exstruct

<p align="center"> <a href="https://harumiweb.github.io/exstruct/"> <img width="600" alt="ExStruct Logo" src="https://github.com/user-attachments/assets/c1d4e616-890f-435c-9d53-fba054f861a8" /> </a> </p> <p align="center"> <em>Excel Structured Extraction Engine</em> </p> <div align="center" style="max-width: 600px; margin: auto;"> [](https://pypi.org/project/exstruct/) [](https://pepy.tech/projects/exstruct)  [](https://github.com/harumiWeb/exstruct/actions/workflows/pytest.yml) [](https://app.codacy.com/gh/harumiWeb/exstruct/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [](https://codecov.io/gh/harumiWeb/exstruct) [](https://deepwiki.com/harumiWeb/exstruct)  </div> <p align="center"> <a href="README.md"> English </a> | <a href="README.ja.md"> 日本語 </a> </p> # ExStruct — Excel Structured Extraction Engine ExStruct reads Excel workbooks into structured data and applies patch-based editing workflows through a shared core. It provides extraction APIs, a JSON-first editing CLI, and an MCP server for host-managed integrations, with options tuned for LLM/RAG preprocessing, reviewable edit flows, and local automation. - In COM/Excel environments (Windows), it performs rich extraction. - In non-COM environments (Linux/macOS): - direct OOXML parsing extracts cells, shapes, charts, table candidates, and print areas on a best-effort basis - if the LibreOffice runtime is available, cells, table candidates, shapes, and charts are also extracted on a best-effort basis Detection heuristics, editing workflows, and output modes are adjustable for LLM/RAG pipelines and local automation. ## Main Features - **Excel -> structured JSON**: outputs cells, shapes, charts, SmartArt, table candidates, merged-cell ranges, print areas, and auto page-break areas by sheet or by area. - **Output modes**: - `light`: cells + table candidates + print areas + shapes/charts (best-effort via direct OOXML parsing) - `libreoffice`: best-effort non-COM mode for `.xlsx/.xlsm`. When the LibreOffice runtime is available, it adds merged cells, shapes, connectors, and charts - `standard`: Excel COM mode with texted shapes + arrows, charts, SmartArt, and merged-cell ranges - `verbose`: outputs all shapes with width/height and also emits cell hyperlinks - **Formula extraction**: emits `formulas_map` (formula string -> cell coordinates) via openpyxl/COM. It is enabled by default in `verbose` and can be controlled with `include_formulas_map`. - **Formats**: JSON (compact by default, `--pretty` for formatting), YAML, and TOON (optional dependencies). - **Workbook editing interfaces**: use the editing CLI for primary ExStruct edit flows, keep MCP for host-owned safety controls, and use `exstruct.edit` only when you need the same patch contract from Python. - **Table detection tuning**: heuristics can be adjusted dynamically through the API. - **Hyperlink extraction**: in `verbose` mode, or with `include_cell_links=True`, cell links are emitted in `links`. - **Safe fallback**: if Excel COM or the LibreOffice runtime is unavailable, the process does not crash and falls back to direct OOXML parsing. ## Installation ```bash pip install exstruct ``` Optional extras: - YAML: `pip install pyyaml` - TOON: `pip install python-toon` - Rendering (PDF/PNG): Excel + `pip install pypdfium2 pillow` (`mode=libreoffice` is not supported) - Install everything at once: `pip install exstruct[yaml,toon,render]` Platform note: - On Debian/Ubuntu/WSL, install LibreOffice together with `python3-uno`. ExStruct probes a compatible system Python automatically for `mode=libreoffice`; if your environment needs an explicit interpreter, set `EXSTRUCT_LIBREOFFICE_PYTHON_PATH=/usr/bin/python3`. - LibreOffice Python detection now runs the bundled bridge in `--probe` mode before selection. An incompatible `EXSTRUCT_LIBREOFFICE_PYTHON_PATH` fails fast instead of surfacing a delayed bridge `SyntaxError` during extraction. - If the isolated temporary LibreOffice profile fails before the UNO socket becomes ready, ExStruct