The Cost and Latency of Defining Complex Graph Structures

Hey everyone! Thank you so much for watching the 37th episode of the Weaviate podcast! This episode discusses some of the ideas behind GPT Index. GPT Index presents really exciting ideas about how we use LLMs to index our data and then traverse these data structures. We began the podcast by discussing the origins of the tool and the ideas behind the Tree Index. We then discussed generalizing these trees to graphs and whether we are headed to the Knowledge Graph 2.0. Another really interesting topic we covered is the inference cost of building and traversing LLM indices like this! I really hope you enjoy this podcast I think these are some of the most cutting edge ideas in AI and Search!

Check out GPT Index (now LlamaIndex here - https://gpt-index.readthedocs.io/en/l...)

Chapters

0:00 Introduction

0:18 Origin Story of GPT Index

2:22 GPT Tree Index

5:53 Search Examples - Podcast Clips

11:22 Knowledge Graph 2.0?

16:05 LLM Writing Data to DB

20:18 Weaviate Classes and Index Hierarchy

23:53 Subindices vs. Tool Use

28:50 Inference Requirements for GPT Index

35:53 Design of GPT Index

37:40 Impact of Cheaper LLMs for this

40:02 Name Change for GPT Index?

42:04 Llama Hub

45:07 Relationship in Software Stack

48:15 Extension to Multimodal, e.g. Vision-Language