uproar, and how you can try it out for yourself.


Installing Ollama for Large Language Models (LLM) in Windows

Installing Ollama for Large Language Models (LLM) in Windows

Interested in getting your own LLM up and running? Ollama, an alternative to software like LM Studio, is a relatively easy and simple tool to get started with. In this article, we'll be going through how to get it set up on Windows.


Reranking Documents Using Cross-Encoders for Retrieval Augmented Generation (RAG)

Reranking Documents Using Cross-Encoders for Retrieval Augmented Generation (RAG)

It's important to ensure that your AI is presenting the absolute best results from your searches with it. In today's AI/ML tutorial by Bruce Nielson, we'll be looking at something called a Reranker, which will allow us to turn good results into best results.


Using Neo4j Graph Database for Retrieval Augmented Generation (RAG)

Using Neo4j Graph Database for Retrieval Augmented Generation (RAG)

In two previous posts, we talked about how to install Neo4j Graph Database for use with the Book Search Archive, our sample project using the Mindfire Technology open-source AI stack. In this post we'll cover the code necessary to use Neo4j as a document store for Retrieval Augmented Generation (RAG).


Installing Neo4j Graph Database Desktop Version

Installing Neo4j Graph Database Desktop Version

Last time in our series of AI tutorials, we covered how to install Neo4J (graph database). For the Mindfire stack, we’re aiming to stick with open-source software, and Neo4j Server Community Edition fits the bill. However, there’s also a free Enterprise edition available (with some limitations) if you use Neo4J Desktop, which we'll be looking at today.


Installing Neo4J Graph Database Self-Managed for Retrieval Augmented Generation (RAG)

Installing Neo4J Graph Database Self-Managed for Retrieval Augmented Generation (RAG)

In previous posts, we learned how to install PosgreSQL and then how to install pgvector (a vector search extension for PostgreSQL.) The Book Search Archive (our toy app to show off Mindfire’s growing low-cost open-source AI stack) currently utilizes a PostgreSQL database with vectors handled by pgvector to do our semantic searches using a HNSW index built-into pgvector. In this post, we'll be looking at how to use Neo4J, a graph database with vector support, to do RAG.


How to Remove a File From a GitHub Repo

How to Remove a File From a GitHub Repo

It can often be helpful to use git as a way to compare text, especially when using git to compare versions of output, so you can then use git-filter-repo to remove that part of the git history. However, it isn't the easiest tool to figure out. Suppose you need to remove a file from a GitHub repo, how would you do that? If you just remove the file and check it in the older commits still contain that file, which means it is still part of the overall repo, what to do? In today's blog, we'll explore how to remove a file from your GitHub repo history.


AI Tutorial: Hybrid Search in Detail

AI Tutorial: Hybrid Search in Detail

In a previous post, we talked about how AI developers can combine lexical and semantic search results to create a better answer/response. Today, in this article by Bruce Nielson, we'll look a bit deeper into how you can make your AI search tools far more intelligent.


A Local Text-to-Speech Model Using Suno Bark

A Local Text-to-Speech Model Using Suno Bark

In our last post, we looked at how to stream a text-to-speech model using the Hugging Face API. We had mixed results at best. So, in this post, we'll be covering how to do text-to-speech (TTS) using a local TTS model with the help of Suno Bark and Hugging Face.


Testing Hugging Face Serverless Text-To-Speech Models

Testing Hugging Face Serverless Text-To-Speech Models

Looking to make your AI a bit more personable or easier to communicate with? In today's article by Bruce Nielson, we'll be testing out some ways of making an LLM have text-to-speech functionality using the Hugging Face framework.