retries once with the shared/default LibreOffice profile as a compatibility fallback and reports per-attempt startup detail if both launches fail. ## Quick Start CLI ```bash exstruct input.xlsx > output.json # compact JSON to stdout by default exstruct input.xlsx -o out.json --pretty # write pretty JSON to a file exstruct input.xlsx --format yaml # YAML (requires pyyaml) exstruct input.xlsx --format toon # TOON (requires python-toon) exstruct input.xlsx --sheets-dir sheets/ # write one file per sheet exstruct input.xlsx --auto-page-breaks-dir auto_areas/ # always shown; execution requires standard/verbose + Excel COM exstruct input.xlsx --alpha-col # output column keys as A, B, ..., AA exstruct input.xlsx --include-backend-metadata # include shape/chart backend metadata exstruct input.xlsx --mode light # cells + table candidates + best-effort OOXML shapes/charts exstruct input.xlsx --mode libreoffice # best-effort extraction of shapes/connectors/charts without COM exstruct input.xlsx --pdf --image # PDF and PNGs (Excel COM required) ``` Auto page-break export is available from both the API and the CLI when Excel/COM is available. The CLI always exposes `--auto-page-breaks-dir`, but validates it at execution time. `mode=libreoffice` rejects `--pdf`, `--image`, and `--auto-page-breaks-dir` early, and `mode=light` also rejects `--auto-page-breaks-dir`. Use `standard` or `verbose` with Excel COM for those features. By default, the CLI keeps legacy 0-based numeric string column keys (`"0"`, `"1"`, ...). Use `--alpha-col` when you need Excel-style keys (`"A"`, `"B"`, ...). By default, serialized shape/chart output omits backend metadata (`provenance`, `approximation_level`, `confidence`) to reduce token usage. Use `--include-backend-metadata` or the corresponding Python/MCP option when you need it. ## Quick Start Editing CLI ```bash exstruct patch --input book.xlsx --ops ops.json --backend openpyxl exstruct patch --input book.xlsx --ops - --dry-run --pretty < ops.json exstruct make --output new.xlsx --ops ops.json --backend openpyxl exstruct ops list exstruct ops describe create_chart --pretty exstruct validate --input book.xlsx --pretty ``` - `patch` and `make` print JSON `PatchResult` to stdout. - `ops list` / `ops describe` expose the public patch-op schema. - `validate` reports workbook readability (`is_readable`, `warnings`, `errors`). Recommended edit flow: 1. Build patch ops. 2. Run `exstruct patch --dry-run` and inspect `PatchResult`, warnings, and diff. 3. Pin `--backend openpyxl` when you want the dry run and the real apply to use the same engine. 4. If you keep `--backend auto`, inspect `PatchResult.engine`; on Windows/Excel hosts the real apply may switch to COM. 5. Re-run without `--dry-run` only after the result is acceptable. ## ExStruct CLI Skill ExStruct also ships one repo-owned Skill for agents that should follow the editing CLI safely instead of rediscovering the workflow each time. Canonical repo source: - `.agents/skills/exstruct-cli/` You can install it with the following single command: ```bash npx skills add harumiWeb/exstruct/.agents/skills --skill exstruct-cli ``` If your runtime cannot use `npx skills add`, place the same folder manually into a local skill directory that discovers `SKILL.md`-based skills. Use this Skill when the agent needs help choosing between `patch`, `make`, `validate`, `ops list`, and `ops describe`, or when it should follow the safe `validate -> dry-run -> inspect -> apply -> verify` workflow. Example prompt for agents: > Use `$exstruct-cli` to choose the right ExStruct editing CLI command, follow a safe validate/dry-run/inspect workflow, and explain any backend constraints for this workbook task. ## MCP Server (stdio) MCP is the integration / compatibility layer around the same editing core. Use it when you need host-managed path restrictions, transport mapping, artifact mirroring, or approval-aware agent execution. For ordinary Python workbook editing, `openpyxl` / `xlwings` are usually a better