expenses_agent: batch LLM calls + skip RAG to fix timeout on large uploads
- auto_rag=False: skip PeerBus odoo_doc_agent call on every execute(); eliminates 30s Ollama semaphore contention before parsing even starts - _batch_parse_receipts(): Phase 1 regex (instant per-receipt: amount, date, bank-statement skip); Phase 2 single batched LLM call for all vendor+product_name instead of N individual calls; vision mode falls back to per-receipt calls (can't batch images); LLM fallback on bad JSON or wrong item count - _act() updated to use _batch_parse_receipts() - 7 new tests covering batch happy path, regex-only amounts, private-key cleanup, bank-statement skip, malformed-JSON fallback, wrong-count fallback, no-products short-circuit (99 tests total, all passing) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Carlos Garcia
@@ -219,6 +219,7 @@ class ExpensesAgent(BaseAgent):
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required_odoo_module = 'hr_expense'
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system_prompt_file = 'expenses_system.txt'
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tools = EXPENSES_TOOLS
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auto_rag = False # Receipt processing needs no RAG docs; skip the 30s peer-bus call
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def __init__(self, odoo, llm, peer_bus=None):
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super().__init__(odoo, llm, peer_bus)
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@@ -343,20 +344,9 @@ class ExpensesAgent(BaseAgent):
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logger.info('ocr filename=%r date_hint=%r ocr_len=%d text_preview=%r',
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r.get('filename'), r.get('date_from_name'), ocr_len, ocr_preview)
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# Parse all receipts concurrently.
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# b64 + mimetype are forwarded so _parse_receipt_text can use the
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# vision LLM path when RECEIPT_VISION_MODE=vision (the default).
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parse_tasks = [
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self._parse_receipt_text(
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r.get('text', ''), r.get('filename', 'receipt'),
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expense_products=expense_products,
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date_hint=r.get('date_from_name'),
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b64=r.get('b64'),
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mimetype=r.get('mimetype'),
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)
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for r in unique_receipts
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]
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raw_parsed = await asyncio.gather(*parse_tasks, return_exceptions=True)
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# Parse all receipts: regex phase is instant; LLM phase is batched into
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# a single call so N receipts cost 1 LLM round-trip instead of N.
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raw_parsed = await self._batch_parse_receipts(unique_receipts, expense_products)
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paired: list[tuple[dict, dict]] = []
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for receipt, parsed in zip(unique_receipts, raw_parsed):
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@@ -522,6 +512,164 @@ class ExpensesAgent(BaseAgent):
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return None
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async def _batch_parse_receipts(self, receipts: list, expense_products: list) -> list:
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"""Parse all receipts with a single LLM call instead of one per receipt.
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Phase 1 — regex (no LLM, instant):
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• amount : _extract_amount_from_text
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• date : filename hint > OCR regex > today
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• skip flag : bank/card statement detection
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Phase 2 — one batched LLM call:
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• vendor + product_name for all non-skipped receipts in one prompt
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• Vision mode (RECEIPT_VISION_MODE=vision): falls back to individual
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calls because images can't be batched in a single Ollama message
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• Falls back to individual _parse_receipt_text calls on any failure
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Returns a list of parsed dicts in the same order as `receipts`.
