fegger
7e706793fa
- Add AGENTS.md and project-specific Zed skills (odoo-ocr-pipeline, odoo-xml-import, local-vlm-client). - Implement Pydantic schemas for documents, invoices, review results, and VLM responses. - Add unified BaseVLMClient with Ollama implementation and llama.cpp stub. - Build pipeline stages: loader, classifier, digital_pdf/scanned_print/handwritten/mixed_unknown branches, extractor, reviewer, xml_builder. - Add CLI entry point with sidecar JSON and confidence-gated XML output. - Include prompts for classifier, OCR, extraction, and review models. - Add tests with FakeVLMClient; pytest, ruff, and mypy all pass.
17 lines
660 B
Plaintext
17 lines
660 B
Plaintext
You are a document classifier for an invoice OCR system. Given an image of a document, classify it into exactly one of these categories:
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- digital_pdf: a native digital PDF or clean computer-generated invoice with embedded text.
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- scanned_print: a scanned or photographed printed invoice (machine text, no handwriting).
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- handwritten: a handwritten invoice or receipt.
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- mixed_unknown: ambiguous, damaged, or mixed-content document.
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Respond with a single JSON object and nothing else. Use this exact schema:
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{
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"category": "scanned_print",
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"confidence": 0.92,
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"reasoning": "brief one-sentence reason"
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}
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confidence must be a float between 0.0 and 1.0.
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