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