wuyang6
769 lines
30 KiB
Python
769 lines
30 KiB
Python
from __future__ import annotations
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import base64
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import json
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import tempfile
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import threading
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import time
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import unittest
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from pathlib import Path
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from unittest.mock import patch
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from src.agent_types import AgentRunResult, ModelConfig, UsageStats
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from src.bundled_skills import BundledSkill
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from src.evaluation_runtime import (
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EvaluationRuntime,
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build_evaluation_prompt,
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calculate_metrics,
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extract_accessed_refs,
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labels_equivalent,
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normalize_label_mapping_output,
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normalize_label_master_result,
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normalize_evaluation_output,
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parse_dataset_bytes,
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prediction_is_allowed,
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transient_model_error_message,
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)
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def _write_test_skill(root: Path) -> None:
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skill_dir = root / 'skills' / 'label-master'
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manifest_dir = skill_dir / 'knowledge' / '索引'
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manifest_dir.mkdir(parents=True)
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(skill_dir / 'SKILL.md').write_text(
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(
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'---\n'
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'name: label-master\n'
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'description: Test label skill.\n'
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'allowed_tools: read_file, grep_search\n'
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'---\n'
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'Read the manifest and classify the query.\n'
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),
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encoding='utf-8',
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)
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(manifest_dir / 'label_manifest.json').write_text(
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json.dumps(
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{
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'counts': {'labels': 2, 'functions': 1},
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'labels': [
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{
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'name': '地图导航',
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'domain': '地图',
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'path': 'knowledge/标签/地图导航.md',
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},
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{
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'name': 'QA',
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'domain': '问答',
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'path': 'knowledge/标签/QA.md',
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},
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],
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'agent_tags': ['地图导航'],
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'functions': [{'name': 'QA'}],
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'intents': [],
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},
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ensure_ascii=False,
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),
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encoding='utf-8',
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)
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def _write_fast_slow_skill(root: Path) -> None:
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skill_dir = root / 'skills' / 'label-fast-slow-routing'
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skill_dir.mkdir(parents=True)
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(skill_dir / 'SKILL.md').write_text(
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(
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'---\n'
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'name: label-fast-slow-routing\n'
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'description: Test fast slow routing skill.\n'
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'---\n'
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'Classify the query as 快、慢 or 模糊.\n'
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),
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encoding='utf-8',
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)
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class EvaluationRuntimeTests(unittest.TestCase):
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def test_parse_csv_and_jsonl(self) -> None:
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csv_rows, csv_format = parse_dataset_bytes(
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'sample.csv',
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'query,label\n打开地图,地图导航\n'.encode(),
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)
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self.assertEqual(csv_format, 'csv')
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self.assertEqual(csv_rows[0]['query'], '打开地图')
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jsonl_rows, jsonl_format = parse_dataset_bytes(
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'sample.jsonl',
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b'{"query":"hello","label":"QA"}\n',
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)
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self.assertEqual(jsonl_format, 'jsonl')
