SSShooter / streaming-mindmap-rendering

Skill Content

---
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.

```bash
npm install mind-elixir
```

## 2. Component Structure

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

```tsx
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

```text
- 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.

````typescript
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 } = await reader.read()
    if (done) break

    const chunk = decoder.decode(value)
    accumulatedText.current += chunk

    // Throttle updates to avoid freezing the UI
    const now = Date.now()
    if (now - lastRenderTime.current > 500) {
      // 500ms throttle
      updateMindmap()
      lastRenderTime.current = now
    }
  }

  // Final update
  updateMindmap()
}

function updateMindmap() {
  try {
    const cleanText = cleanStreamContent(accumulatedText.current)
    const data = plaintextToMindElixir(cleanText)
    setMindmapData(data) // This triggers the useEffect in MindmapRenderer
  } catch (e) {
    // Ignore parse errors from incomplete chunks
    console.warn('Partial parse error ignored')
  }
}
````

## 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.

```typescript
// 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.