Interact with LLM's via VS Code notebooks.
To begin, make a *.llm file and this extension will automatically take it from there.
Note: You can also use
*.llm.jsonfile, which functions identically but allows importing into scripts without needing to specifically configure a loader.
As compared to ChatGPT where you only have control over the user message, this allows for precisely tuning all of the system, user, and assistant messages to best suit the task at hand (aka "Prompt Engineering"):
Fun fact! The .llm format used by notebooks is on-disk represented in the official OpenAI "Chat Format" as JSON, meaning the tuned prompt notebook files can be loaded straight from disk and incorporated with the rest of your pipeline.
The extension is free to use. OpenAI isn't. Configure llm-book.openAI.dollarsPerKiloToken to show how much a given cell or notebook will cost to execute. Configure llm-book.openAI.showTokenCount to hide the token counts on cells and notebooks.
There is initial support for LLaMa models (anything CLI-powered, really) but it's wonky (the prompt is echoed back in the response, for one). Also, the base LLaMa models aren't well suited for conversational settings, and do not support the system, user, assistant breakdown. If you are interested in furthering this support, PR's are more than welcome. Set llm-book.LLaMa.binary to begin.
By default the extension queries against OpenAI APIs (https://api.openai.com/v1/chat/completions), however this is easily configured via the llm-book.openAI.endpoint setting. Your API key is added in the notebook's controls.