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This post had robust discussion on HN and Twitter.

In October 2022, Robust Intelligence hosted an internal hackathon to play around with LLMs which led to the creation of two of the most important AI Engineering tools: LangChain 🦜⛓️ (our interview with Harrison here) and LlamaIndex 🦙 by Jerry Liu, which we’ll cover today. In less than a year, LlamaIndex has crossed 600,000 monthly downloads, raised $8.5M from Greylock, has a fast growing open source community that contributes to LlamaHub, and it doesn’t seem to be slowing down.

LlamaIndex’s Origin (aka GPT Tree Index)

Jerry struggled to make large amounts of data work with GPT-3 (which had a 4,096 tokens context window). Today LlamaIndex is at the forefront of the RAG wave (Retrieval Augmented Generation), but in the beginning Jerry wasn’t focused on embeddings and search, but rather on understanding how models could summarize, link, and reason about data.

On November 5th, Jerry pushed the first version to Github under the name “GPT Tree Index”:

The GPT Tree Index first takes in a large dataset of unprocessed text data as input. It then builds up a tree-index in a bottom-up fashion; each parent node is able to summarize the children nodes using a general summarization prompt; each intermediate node containing summary text summarizing the components below. Once the index is built, it can be saved to disk and loaded for future use.

Then, say the user wants to use GPT-3 to answer a question. Using a query prompt template, GPT-3 will be able to recursively perform tree traversal in a top-down fashion in order to answer a question. For example, in the very beginning GPT-3 is tasked with selecting between *n* top-level nodes which best answers a provided query, by outputting a number as a multiple-choice problem. The GPT Tree Index then uses the number to select the corresponding node, and the process repeats recursively among the children nodes until a leaf node is reached.

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How is this better than an embeddings-based approach / other state-of-the-art QA and retrieval methods?

The intent is not to compete against existing methods. A simpler embedding-based technique could be to just encode each chunk as an embedding and do a simple question-document embedding look-up to retrieve the result. This project is a simple exercise to test how GPT can organize and lookup information.

The project attracted a lot of attention early on (the announcement tweet has ~330 likes), but it wasn’t until ~February 2023 that the open source community really started to explode, which was around the same time that LlamaHub was released. LlamaHub made it easy for developers to import data from Google Drive, Discord, Slack, databases, and more into their LlamaIndex projects.

What is LlamaIndex?

As we mentione