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
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Apple
261
services/router/llm_enrichment.py
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261
services/router/llm_enrichment.py
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"""
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llm_enrichment.py — Optional LLM enrichment for Risk Attribution (strictly bounded).
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Design constraints:
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- LLM output is explanatory ONLY — never changes scores or decisions.
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- Default mode is OFF (llm_mode="off").
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- Local mode calls a local HTTP model runner (Ollama-compatible by default).
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- Triggers are checked before every call: off if delta < warn OR band not high/critical.
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- Input is hard-truncated to llm_max_chars_in.
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- Output is hard-truncated to llm_max_chars_out.
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- Any error → graceful skip, returns {enabled: false, text: null}.
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Hardening guards (new):
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- model_allowlist: model must be in allowlist or call is skipped.
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- max_calls_per_digest: caller passes a mutable counter dict; stops after limit.
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- per_day_dedupe: in-memory key per (date, service, env) prevents duplicate calls.
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Usage:
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from llm_enrichment import maybe_enrich_attribution
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call_counter = {"count": 0}
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report["llm_enrichment"] = maybe_enrich_attribution(
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attribution_report, risk_report, attr_policy,
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call_counter=call_counter,
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)
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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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from typing import Dict, Optional
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logger = logging.getLogger(__name__)
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# ─── Per-day dedupe store (module-level in-memory) ───────────────────────────
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# key: "risk_enrich:{YYYY-MM-DD}:{service}:{env}" → True
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_dedupe_store: Dict[str, bool] = {}
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def _dedupe_key(service: str, env: str) -> str:
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date = datetime.datetime.utcnow().strftime("%Y-%m-%d")
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return f"risk_enrich:{date}:{service}:{env}"
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def _is_deduped(service: str, env: str) -> bool:
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return _dedupe_store.get(_dedupe_key(service, env), False)
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def _mark_deduped(service: str, env: str) -> None:
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_dedupe_store[_dedupe_key(service, env)] = True
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def _clear_dedupe_store() -> None:
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"""Test helper to reset per-day dedup state."""
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_dedupe_store.clear()
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# ─── Trigger guard ────────────────────────────────────────────────────────────
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def _should_trigger(risk_report: Dict, attr_policy: Dict) -> bool:
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"""
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Returns True only if triggers are met:
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delta_24h >= risk_delta_warn OR band in band_in
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Both conditions are OR — either is enough.
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"""
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triggers = attr_policy.get("llm_triggers", {})
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delta_warn = int(triggers.get("risk_delta_warn", 10))
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band_in = set(triggers.get("band_in", ["high", "critical"]))
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band = risk_report.get("band", "low")
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delta_24h = (risk_report.get("trend") or {}).get("delta_24h")
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if band in band_in:
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return True
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if delta_24h is not None and delta_24h >= delta_warn:
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return True
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return False
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# ─── Prompt builder ───────────────────────────────────────────────────────────
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def _build_prompt(
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attribution_report: Dict,
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risk_report: Dict,
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max_chars: int,
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) -> str:
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"""Build a compact prompt for local LLM enrichment."""
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service = attribution_report.get("service", "?")
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env = attribution_report.get("env", "prod")
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score = risk_report.get("score", 0)
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band = risk_report.get("band", "?")
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delta = attribution_report.get("delta_24h")
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causes = attribution_report.get("causes", [])[:3]
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reasons = risk_report.get("reasons", [])[:4]
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causes_text = "\n".join(
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f" - {c['type']} (score={c['score']}, confidence={c['confidence']}): "
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+ "; ".join(c.get("evidence", []))
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for c in causes
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)
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reasons_text = "\n".join(f" - {r}" for r in reasons)
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prompt = (
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f"You are a platform reliability assistant. Provide a 2-3 sentence human-readable "
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f"explanation for a risk spike in service '{service}' (env={env}).\n\n"
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f"Risk score: {score} ({band}). "
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+ (f"Delta 24h: +{delta}.\n\n" if delta is not None else "\n\n")
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+ f"Risk signals:\n{reasons_text}\n\n"
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f"Attributed causes:\n{causes_text}\n\n"
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f"Write a concise explanation (max 3 sentences). Do NOT include scores or numbers "
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f"from above verbatim. Focus on actionable insight."
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)
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return prompt[:max_chars]
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# ─── Local model call ─────────────────────────────────────────────────────────
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def _is_model_allowed(model: str, attr_policy: Dict) -> bool:
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"""Return True if model is in llm_local.model_allowlist (or list is empty/absent)."""
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allowlist = attr_policy.get("llm_local", {}).get("model_allowlist")
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if not allowlist:
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return True # no restriction configured
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return model in allowlist
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def _call_local_llm(
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prompt: str,
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attr_policy: Dict,
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max_out: int,
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) -> Optional[str]:
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"""
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Calls Ollama-compatible local endpoint.