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self.assertEqual(jsonl_rows[0]['label'], 'QA')
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def test_dataset_mapping_snapshot_and_experiment(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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_write_test_skill(root)
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runtime = EvaluationRuntime(
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root=root / 'evaluations',
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cwd_for_account=lambda _account: root,
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model_config_for=lambda _account: ModelConfig(model='test-model'),
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account_paths_for=lambda _account: {'python_env': root / '.venv'},
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max_workers=1,
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)
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try:
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metadata = runtime.metadata('alice')
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self.assertEqual(metadata['default_skill'], 'label-master')
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self.assertEqual(
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[item['name'] for item in metadata['label_catalog']['labels']],
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['地图导航', 'QA'],
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)
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content = base64.b64encode(
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'rid,query,人工标签\n1,导航去公司,Agent(tag="地图导航")\n'.encode()
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).decode()
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dataset = runtime.create_dataset(
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account_id='alice',
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name='test',
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filename='test.csv',
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content_base64=content,
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)
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self.assertEqual(dataset['mapping']['fields']['query'], 'query')
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self.assertEqual(
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dataset['mapping']['fields']['gold_label'],
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'人工标签',
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)
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self.assertEqual(dataset['mapping']['fields']['request_id'], 'rid')
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experiment = runtime.create_experiment(
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account_id='alice',
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dataset_id=dataset['id'],
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name='first',
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)
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self.assertEqual(experiment['total_cases'], 1)
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self.assertEqual(experiment['skill_name'], 'label-master')
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self.assertEqual(len(experiment['skill_version']), 12)
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self.assertEqual(experiment['cases'][0]['request_id'], '1')
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finally:
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runtime.shutdown()
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def test_fast_slow_skill_is_the_evaluation_default(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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_write_test_skill(root)
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_write_fast_slow_skill(root)
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runtime = EvaluationRuntime(
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root=root / 'evaluations',
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cwd_for_account=lambda _account: root,
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model_config_for=lambda _account: ModelConfig(model='test-model'),
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account_paths_for=lambda _account: {'python_env': root / '.venv'},
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max_workers=1,
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)
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try:
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metadata = runtime.metadata('alice')
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self.assertEqual(
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metadata['default_skill'],
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'label-fast-slow-routing',
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)
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defaults = [
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item['name']
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for item in metadata['skills']
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if item['default']
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]
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self.assertEqual(defaults, ['label-fast-slow-routing'])
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finally:
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runtime.shutdown()
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def test_skill_versions_are_immutable_and_account_scoped(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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_write_test_skill(root)
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runtime = EvaluationRuntime(
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root=root / 'evaluations',
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cwd_for_account=lambda _account: root,
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model_config_for=lambda _account: ModelConfig(model='test-model'),
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account_paths_for=lambda _account: {'python_env': root / '.venv'},
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max_workers=1,
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)
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try:
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versions = runtime.list_skill_versions('alice', 'label-master')
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self.assertEqual(len(versions['versions']), 1)
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current_id = versions['current_snapshot_id']