fit. For local shell or agent workflows, prefer the editing CLI. ### Quick Start with `uvx` (recommended) You can run it directly without installation: ```bash uvx --from 'exstruct[mcp]' exstruct-mcp --root C:\data --log-file C:\logs\exstruct-mcp.log --on-conflict rename ``` Benefits: - no `pip install` required - automatic dependency management - isolated environment - easy version pinning: `uvx --from 'exstruct[mcp]==0.4.4' exstruct-mcp` ### Traditional installation You can also install it with pip: ```bash pip install exstruct[mcp] exstruct-mcp --root C:\data --log-file C:\logs\exstruct-mcp.log --on-conflict rename ``` Available tools: | Tool name | Description | | ------------------------------- | -------------------------------------- | | `exstruct_extract` | Extracts data from a workbook. | | `exstruct_capture_sheet_images` | Captures sheet images. | | `exstruct_make` | Creates a new workbook. | | `exstruct_patch` | Applies editing patches to a workbook. | | `exstruct_read_json_chunk` | Reads extracted JSON chunks. | | `exstruct_read_range` | Reads cells from a specified range. | | `exstruct_read_cells` | Reads data cell by cell. | | `exstruct_read_formulas` | Reads cell formulas. | | `exstruct_validate_input` | Validates input data. | For more details and API usage, see the documentation site: [MCP Server](https://harumiweb.github.io/exstruct/mcp/) ## Quick Start Python Extraction ```python from pathlib import Path from exstruct import extract, export, set_table_detection_params # Tune table detection (optional) set_table_detection_params(table_score_threshold=0.3, density_min=0.04) # Modes: "light" / "standard" / "verbose" wb = extract("input.xlsx", mode="standard") # standard does not emit links by default export(wb, Path("out.json"), pretty=False) # compact JSON export(wb, Path("out.json"), include_backend_metadata=True) # opt into backend metadata # Helpful model methods: iteration, indexing, and direct serialization first_sheet = wb["Sheet1"] # get a sheet with __getitem__ for name, sheet in wb: # __iter__ yields (name, SheetData) print(name, len(sheet.rows)) wb.save("out.json", pretty=True) # save WorkbookData based on extension first_sheet.save("sheet.json") # save SheetData the same way print(first_sheet.to_yaml()) # YAML string (requires pyyaml) print(first_sheet.to_json(include_backend_metadata=True)) # opt in when needed # ExStructEngine: per-instance configuration from exstruct import ( DestinationOptions, ExStructEngine, FilterOptions, FormatOptions, OutputOptions, StructOptions, export_auto_page_breaks, ) engine = ExStructEngine( options=StructOptions(mode="verbose"), # verbose includes hyperlinks by default output=OutputOptions( format=FormatOptions(pretty=True), filters=FilterOptions( include_shapes=False, include_backend_metadata=True, ), # opt into backend metadata when needed destinations=DestinationOptions(sheets_dir=Path("out_sheets")), # save per-sheet files ), ) wb2 = engine.extract("input.xlsx") engine.export(wb2, Path("out_filtered.json")) # Enable hyperlinks in standard mode engine_links = ExStructEngine(options=StructOptions(mode="standard", include_cell_links=True)) with_links = engine_links.extract("input.xlsx") # Export one file per print area from exstruct import export_print_areas_as export_print_areas_as(wb, "areas", fmt="json", pretty=True) # Extract / export auto page-break areas (COM only; raises if no auto breaks exist) engine_auto = ExStructEngine( output=OutputOptions( destinations=DestinationOptions(auto_page_breaks_dir=Path("auto_areas")) ) ) wb_auto = engine_auto.extract("input.xlsx") # includes SheetData.auto_print_areas engine_auto.export(wb_auto, Path("out_with_auto.json")) export_auto_page_breaks(wb_auto, "auto_areas", fmt="json", pretty=True) ``` **Note (non-COM environments):** even when Excel COM is unavailable, cells + `table_candidates` are still returned, and `.xlsx` / `.xlsm` keep best-effort OOXML `shapes` / `charts` when available. ## Table Detection Parameters ```python from exstruct