Introduce 智能评估 as an evaluation paradigm parallel to static evaluation, driven by OpenClaw. The platform supplies storage, lifecycle, and reporting; OpenClaw plans and executes. - Data model: IntelligentEval + Session + Message tables (new, not reusing exploration) - Lifecycle state machine: draft → planning → pending_approval → executing → completed/cancelled/failed - Session API: create/message (channel-forwarded)/close with turn accounting - Report API: pydantic-validated structured report, executing → completed, Markdown export (pure renderer) - Alembic migration for the three tables; domain glossary added to CONTEXT.md |
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| .. | ||
| __init__.py | ||
| auth.py | ||
| campaigns.py | ||
| exploration.py | ||
| files.py | ||
| intelligent_evals.py | ||
| model_configs.py | ||
| proxy.py | ||
| reports.py | ||
| runs.py | ||
| scenarios.py | ||
| stats.py | ||
| targets.py | ||