subspace.ai - PKM + REPL + AI
The long-term goal of subspace is to be/have three things:
- PKM (Personal Knowledge Management system) like Roam Research or Tana.
- REPL-like (Read Evaluate Print Loop) capabilities. Should be able to execute individual code cells in the JVM backend and rendered in the frontend with Electric. Similar behaviour can be achieved with other languages via Jupyter kernels and JavaScript.
- AI (Artificial Intelligence) integrations. Should be integrated with LLMs - e.g. write GPT-4 queries in subspace, and incorporate the response to your personal knowledge base as a new node. Intelligent search and LLM-based summaries and reasoning over the existing knowledge base, e.g. via LangChain.
The overall design should be open-ended, allowing for easy forking and providing custom node types / rendering functions. The goal is not to be just a storage of information, but a control panel for commonly used workflows. So that you can create convenient shortcuts and informative output views with Clojure + Electric. Since you persist which actions you took over time, you can search for past outputs and interleave these with your personal notes. Later query your knowledge base with LangChain in natural language.
For example, additional customizations could be:
- Wrappers for any babashka / shell scripts you already have.
- Wrapper functions to MLOps platform (or some other task manager) to trigger jobs, query stats and logs from past train runs. Build dashboards as subspace nodes from the result of such queries with Electric+HTML.
- Wrappers for common Kubernetes / AWS / GCP commands.
- Wrappers that pull the contents of arxiv documents as subspace nodes.
- Spaced repetition learning of content (of nodes which you mark to be remembered).
Running:
$ XTDB_ENABLE_BYTEUTILS_SHA1=true clj -A:dev -X user/main
Starting Electric compiler and server...
shadow-cljs - server version: 2.20.1 running at http://localhost:9630
shadow-cljs - nREPL server started on port 9001
[:app] Configuring build.
[:app] Compiling ...
[:app] Build completed. (224 files, 0 compiled, 0 warnings, 1.93s)
👉 App server available at http://0.0.0.0:8080
Why the name?
In mathematics, a subspace is some region in a vector space. In machine learning, we often operate with neural embedding vectors (of text, images, etc) where embeddings of similar concepts have vectors nearby in this "latent space".
As a metaphor, you can imagine each node in your PKM tool to occupy one point in a very high-dimensional latent space of knowledge. Navigating to a node in the PKM is like zooming in to a region in this latent space, which contains many nodes (points) nested under the main node - so in a way, you're viewing a subspace of your knowledge base.
Credits
- Adapted from electric-xtdb-started
- Which was adapted from xtdb-in-a-box
Contributions welcome! Raise an issue/PR or reach out via Twittwer @mattiasarro.