"""Measure generated answers on a declared, frozen graph benchmark. Regression mode repeats known questions; it is never a new independent TEST. The harness supplies retrieval context and does not test native tool routing. """ from __future__ import annotations import argparse import concurrent.futures import csv import hashlib import json import os import time from collections import Counter from pathlib import Path from tools.graphrag.graph_db import create_neo4j_driver, get_neo4j_settings from tools.graphrag.graph_search import search_graph_with_session from tools.graphrag.build_graphrag import graph_version from tools.graphrag.evaluation.benchmark_v2 import BASE from tools.graphrag.evaluation.check_openwebui import grade_claims from tools.graphrag.evaluation.reproducibility import graph_fingerprint, run_metadata, sha256_file, verify_freeze from tools.graphrag.evaluation.source_oracle import load_source_oracle, norm from tools.rag.evaluation.rag_runner import DEFAULT_MODEL, OPENWEBUI_URL, request_json from .checked_generation import generate_checked_answer from .context_policy import joined_candidates, citation_check, allowed_urls, answer_context_issues HERE = Path(__file__).resolve().parent def graph_context(result: dict) -> dict: """Remove storage/profile metadata that cannot answer the actual question. Uses only actual retrieval output and query intent, never gold annotations. Values/subjects/source paths are copied without inventing or rewriting facts. """ facts = []; intent = result['constraints'] for row in result['facts']: kind = row.get('fact_type', '') if kind.startswith('shared_'): field = {'shared_keyword': 'keyword', 'shared_category': 'category', 'shared_document_author': 'document_author'}.get(kind) if field: for side in ['left', 'right']: facts.append({'person': row[f'{side}_person'], field: row[field], 'document_path': row.get(f'{side}_document_path'), 'source_url': row.get(f'{side}_source_url')}) continue if not row.get('person'): continue allowed = {'person', 'document_path', 'source_url', 'keyword', 'category', 'document_author'} if intent['mentions_title']: allowed.add('title') if intent['mentions_title'] or intent['mentions_academic_year'] or intent['years']: allowed.add('year') if intent['mentions_start_year']: allowed.add('start_year') fact = {k: v for k, v in row.items() if k in allowed and v is not None} if fact not in facts: facts.append(fact) sources=result.get('sources') if sources is None: sources=[{k:d[k] for k in ['document_path','source_url'] if d.get(k)} for d in result.get('documents',[])] context={'status':result['status'],'reason':result['reason'],'facts':facts,'sources':sources, 'aggregation':result['aggregation'],'complete':result.get('complete'), 'truncated':result.get('truncated',False), 'truncation_reasons':result.get('truncation_reasons',[]), 'index_version':result.get('index_version')} context['answer_candidates']=joined_candidates(facts,intent) return context def grade_answer(case: dict, answer: dict, oracle: dict, *, context: dict | None = None) -> dict: # JSON null is absence, not a positive factual assertion. The same rule is # applicable to all rounds, including the archived initial sample. normalized = dict(answer) if isinstance(answer.get('claims'), list): normalized['claims'] = [{k: v for k, v in claim.items() if v is not None} if isinstance(claim, dict) else claim for claim in answer['claims']] grade = grade_claims(case, normalized, oracle) urls=(allowed_urls(context) if context is not None else {d['source_url'] for d in oracle.get('documents',{}).values() if d.get('source_url')}) citations=citation_check(answer,urls) grade.update(citations) policy_issues=answer_context_issues(normalized,context) if context is not None else [] grade['context_issues']=policy_issues text = norm(answer.get('answer', '')) missing = [v for v in case.get('expected_answer_contains', []) if norm(v) not in text] # The initial sample exposed a real contradiction in otherwise correct # category claims. Check it explicitly rather than silently accepting prose. wrong_thesis_label = 'dizertac' in text and any(str(b.get('category', '')).startswith('dp') for b in case['graph']['expected_bindings']) and not any( str(b.get('category', '')).startswith('phd') for b in case['graph']['expected_bindings']) grade.update(text_values_ok=not missing, missing_text_values=missing, contradictory_thesis_label=wrong_thesis_label) grade['strict'] = bool(grade['strict'] and not missing and not wrong_thesis_label and citations['citations_ok'] and not policy_issues) return grade def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--output-dir', type=Path, required=True) parser.add_argument('--sample', type=Path, default=BASE / 