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Each dict: {vendor, amount, date, time, product_name, skip?}
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"""
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today = _date.today().isoformat()
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results: list[dict] = []
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needs_llm: list[int] = [] # indices into results that need vendor/cat
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# ── Phase 1: fast per-receipt regex ──────────────────────────────────
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for r in receipts:
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filename = r.get('filename', 'receipt')
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stripped = (r.get('text', '') or '').strip()
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ocr_failed = not stripped or stripped.startswith('[')
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if not ocr_failed and _is_likely_bank_statement(stripped):
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n = sum(1 for line in stripped.splitlines() if _ANY_DOLLAR_RE.search(line))
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logger.warning('receipt %s: bank statement (%d amount lines) — skip', filename, n)
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results.append({'vendor': filename, 'amount': 0.0,
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'date': r.get('date_from_name') or today, 'time': None,
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'product_name': '', 'skip': True})
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continue
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amount = _extract_amount_from_text(stripped) if not ocr_failed else 0.0
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date_hint = r.get('date_from_name')
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date = (date_hint or
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(_extract_date_from_text(stripped) if not ocr_failed else None) or
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today)
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results.append({'vendor': filename, 'amount': amount, 'date': date,
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'time': None, 'product_name': '',
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# internal keys stripped before returning
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'_ocr_failed': ocr_failed, '_stripped': stripped,
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'_b64': r.get('b64'), '_mimetype': r.get('mimetype'),
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'_filename': filename})
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needs_llm.append(len(results) - 1)
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product_list = ', '.join(f'"{p["name"]}"' for p in expense_products)
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if not needs_llm or not product_list:
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for entry in results:
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for k in list(entry):
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if k.startswith('_'):
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del entry[k]
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return results
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# ── Phase 2a: vision mode → individual calls (can't batch images) ────
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use_vision = (
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_get_vision_mode() == 'vision'
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and any(results[i].get('_b64') and
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results[i].get('_mimetype') in _VISION_MIMETYPES
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for i in needs_llm)
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)
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if use_vision:
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tasks = [
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self._parse_receipt_text(
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results[i]['_stripped'], results[i]['_filename'],
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expense_products=expense_products,
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b64=results[i].get('_b64'),
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mimetype=results[i].get('_mimetype'),
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)
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for i in needs_llm
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]
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individual = await asyncio.gather(*tasks, return_exceptions=True)
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for i, parsed in zip(needs_llm, individual):
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if isinstance(parsed, Exception) or not isinstance(parsed, dict):
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continue
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results[i]['vendor'] = parsed.get('vendor', results[i]['_filename'])
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results[i]['product_name'] = parsed.get('product_name', '')
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for entry in results:
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for k in list(entry):
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if k.startswith('_'):
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del entry[k]
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return results
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# ── Phase 2b: text mode → single batched LLM call ────────────────────
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_cat_guide = (
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'restaurant/cafe/food court/bar → food/meal product; '
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'airline/airport/transit/taxi/parking/rental car → travel product; '
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'gas station/petrol/fuel → fuel product; '
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'hotel/motel/lodging → accommodation product; '
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'hardware/home improvement/tech/office supply → supplies product; '
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'return "" if nothing fits'
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)
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receipts_block = ''
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for seq, i in enumerate(needs_llm, 1):
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entry = results[i]
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if entry['_ocr_failed']:
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excerpt = f'[filename: {entry["_filename"]}]'
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else:
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excerpt = entry['_stripped'][:300]
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receipts_block += f'\n=== Receipt {seq} ({entry["_filename"]}) ===\n{excerpt}\n'
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n = len(needs_llm)
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batch_prompt = (
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f'Return ONLY a JSON array with exactly {n} objects, one per receipt below.\n'
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f'Each object must have exactly two keys:\n'
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f'"vendor": business name from the receipt header '
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f'(first 1-3 lines; ignore slogans and item names; '
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f'do NOT substitute a brand not clearly present).\n'
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f'"product_name": single best match from [{product_list}].\n'
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f'Category guide: {_cat_guide}\n'
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f'JSON array only:\n{receipts_block}'
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)
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try:
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resp = await self._llm.submit(
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[{'role': 'user', 'content': batch_prompt}],
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caller='expenses_agent_receipt_parser',
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)
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raw = (resp.content or '').strip()
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first, last = raw.find('['), raw.rfind(']')
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if first == -1 or last <= first:
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raise ValueError(f'No JSON array in response: {raw[:200]}')
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batch_data = json.loads(raw[first:last + 1])
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if len(batch_data) != n:
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raise ValueError(f'Expected {n} items, got {len(batch_data)}')
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for i, item in zip(needs_llm, batch_data):
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v = str(item.get('vendor', '') or '').strip()
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if v:
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results[i]['vendor'] = v
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results[i]['product_name'] = str(item.get('product_name', '') or '').strip()
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logger.info('expenses_agent: batch LLM parsed %d receipts in 1 call', n)
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except Exception as exc:
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logger.warning('expenses_agent: batch LLM failed (%s) — falling back to individual calls', exc)
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fallback_tasks = [
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self._parse_receipt_text(
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results[i]['_stripped'], results[i]['_filename'],
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expense_products=expense_products,
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)
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for i in needs_llm
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]
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fallback = await asyncio.gather(*fallback_tasks, return_exceptions=True)
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for i, parsed in zip(needs_llm, fallback):
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if isinstance(parsed, Exception) or not isinstance(parsed, dict):
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continue
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v = str(parsed.get('vendor', '') or '').strip()
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if v:
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results[i]['vendor'] = v
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results[i]['product_name'] = str(parsed.get('product_name', '') or '').strip()
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# Strip internal bookkeeping keys before returning
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for entry in results:
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for k in list(entry):
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if k.startswith('_'):
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del entry[k]
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return results
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async def _parse_receipt_text(self, text: str, filename: str,
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expense_products: list = None,
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date_hint: str = None,
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