Context Enrichment

The AI plug automatically enriches chat messages with relevant context before sending them to the LLM. This helps the LLM understand your notes and provide more relevant responses.

How It Works

When you send a message in chat, the plug can:

1. Parse wiki-links - Extract content from linked pages
2. Search embeddings - Find semantically related notes
3. Expand templates - Render SilverBullet queries and templates
4. Run custom functions - Execute your own enrichment logic

Each piece of context is wrapped as an "attachment" and inserted into the conversation in a cache-friendly order.

Attachment Types

Type	Description
note	Content from a wiki-linked page like [PageName](PageName.md)
embedding	Content from semantically similar pages found via embedding search
custom	Content added by custom enrichment functions

Configuration

Enable context enrichment features in your config:

config.set("ai", {
  chat = {
    parseWikiLinks = true,      -- Extract content from [wiki-links](wiki-links.md)
    searchEmbeddings = true,    -- Search indexed embeddings for context
    bakeMessages = true,        -- Render templates/queries before sending
    customEnrichFunctions = {}  -- List of custom function names
  }
})

Wiki-Link Context

When parseWikiLinks is enabled, any [PageName](PageName.md) references in your message will have their content fetched and included as context.

Example:

Can you summarize the key points from [Project Notes](Project Notes.md)?

The content of the "Project Notes" page will be attached to your message, giving the LLM access to that information.

Agent context: Page-based agents (see Agents) can also include wiki-links in their page body. These become attachments that persist across the entire chat session.

Embedding Search

When searchEmbeddings is enabled and you have embeddings configured, the plug searches your indexed notes for content semantically related to your message.

Relevant excerpts are automatically included as context, enabling RAG (Retrieval Augmented Generation).

Template Expansion

When bakeMessages is enabled, SilverBullet templates and queries in your message are rendered before sending:

What should I work on today?

Current tasks:
${query[from index.tag("task") where _.done == false](from index.tag("task") where _.done == false.md)}

The query results will be included in the message sent to the LLM.

Custom Enrichment Functions

You can add your own enrichment logic using Space Lua. Define a function and register it:

-- In a Space Lua block
function myCustomEnricher(content)
  -- Add custom context based on the message
  local extra = "Current date: " .. os.date("%Y-%m-%d")
  return content .. "\n\n" .. extra
end

Then add it to the configuration:

config.set("ai", {
  chat = {
    customEnrichFunctions = {"myCustomEnricher"}
  }
})

Event-Based Enrichment

You can also listen to the ai:enrichMessage event to dynamically add enrichment functions:

event.listen {
  name = "ai:enrichMessage",
  run = function(data)
    -- Return function names to run based on message content
    if string.find(data.enrichedContent, "weather") then
      return {"addWeatherContext"}
    end
    return {}
  end
}

Context Format

Attachments are formatted as XML-like context blocks when sent to the LLM:

<context type="note" name="Project Notes">
Content of the page goes here...
</context>

This format helps the LLM distinguish between your message and the attached context. It's also inserted as it's own chat message to help preserve caching with providers that support it.

Disabling Enrichment

To skip enrichment for a specific message, add [enrich:false] anywhere in your message:

[enrich:false] What is 2+2?

The attribute is removed before sending, and no enrichment is performed for that message.