Unlocking the Power of Large Language Models: The ELI5 LLM Agent

In the rapidly evolving world of artificial intelligence, large language models (LLMs) are transforming the way we interact with technology. My contribution to this revolution is the ELI5 LLM Agent, a project designed to engage with the vibrant community of the r/explainlikeimfive subreddit, providing insightful, easy-to-understand responses to complex questions. I'm eager to share my code and help others get started with LLM agents.

Check out my GitHub repository: https://github.com/Steffanic/ELI5Bot

If you want to read the comments, visit my Reddit account: https://www.reddit.com/user/ELI5LLM/

The ELI5 LLM Agent leverages the latest advancements in LLM technology and is powered by LlamaIndex and Ollama, making it surprisingly straightforward to implement and deploy. Ollama is an LLM server that allows users to download and use models with an extremely simple interface. The built-in REST API allows you to run it both locally and remotely, and it is very easy to install. LlamaIndex is a framework for building context-augmented generative AI applications with LLMs. While its interface is a bit more opaque than Ollama's, setup was easy with myriad tutorials and high-quality documentation.

So what does the agent actually do? Each submission pulled from the subreddit is passed into a LlamaIndex agent that has access to an answer drafting tool. Based on the answers drafted, along with the internal dialogue of the agent, a response is generated and submitted as a comment. I attempted to incorporate Wikipedia in a Retrieval-Augmented Generation (RAG) framework, but found that this led to mode collapse for the agent. The retrieved pages did not provide the necessary context to answer the questions, causing the agent to get stuck asking for the same pages repeatedly.

Overall, the performance is acceptable. Responses are generally aligned with the questions, often insightful, and sometimes even funny. There is much more to do to bring this to production quality, particularly in terms of evaluation. Fortunately, there are ground truth examples for every submission.

Working with the libraries available in the LLM space has been incredible. They have made it possible to intuitively use advanced AI tools with only a few lines of code. I can't wait to apply this knowledge in a business setting, whether it be building internal or customer-facing RAG applications, or more complex LLM applications that I have yet to ideate.