Getting Started

Enzyme is a REPL to digest your thoughts

Enzyme is designed to be a notebook that you can chat with — at their core, everything is plain text, meaning that you can remove chat messages of your own or ones that were AI-generated.

It borrows from the idea of REPL / Jupyter notebook cells, where each cell contains enough metadata to be self-sufficient and atomic.

Setting up

• Install Enzyme via Community Plugins (the name in that catalog is still Reason from before a recent rename; once a PR has been merged this should grow more consistent)
• Enable Enzyme in your Community Plugins settings
• Enter an API Key for the LLM
  • The default is Claude Haiku; also works well with other Anthropic and OpenAI models
  • You can select a different one or add your own configuration that uses the OpenAI API (such as TogetherAI or local models such as LM Studio, Ollama, Llama.cpp’s own model server, Nitro, etc. You can follow a tutorial for local model setup in Enzyme with LM Studio here)

Enzyme makes use of a few plugins:

• The excellent Dataview plugin in order to retrieve notes for synthesis. Dataview provides a live index with which to query the most up-to-date contents from your Obsidian notes. Make sure to install and enable this.
• It also works well when paired with the Readwise plugin, which can import highlights from books, articles, and podcasts into Obsidian. More on the author’s workflow with these two plugins in a blog post.

Easily reach for ideas in the back of your brain

Enzyme helps you quickly access and connect ideas from your notes. Here’s how it works:

Default behavior

```enzyme
remind me of some of the ideas that have been spinning around in my head recently.
```

When you use an Enzyme block like this:

1. It finds the tags and links you’ve used most recently
2. It extracts the text around those mentions
3. It uses this information to answer your prompt
4. It shows you the relevant parts of your notes as block embeds

This makes it easier to connect scattered thoughts across your notes.

Creating Enzyme blocks from text

You can also create Enzyme blocks from any text in your editor. Here are some examples:

• Thoughts on #pkm

  • Finds 5 recent notes with the #pkm tag
  • Extracts text around the tag
  • Synthesizes your thoughts on Personal Knowledge Management

• [[Jean Deaux]]<10

  • Finds your 10 most recent mentions of [[Jean Deaux]]
  • Connects and summarizes these mentions

• Insights on public speaking from Readwise/Podcasts/

  • Finds 5 recent files in the Readwise/Podcasts/ folder
  • Synthesizes highlights about public speaking

Quick tip

For easy access, set up a hotkey (like Cmd + Shift + P) to run the command: Enzyme: Build an Enzyme block from selection

Understanding Enzyme Blocks

Enzyme blocks can be placed anywhere in your notes. They use YAML syntax and consist of two main parts:

1. A sources section with Dataview Query Language (DQL)
2. A guidance prompt

Anatomy of a Source

A Source typically includes:

• A Dataview query to retrieve notes from your vault
• A strategy for extracting content from notes
• An evergreen (required for the SingleEvergreenReferrer strategy)

Here’s a simple Source example:

```enzyme
sources:
sources:
  - dql: LIST FROM "Readwise/Articles"
guidance: Summarize key insights from my article highlights
```

This uses the default extraction behavior, which includes the entire content of each note. For more extraction options, see Extraction Strategies.

Using Multiple Sources

You can define multiple Sources for more complex queries:

```enzyme
sources:
  - strategy: SingleEvergreenReferrer
    dql: |
    LIST FROM "Readwise/Books"
    WHERE contains(file.outlinks, [[ecology of technology]])
  evergreen: [[ecology of technology]]
  - dql: LIST FROM "Readwise/Articles"
guidance: [[ecology of technology]] is an idea that has been spinning around in my head, connecting spirituality and futurism. Help me identify some common overarching ideas to summarize my thoughts on it.
```

This example combines book notes mentioning “ecology of technology” with relevant articles, allowing for a more comprehensive analysis.

Using the Digest Feature

Once you’ve set up your Enzyme block:

1. Click “Digest” to process your notes and generate a digest.
2. Review the output to see how well the AI understood your guidance.
3. Refine your results:
   • Create follow-up Enzyme blocks to explore specific aspects or add context.
   • ==Highlight== parts of the digest output to include as context in follow-ups.
   • Add new sources in follow-up blocks for broader analysis.

Note: Follow-up Enzyme blocks automatically include context from previous blocks and sources in their analysis.

Tip: Since Enzyme blocks are plain text, you can easily modify or delete the original block to start over if needed.