Author Archives: Jesse Liberty
Creating a multi-agent application – Part 5 (final)
In part 4 of this series we created our final two agents. In this final part of the series we’ll review the workflow that we create with the StateGraph class of LangGraph.
Posted in AI, Programming, Python
Leave a comment
Creating a multi-agent application – Part 4
In part 3 we looked at creating the researcher. As promised, today we’ll look at the author. You’ll notice in the following code a great deal of similarity to what we’ve seen before. The goal is to create a code … Continue reading
Posted in Agents, AI, Programming, Python
Leave a comment
Creating a multi-agent application — Part 3
In the previous post, we examined how to load the libraries we need and how to create the Blogger agent. In this post, we’ll examine the Research agent. You’ll no doubt notice the pattern of defining the template, the agent … Continue reading
Posted in AI, Programming, Python
Leave a comment
Creating a multi-agent application. Part 2
In my previous post, I showed the output of a multi-agent application I wrote to create blog posts (not to worry, it is for demonstration purposes only). In this post, I will begin the process of working through the code, … Continue reading
Posted in Essentials
Leave a comment
Creating a multi-agent application – Part 1
The following text was created by a multi-agent application designed to create blog posts. In my next post we’ll take the application apart, step by step. For now, here is a test run with the prompt Use of multiagents in … Continue reading
Posted in AI
Leave a comment
ReAct and Agents in AI
In the previous post, we looked at the use of Chain of Thought (CoT) reasoning in the context of LLMs. For an LLM to take action in the world, however, it needs agents. The paradigm for this is called ReAct—that … Continue reading
AI Reasoning and Planning
Until very recently, it was observed that LLMs had a very hard time with complex problems. Context was lost, memory of previous steps was distorted, and so forth. This led to unreliable results (hallucinations) and, consequently, to a lack of … Continue reading
PEAS for Agent AI
A classic AI framework to define an agent’s task environment is PEAS. It stands for:
The R in RAG
In my previous post we looked at saving to the vector store. In this short post we’ll look at retrieving that information. The simple search is a good starting point and depends on writing a good prompt, but we can … Continue reading
Deeper into RAG
In the previous post we walked through creating a RAG example, line by line. Let’s take a closer conceptual look at the steps involved in creating a RAG
Posted in Essentials
Leave a comment
RAG In Detail
In my previous post I walked through a RAG example but glossed over the details. In this post I’ll back up and walk through the program line by line. The key steps in RAG are
RAG – A Quick Example
In the previous blog post, we imported a few Python modules and configured our AI key, using Colab. In this blog post we’ll use Retrieval-Augmented Generation (RAG) to extend an LLM that we’ll get from OpenAI. I’ll use a number … Continue reading