'results/final_v4_2026-10-25/openwebui_sample.json') parser.add_argument('--workers', type=int, default=3) parser.add_argument('--model', default=DEFAULT_MODEL) parser.add_argument('--split', choices=['dev', 'test', 'regression'], default='test') parser.add_argument('--questions', type=Path, default=BASE/'graphrag_questions_v4.json') parser.add_argument('--audit', type=Path, default=BASE/'results/v4_preparation/source_audit.json') parser.add_argument('--freeze-manifest', type=Path, default=BASE/'frozen_v4.json') parser.add_argument('--dev-ids', type=Path) parser.add_argument('--dev-results', type=Path, help='Required completed DEV before final regression.') args = parser.parse_args() if args.output_dir.exists(): parser.error('Refusing to overwrite an answer-level TEST.') if not os.getenv('OPENWEBUI_API_KEY'): parser.error('OPENWEBUI_API_KEY missing.') if args.workers < 1: parser.error('--workers must be positive.') if args.split=='regression' and args.dev_results is None: parser.error('Final regression requires completed --dev-results.') dataset=args.questions; audit=args.audit; frozen=args.freeze_manifest manifest = json.loads(frozen.read_text()) questions = json.loads(dataset.read_text()); oracle = load_source_oracle() sample = json.loads(args.sample.read_text()); exposed = set(sample['selected_ids']) test = [q for q in questions if q['split'] == 'test' and q['id'] not in exposed] assert len(exposed) == 20 and len(test) == 300 development=set(json.loads(args.dev_ids.read_text())) if args.dev_ids else exposed selected = ([q for q in questions if q['split']=='test'] if args.split=='regression' else test if args.split=='test' else [q for q in questions if q['id'] in development]) if args.split=='regression' and manifest.get('evaluation_kind')!='historical_regression': parser.error('Regression requires an explicitly declared historical regression freeze.') prompt_path = HERE / 'graph_answer_prompt.txt'; prompt = prompt_path.read_text() args.output_dir.mkdir(parents=True) metadata = run_metadata(dataset); settings = get_neo4j_settings(); contexts = {} with create_neo4j_driver(settings) as driver, driver.session(database=settings.database) as session: verify_freeze(manifest, metadata, graph_fingerprint(session), audit=audit, **manifest['parameters']) metadata['index_version']=graph_version(session) # Generate every context before making any external request. for case in selected: result=search_graph_with_session(session,case['question'],**manifest['parameters']) result['index_version']=metadata['index_version'] contexts[case['id']]=graph_context(result) code_files = [Path(__file__), prompt_path, HERE/'answer_format.py',HERE/'context_policy.py',HERE/'checked_generation.py', Path('tools/rag/evaluation/rag_runner.py')] hashes = {str(p): sha256_file(p) for p in code_files} if args.split=='regression': dev=json.loads(args.dev_results.read_text()) if (dev['configuration']['selected_split']!='dev' or set(dev['configuration']['selected_ids'])!=development or dev['summary']['questions']!=len(development) or {row['id'] for row in dev['questions']}!=development or len(dev['questions'])!=len(development) or dev['configuration']['generation_files']!=hashes or dev['run_metadata']['fingerprints']!=metadata['fingerprints'] or dev['configuration']['graph_freeze_sha256']!=sha256_file(frozen) or dev['configuration']['model']!=args.model or dev['run_metadata'].get('index_version')!=metadata['index_version']): raise RuntimeError('Generation code, prompt, data or complete DEV differs; rerun DEV before final freeze.') declaration = {'method': ('Previously exposed LLM questions; regression, not independent TEST.' if manifest.get('evaluation_kind')=='historical_regression' else 'Fresh LLM-generation holdout over previously measured, frozen graph queries.'), 'graph_freeze_sha256': sha256_file(frozen), 'audit_sha256': sha256_file(audit), 'generation_files': hashes, 'model': args.model, 'temperature': 0, 'workers': args.workers, 'dev_ids': sorted(development), 'test_ids': [q['id'] for q in test], 'dev_count': len(development), 'test_count': len(test), 'target_accuracy': 0.92, 'selected_split': args.split, 'selected_ids': [q['id'] for q in selected], 'selected_count':len(selected), 'selected_dev_overlap': len(development & {q['id'] for q in selected}), 'corrective_request_limit':1,'format_repair_limit':1, 'repair_policy':'At most one total corrective request for format, citations or retrieval-context violations. Same question/context, no gold; retain all attempts.', 'dev_results_sha256':sha256_file(args.dev_results) if args.dev_results else None, 'policy': ('Evaluate every declared regression case, including DEV overlap; retain all failures and stop tuning after this freeze.' if manifest.get('evaluation_kind')=='historical_regression' else 'Evaluate all 300; retain every failure; do not tune on this TEST.'), 'selected_categories': dict(Counter(q['category'] for q in selected)), 'limitations': [('All questions and previous model outputs are known; this is historical regression.' if manifest.get('evaluation_kind')=='historical_regression' else 'Known corpus/profiles and templates; model outputs for these 300 questions were not previously observed.'), 'Harness supplies graph context; native OpenWebUI tool choice and browser UI are outside this experiment.', 'Checks structured factual claims, their source support/citations and required prose values; not a general semantic judge of arbitrary prose.']