zk-rag 1.0.5

zk-rag is a command-line chat tool for your zettelkasten

Query your Zettelkasten

This is a simple implementation of Retrieval Augmented Generation (RAG) for your Zettelkasten. It breaks your markdown documents into chunks, and uses a cosine-distance query to place relevant text chunks from your Zettelkasten into an LLM chat context in order to answer your questions.

It uses chromadb for a vector database (indexing the chunks, and doing distance queries for chunk selection) and ollama in order to generate the responses.

This project has many limitations, it is simply a starting point upon which I am building a larger system. Treat it like an example project, and build your own work on top of it.

Limitations

• command-line only

That said, it includes all of the critical elements for doing RAG queries across a document base.

Requirements

You must have ollama installed and functional.

You must have a local knowledgebase / zettelkasten with content in markdown format. I use Obsidian, because I favour working locally, and I favour using the markdown format for notes - because everything's local, and in plain text, I can simply point this tool at a Vault folder.

Workstation setup

I recommend you setting up a local virtual Python environment, to keep it clean, but you can install it globally.

pip install zk-rag

Run zk_reindex to start the configuration. You will need to provide the path to your root Zettelkasten / Obsidian vault folder, and the name of the LLM model you want to use in your Ollama installation.

Use ollama list to check which ones you have set up.

Run zk_reindex to re-index the contents of your zettelkasten.

Run zk_chat to query your Zettelkasten. Note that this is not a true chat, it will not take into account the history of your queries, every query is answered as a stand-alone question. Press enter (blank line) to exit the query loop.