streaming-mindmap-rendering

name	Streaming Mindmap Rendering
description	Implement real-time streaming mindmap rendering using Mind Elixir in web applications. Supports streaming text parsing and incremental updates.

Streaming Mindmap Rendering

This skill guides you through implementing a streaming mindmap renderer using mind-elixir. This technique allows you to display a mindmap that grows in real-time as data is generated by an AI model or fetched from a stream.

Prerequisites

• React (or any frontend framework, examples use React)
• mind-elixir library

1. Install Dependencies

First, ensure you have mind-elixir installed.

npm install mind-elixir

2. Component Structure

Create a wrapper component for mind-elixir to handle the lifecycle and updates.

import MindElixir, {
  type MindElixirData,
  type MindElixirInstance,
} from 'mind-elixir'
import { useEffect, useRef } from 'react'

export function MindmapRenderer({ data }: { data: MindElixirData | null }) {
  const elRef = useRef<HTMLDivElement>(null)
  const meRef = useRef<MindElixirInstance | null>(null)

  useEffect(() => {
    if (!elRef.current) return

    meRef.current = new MindElixir({
      el: elRef.current,
      direction: MindElixir.RIGHT,
    })

    // Initial empty state or loading state
    meRef.current.init(
      data || { nodeData: { topic: 'Loading...', id: 'root' } }
    )

    return () => {
      // Cleanup if necessary
    }
  }, [])

  // Update effect
  useEffect(() => {
    if (meRef.current && data) {
      // Refresh the graph with new data
      meRef.current.refresh(data)
    }
  }, [data])

  return <div ref={elRef} style={{ height: '500px', width: '100%' }} />
}

3. Streaming & Parsing Logic

The core of this skill is efficiently handling the stream and parsing potentially incomplete data.

Data Formats

Mind Elixir supports two main formats:

1. JSON (Native): Hierarchical tree structure. Hard to stream because JSON is invalid until complete.
2. Plain Text (Recommended for Streaming): Indentation-based or markdown-list-based text. Easier to parse partially.

Plain Text Format Example

- Root Node
  - Child Node 1
    - Child Node 1-1
    - Child Node 1-2
    - Child Node 1-3
    - }:2 Summary of first two nodes
  - Child Node 2
    - Child Node 2-1 [^id1]
    - Child Node 2-2 [^id2]
    - Child Node 2-3 {color: #e87a90}
    - > [^id1] <-Bidirectional Link-> [^id2]
  - Child Node 3
    - Child Node 3-1 [^id3]
    - Child Node 3-2 [^id4]
    - Child Node 3-3 [^id5]
    - > [^id3] >-Unidirectional Link-> [^id4]
    - > [^id3] <-Unidirectional Link-< [^id5]
  - Child Node 4
    - Child Node 4-1 [^id6]
    - Child Node 4-2 [^id7]
    - Child Node 4-3 [^id8]
    - } Summary of all previous nodes
    - Child Node 4-4
  - > [^id1] <-Link position is not restricted, as long as the id can be found during rendering-> [^id8]

Parsing Implementation

Use mind-elixir/plaintextConverter (or a custom parser) to convert text to the Mind Elixir JSON format.

import { plaintextToMindElixir } from 'mind-elixir/plaintextConverter'

// Helper to clean Markdown code blocks if your stream includes them
function cleanStreamContent(content: string): string {
  return content
    .replace(/^```[\w]*\n?/gm, '')
    .replace(/```$/gm, '')
    .trim()
}

// State hooks in your parent component
const [mindmapData, setMindmapData] = useState<MindElixirData | null>(null)
const accumulatedText = useRef('')
const lastRenderTime = useRef(0)

// Streaming function (Generic Example)
async function startStreaming(url: string) {
  const response = await fetch(url)
  const reader = response.body?.getReader()
  const decoder = new TextDecoder()

  if (!reader) return

  while (true) {
    const { done, value } =  reader.()
     (done) 

     chunk = decoder.(value)
    accumulatedText. += chunk

    
     now = .()
     (now - lastRenderTime. > ) {
      
      ()
      lastRenderTime. = now
    }
  }

  
  ()
}

 () {
   {
     cleanText = (accumulatedText.)
     data = (cleanText)
    (data) 
  }  (e) {
    
    .()
  }
}

4. Optimization Tips

• Throttling: Do not re-parse and re-render on every single byte. Use a throttle (e.g., 200-500ms).
• Stable Root: Ensure the parsing logic maintains a stable root ID if possible, to prevent the whole graph from flashing.
• Scroll to Last: To follow the generation, you can programmatically scroll to the last added node.

// Scroll to last node (inside MindmapRenderer update effect)
const lastNode = findLastNode(data.nodeData) // Implement traversal to find last node
if (lastNode?.id) {
  const nodeEle = meRef.current.findEle(lastNode.id)
  if (nodeEle) meRef.current.scrollIntoView(nodeEle)
}

5. Integrating with AI Prompts

When generating mindmaps with LLMs, instruct the model to use the plaintext format.