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self.assertTrue(versions['versions'][0]['is_current'])
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manifest = runtime.get_skill_version_manifest(
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'alice',
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'label-master',
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current_id,
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)
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skill_file = next(
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item
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for item in manifest['files']
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if item['path'] == 'SKILL.md'
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)
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self.assertTrue(skill_file['editable'])
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current_file = runtime.read_skill_version_file(
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'alice',
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'label-master',
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current_id,
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'SKILL.md',
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)
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updated_content = current_file['content'].replace(
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'Read the manifest',
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'Read the manifest carefully',
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)
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saved = runtime.save_skill_version(
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'alice',
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'label-master',
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base_snapshot_id=current_id,
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version_name='边界调整 v1',
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note='测试版本',
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files={'SKILL.md': updated_content},
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)
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self.assertEqual(saved['version_name'], '边界调整 v1')
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self.assertEqual(saved['source_type'], 'local')
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self.assertNotEqual(saved['id'], current_id)
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original = runtime.read_skill_version_file(
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'alice',
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'label-master',
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current_id,
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'SKILL.md',
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)
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forked = runtime.read_skill_version_file(
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'alice',
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'label-master',
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saved['id'],
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'SKILL.md',
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)
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self.assertNotIn('carefully', original['content'])
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self.assertIn('carefully', forked['content'])
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dataset = runtime.create_dataset(
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account_id='alice',
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name='versioned',
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filename='versioned.csv',
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rows=[{'query': '导航去公司'}],
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)
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experiment = runtime.create_experiment(
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account_id='alice',
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dataset_id=dataset['id'],
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name='local version',
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snapshot_id=saved['id'],
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)
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self.assertEqual(experiment['snapshot_id'], saved['id'])
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self.assertEqual(
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experiment['skill_version'],
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saved['content_hash'][:12],
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)
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with self.assertRaisesRegex(
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ValueError,
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'Skill 版本不存在',
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):
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runtime.read_skill_version_file(
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'bob',
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'label-master',
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saved['id'],
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'SKILL.md',
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)
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finally:
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runtime.shutdown()
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def test_result_normalization_and_label_equivalence(self) -> None:
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result = normalize_evaluation_output(
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json.dumps(
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{
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'prediction': '地图导航',
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'function_output': 'Agent(tag="地图导航")',
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'complex': False,
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'reason': '导航执行',
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'candidates': ['地图导航', '地图问答'],
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'evidence_refs': ['knowledge/标签/地图导航.md'],
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'confidence': 1.4,
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'uncertainty': '',
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},
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ensure_ascii=False,
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)
