- name
- extracting-mistral-ocr
- description
- Extracts text, tables, and images from PDFs (including scanned PDFs) using the Mistral OCR API. Use when user asks to OCR a PDF/image, extract text from a PDF, parse a scanned document, convert a PDF to Markdown, or extract structured fields from a document.
- compatibility
- Requires network access and a MISTRAL_API_KEY environment variable. Expects Python 3.9+ and the mistralai package.
- allowed-tools
- Read,Write,Bash(python:*)
- metadata
- {"author":"generated-by-chatgpt","version":"0.1.0","api":"mistral","default-model":"mistral-ocr-latest"}
# Mistral OCR PDF extraction
## Quick start (default)
Run the bundled script to OCR a local PDF and write Markdown + JSON outputs:
```bash
python {baseDir}/scripts/mistral_ocr_extract.py --input path/to/file.pdf --out out/ocr
```
Output directory layout:
- `combined.md` (all pages concatenated)
- `pages/page-000.md` (per-page markdown)
- `raw_response.json` (full OCR response)
- `images/` (decoded embedded images, if requested)
- `tables/` (separate tables, if requested)
## Workflow
1. **Pick input mode**
- **Local PDF** (most common): upload via Files API, then OCR via `file_id`.
- **Public URL**: OCR directly via `document_url`.
2. **Choose output fidelity** (defaults are safe for RAG)
- Keep `table_format=inline` unless the user explicitly wants tables split out.
- Set `--include-image-base64` when the user needs figures/diagrams extracted.
- Use `--extract-header/--extract-footer` if header/footer noise hurts downstream search.
3. **Run OCR**
- Use `scripts/mistral_ocr_extract.py` to produce a deterministic on-disk artefact set.
4. **(Optional) Structured extraction from the whole document**
- If the user wants fields (invoice totals, contract parties, etc.), provide an annotation prompt.
- The OCR API can return a document-level `document_annotation` in addition to page markdown.
Example:
```bash
python {baseDir}/scripts/mistral_ocr_extract.py \
--input invoice.pdf \
--out out/invoice \
--annotation-prompt "Extract supplier_name, invoice_number, invoice_date (ISO-8601), currency, total_amount. Return JSON." \
--annotation-format json_object
```
## Decision rules
- **If the PDF is local and not publicly accessible**, upload it (the script does this automatically).
- **If the PDF URL is private or requires authentication**, do not pass it as `document_url`; upload instead.
- **If output quality is critical**, prefer `table_format=html` for downstream parsing over brittle regex.
## Common failure modes
- **Missing `MISTRAL_API_KEY`**: set it in the environment before running.
- **URL OCR fails**: the URL likely is not publicly accessible; upload the file.
- **Large files**: upload supports large files, but very large PDFs may need page selection (`--pages`) or batch processing.
## References
- API + parameters: `references/mistral_ocr_api.md`
- Output mapping rules (placeholders to extracted images/tables): `references/output_mapping.md`
- Example annotation prompts for common document types: `references/annotation_prompts.md`
عرض على GitHub