When RECEIPT_VISION_MODE=vision (default), uploaded receipt images are sent
directly to the vision-capable LLM (llama3.2-vision via Ollama) instead of
the OCR text excerpt. The model can read logos, stylised fonts, and layouts
that Tesseract OCR mangles (Home Depot, HMSHost/Sergio's, etc.).
Architecture:
- amount + date: always from Tesseract regex (deterministic, never LLM)
- vendor + category: vision LLM when image available, text LLM as fallback
- Fallthrough: if vision call fails for any reason, text path is tried next
- PDF/TXT/HTML receipts: always use text path (not visual media)
Revert instantly without a rebuild:
echo "RECEIPT_VISION_MODE=text" >> /root/odoo/odoo-ai/.env
docker compose up -d agent-service
config.py: add receipt_vision_mode setting (default 'vision')
expenses_agent.py: _VISION_MIMETYPES, _get_vision_mode() helper,
dual-path _parse_receipt_text (b64/mimetype params), _act() passes b64
tests: 92 passing — 4 new vision tests, 2 existing prompt tests
pinned to text mode via _get_vision_mode patch
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove "NeDonald's → McDonald's" from LLM vendor correction examples; the
example was biasing the model to return McDonald's for any ambiguous receipt
(Home Depot, Sergio's/HMSHost). Replace with neutral brand examples and add
an explicit instruction not to substitute a brand name absent from the OCR text.
- Add `net\s*fee` to _TOTAL_RE so MIA Parking kiosk receipts ("net fee: 150.00 USD")
are captured by Pass 1 rather than the max-scan which could pick a larger line.
- Add Step 5b grayscale fallback in receipt_parser: if all binarized PSM attempts
yield < 20 chars, retry OCR on the pre-binarization grayscale image. Fixes
dot-matrix and certain thermal-print fonts destroyed by the 160-threshold.
- Tests: 88 passing (test_net_fee_parking, test_vendor_prompt_does_not_contain_mcdonalds,
test_vendor_prompt_instructs_not_to_guess_absent_brand).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LAYAL CAFE ($2.80 instead of $42.90):
- Add (?!\s*tax) lookahead to _TOTAL_RE so "Total Taxes $2.80" is never
confused with the receipt total when OCR drops the "Taxes" word
- Change Pass 1 from matches[-1] to max() so the largest labeled amount
always wins, regardless of line order in the OCR output
United Airlines (Subway/$0/wrong date):
- Add OSD-based rotation correction in receipt_parser.py: after EXIF
transpose, ask Tesseract's orientation-detection engine (--psm 0) what
angle to rotate; applies to receipts photographed lying sideways where
EXIF metadata cannot help
- Add month-name date patterns (DD MON YYYY / MON DD YYYY) to
_extract_date_from_text for airline/hotel receipts that print dates
like "05 MAY 2026" instead of "05/07/26"
85 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Gas station receipts (Costco, Shell, etc.) print "Total Sale $X.XX" — the
word "Sale" between "Total" and the amount prevented _TOTAL_RE from matching,
causing the Costco receipt to fall through to the max-scan heuristic and
return a garbled OCR value instead of the correct total.
Also add "Net Sale" and "Sale Total" variants for broader coverage.
79 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add _is_likely_bank_statement(): if OCR text has ≥10 lines with dollar
amounts it is almost certainly a bank/card statement screenshot, not a
single receipt. Return skip=True so _act() skips it and adds a note to
the escalations list instead of creating a $1,699 expense line.
- Fix default product selection in _act(): prefer "Meals" over whatever
happens to be first in Odoo's expense product list ("Communication"),
so unrecognised receipts get a sensible fallback category.
- Improve LLM category prompt: remove hardcoded product names (airline →
Transport) that don't exist in every Odoo install; describe business
types semantically so the model picks from the actual available list.
- Mention skipped statements in the final summary message.
- 77 tests, all passing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove card brands (VISA/MC/Amex) from _SKIP_LINE_RE so card-terminal
lines like "VISA USD$ 36.78" are no longer skipped
- Replace bottom-50% scan with full-text max scan (Pass 2): scans every
line in the receipt and returns the largest dollar amount, correctly
handling display-style receipts that show the charge at the top with
no label (e.g. LAYAL CAFE $40.10 before the item list)
- Update vendor LLM prompt to ask the model to correct OCR garbling
(e.g. "NeDonald's" → "McDonald's") and detect bank statements
- Add 4 new tests covering top-amount, card-terminal, max-beats-items,
and change-exclusion scenarios (71 tests, all passing)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Logs per-receipt: OCR text length, first 120 chars of OCR output,
and final parsed vendor/amount/date/product_name.
This will show whether Tesseract is producing usable text.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Receipt quality: replace LLM amount/date extraction with regex.
LLM was hallucinating 2021/2022 dates and returning '198.40 USD' strings.
Amounts now use deterministic regex (Total:/Grand Total:/Amount Due:).
Dates: filename timestamp > OCR regex > today (no LLM date guessing).
LLM only asked for vendor name + product category.
Approval: fix GET /approval/pending 500 by using correct column
name 'started_at' instead of 'created_at' (which does not exist).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Two sources of hallucinated values in receipt parsing:
1. The LLM extraction prompt had no explicit "don't guess" constraint, so
when Tesseract produced garbled OCR text the LLM substituted plausible-
looking values (wrong vendor names, wrong totals) instead of returning
safe defaults.
2. The date field asked the LLM to extract the date from the OCR text even
when date_hint (from the filename timestamp, e.g. 20260509_180857.jpg)
was already available — a reliable signal that was being ignored.
expenses_agent._parse_receipt_text:
- LLM path: new prompt leads with "copy values EXACTLY, do NOT guess or
infer"; adds "if OCR looks corrupted, return safe default rather than
a more logical value"; injects date_hint directly as an authoritative
value when available so the LLM never needs to extract the date.
