156 lines
8.7 KiB
Python
156 lines
8.7 KiB
Python
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"""Frozen mixed diagnostic via the installed CLI's shared engine and MCP API."""
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import argparse
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from datetime import datetime, timezone
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import hashlib
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import importlib.util
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import json
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import os
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from pathlib import Path
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import shutil
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import subprocess
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import tempfile
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ROOT = Path(__file__).resolve().parents[2]
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FIXTURE = ROOT / 'fixtures/mixed'
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def digest(path):
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with path.open('rb') as stream:
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return hashlib.file_digest(stream, 'sha256').hexdigest()
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def load_fixture(directory=FIXTURE):
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raw = (directory / 'fixture.json').read_bytes()
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lock = json.loads((directory / 'labels.lock.json').read_bytes())
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if hashlib.sha256(raw).hexdigest() != lock['fixture_sha256']:
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raise ValueError('Frozen labels changed; create a new version, never refreeze v1')
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fixture = json.loads(raw)
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allowed = directory.parent.resolve()
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ids, names = set(), set()
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for asset in fixture['assets']:
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path = (directory / asset['path']).resolve()
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if not path.is_relative_to(allowed) or not path.is_file():
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raise ValueError('Asset must be an existing file below fixtures')
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if asset['id'] in ids or path.name in names:
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raise ValueError('Asset IDs and materialized filenames must be unique')
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ids.add(asset['id']); names.add(path.name)
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if path.stat().st_size != asset['bytes'] or digest(path) != asset['sha256']:
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raise ValueError('Frozen asset changed: ' + asset['id'])
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for query in fixture['queries']:
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# Temporal/media labels can include structured entries; normalize below.
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if any(identifier not in ids for identifier in expected_ids(query)):
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raise ValueError('Unknown expected asset')
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return fixture, lock
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def expected_ids(query):
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return {item if isinstance(item, str) else item['asset'] for item in query['expected']}
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def score(query, hits):
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expected = expected_ids(query)
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unique = list(dict.fromkeys(hit['asset'] for hit in hits))
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ranks = [i + 1 for i, asset in enumerate(unique) if asset in expected]
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first = min(ranks, default=None)
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return {'first_relevant_rank': first,
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'top1': first == 1 if expected else None,
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'recall_at_5': len(expected.intersection(unique[:5])) / len(expected) if expected else None,
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'reciprocal_rank': 1 / first if first else 0 if expected else None,
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'no_answer_returned_any': bool(hits) if not expected else None,
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'hard_negative_in_top5': any(asset in query.get('hard_negatives', []) for asset in unique[:5])}
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def summarize(rows):
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groups = {}
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for row in rows:
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if 'unsupported' in row:
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continue
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key = (row['mode'], row['scope'], row['query']['category'], row['query']['language'])
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groups.setdefault(key, []).append(row['score'])
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result = []
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for key, values in sorted(groups.items()):
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group = dict(zip(('mode', 'scope', 'category', 'language'), key))
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group['queries'] = len(values)
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for metric in ('top1', 'recall_at_5', 'reciprocal_rank', 'no_answer_returned_any', 'hard_negative_in_top5'):
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numbers = [v[metric] for v in values if v[metric] is not None]
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group[metric] = sum(numbers) / len(numbers) if numbers else None
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result.append(group)
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return result
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def run(args):
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if os.environ.get('SEARCHI_FORCE_CPU') != '1':
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raise ValueError('Set SEARCHI_FORCE_CPU=1; this evaluator must never run GPU inference')
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fixture, lock = load_fixture()
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manifest_path = ROOT / 'tools/embeddinggemma2/model-manifest.json'
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manifest = json.loads(manifest_path.read_bytes())
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for model in manifest['files']:
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path = args.models / model['path']
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if path.stat().st_size != model['bytes'] or digest(path) != model['sha256']:
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raise ValueError('Model bytes differ from the pinned Google manifest')
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spec = importlib.util.spec_from_file_location('searchi_benchmark', ROOT / 'tools/embeddinggemma2/benchmark.py')
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benchmark = importlib.util.module_from_spec(spec); spec.loader.exec_module(benchmark)
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args.output.parent.mkdir(parents=True, exist_ok=True)
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rows = []
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with tempfile.TemporaryDirectory(prefix='searchi-mixed-') as temporary:
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base = Path(temporary); corpus = base / 'corpus'; corpus.mkdir(); database = base / 'index.sqlite'
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names = {}
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for asset in fixture['assets']:
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source = (FIXTURE / asset['path']).resolve()
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shutil.copyfile(source, corpus / source.name)
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names[source.name] = asset['id']
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subprocess.run([str(args.cli), 'index', '--db', str(database), '--root', str(corpus)], check=True, timeout=300)
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subprocess.run([str(args.cli), 'embed', '--db', str(database), '--model-dir', str(args.models),
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'--embedding-worker', str(args.worker), '--cpu-threads', '1',
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'--ffmpeg', str(args.ffmpeg), '--ffprobe', str(args.ffprobe)], check=True, timeout=3600)
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for mode in ('lexical_only', 'hybrid'):
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command = [str(args.cli), 'mcp', '--db', str(database)]
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if mode == 'hybrid':
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command += ['--model-dir', str(args.models), '--embedding-worker', str(args.worker), '--cpu-threads', '1']
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session = benchmark.Session(command)
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try:
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session.mcp(1, 'initialize', {'protocolVersion': '2025-03-26', 'capabilities': {}, 'clientInfo': {'name': 'mixed-eval', 'version': '1'}})
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session.send({'jsonrpc': '2.0', 'method': 'notifications/initialized'})
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request = 2
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for query in fixture['queries']:
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scopes = ('all_files', 'code') if query['category'] == 'code' else ('all_files',)
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for scope in scopes:
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options = {'query': query['text'], 'limit': 20, 'code_only': scope == 'code'}
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filters = query.get('filters', {})
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if 'time_range_s' in filters:
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rows.append({'mode': mode, 'scope': scope, 'query': query, 'unsupported': 'Temporal-range search filter is not supported'})
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continue
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if 'extension' in filters:
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options['extension'] = filters['extension']
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response, elapsed = session.mcp(request, 'tools/call', {'name': 'search', 'arguments': options}); request += 1
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if response.get('isError'):
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raise RuntimeError('Shared search failed: ' + json.dumps(response))
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content = response['structuredContent']
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if content['mode'] != mode:
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raise RuntimeError('Unexpected semantic fallback; not valid quality evidence')
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hits = [{'asset': names[hit['filename']], 'score': hit['score'], 'match_kind': hit['match_kind'], 'source': hit.get('source')} for hit in content['results']]
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rows.append({'mode': mode, 'scope': scope, 'query': query, 'hits': hits, 'score': score(query, hits), 'roundtrip_ms': elapsed})
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# Retain partial actual evidence if a later request fails.
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args.output.write_text(json.dumps({'complete': False, 'rows': rows}, indent=2, ensure_ascii=False), encoding='utf-8')
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finally:
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session.close()
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report = {'complete': True, 'recorded_at': datetime.now(timezone.utc).isoformat(), 'fixture_sha256': lock['fixture_sha256'],
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'model_manifest_sha256': digest(manifest_path), 'model': manifest,
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'device': 'cpu', 'cpu_threads': 1, 'cli_sha256': digest(args.cli), 'worker_sha256': digest(args.worker),
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'scope_note': 'Small mixed diagnostic, reused previously tested media labels; file-level relevance only. No temporal-localization or production-scale claims. MCP roundtrip excludes initialization and is not UI latency.',
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'rows': rows, 'summary': summarize(rows)}
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args.output.write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding='utf-8')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--verify-only', action='store_true')
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for flag in ('cli', 'models', 'worker', 'ffmpeg', 'ffprobe', 'output'):
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parser.add_argument('--' + flag, type=Path)
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args = parser.parse_args()
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if args.verify_only:
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fixture, lock = load_fixture(); print('Verified', len(fixture['assets']), 'assets and', len(fixture['queries']), 'frozen queries', lock['fixture_sha256'])
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else:
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if any(getattr(args, flag) is None for flag in ('cli', 'models', 'worker', 'ffmpeg', 'ffprobe', 'output')):
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parser.error('Supply all explicit runtime paths and output, or use --verify-only')
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run(args)
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