feat(platform): add new services, tools, tests and crews modules
New router intelligence modules (26 files): alert_ingest/store, audit_store, architecture_pressure, backlog_generator/store, cost_analyzer, data_governance, dependency_scanner, drift_analyzer, incident_* (5 files), llm_enrichment, platform_priority_digest, provider_budget, release_check_runner, risk_* (6 files), signature_state_store, sofiia_auto_router, tool_governance New services: - sofiia-console: Dockerfile, adapters/, monitor/nodes/ops/voice modules, launchd, react static - memory-service: integration_endpoints, integrations, voice_endpoints, static UI - aurora-service: full app suite (analysis, job_store, orchestrator, reporting, schemas, subagents) - sofiia-supervisor: new supervisor service - aistalk-bridge-lite: Telegram bridge lite - calendar-service: CalDAV calendar service with reminders - mlx-stt-service / mlx-tts-service: Apple Silicon speech services - binance-bot-monitor: market monitor service - node-worker: STT/TTS memory providers New tools (9): agent_email, browser_tool, contract_tool, observability_tool, oncall_tool, pr_reviewer_tool, repo_tool, safe_code_executor, secure_vault New crews: agromatrix_crew (10 modules: depth_classifier, doc_facts, doc_focus, farm_state, light_reply, llm_factory, memory_manager, proactivity, reflection_engine, session_context, style_adapter, telemetry) Tests: 85+ test files for all new modules Made-with: Cursor
This commit is contained in:
Apple
341
services/router/risk_digest.py
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341
services/router/risk_digest.py
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"""
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risk_digest.py — Daily Risk Digest generator (deterministic, no LLM).
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Produces:
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ops/reports/risk/YYYY-MM-DD.json
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ops/reports/risk/YYYY-MM-DD.md
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Content:
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- Top risky services (score desc)
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- Top regressions (delta_24h desc)
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- SLO violation summary
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- Deterministic action list based on risk state
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"""
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from __future__ import annotations
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import datetime
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import json
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import logging
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import math
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import os
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from pathlib import Path
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from typing import Dict, List, Optional
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logger = logging.getLogger(__name__)
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_ACTION_TEMPLATES = {
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"regression_fail": "🚨 **Regression detected**: {service} score +{delta} in 24h. Freeze deployments; inspect recent incidents/followups immediately.",
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"regression_warn": "⚠️ **Score rising**: {service} +{delta} in 24h. Review open incidents and overdue follow-ups.",
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"critical_band": "🔴 **Critical risk**: {service} (score {score}). Oncall review required within 2h.",
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"high_band": "🟠 **High risk**: {service} (score {score}). Coordinate with oncall before next release.",
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"overdue_followups": "📋 **Overdue follow-ups**: {service} has {count} overdue follow-up(s). Close them to reduce risk score.",
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"slo_violation": "📉 **SLO violation**: {service} has {count} active SLO violation(s). Avoid deploying until clear.",
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}
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def _now_date() -> str:
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return datetime.datetime.utcnow().strftime("%Y-%m-%d")
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def _clamp(text: str, max_chars: int) -> str:
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if len(text) <= max_chars:
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return text
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truncated = text[:max_chars]
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return truncated + "\n\n_[digest truncated to policy max_chars]_"
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def _build_action_list(reports: List[Dict]) -> List[str]:
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actions = []
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for r in reports[:10]:
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service = r.get("service", "?")