import set_table_detection_params set_table_detection_params( table_score_threshold=0.35, # raise it to be stricter density_min=0.05, coverage_min=0.2, min_nonempty_cells=3, ) ``` Higher values reduce false positives. Lower values reduce missed detections. ## Output Modes - **light**: cells + table candidates + best-effort OOXML shapes/connectors/charts for `.xlsx` / `.xlsm` (no COM required). - **standard**: texted shapes + arrows, charts (when COM is available), and table candidates. Cell hyperlinks are emitted only when `include_cell_links=True`. - **verbose**: all shapes, charts, `table_candidates`, hyperlinks, and `colors_map`. ## Error Handling / Fallback - If Excel COM is unavailable, extraction falls back to cells + table candidates automatically; `.xlsx` / `.xlsm` still preserve best-effort OOXML shapes/charts when available. - If a rich-extraction step fails, ExStruct still returns cells + table candidates and keeps any already recovered best-effort artifacts where safe. - The CLI writes errors to stdout/stderr and exits with a non-zero status on failure. ## Optional Rendering Excel and `pypdfium2` are required: ```bash exstruct input.xlsx --pdf --image --dpi 144 ``` This writes `<output>.pdf` and PNG files under `<output>_images/`. ## Example 1: Excel Structuring Demo To show how far exstruct can structure Excel, we parse an Excel workbook that combines the following three elements on a single sheet and show an LLM reasoning example based on the JSON output. - a table (sales data) - a line chart - a flowchart built only with shapes The image below is the actual sample Excel sheet.  Sample Excel: `sample/sample.xlsx` ### 1. Input: Excel Sheet Overview This sample Excel contains the following data: ### 1) Table (sales data) | Month | Product A | Product B | Product C | | ------ | --------- | --------- | --------- | | Jan-25 | 120 | 80 | 60 | | Feb-25 | 135 | 90 | 64 | | Mar-25 | 150 | 100 | 70 | | Apr-25 | 170 | 110 | 72 | | May-25 | 160 | 120 | 75 | | Jun-25 | 180 | 130 | 80 | ### 2) Chart (line chart) - Title: Sales Data - Series: Product A / Product B / Product C (six months) - Y-axis: 0-200 ### 3) Flowchart made with shapes The sheet includes the following flow: - Start / End - Format check - Loop (items remaining?) - Error handling - Yes/No decision for sending email ### 2. Output: structured JSON generated by exstruct (excerpt) Below is a shortened JSON output example from parsing the workbook above. ```json { "book_name": "sample.xlsx", "sheets": { "Sheet1": { "rows": [ { "r": 3, "c": { "1": "月", "2": "製品A", "3": "製品B", "4": "製品C" } }, ... ], "shapes": [ { "id": 1, "text": "開始", "l": 148, "t": 220, "kind": "shape", "type": "AutoShape-FlowchartProcess" }, { "id": 2, "text": "入力データ読み込み", "l": 132, "t": 282, "kind": "shape", "type": "AutoShape-FlowchartProcess" }, { "l": 193, "t": 246, "kind": "arrow", "begin_arrow_style": 1, "end_arrow_style": 2, "begin_id": 1, "end_id": 2, "direction": "N" }, ... ], "charts": [ { "name": "Chart 1", "chart_type": "Line", "title": "売上データ", "y_axis_range": [ 0.0, 200.0 ], "series": [ { "name": "製品A", "name_range": "Sheet1!$C$3", "x_range": "Sheet1!$B$4:$B$9", "y_range": "Sheet1!$C$4:$C$9" }, ... ], "l": 377, "t": 25 } ], "table_candidates": [ "B3:E9" ] } } } ``` ### 3. How AI (Copilot / LLM) interprets the JSON ````md Below is a Markdown reconstruction of the Excel workbook. It expresses the table, chart, and flowchart as separate structures. --- ## Sales Data Table | Month | Product A | Product B | Product C | | ---------- | --------- | --------- | --------- | | 2025-01-01 | 120 | 80 | 60 | | 2025-02-01 | 135 | 90 | 64 | | 2025-03-01 | 150 | 100 | 70 | | 2025-04-01 | 170 | 110 | 72 | | 2025-05-01 | 160 | 120 | 75 | | 2025-06-01 | 180 | 130 | 80 | --- ## Sales Data (Line Chart) - Chart title: **売上データ** - Chart type: line chart - Y-axis range: 0 to 200 - Data series: - Product A: 120 -> 135 -> 150 -> 170 -> 160 -> 180 - Product B: 80 -> 90 -> 100 -> 110 -> 120 -> 130 - Product C: 60 -> 64 -> 70 -> 72 -> 75 -> 80 --- ## Process Flow (Mermaid Flowchart) ```mermaid flowchart TD A[Start] B[Load input data] C{Is the format valid?