feat: add structured content extraction and batch embedding optimization to highlight()

Fix highlight() hanging on structured text input by addressing 3 root causes:

1. embedBatch() now uses native WASM batch API (single forward pass instead
   of N individual embed() calls via Promise.all)

2. highlight() auto-detects content type (plain text, rich-text JSON, HTML,
   Markdown) and extracts meaningful text segments. Supports TipTap, Slate.js,
   Lexical, Draft.js, and Quill Delta formats. New contentType hint and
   contentExtractor callback for custom parsers.

3. Semantic matching phase has 10s timeout - falls back to text-only matches
   instead of hanging indefinitely.

Also fixes extractTextContent() array check: uses type-based detection
(typeof data[0] === 'number') instead of length-based (data.length > 10)
so arrays of objects are properly indexed for text search.

New types: ContentType, ContentCategory, ExtractedSegment
New fields: HighlightParams.contentType, HighlightParams.contentExtractor,
            Highlight.contentCategory

docs/API_REFERENCE.md

@@ -507,14 +507,16 @@ console.log(vector.length) // 384
 
 ---
 
-### `async embedBatch(texts: string[]): Promise<number[][]>` ✨ *v7.1.0*
-Batch embed multiple texts efficiently.
+### `async embedBatch(texts: string[]): Promise<number[][]>` ✨ *v7.1.0, Optimized v7.9.0*
+Batch embed multiple texts using native WASM batch API (single forward pass).
 
 **Parameters:**
 - `texts` - Array of strings to embed
 
 **Returns:** Array of 384-dimensional vectors
 
+> **v7.9.0**: Uses the engine's native `embed_batch()` for a single model forward pass instead of N individual `embed()` calls.
+
 **Example:**
 ```typescript
 const embeddings = await brain.embedBatch([