Main API for RLM document indexing - extracts structured data from documents.

Primary functions:
- `build-index` - Extract structure from file path or string content
- `index!` - Index and save to EDN + PNG files
- `load-index` - Load indexed document from EDN directory
- `inspect` - Print full document summary with TOC tree
- `print-toc-tree` - Print a formatted TOC tree from TOC entries

Supported file types:
- PDF (.pdf) - Uses vision LLM for node-based extraction
- Markdown (.md, .markdown) - Parses heading structure into pages (no LLM needed)
- Plain text (.txt, .text) - Uses LLM for text extraction
- Images (.png, .jpg, .jpeg, .gif, .bmp, .webp) - Direct vision LLM extraction

Markdown files are parsed deterministically by heading structure:
- Top-level headings (h1, or first level found) become page boundaries
- Nested headings become section nodes within each page
- No LLM required for structure extraction

Post-processing:
1. Translates local node IDs to globally unique UUIDs
2. If no TOC exists in document, generates one from Section/Heading structure
3. Links TocEntry target-section-id to matching Section nodes
4. Generates document abstract from all section descriptions using Chain of Density

Usage:
(require '[com.blockether.svar.internal.rlm.internal.pageindex.core :as pageindex])

;; Index a PDF
(def doc (pageindex/build-index "manual.pdf"))

;; Index and save to EDN + PNG files
(pageindex/index! "manual.pdf")
;; => {:document {...} :output-path "manual.pageindex"}

;; Load and inspect (includes TOC tree)
(pageindex/inspect "manual.pageindex")

Markdown parsing for RLM - extracts hierarchical structure from markdown files.

Primary functions:
- `markdown->pages` - Main API: convert markdown string to page-based format
- `markdown-file->pages` - Convenience: reads file and calls markdown->pages

Design:
- Top-level headings (h1, or first heading level found) become 'pages'
- Nested headings become nodes within each page
- Code blocks are skipped when parsing headings
- Each section includes text from heading to next heading
- No LLM required - deterministic parsing

PDF to images conversion and metadata extraction using Apache PDFBox.

Provides:
- `pdf->images` - Convert PDF file to vector of BufferedImage objects
- `page-count` - Get total page count of a PDF file
- `pdf-metadata` - Extract PDF metadata (author, title, dates, etc.)
- `detect-text-rotation` - Detect content rotation per page using text position heuristics

Uses PDFBox for reliable PDF rendering at configurable DPI.
Handles error cases: encrypted PDFs, corrupted files, file not found.

Comprehensive clojure.spec definitions for RLM data structures.

This namespace centralizes ALL specs for the RLM system to provide
a clear view of the complete data model. Individual namespaces will require
this namespace and use these specs for validation.

Data Model Philosophy:
- FLAT structure with parent references (Datalevin-style)
- All keywords are namespaced (`:node/*`, `:page/*`, `:toc/*`)
- Vector of maps output (not nested trees)
- `:node/parent-id` creates hierarchy (nil for root nodes)

Vision/LLM-based text extraction from documents.

Provides:
- `image->base64` - Convert BufferedImage to base64 PNG string
- `image->bytes` - Convert BufferedImage to PNG byte array
- `image->bytes-region` - Extract and convert a bounding-box region to PNG bytes
- `extract-image-region` - Crop a BufferedImage to a bounding-box region
- `scale-and-clamp-bbox` - Scale and clamp bounding box coordinates to image dimensions
- `extract-text-from-image` - Extract structured nodes from a single BufferedImage (vision)
- `extract-text-from-pdf` - Extract structured nodes from all pages of a PDF (vision)
- `extract-text-from-text-file` - Extract from text/markdown file (LLM, no image rendering)
- `extract-text-from-image-file` - Extract from image file (vision)
- `extract-text-from-string` - Extract from string content (LLM, no image rendering)
- `infer-document-title` - Infer a document title from page content using LLM

Configuration is passed explicitly via opts maps.
Uses multimodal LLM for both image and text extraction.
Parallel extraction using core.async channels for PDFs.