- Vision fast path: normalise "null" string for date the same way as time;
prefer date_hint over a null date returned by the vision model.
receipt_parser._ocr_image_vision:
- Vision prompt now leads with the same "copy exactly, do not guess"
constraint and explicitly accepts null for date/time when not clearly
visible, matching the conservative tone of the LLM extraction prompt.
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>
- All specialist agents: handle_peer_request(request_type, params, directive_id)
replaces handle_peer_request(request: dict) so callers pass structured args
- ab_ai_bot: force-write bus_presence.status via SQL so Odoo 18 WebSocket presence
shows the correct colour immediately (ORM compute does not trigger on last_poll writes)
- odoo_client: wrap XML-RPC executor calls in asyncio.wait_for to enforce timeout
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The old flow required a "confirm" reply after showing a parsed-receipt
table, but that follow-up dispatch call carries no receipts (they only
exist in the /upload context). The confirmation gate was architecturally
broken: the second turn would always create nothing.
Fix: create the expense sheet immediately when receipts are present.
Byte-exact and semantic duplicates are auto-skipped; the count of
skipped items is reported in the success message. The report is always
created in Odoo as a draft so users can review amounts and submit
manually via Odoo > Expenses.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Before writing any expense records the bot now posts a numbered table
of parsed vendor/amount/date for every receipt, with duplicate entries
flagged inline. User replies 'confirm' (skips dups) or 'confirm, keep
all'. This catches OCR amount misreads before they land in Odoo.
Also removes the separate awaiting_dup_approval step; duplicate review
is now part of the single confirmation table.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pass 1 unchanged: same date + amount within 0.05 + vendor similarity 60%.
Pass 2 (new): same vendor (>= 80% similarity) + same date, regardless
of amount, to catch receipts where OCR misread the total.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaced 'pick the largest one' guidance with 'bottom-most total' and
'return 0 if no clear total found' to avoid picking line items or tips.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- master_agent: thread raw user message into extra_context and peer_data so
expenses_agent can check it directly without relying on LLM intent_summary
- master_agent: when receipts are in extra_context always route to expenses_agent,
so replies like 'skip duplicates' still trigger expense processing
- expenses_agent: _plan() checks peer_data raw_message alongside task so
skip/keep keywords are detected even when master rewrites the intent
- ab_ai_mail: wrap clarification message HTML in Markup() so Odoo does not
re-escape the tags; use <br> instead of <br/>
- ab_ai_mail: convert agent plain-text replies newlines to <br> for proper
line-break rendering in Discuss
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- expenses_agent: extract transaction time (HH:MM) from OCR receipt text
- expenses_agent: _find_semantic_duplicate uses time to rule out false positives (>30 min apart = different receipts)
- expenses_agent: pause when duplicates found, set mode=awaiting_dup_approval, ask user before creating sheet
- expenses_agent: _report formats approval message listing each dup pair with vendor/amount/date/times/filenames
- ab_ai_mail: _find_pending_attachments recognises dup-approval bot message so ZIP re-attaches on user reply
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
After parsing all receipts, identify photos that are different shots of
the same physical receipt by comparing amount + date + vendor similarity
(difflib ratio >= 0.6). When a duplicate is found, keep whichever photo
produced the most OCR text (clearest shot) and report the skipped ones.
Zero-amount receipts (OCR failed entirely) are excluded from semantic
dedup to avoid false positives.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile: add tesseract-ocr-osd for orientation detection data
- receipt_parser: resize large phone photos to 1800px, convert to
grayscale, sharpen before OCR; use psm 1 (auto + OSD) so rotated
receipts are correctly oriented before text extraction
- expenses_agent: tighten amount extraction prompt to pick the FINAL
total, not subtotal or tax line, reducing misreads like 42.90->409.00
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ab_ai_bot: raise requests.post timeout 120s -> 600s so long OCR+LLM
runs don't silently drop the reply in Discuss
- upload: run parse_upload in ThreadPoolExecutor so tesseract OCR
doesn't block the FastAPI event loop
- expenses_agent: parse all receipts concurrently with asyncio.gather
(Ollama semaphore caps parallelism at 2); reduces 13-receipt LLM
time from ~39s sequential to ~20s parallel
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile: install tesseract-ocr so Pillow+pytesseract can OCR receipt images
- operational_store: JSON-serialize raw_data before passing to asyncpg JSONB
- receipt_parser: add SHA256 hash + date extracted from filename timestamps
- expenses_agent: deduplicate receipts by hash before creating expense records
- expenses_agent: fetch all expensable Odoo products, pass list to LLM for
category selection (Meals, Flights, etc.) per receipt
- expenses_agent: pass date_hint from filename (e.g. 20260509_180857.jpg -> 2026-05-09)
as fallback when OCR text is unavailable
- expenses_tools: add get_expense_products() to fetch all expensable products
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Discuss bot now reads ir.attachment from incoming messages; file-only
messages no longer silently dropped
- ZIP files are described (contents listed) and bot asks clarifying
question before acting; user's follow-up reply looks back for pending
attachments so files don't need to be re-uploaded
- receipt_parser: extracts text from ZIP (recursive), JPG/PNG/etc (OCR),
PDF (pdfplumber), HTML, TXT
- expenses_agent: full rewrite fixing broken method signatures; adds
create_expense_sheet / create_expense / attach_receipt flow driven by
LLM receipt parsing (Ollama, HIPAA-locked)
- master_agent: extra_context threads receipts + user_id into directives
- FastAPI /upload multipart endpoint; registered in main.py
- Odoo /ai/upload controller proxies files to agent service
- ab_ai_bot: dispatch_message_with_files() for multipart uploads
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
