| """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} |
|
|
| |
| 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, |
| ) |
|
|
| |
| |
| 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 |
|
|