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)
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self.assertEqual(result['prediction'], '地图导航')
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self.assertEqual(result['confidence'], 1.0)
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duplicated = normalize_evaluation_output(
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'{"prediction":"慢","complex":false}'
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'{"prediction":"慢","complex":false}'
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)
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self.assertEqual(duplicated['prediction'], '慢')
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self.assertTrue(labels_equivalent('Agent(tag="地图导航")', '地图导航'))
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self.assertTrue(
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labels_equivalent(
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'complex=false\nAgent(tag="地图导航")',
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'地图导航',
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)
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)
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self.assertTrue(labels_equivalent('慢系统', '慢'))
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self.assertTrue(
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labels_equivalent(
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'complex=Ture\n慢系统',
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'慢',
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True,
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)
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)
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self.assertTrue(labels_equivalent('complex=Ture', '慢', True))
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self.assertFalse(
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labels_equivalent(
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'complex=Ture\n慢系统',
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'慢',
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False,
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)
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)
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snapshot_metadata = json.dumps(
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{
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'label_catalog': {
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'labels': ['地图导航'],
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'agent_tags': ['地图导航'],
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'functions': ['QA'],
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'intents': [],
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}
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},
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ensure_ascii=False,
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)
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self.assertTrue(
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prediction_is_allowed(
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'label-master',
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'Agent(tag="地图导航")',
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snapshot_metadata,
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)
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)
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self.assertFalse(
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prediction_is_allowed(
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'label-master',
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'已完成第一步知识读取',
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snapshot_metadata,
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)
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)
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self.assertTrue(
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prediction_is_allowed(
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'label-fast-slow-routing',
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'模糊',
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'{}',
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)
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)
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self.assertFalse(
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prediction_is_allowed(
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'label-fast-slow-routing',
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'fast',
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'{}',
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)
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)
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normalized_label = normalize_label_master_result(
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{
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**result,
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'prediction': 'Agent(tag="地图导航")',
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'function_output': '一段解释文字',
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},
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snapshot_metadata,
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)
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self.assertEqual(normalized_label['prediction'], '地图导航')
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self.assertEqual(
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normalized_label['function_output'],
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'Agent(tag="地图导航")',
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)
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mapping = normalize_label_mapping_output(
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json.dumps(
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{
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'label_map': {
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'慢系统': '慢',
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'complex=Ture': 'complex=true',
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},
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'unmapped': [],
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'reason': '统一快慢和复杂度格式',
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},
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ensure_ascii=False,
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),
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raw_labels=['慢系统', 'complex=Ture', '未知'],
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skill_name='label-fast-slow-routing',
