Big new release of my LLM CLI tool and Python library for working with Large Language models (Llama 2, GPT-4 etc)
LLM 0.9 adds support for embedding models, installed via plugins
If you aren't familiar with embeddings I have a very detailed explanation of what they can do and how you can use them here:
Here's a fun example of something you can now do with LLM: search for every README.md file in your home directory and store embeddings for all of them in a collection called "readmes":
```
llm embed-multi readmes \
--model sentence-transformers/all-MiniLM-L6-v2 \
--files ~/ '**/README.md'
```
Then run a similarity search for "sqlite" like this:
```
llm similar readmes -c sqlite
```
Also new today: the llm-cluster plugin, which derives clusters of documents from a collection of embeddings
A fun trick with that is that you can ask it to pass the items in each cluster through an LLM in order to generate titles for each cluster!
I just released a new version of my Symbex tool, which finds functions and classes in a Python codebase
It can now export the code it finds in a format that can then be piped to "llm embed-multi" to embed those functions
@impersonal totally reasonable question! I'm still figuring out what you can do with embeddings of Python functions myself
My hunch is that they'll get interesting when combined with other tricks - like finding the example code most relevant to a question posed to an LLM
Simon Willison
lucrianami
Impersonal