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score = r.get("score", 0)
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band = r.get("band", "low")
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trend = r.get("trend") or {}
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comp = r.get("components", {})
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delta_24h = trend.get("delta_24h")
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reg = trend.get("regression", {})
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if reg.get("fail") and delta_24h is not None and delta_24h > 0:
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actions.append(_ACTION_TEMPLATES["regression_fail"].format(
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service=service, delta=delta_24h))
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elif reg.get("warn") and delta_24h is not None and delta_24h > 0:
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actions.append(_ACTION_TEMPLATES["regression_warn"].format(
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service=service, delta=delta_24h))
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if band == "critical":
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actions.append(_ACTION_TEMPLATES["critical_band"].format(
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service=service, score=score))
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elif band == "high":
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actions.append(_ACTION_TEMPLATES["high_band"].format(
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service=service, score=score))
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overdue = (
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(comp.get("followups") or {}).get("P0", 0)
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+ (comp.get("followups") or {}).get("P1", 0)
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+ (comp.get("followups") or {}).get("other", 0)
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)
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if overdue:
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actions.append(_ACTION_TEMPLATES["overdue_followups"].format(
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service=service, count=overdue))
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slo_count = (comp.get("slo") or {}).get("violations", 0)
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if slo_count:
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actions.append(_ACTION_TEMPLATES["slo_violation"].format(
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service=service, count=slo_count))
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return actions[:20] # cap
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def _build_markdown(
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date_str: str,
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env: str,
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reports: List[Dict],
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top_regressions: List[Dict],
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improving: List[Dict],
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actions: List[str],
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band_counts: Dict,
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) -> str:
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lines = [
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f"# Risk Digest — {date_str} ({env})",
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"",
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f"Generated: {datetime.datetime.utcnow().isoformat()} UTC",
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"",
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"## Band Summary",
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"",
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"| Band | Count |",
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"|------|-------|",
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]
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for band in ("critical", "high", "medium", "low"):
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lines.append(f"| {band} | {band_counts.get(band, 0)} |")
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lines += [
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"",
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"## Top Risky Services",
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"",
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"| Service | Score | Band | Δ24h | Δ7d |",
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"|---------|-------|------|------|-----|",
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]
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for r in reports:
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t = r.get("trend") or {}
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d24 = t.get("delta_24h")
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d7 = t.get("delta_7d")
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d24_str = (f"+{d24}" if d24 and d24 > 0 else str(d24)) if d24 is not None else "—"
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d7_str = (f"+{d7}" if d7 and d7 > 0 else str(d7)) if d7 is not None else "—"
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lines.append(
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f"| {r['service']} | {r.get('score', 0)} | {r.get('band', '?')} "
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f"| {d24_str} | {d7_str} |"
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)
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if top_regressions:
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lines += ["", "## Top Regressions (Δ24h)", ""]
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for item in top_regressions:
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delta = item.get("delta_24h", 0)
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lines.append(f"- **{item['service']}**: +{delta} points in 24h")
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# ── Likely Causes (Attribution) ───────────────────────────────────────────
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regressions_with_attribution = [
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r for r in reports
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if (r.get("trend") or {}).get("delta_24h") is not None
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and r["trend"]["delta_24h"] > 0
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and r.get("attribution") is not None
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and r["attribution"].get("causes")
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]
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regressions_with_attribution = sorted(
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regressions_with_attribution,
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key=lambda r: -(r.get("trend") or {}).get("delta_24h", 0),
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)[:5]
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if regressions_with_attribution:
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lines += ["", "## Likely Causes (Top Regressions)", ""]
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for r in regressions_with_attribution:
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svc = r["service"]
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attr = r["attribution"]
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delta = r["trend"]["delta_24h"]
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summary = attr.get("summary", "")
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lines.append(f"### {svc} (+{delta} pts)")
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if summary:
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lines.append(f"> {summary}")
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causes = attr.get("causes", [])[:2]
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for c in causes:
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evid = "; ".join(c.get("evidence", []))
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lines.append(
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f"- **{c['type']}** (confidence: {c.get('confidence', '?')}): {evid}"
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)
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# LLM text if available
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llm = attr.get("llm_enrichment") or {}
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if llm.get("enabled") and llm.get("text"):
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lines += ["", f" _LLM insight_: {llm['text'][:400]}"]
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lines.append("")
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# ── Change Timeline (Top Regressions) ────────────────────────────────────
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regressions_with_timeline = [
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r for r in regressions_with_attribution
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if r.get("attribution") and r["attribution"].get("timeline")
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]
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if regressions_with_timeline:
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lines += ["", "## Change Timeline (Top Regressions)", ""]
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for r in regressions_with_timeline:
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svc = r["service"]
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timeline = r["attribution"]["timeline"][:5] # top 5 per service
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lines.append(f"### {svc}")
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for item in timeline:
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ts = (item.get("ts") or "")[:16]
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label = item.get("label", "")
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ev_type = item.get("type", "")
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lines.append(f"- `{ts}` [{ev_type}] {label}")
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lines.append("")
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if improving:
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lines += ["", "## Improving Services (Δ7d)", ""]
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for item in improving:
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delta = item.get("delta_7d", 0)
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lines.append(f"- **{item['service']}**: {delta} points over 7d")
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if actions:
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lines += ["", "## Action List", ""]
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for action in actions:
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lines.append(f"- {action}")
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lines += ["", "---", "_Generated by DAARION.city Risk Digest (deterministic, no LLM by default)_"]
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return "\n".join(lines)
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def daily_digest(
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env: str = "prod",
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*,
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service_reports: Optional[List[Dict]] = None,
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policy: Optional[Dict] = None,
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date_str: Optional[str] = None,
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output_dir: Optional[str] = None,
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write_files: bool = True,
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) -> Dict:
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"""
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Build and optionally persist the daily risk digest.
