518b8eca85
md-first memory extraction framework for AI agents. Markdown is the single source of truth; SQLite holds state and LanceDB provides the rebuildable vector + BM25 + scalar index. The codebase follows a single-direction DDD layering (entrypoints -> service -> memory -> infra, with component / core / config cross-cutting) enforced by import-linter. Engineering surface: - Coding conventions in .claude/rules/ (path-scoped) and workflows in .claude/skills/ (/commit, /new-branch, /pr). - GitHub Actions CI runs make lint + test + integration; pre-commit mirrors the gates locally (ruff, hygiene hooks, gitlint commit-msg). - Commit messages follow Conventional Commits, enforced by gitlint. - make lint also enforces datetime two-zone discipline and OpenAPI drift.
84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
"""Re-run probes against an existing corpus + regenerate the report.
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Reuses everything from :mod:`_run_full_report` except the ingest step —
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points at the already-populated ``~/.everos-report-corpus`` and only
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re-runs the search probes + report rendering. Useful when the corpus
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is already there from a previous run and you just want to refresh the
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retrieval section without paying for LLM ingestion again.
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"""
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from __future__ import annotations
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import asyncio
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import os
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from pathlib import Path
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import httpx
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from dotenv import load_dotenv
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_PROJECT_ROOT = Path(__file__).resolve().parents[3]
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load_dotenv(_PROJECT_ROOT / ".env", override=False)
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from _run_full_report import ( # noqa: E402
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CONVERSATION,
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CORPUS_ROOT,
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REPORT_PATH,
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inspect_artifacts,
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render_report,
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run_probes,
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)
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async def main() -> None:
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if not (CORPUS_ROOT / "users").is_dir():
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raise SystemExit(f"{CORPUS_ROOT} not populated — run _run_full_report.py first")
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os.environ["EVEROS_MEMORY__ROOT"] = str(CORPUS_ROOT)
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from everos.config import load_settings
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load_settings.cache_clear()
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print(f"[1/3] using corpus at {CORPUS_ROOT}")
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from everos.entrypoints.api.app import create_app
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app = create_app()
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transport = httpx.ASGITransport(app=app)
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async with (
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app.router.lifespan_context(app),
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httpx.AsyncClient(transport=transport, base_url="http://test") as client,
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):
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print("[2/3] inspecting artifacts + running probes ...")
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artifacts = await inspect_artifacts(CORPUS_ROOT)
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probes = await run_probes(client)
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print("[3/3] re-rendering report ...")
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md = render_report(
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memory_root=CORPUS_ROOT,
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ingest_summary={
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"batches": [
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{
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"idx": i,
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"msg_count": len(b),
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"status": "extracted (cached)",
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"returned_count": len(b),
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}
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for i, b in enumerate(CONVERSATION)
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],
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"flush_status": "extracted (cached)",
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},
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cascade_summary={
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"note": "cascade was force-completed via _rerun_probes.py "
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"after initial run; counts below are post-completion."
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},
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artifacts=artifacts,
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probes=probes,
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)
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REPORT_PATH.write_text(md, encoding="utf-8")
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print(f" → {REPORT_PATH}")
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if __name__ == "__main__":
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asyncio.run(main())
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