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Skips if model is not in model_allowlist.
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Returns text or None on failure.
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"""
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llm_cfg = attr_policy.get("llm_local", {})
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endpoint = llm_cfg.get("endpoint", "http://localhost:11434/api/generate")
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model = llm_cfg.get("model", "llama3")
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timeout = int(llm_cfg.get("timeout_seconds", 15))
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if not _is_model_allowed(model, attr_policy):
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logger.warning("llm_enrichment: model '%s' not in allowlist; skipping", model)
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return None
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try:
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import urllib.request
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payload = json.dumps({
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"model": model,
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"prompt": prompt,
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"stream": False,
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"options": {"num_predict": max_out // 4}, # approx token budget
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}).encode()
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req = urllib.request.Request(
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endpoint,
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data=payload,
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headers={"Content-Type": "application/json"},
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method="POST",
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)
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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body = json.loads(resp.read())
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text = body.get("response", "") or ""
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return text[:max_out] if text else None
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except (Exception, OSError, ConnectionError) as e:
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logger.warning("llm_enrichment: local LLM call failed: %s", e)
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return None
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# ─── Public interface ─────────────────────────────────────────────────────────
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def maybe_enrich_attribution(
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attribution_report: Dict,
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risk_report: Dict,
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attr_policy: Optional[Dict] = None,
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*,
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call_counter: Optional[Dict] = None,
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) -> Dict:
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"""
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Conditionally enrich attribution_report with LLM text.
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Hardening guards (checked in order):
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1. llm_mode must be "local" (not "off" or "remote")
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2. triggers must be met (delta >= warn OR band in high/critical)
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3. model must be in model_allowlist
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4. max_calls_per_digest not exceeded (via mutable `call_counter` dict)
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5. per-day dedupe: (service, env) pair not already enriched today
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Returns:
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{"enabled": True/False, "text": str|None, "mode": str}
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Never raises. LLM output does NOT alter scores.
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"""
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if attr_policy is None:
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try:
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from risk_attribution import load_attribution_policy
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attr_policy = load_attribution_policy()
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except Exception:
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return {"enabled": False, "text": None, "mode": "off"}
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mode = (attr_policy.get("defaults") or {}).get("llm_mode", "off")
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if mode == "off":
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return {"enabled": False, "text": None, "mode": "off"}
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# Guard: triggers
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if not _should_trigger(risk_report, attr_policy):
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return {"enabled": False, "text": None, "mode": mode,
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"skipped_reason": "triggers not met"}
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service = attribution_report.get("service", "")
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env = attribution_report.get("env", "prod")
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# Guard: model allowlist (checked early so tests can assert without calling LLM)
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if mode == "local":
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llm_local_cfg_early = attr_policy.get("llm_local", {})
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model_cfg = llm_local_cfg_early.get("model", "llama3")
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if not _is_model_allowed(model_cfg, attr_policy):
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logger.warning("llm_enrichment: model '%s' not in allowlist; skipping", model_cfg)
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return {"enabled": False, "text": None, "mode": mode,
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"skipped_reason": f"model '{model_cfg}' not in allowlist"}
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# Guard: per-day dedupe
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llm_local_cfg = attr_policy.get("llm_local", {})
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if llm_local_cfg.get("per_day_dedupe", True):
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if _is_deduped(service, env):
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return {"enabled": False, "text": None, "mode": mode,
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"skipped_reason": "per_day_dedupe: already enriched today"}
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# Guard: max_calls_per_digest
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if call_counter is not None:
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max_calls = int(llm_local_cfg.get("max_calls_per_digest", 3))
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if call_counter.get("count", 0) >= max_calls:
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return {"enabled": False, "text": None, "mode": mode,
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"skipped_reason": f"max_calls_per_digest={max_calls} reached"}
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defaults = attr_policy.get("defaults", {})
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max_in = int(defaults.get("llm_max_chars_in", 3500))
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max_out = int(defaults.get("llm_max_chars_out", 800))
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prompt = _build_prompt(attribution_report, risk_report, max_in)
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if mode == "local":
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try:
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text = _call_local_llm(prompt, attr_policy, max_out)
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except Exception as e:
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logger.warning("llm_enrichment: local call raised: %s", e)
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text = None
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if text is not None:
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# Update guards on success
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_mark_deduped(service, env)
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if call_counter is not None:
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call_counter["count"] = call_counter.get("count", 0) + 1
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return {
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"enabled": text is not None,
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"text": text,
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"mode": "local",
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}
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# mode == "remote" — not implemented; stub for future extensibility
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logger.debug("llm_enrichment: remote mode not implemented; skipping")
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return {"enabled": False, "text": None, "mode": "remote",
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"skipped_reason": "remote not implemented"}
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