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service_reports — pre-fetched+enriched list of RiskReports (with trend).
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Returns {json_path, md_path, json_data, markdown, date, env}
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"""
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from risk_engine import load_risk_policy, compute_risk_dashboard
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if policy is None:
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policy = load_risk_policy()
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digest_cfg = policy.get("digest", {})
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top_n = int(digest_cfg.get("top_n", 10))
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max_chars = int(digest_cfg.get("markdown_max_chars", 8000))
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cfg_output_dir = digest_cfg.get("output_dir", "ops/reports/risk")
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effective_output_dir = output_dir or cfg_output_dir
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effective_date = date_str or _now_date()
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reports = sorted(service_reports or [], key=lambda r: -r.get("score", 0))[:top_n]
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# Band counts
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band_counts: Dict[str, int] = {"critical": 0, "high": 0, "medium": 0, "low": 0}
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for r in reports:
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b = r.get("band", "low")
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band_counts[b] = band_counts.get(b, 0) + 1
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# Top regressions
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top_regressions = sorted(
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[r for r in reports if (r.get("trend") or {}).get("delta_24h") is not None
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and r["trend"]["delta_24h"] > 0],
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key=lambda r: -r["trend"]["delta_24h"],
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)[:5]
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top_regressions_out = [
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{"service": r["service"], "delta_24h": r["trend"]["delta_24h"],
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"attribution_causes": [
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{"type": c["type"], "score": c["score"],
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"confidence": c.get("confidence", "low"),
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"evidence": c.get("evidence", [])[:2],
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"refs": c.get("refs", [])[:3]}
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for c in (r.get("attribution") or {}).get("causes", [])[:2]
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],
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"timeline_preview": (r.get("attribution") or {}).get("timeline", [])[:3],
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}
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for r in top_regressions
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]
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# Improving services
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improving = sorted(
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[r for r in reports if (r.get("trend") or {}).get("delta_7d") is not None
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and r["trend"]["delta_7d"] < 0],
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key=lambda r: r["trend"]["delta_7d"],
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)[:5]
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improving_out = [
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{"service": r["service"], "delta_7d": r["trend"]["delta_7d"]}
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for r in improving
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]
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actions = _build_action_list(reports)
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markdown_raw = _build_markdown(
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date_str=effective_date,
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env=env,
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reports=reports,
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top_regressions=top_regressions_out,
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improving=improving_out,
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actions=actions,
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band_counts=band_counts,
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)
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markdown = _clamp(markdown_raw, max_chars)
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json_data = {
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"date": effective_date,
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"env": env,
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"generated_at": datetime.datetime.utcnow().isoformat(),
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"band_counts": band_counts,
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"top_services": [
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{
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"service": r.get("service"),
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"score": r.get("score"),
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"band": r.get("band"),
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"delta_24h": (r.get("trend") or {}).get("delta_24h"),
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"delta_7d": (r.get("trend") or {}).get("delta_7d"),
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"regression": (r.get("trend") or {}).get("regression"),
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"reasons": r.get("reasons", [])[:5],
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"attribution_summary": (r.get("attribution") or {}).get("summary"),
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"top_causes": [
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{"type": c["type"], "score": c["score"],
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"confidence": c.get("confidence", "low"),
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"evidence": c.get("evidence", [])[:2],
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"refs": c.get("refs", [])[:3]}
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for c in (r.get("attribution") or {}).get("causes", [])[:2]
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],
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"timeline_preview": (r.get("attribution") or {}).get("timeline", [])[:3],
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"evidence_refs": (r.get("attribution") or {}).get("evidence_refs", {}),
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}
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for r in reports
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],
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"top_regressions": top_regressions_out,
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"improving_services": improving_out,
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"actions": actions,
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}
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json_path: Optional[str] = None
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md_path: Optional[str] = None
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if write_files:
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try:
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out = Path(effective_output_dir)
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out.mkdir(parents=True, exist_ok=True)
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json_path = str(out / f"{effective_date}.json")
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md_path = str(out / f"{effective_date}.md")
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with open(json_path, "w") as f:
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json.dump(json_data, f, indent=2)
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with open(md_path, "w") as f:
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f.write(markdown)
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logger.info("Risk digest written: %s, %s", json_path, md_path)
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except Exception as e:
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logger.warning("Risk digest write failed: %s", e)
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json_path = md_path = None
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return {
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"date": effective_date,
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"env": env,
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"json_path": json_path,
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"md_path": md_path,
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"json_data": json_data,
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"markdown": markdown,
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}
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Reference in New Issue
Block a user