} (args.output_dir / 'frozen_answers.json').write_text(json.dumps(declaration, ensure_ascii=False, indent=2) + '\n') results = {} def run(case): started = time.perf_counter(); context = contexts[case['id']] row = {'id': case['id'], 'category': case['category'], 'question': case['question'], 'graph_context': context, 'strict': False} def complete(messages): return request_json(OPENWEBUI_URL, method='POST', timeout=90, max_attempts=2, headers={'Authorization':f'Bearer {os.environ["OPENWEBUI_API_KEY"]}','Content-Type':'application/json'}, payload={'model':args.model,'temperature':0,'stream':False, 'response_format':{'type':'json_object'},'messages':messages}) generated=generate_checked_answer(case['question'],context,prompt,complete) row.update(generated) # Grading occurs after all generation/correction decisions, never inside # the generator. Gold cannot influence whether another request is sent. for attempt in row['attempts']: attempt['strict']=(grade_answer(case,attempt['answer'],oracle,context=context)['strict'] if attempt.get('answer') else False) if row.get('answer'): row.update(grade_answer(case,row['answer'],oracle,context=context)) row['strict']=bool(row['strict'] and row['accepted_for_delivery']) row['latency_seconds'] = time.perf_counter() - started return row output = {'configuration': declaration, 'run_metadata': metadata, 'questions': []} with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as executor: futures = [executor.submit(run, case) for case in selected] for future in concurrent.futures.as_completed(futures): row = future.result(); results[row['id']] = row output['questions'] = [results[q['id']] for q in selected if q['id'] in results] output['summary'] = {'questions': len(results), 'correct': sum(r['strict'] for r in results.values()), 'accuracy': sum(r['strict'] for r in results.values()) / len(results), 'errors': sum(bool(r.get('error')) for r in results.values()), 'first_pass_correct':sum(bool(r.get('attempts') and r['attempts'][0].get('strict')) for r in results.values()), 'format_repair_attempted':sum(bool(r.get('attempts') and r['attempts'][0].get('format_error') and len(r['attempts'])>1) for r in results.values()), 'corrective_request_count':sum(len(r.get('attempts',[]))>1 for r in results.values()), 'context_repair_attempted':sum(bool(r.get('attempts') and r['attempts'][0].get('context_issues') and len(r['attempts'])>1) for r in results.values()), 'normalized_first_response':sum(bool(r.get('attempts') and r['attempts'][0].get('format_normalized')) for r in results.values())} (args.output_dir / f'answers_{args.split}.json').write_text(json.dumps(output, ensure_ascii=False, indent=2) + '\n') print(f'{len(results)}/{len(selected)} {row["id"]}: {"OK" if row["strict"] else "FAIL"} {row["latency_seconds"]:.1f}s', flush=True) with create_neo4j_driver(settings) as driver, driver.session(database=settings.database) as session: verify_freeze(manifest, run_metadata(dataset), graph_fingerprint(session), audit=audit, **manifest['parameters']) assert graph_version(session)==metadata['index_version'],'Index version changed during generation.' assert hashes == {str(p): sha256_file(p) for p in code_files}, 'Generation code changed during TEST.' output['by_category'] = {category: {'questions': len(rows), 'correct': sum(r['strict'] for r in rows), 'accuracy': sum(r['strict'] for r in rows) / len(rows)} for category in sorted({q['category'] for q in selected}) for rows in [[r for r in output['questions'] if r['category'] == category]]} (args.output_dir / f'answers_{args.split}.json').write_text(json.dumps(output, ensure_ascii=False, indent=2) + '\n') fields=['id','category','strict','error','latency_seconds','attempts','first_pass_strict'] with (args.output_dir/f'answers_{args.split}.csv').open('w',newline='',encoding='utf-8') as handle: writer=csv.DictWriter(handle,fieldnames=fields);writer.writeheader() for row in output['questions']: writer.writerow({**{k:row.get(k) for k in fields if k not in {'attempts','first_pass_strict'}}, 'attempts':len(row.get('attempts',[])), 'first_pass_strict':bool(row.get('attempts') and row['attempts'][0].get('strict'))}) print(json.dumps(output['summary'])) if __name__ == '__main__': main()