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)
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self.assertEqual(mapping['label_map']['慢系统'], '慢')
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self.assertEqual(mapping['label_map']['complex=Ture'], 'complex=true')
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self.assertEqual(mapping['unmapped'], ['未知'])
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self.assertIn(
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'HTTP 429',
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transient_model_error_message(
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'HTTP 429 from local model backend: Too many requests'
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),
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)
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def test_transient_model_failure_retries_before_validation(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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runtime = EvaluationRuntime(
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root=root / 'evaluations',
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cwd_for_account=lambda _account: root,
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model_config_for=lambda _account: ModelConfig(model='test-model'),
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account_paths_for=lambda _account: {'python_env': root / '.venv'},
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max_workers=1,
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)
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transient = AgentRunResult(
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final_output='HTTP 429: Too many requests',
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turns=0,
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tool_calls=0,
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transcript=(),
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usage=UsageStats(),
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)
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success = AgentRunResult(
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final_output='{"prediction":"快"}',
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turns=1,
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tool_calls=0,
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transcript=(),
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usage=UsageStats(),
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)
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|
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class StubAgent:
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def __init__(self) -> None:
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self.calls = 0
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def run(self, *_args: object, **_kwargs: object) -> AgentRunResult:
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self.calls += 1
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return transient if self.calls == 1 else success
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|
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agent = StubAgent()
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try:
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with patch.object(runtime, '_extend_model_cooldown') as cooldown:
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result = runtime._run_agent_with_transient_backoff(
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agent, # type: ignore[arg-type]
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'prompt',
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session_id='session',
|
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runtime_context='context',
|
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cancel_event=threading.Event(),
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)
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self.assertIs(result, success)
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self.assertEqual(agent.calls, 2)
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cooldown.assert_called_once()
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finally:
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runtime.shutdown()
|
|
|
|
def test_fast_slow_prompt_uses_skill_contract_and_relative_paths(self) -> None:
|
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skill = BundledSkill(
|
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name='label-fast-slow-routing',
|
|
description='Test fast slow routing skill.',
|
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source='directory',
|
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get_prompt=lambda _agent, _args: 'Read references/policy.md.',
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)
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prompt, runtime_context = build_evaluation_prompt(
|
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skill=skill,
|
|
snapshot_root=Path('/tmp/evaluation-snapshot'),
|
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canonical_case={
|
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'query': '打开空调',
|
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'gold_label': '快',
|
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'source_row': {
|
|
'query': '打开空调',
|
|
'人工标签': '快',
|
|
'secret': 'should-not-leak',
|
|
},
|
|
},
|
|
)
|
|
self.assertIn('填写“快”“慢”或“模糊”', prompt)
|
|
self.assertNotIn('填写 fast 或 slow', prompt)
|
|
self.assertIn('`references/...`', runtime_context)
|
|
self.assertNotIn(
|
|
'不要添加 `skills/label-fast-slow-routing/` 前缀',
|
|
prompt,
|
|
)
|
|
self.assertNotIn('should-not-leak', prompt)
|
|
self.assertNotIn('"人工标签"', prompt)
|
|
|
|
def test_label_mapping_suggestion_uses_one_agent_run(self) -> None:
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
_write_fast_slow_skill(root)
|
|
runtime = EvaluationRuntime(
|
|
root=root / 'evaluations',
|
|
cwd_for_account=lambda _account: root,
|
|
model_config_for=lambda _account: ModelConfig(model='test-model'),
|
|
account_paths_for=lambda _account: {'python_env': root / '.venv'},
|
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max_workers=1,
|
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)
|
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try:
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|
dataset = runtime.create_dataset(
|
|
account_id='alice',
|
|
name='fast-slow',
|
|
filename='fast-slow.csv',
|
|
rows=[
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{'query': '打开空调', 'label': '快系统'},
|
|
{'query': '帮我规划路线', 'label': '慢系统'},
|
|
],
|
|
)
|
|
fake_result = AgentRunResult(
