Continuing on from last week's article on Retrieval Augmented Generation (RAG for short), Bruce Nielson continues to expand on his series of AI and ML articles with a Part 2 on RAG. If you're interested in learning the ins and outs of how AI works, look no further than Bruce's ongoing series of tutorials and demos.
Interested in learning more about how ML actually works? Look no further than our continuing series of tutorials and demos on ML and AI, including this blog post by Bruce Nielson, where he continues breaking down how Retrieval Augmented Generation (RAG) works with Haystack and pgvector.
Most people who know about AI have heard of Stable Diffusion, one of the leading AI models in text-to-image generation. In today's article our in-house expert on ML and AI, Bruce Nielson, will walk you through the steps of setting up a working version of Stable Diffusion right on your own laptop.
In a previous post, we introduced Google's Gemma, but didn't dive very deep into it. In this new post by Bruce Nielson, we will showcase exactly how to setup a LLM with Gemma that can work on your laptop.
In a previous post, we used Haystack with pgvector to create a PostgreSQL document store. In this post we’re going to go over how to create a custom Haystack pipeline component that contains our custom programming. This will allow us to then implement a Haystack pipeline for our EPUB document converter.
This post is your one stop shop for how to setup your environment to follow my blog posts on your local machine to do Retrieval Augmented Generation (RAG) using Haystack, PostgreSQL, and pgvector along with Hugging Face for the open-source Large Language Model.
In this next AI tutorial by Bruce Nielson, we'll be diving into how you can load EPUB files into a Postgres database for the purpose of semantic searches, including a demo example. This is demo is similar to a previous demo we have done but is far quicker to get going thanks to pgvector.
Continuing with our tutorials on how to get started with Machine Learning, our in-house expert on AI and ML, Bruce Nielson, shares how you can use pgvector and Psycopg to do RAG.
Continuing with our series of tutorials on getting ready for Retrieval Augmented Generation, Bruce Nielson shares how you can get Haystack installed and working with your PostgreSQL database.
In our last two posts, we first installed PostgreSQL and then installed the pgvector extension to add functionality to PostgreSQL to allow us to use PostgreSQL as our document store for use with RAG. Today, we will briefly explain how pgvector handles similarity comparisons, and how it differs from our simple cosine similarity method.
