The llama3.2-vision model was producing unreliable structured data
(wrong vendors, amounts, dates) making expense reports worse than
Tesseract + LLM extraction. Removes _ocr_image_vision(), the
vision JSON fast path in _parse_receipt_text(), _match_category(),
and the vision_ocr_model config setting entirely.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
receipt_parser: change _ocr_image_vision() to extract structured JSON
{vendor,amount,date,time,category} directly from the image instead of
transcribing raw text, so the downstream LLM extraction step is
unnecessary and the two-step error-compounding is eliminated.
expenses_agent: add _match_category() helper to map vision category
labels to expense product names via substring/fuzzy match; add fast
path in _parse_receipt_text() that detects pre-extracted vision JSON
(text starts with '{') and skips the second LLM submit call entirely.
Fix text[:2000] truncation that discarded receipt totals — now keeps
first 1500 + last 1500 chars of long receipts so the grand total at
the bottom is always included.
tests: fix stale test_act_enters_awaiting_confirmation_on_first_pass
(confirmation gate was removed); add TestMatchCategory and three new
tests for the vision JSON fast path and LLM fallthrough.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds module-level label and cross-reference to the new doc.
TEST_EXPENSES_AGENT.md documents every test group, case, and the
real-world bug each test guards against (e.g. In-N-Out OCR mismatch).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
'Restaurant A' vs 'Restaurant Z' differ by 1 char so difflib scores
them at ~91% -- correctly above the 80% threshold. Use clearly
different vendors (Starbucks Coffee vs McDonalds Burger) instead.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
