Demystifying Large Language Models: What's the Role of Vector Databases? - Eden Marco

Introduction

Welcome to this deep dive into the world of Large Language Models (LLMs) and vector databases for the Crazy Wisdom Podcast. Our guest today is Eden Marco (follow him on Twitter), a Customer Engineer at Google Cloud and a best-selling Udemy instructor with a passion for General Artificial Intelligence (GenAI). In this episode, we unpack the complexities and intricacies of LLMs, explore the role of vector databases, and discuss the future of autonomous agents and machine learning.

Key Discussion Points

1. Vector Databases & Similarity Search: Vectors play a critical role in similarity searches, a common use case in vector databases. These databases are pivotal in developing LLMs and help in getting the right context to prompts. They are also used to truncate long pieces of text into paragraphs for vector outputs.

2. Understanding LLMs and Context: How does an LLM answer questions about things it hasn't been trained on? The answer lies in in-context learning. We delve into the main problems LLMs face in understanding context and the role of vector stores in this process.

3. LLMs and Long Term Memory: We discuss the concept of coreference resolution in LLMs, the issue of growing queries due to token limitations, and techniques to handle these challenges. Eden explains the human-like behavior of LLMs and how autonomous agents interact with other agents using memory as context.

4. Prompt Engineering & Autnomous Agents: What is sophisticated prompt engineering? It's the art of getting the LLM to do what you want. We explore autonomous agents with Langchain, the process of augmenting prompts, and the growing importance of prompt engineering.

5. Human Simulation & Machine Learning: Despite advancements, a real human simulation remains distant. We touch upon the statistical nature of machines and humans and discuss whether machine learning could be considered a parasite in the digital ecosystem.

6. Twitter API & Coding: We discuss the implications and challenges of using Twitter's API for scraping tweets, and how coding can be used to overcome limitations and navigate permissions.

7. Chroma - A Vector Database: An introduction to Chroma, a vector database that facilitates in-context learning and filtering. Eden sheds light on the competitive vector database market, the benefits of using managed servers, and the potential of combining vector databases with relational ones for enhanced utility.

8. Long Term Memory and Scaling Databases: We discuss the potential of using vector databases for long term memories, the importance of cataloging memories, and the ease of scaling databases with cloud services.

9. People to Follow: Hari Sanchez, co-founder, and creator of the Langchain framework, is a must-follow for anyone interested in long-term memory and LLMs.

10. The Gap in Machine Learning: We wrap up the discussion with insights into how the gap between machine learning programmers, data scientists, and PhDs has been bridged, and the potential future of open-source models in the face of state-of-the-art LLMs.

Websites & Resources Mentioned

• Chain of Thought Paper: A resource that describes how LLMs think.
• Eden Marco's Udemy Class: A comprehensive course by Eden Marco on GenAI.
• Langchain by Hari Sanchez: A groundbreaking framework for autonomous agents.
• Eden Marco's Twitter Profile: Stay updated with Eden's latest insights and work.