} D[Process one item] E{Items remaining?} F[Generate output] G{Send email?} H[Show error] I[Send email] J[Finish] A --> B B --> C C -->|yes| D C --> H D --> E E --> F F --> G G -->|yes| I G -->|no| J H --> J I --> J ``` ```` ## Example 2: A General Application Form ### Excel data  ### ExStruct JSON (Truncated for brevity) ```json { "book_name": "ja_form.xlsx", "sheets": { "Sheet1": { "rows": [ { "r": 1, "c": { "0": "介護保険負担限度額認定申請書" } }, { "r": 3, "c": { "0": "（申請先）", "7": " 　　　　年　　　　月　　　　日" } }, { "r": 4, "c": { "1": "X市長　" } }, ... ], "table_candidates": ["B25:C26", "C37:D50"], "merged_cells": { "schema": ["r1", "c1", "r2", "c2", "v"], "items": [ [55, 5, 55, 10, "申請者が被保険者本人の場合には、下記について記載は不要です。"], [54, 8, 54, 10, " "], [51, 5, 52, 6, "有価証券"], ... ] } } } } ``` ### ExStruct JSON -> Markdown via LLM reasoning ```md # Long-Term Care Insurance Burden Limit Certification Application (Submitted to) Year Month Day Mayor of City X Attach the related documents below and apply for certification of the burden limit for food and housing expenses. --- ## Insured Person Information | Item | Value | | ---- | ----- | | Furigana | | | Name | | | Insured Person Number | | | Personal Number | | | Date of Birth | Meiji / Taisho / Showa Year Month Day | | Address | | | Contact | | --- ## Long-Term Care Facility Entered / Hospitalized In | Item | Value | | ---- | ----- | | Facility name / location | | | Contact | | | Date of entry / admission | Year Month Day | If the applicant has not entered a care insurance facility, or uses short stay only, this section is not required. --- ## Presence of a Spouse | Item | Value | | ---- | ----- | | Spouse | Yes / No | If "No", the following spouse section is not required. --- ## Spouse Information | Item | Value | | ---- | ----- | | Furigana | | | Name | | | Date of Birth | Meiji / Taisho / Showa Year Month Day | | Personal Number | | | Address | Postal code | | Contact | | | Address as of January 1 of this year (if different) | Postal code | | Tax status | Municipal resident tax: taxable / non-taxable | --- ## Declaration of Income and Other Status Check the applicable item below. - □ 1. Livelihood protection recipient - □ 2. Old-age welfare pension recipient in a household exempt from municipal resident tax - □ 3. Person exempt from municipal resident tax whose taxable pension income + survivor/disability pension + other income totals **800,000 JPY or less per year** - □ 4. Same as above, but **over 800,000 JPY up to 1,200,000 JPY** - □ 5. Same as above, but **over 1,200,000 JPY** Survivor pension includes widow's pension, widower's pension, mother's pension, quasi-mother's pension, and orphan's pension. --- ## Declaration of Deposits and Other Assets - □ The total amount of deposits, securities, and other assets is below the following threshold: - Category 2: 10 million JPY (20 million JPY for couples) - Category 3: 6.5 million JPY (16.5 million JPY for couples) - Category 4: 5.5 million JPY (15.5 million JPY for couples) - Category 5: 5 million JPY (15 million JPY for couples) - Second insured persons (ages 40-64): Categories 3-5 must be 10 million JPY or less (20 million JPY for couples) ### Asset breakdown | Item | Amount | | ---- | ------ | | Deposits | JPY | | Securities (estimated value) | JPY | | Other (including cash / debt) | JPY (describe) | --- ## Applicant Information (not required when the applicant is the insured person) | Item | Value | | ---- | ----- | | Applicant name | | | Contact (home / office) | | | Applicant address | | | Relationship to insured person | | --- ## Notes 1. In this application, "spouse" includes a spouse living separately and a common-law partner. 2. If you own multiple assets of the same kind, list all of them and attach copies of bankbooks or equivalent documents. 