extracting-tables

<!-- AI-RULEZ :: GENERATED FILE — DO NOT EDIT Content-Hash: blake3:7667a52a8674605a45cc61b67e7879a0104d5e86c0d82b4bde5ced9e6e3463a8 Source-Hash: blake3:3fca38eb564d0ed9c9194d070fd8dec96de727edc92e82954cb4b9af52cddbaf Schema-Version: v1 --> # Extracting tables Use this when the user wants structured tabular data — financial statements, scientific tables, invoices, spreadsheet-style PDFs. Xberg detects tables via a layout model (RT-DETR v2) and reconstructs cell structure with a configurable table model. ## Basic usage ```bash # Markdown tables embedded in the content stream xberg extract report.pdf --layout --content-format markdown # Structured JSON output, tables appear under result.tables xberg extract report.pdf --layout --format json ``` `--layout` turns on layout-aware extraction; without it, tables fall back to plain text reflow and you lose cell boundaries. ## Output shapes Two surfaces, picked via `--format` (CLI shape) and `--content-format` (content rendering): - **Markdown tables in `content`** — `--content-format markdown`. Tables appear inline as `| col | col |` blocks. Good for LLM ingestion. - **Structured `tables` array** — `--format json`. Each entry has `cells[][]` (rows × cols), `markdown` (pre-rendered), `page_number`, `bounding_box`. Use this when downstream code needs exact cell access. (`bounding_box` is omitted when no position data is available.) Both are populated at once when `--layout` is on. The `tables` array is always structured; the `content` stream switches representation. ```bash xberg extract financials.pdf --layout --format json \ | jq '.result.tables[] | {page: .page_number, rows: (.cells | length)}' ``` ## Table models `--layout-table-model` picks the reconstruction backend: | Model | Best for | Notes | | ------------------ | ----------------------------------------------------- | ------------------------------------------- | | `tatr` | dense complex tables (academic, financial) | **Default.** Heaviest, highest accuracy. | | `slanet_auto` | dispatches per-table to wired/wireless | Good when table styles are mixed. | | `slanet_wired` | tables with visible borders | Faster than tatr. | | `slanet_wireless` | tables without borders (whitespace-separated) | For invoices, simple grids. | | `slanet_plus` | hybrid wired / wireless | Lighter than `slanet_auto`. | | `disabled` | layout detection only, no table structure | Use to skip table model cost. | ```bash xberg extract bank-statement.pdf \ --layout --layout-table-model tatr --content-format markdown ``` Drop `--layout-confidence` when the layout model misses tables (default threshold ~0.5): ```bash xberg extract noisy-scan.pdf --layout --layout-confidence 0.3 ``` ## Spreadsheets `.xlsx`, `.ods`, `.csv`, `.tsv` are extracted by dedicated parsers — no layout model needed. Each sheet becomes a markdown table (or structured table) automatically: ```bash xberg extract workbook.xlsx --content-format markdown xberg extract data.csv --format json ``` Pass `--no-cache=true` only when iterating on the same file with different configs. ## Config file alternative ```toml # `output_format` in config files equals `--content-format` on the CLI. output_format = "markdown" [layout] confidence_threshold = 0.5 table_model = "tatr" ``` Then: ```bash xberg extract report.pdf --format json ``` ## Programmatic access From Python, structured tables live on the document in the result envelope (`result.results[0].tables`): ```python from xberg import ExtractInput, extract, ExtractionConfig, LayoutDetectionConfig config = ExtractionConfig( layout=LayoutDetectionConfig(table_model="tatr"), output_format="markdown", ) result = await extract(ExtractInput(uri="report.pdf"), config) for table in result.results[0].tables: print(table.markdown) # rendered markdown print(table.cells[0][0]) # cell access ``` Node.js mirrors this (`extract`, `output.results[0].tables`, camelCase fields). See `references/python-api.md` and `references/nodejs-api.md` in the sibling `xberg` skill for full type signatures. ## Known limitations - **Merged cells** — reconstructed as repeated values across the spanned region; the merge is not preserved as metadata. - **Rotated tables** — enable `--ocr-auto-rotate true` for image-based PDFs before extraction. - **Nested tables** — flattened. Detection succeeds; structural nesting is lost. - **Multi-page tables** — each page yields a separate `tables[]` entry. Stitch by matching column headers if needed. - **ONNX Runtime required** — layout and table models are unavailable in WASM builds and on the Android x86_64 emulator; native targets ship full support. ## Common failure modes - **Empty `tables` with `--layout` on** — confidence threshold too high or table model mismatched. Drop `--layout-confidence` to 0.3, try `--layout-table-model tatr`. - **Markdown tables look ragged** — switch `--layout-table-model` to `slanet_wired` for bordered grids or `slanet_wireless` for invoices. - **Slow extraction** — `tatr` is heavy. Use `slanet_auto` or `slanet_plus` as a default; reach for `tatr` only when accuracy matters. See `references/cli-reference.md` for the full layout flag set and `references/advanced-features.md` for the layout pipeline internals.