File size: 4,114 Bytes
7ff7119 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | """compare_node — three-way matching: invoice + delivery note + purchase order.
The 535-line ``validation/compare.py`` implements the algorithm; this node
glues it to the graph state:
1. Find the first three-way (invoice + delivery_note + purchase_order)
2. Call ``validation.compare.three_way_match()``
3. Wrap the result into a ``ComparisonReport`` Pydantic model in the parent state
4. Convert critical mismatches to Risks (``kind="cross_check"``)
"""
from __future__ import annotations
from graph.states.pipeline_state import (
ComparisonReport,
PipelineState,
ProcessedDocument,
Risk,
)
from validation.compare import three_way_match
def _to_pydantic_report(
result, invoice_name: str, delivery_name: str, order_name: str,
) -> ComparisonReport:
"""``ComparisonResult`` (dataclass) → ``ComparisonReport`` (Pydantic) conversion."""
overall = "ok"
if result.critical_count > 0:
overall = "critical"
elif result.warning_count > 0:
overall = "warning"
elif result.missing_count > 0:
overall = "missing"
summary = (
f"3-way match: {invoice_name} / {delivery_name} / {order_name} -- "
f"{result.total_checks} checks, {result.ok_count} ok, "
f"{result.warning_count} warning, {result.critical_count} critical, "
f"{result.missing_count} missing"
)
return ComparisonReport(
invoice_filename=invoice_name,
delivery_note_filename=delivery_name,
purchase_order_filename=order_name,
matches=[m.to_dict() for m in result.matches],
total_checks=result.total_checks,
ok_count=result.ok_count,
warning_count=result.warning_count,
critical_count=result.critical_count,
missing_count=result.missing_count,
overall_status=overall,
summary=summary,
)
async def compare_node(state: PipelineState) -> dict:
"""Three-way match on the first invoice + delivery_note + purchase_order trio."""
documents: list[ProcessedDocument] = state.get("documents") or []
invoices = [d for d in documents if d.classification and d.classification.doc_type == "invoice"]
delivery_notes = [d for d in documents if d.classification and d.classification.doc_type == "delivery_note"]
purchase_orders = [d for d in documents if d.classification and d.classification.doc_type == "purchase_order"]
if not (invoices and delivery_notes and purchase_orders):
return {"comparison": None}
inv = invoices[0]
dn = delivery_notes[0]
po = purchase_orders[0]
if not (inv.extracted and dn.extracted and po.extracted):
return {"comparison": None}
# 4-pass item matching + apples-to-apples amount comparison
result = three_way_match(
invoice=inv.extracted.raw,
delivery_note=dn.extracted.raw,
purchase_order=po.extracted.raw,
)
report = _to_pydantic_report(
result,
invoice_name=inv.ingested.file_name,
delivery_name=dn.ingested.file_name,
order_name=po.ingested.file_name,
)
# Convert critical / warning matches → Risks (kind="cross_check"), with
# description-level dedup.
risks: list[Risk] = []
seen: set[str] = set()
for m in result.matches:
if m.severity == "ok":
continue
msg = m.message
if msg in seen:
continue
seen.add(msg)
if m.severity == "critical":
risks.append(Risk(
description=msg,
severity="high",
rationale="Critical discrepancy across documents",
kind="cross_check",
source_check_id="compare_three_way",
))
elif m.severity == "warning":
risks.append(Risk(
description=msg,
severity="medium",
rationale="Warning-level discrepancy",
kind="cross_check",
source_check_id="compare_three_way",
))
out: dict = {"comparison": report}
if risks:
out["risks"] = risks
return out
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