|
|
final_output=json.dumps(
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{
|
|
'label_map': {
|
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'快系统': '快',
|
|
'慢系统': '慢',
|
|
},
|
|
'unmapped': [],
|
|
'reason': '统一路由标签',
|
|
},
|
|
ensure_ascii=False,
|
|
),
|
|
turns=1,
|
|
tool_calls=1,
|
|
transcript=(),
|
|
usage=UsageStats(input_tokens=10, output_tokens=5),
|
|
)
|
|
with patch(
|
|
'src.evaluation_runtime.LocalCodingAgent.run',
|
|
return_value=fake_result,
|
|
) as run:
|
|
suggestion = runtime.suggest_label_mapping(
|
|
dataset['id'],
|
|
'alice',
|
|
mapping=dataset['mapping'],
|
|
skill_name='label-fast-slow-routing',
|
|
)
|
|
self.assertEqual(run.call_count, 1)
|
|
self.assertEqual(
|
|
suggestion['label_map'],
|
|
{'快系统': '快', '慢系统': '慢'},
|
|
)
|
|
finally:
|
|
runtime.shutdown()
|
|
|
|
def test_single_analysis_uses_snapshot_without_dataset_records(self) -> None:
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
_write_test_skill(root)
|
|
runtime = EvaluationRuntime(
|
|
root=root / 'evaluations',
|
|
cwd_for_account=lambda _account: root,
|
|
model_config_for=lambda _account: ModelConfig(model='test-model'),
|
|
account_paths_for=lambda _account: {'python_env': root / '.venv'},
|
|
max_workers=1,
|
|
)
|
|
fake_result = AgentRunResult(
|
|
final_output=json.dumps(
|
|
{
|
|
'prediction': '地图导航',
|
|
'function_output': 'Agent(tag="地图导航")',
|
|
'complex': False,
|
|
'reason': '导航执行',
|
|
'candidates': ['地图导航'],
|
|
'evidence_refs': [
|
|
'knowledge/索引/label_manifest.json'
|
|
],
|
|
'confidence': 0.98,
|
|
'uncertainty': '',
|
|
},
|
|
ensure_ascii=False,
|
|
),
|
|
turns=1,
|
|
tool_calls=1,
|
|
transcript=(
|
|
{
|
|
'role': 'assistant',
|
|
'tool_calls': [
|
|
{
|
|
'function': {
|
|
'name': 'read_file',
|
|
'arguments': json.dumps(
|
|
{
|
|
'path': (
|
|
'knowledge/索引/'
|
|
'label_manifest.json'
|
|
)
|
|
},
|
|
ensure_ascii=False,
|
|
),
|
|
}
|
|
}
|
|
],
|
|
},
|
|
),
|
|
usage=UsageStats(input_tokens=10, output_tokens=5),
|
|
)
|
|
try:
|
|
with patch(
|
|
'src.evaluation_runtime.LocalCodingAgent.run',
|
|
return_value=fake_result,
|
|
):
|
|
result = runtime.analyze_case(
|
|
account_id='alice',
|
|
query='导航去公司',
|
|
)
|
|
self.assertEqual(result['prediction'], '地图导航')
|
|
self.assertEqual(result['skill_name'], 'label-master')
|
|
self.assertEqual(result['model'], 'test-model')
|
|
self.assertEqual(runtime.list_datasets('alice'), [])
|
|
self.assertEqual(runtime.list_experiments('alice'), [])
|
|
history = runtime.list_single_analyses('alice')
|
|
self.assertEqual(len(history), 1)
|
|
self.assertEqual(history[0]['id'], result['id'])
|
|
self.assertEqual(history[0]['query'], '导航去公司')
|
|
self.assertEqual(history[0]['prediction'], '地图导航')
|
|
self.assertNotIn('raw_output', history[0])
|
|
detail = runtime.get_single_analysis(result['id'], 'alice')
|
|
self.assertEqual(detail['prediction'], '地图导航')
|
|
self.assertEqual(detail['canonical']['query'], '导航去公司')
|
|
finally:
|
|
runtime.shutdown()
|
|
|
|
def test_background_runner_completes_cases(self) -> None:
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
_write_test_skill(root)
|
|
runtime = EvaluationRuntime(
|
|
root=root / 'evaluations',
|
|
cwd_for_account=lambda _account: root,
|
|
model_config_for=lambda _account: ModelConfig(model='test-model'),
|
|
account_paths_for=lambda _account: {'python_env': root / '.venv'},
|
|
max_workers=1,
|
|
)
|
|
try:
|
|
dataset = runtime.create_dataset(
|
|
account_id='alice',
|
|
name='test',
|
|
filename='test.csv',
|
|
rows=[
|
|
{'query': '导航去公司', 'label': '地图导航'},
|
|
{'query': '导航回家', 'label': '地图导航'},
|
|
],
|
|
)
|
|
experiment = runtime.create_experiment(
|
|
account_id='alice',
|
|
dataset_id=dataset['id'],
|
|
name='runner',
|
|
concurrency=1,
|
|
)
|
|
fake_result = AgentRunResult(
|
|
final_output=json.dumps(
|
|
{
|
|
'prediction': '地图导航',
|
|
'function_output': 'Agent(tag="地图导航")',
|
|
'complex': False,
|
|
'reason': '导航执行',
|
|
'candidates': ['地图导航'],
|
|
'evidence_refs': ['knowledge/标签/地图导航.md'],
|
|
'confidence': 0.98,
|
|
'uncertainty': '',
|
|
},
|
|
ensure_ascii=False,
|
|
),
|
|
turns=1,
|
|
tool_calls=1,
|
|
transcript=(
|
|
{
|
|
'role': 'assistant',
|
|
'tool_calls': [
|
|
{
|
|
'function': {
|
|
'name': 'read_file',
|
|
'arguments': json.dumps(
|
|
{
|
|
'path': (
|
|
'knowledge/索引/'
|
|
'label_manifest.json'
|
|
)
|
|
},
|
|
ensure_ascii=False,
|
|
),
|
|
}
|
|
}
|
|
],
|
|
},
|
|
),
|
|
usage=UsageStats(input_tokens=10, output_tokens=5),
|
|
)
|
|
with patch(
|
|
'src.evaluation_runtime.LocalCodingAgent.run',
|
|
return_value=fake_result,
|
|
):
|
|
runtime.start(experiment['id'], 'alice')
|
|
deadline = time.time() + 3
|
|
current = runtime.get_experiment(experiment['id'], 'alice')
|
|
while (
|
|
current['status'] not in {'completed', 'completed_with_errors'}
|
|
and time.time() < deadline
|
|
):
|
|
time.sleep(0.02)
|
|
current = runtime.get_experiment(
|
|
experiment['id'],
|
|
'alice',
|
|
)
|
|
self.assertEqual(current['status'], 'completed')
|
|
self.assertEqual(current['completed_cases'], 2)
|
|
self.assertEqual(current['metrics']['accuracy'], 1.0)
|
|
finally:
|
|
runtime.shutdown()
|
|
|
|
def test_accessed_refs_only_include_snapshot_paths(self) -> None:
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
snapshot_root = Path(directory)
|
|
evidence = snapshot_root / 'skills' / 'label-master' / 'knowledge.md'
|
|
evidence.parent.mkdir(parents=True)
|
|
evidence.write_text('evidence', encoding='utf-8')
|
|
transcript = (
|
|
{
|
|
'role': 'assistant',
|
|
'tool_calls': [
|
|
{
|
|
'function': {
|
|
'name': 'read_file',
|
|
'arguments': json.dumps(
|
|
{
|
|
'path': (
|
|
'skills/label-master/knowledge.md'
|
|
)
|
|
}
|
|
),
|
|
}
|
|
},
|
|
{
|
|
'function': {
|
|
'name': 'read_file',
|
|
'arguments': json.dumps({'path': '/etc/hosts'}),
|
|
}
|
|
},
|
|
],
|
|
},
|
|
)
|
|
self.assertEqual(
|
|
extract_accessed_refs(
|
|
transcript,
|
|
snapshot_root=snapshot_root,
|
|
),
|
|
[
|
|
{
|
|
'tool': 'read_file',
|
|
'path': 'skills/label-master/knowledge.md',
|
|
}
|
|
],
|
|
)
|
|
|
|
def test_metrics_count_disagreements(self) -> None:
|
|
metrics = calculate_metrics(
|
|
[
|
|
{
|
|
'status': 'completed',
|
|
'gold_label': 'A',
|
|
'prediction': 'A',
|
|
'correct': True,
|
|
'confidence': 0.9,
|
|
'evidence_refs': ['a.md'],
|
|
'domain': 'one',
|
|
},
|
|
{
|
|
'status': 'completed',
|
|
'gold_label': 'A',
|
|
'prediction': 'B',
|
|
'correct': False,
|
|
'confidence': 0.5,
|
|
'evidence_refs': [],
|
|
'domain': 'one',
|
|
},
|
|
]
|
|
)
|
|
self.assertEqual(metrics['labeled'], 2)
|
|
self.assertEqual(metrics['disagreements'], 1)
|
|
self.assertEqual(metrics['accuracy'], 0.5)
|
|
self.assertEqual(metrics['low_confidence'], 1)
|
|
self.assertEqual(metrics['no_evidence'], 1)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|