3. If there is not enough space, write on the margin or on a separate sheet and attach it. 4. If benefits are obtained through a false declaration, the paid amount and up to twice that amount as an additional charge may need to be repaid under Article 22, Paragraph 1 of the Long-Term Care Insurance Act. ``` ## Discussion The result above shows the following clearly: **ExStruct JSON is already in a format that AI can understand semantically as-is.** Other LLM inference samples built with this library are available in the following directories: - [Basic Excel](sample/basic/) - [Flowchart](sample/flowchart/) - [Gantt Chart](sample/gantt_chart/) - [Application forms with many merged cells](sample/forms_with_many_merged_cells/) ### 4. Summary This benchmark demonstrates that the library can: - analyze tables, charts, and shapes (flowcharts) at the same time - convert Excel's semantic structure into JSON - let AI/LLMs read that JSON directly and reconstruct workbook content In short, **exstruct = "an engine that converts Excel into a format AI can understand."** ## Benchmark  This repository includes benchmark reports focused on RAG/LLM preprocessing of Excel documents. We track two perspectives: (1) core extraction accuracy and (2) reconstruction utility for downstream structure queries (RUB). See `benchmark/REPORT.md` for the working summary and `benchmark/public/REPORT.md` for the public bundle. Current results are based on n=12 cases and will be expanded further. ## Notes - Default JSON is compact to reduce token usage. Use `--pretty` / `pretty=True` when readability matters. - The field name is `table_candidates` (replacing the old `tables`). Adjust downstream schemas accordingly. ## Enterprise Use ExStruct is intended primarily for **library** use, not as a service. - no official support or SLA is provided - long-term stability is prioritized over rapid feature growth - enterprise use is expected to involve forking or internal customization It is suitable for teams that: - need transparency instead of black-box tooling - are comfortable maintaining internal forks when needed ## Print Areas and Auto Page-Break Areas (PrintArea / PrintAreaView) - `SheetData.print_areas` contains print areas (cell coordinates) in `light` / `standard` / `verbose`. - `SheetData.auto_print_areas` contains Excel COM-computed auto page-break areas only when auto page-break extraction is enabled (COM only). - Use `export_print_areas_as(...)` or CLI `--print-areas-dir` to export one file per print area. If no print areas exist, nothing is written. - Use CLI `--auto-page-breaks-dir` (COM only), `DestinationOptions.auto_page_breaks_dir` (recommended), or `export_auto_page_breaks(...)` to export one file per auto page-break area. `export_auto_page_breaks(...)` raises `ValueError` when no auto page breaks exist. - `PrintAreaView` includes rows and table candidates inside the area, plus shapes/charts that intersect the area. When shape size is unknown, point-based overlap is used. With `normalize=True`, row/column indices are rebased to the area origin. ## Architecture ExStruct adopts a pipeline-oriented architecture that separates extraction strategy (Backend), orchestration (Pipeline), and semantic modeling. See: [dev-docs/architecture/pipeline.md](dev-docs/architecture/pipeline.md) ## Contributing If you plan to extend ExStruct internals, read the contributor architecture guide first. See: [dev-docs/architecture/contributor-guide.md](dev-docs/architecture/contributor-guide.md) ## Coverage Note The cell-structure inference logic (`cells.py`) depends on heuristic rules and Excel-specific behavior. Full coverage is intentionally not pursued, because exhaustive tests would not reflect real-world reliability. ## License BSD-3-Clause. See `LICENSE` for details. ## Documentation - API reference (GitHub Pages): https://harumiweb.github.io/exstruct/ - JSON schemas are stored in `schemas/`, one file per model. Regenerate them with `python scripts/gen_json_schema.py` after model changes. ## Star History [](https://www.star-history.com/?repos=harumiWeb%2Fexstruct&type=date&legend=top-left)