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Implement ledger metrics for observability and add tests for Ruby packages endpoints 
- Added `LedgerMetrics` class to record write latency and total events for ledger operations.
- Created comprehensive tests for Ruby packages endpoints, covering scenarios for missing inventory, successful retrieval, and identifier handling.
- Introduced `TestSurfaceSecretsScope` for managing environment variables during tests.
- Developed `ProvenanceMongoExtensions` for attaching DSSE provenance and trust information to event documents.
- Implemented `EventProvenanceWriter` and `EventWriter` classes for managing event provenance in MongoDB.
- Established MongoDB indexes for efficient querying of events based on provenance and trust.
- Added models and JSON parsing logic for DSSE provenance and trust information.
2025-11-13 09:29:09 +02:00
..
AGENTS.md
Update AGENTS.md files across multiple modules to standardize task status update instructions and introduce a new document for Secret Leak Detection operations.
2025-11-05 11:58:32 +02:00
architecture.md
Implement ledger metrics for observability and add tests for Ruby packages endpoints
2025-11-13 09:29:09 +02:00
implementation_plan.md
Update AGENTS.md files across multiple modules to standardize task status update instructions and introduce a new document for Secret Leak Detection operations.
2025-11-05 11:58:32 +02:00
orchestration-pipeline.md
feat: Implement Policy Engine Evaluation Service and Cache with unit tests
2025-11-05 09:44:37 +02:00
README.md
feat: Enhance Authority Identity Provider Registry with Bootstrap Capability
2025-11-09 12:18:14 +02:00

README.md

StellaOps Advisory AI

Advisory AI is the retrieval-augmented assistant that synthesizes advisory and VEX evidence into operator-ready summaries, conflict explanations, and remediation plans with strict provenance.

Responsibilities

  • Generate policy-aware advisory summaries with citations back to Conseiller and Excititor evidence.
  • Explain conflicting advisories/VEX statements using weights from VEX Lens and Policy Engine.
  • Propose remediation hints aligned with Offline Kit staging and export bundles.
  • Expose API/UI surfaces with guardrails on model prompts, outputs, and retention.

Key components

  • RAG pipeline drawing from Conseiller, Excititor, VEX Lens, Policy Engine, and SBOM Service data.
  • Prompt templates and guard models enforcing provenance and redaction policies.
  • Vercel/offline inference workers with deterministic caching of generated artefacts.

Integrations & dependencies

  • Authority for tenant-aware access control.
  • Policy Engine for context-specific decisions and explain traces.
  • Console/CLI for interaction surfaces.
  • Export Center/Vuln Explorer for embedding generated briefs.

Operational notes

  • Model cache management and offline bundle packaging per Epic 8 requirements.
  • Usage/latency dashboards for prompt/response monitoring with advisory_ai_latency_seconds, guardrail block/validation counters, and citation coverage histograms wired into the default “Advisory AI” Grafana dashboard.
  • Alert policies fire when advisory_ai_guardrail_blocks_total or advisory_ai_validation_failures_total breach burn-rate thresholds (5 blocks/min or validation failures > 1% of traffic) and when latency p95 exceeds 30s.
  • Redaction policies validated against security/LLM guardrail tests.
  • Guardrail behaviour, blocked phrases, and operational alerts are detailed in /docs/security/assistant-guardrails.md.

Deployment & configuration

  • Containers: advisory-ai-web fronts the API/cache while advisory-ai-worker drains the queue and executes prompts. Both containers mount a shared RWX volume providing /var/lib/advisory-ai/{queue,plans,outputs}.
  • Remote inference toggle: Set ADVISORYAI__AdvisoryAI__Inference__Mode=Remote to send sanitized prompts to an external inference tier. Provide ADVISORYAI__AdvisoryAI__Inference__Remote__BaseAddress (and optional ...ApiKey) to complete the circuit; failures fall back to the sanitized prompt and surface inference.fallback_* metadata.
  • Helm/Compose: Bundled manifests wire the SBOM base address, queue/plan/output directories, and inference options via the AdvisoryAI configuration section. Helm expects a PVC named stellaops-advisory-ai-data. Compose creates named volumes so the worker and web instances share deterministic state.

CLI usage

  • stella advise run <summary|conflict|remediation> --advisory-key <id> [--artifact-id id] [--artifact-purl purl] [--policy-version v] [--profile profile] [--section name] [--force-refresh] [--timeout seconds]
    • Requests an advisory plan from the web service, enqueues execution, then polls for the generated output (default wait 120 s, single check if --timeout 0).
    • Renders plan metadata (cache key, prompt template, token budget), guardrail state, provenance hashes, signatures, and citations in a deterministic table view.
    • Honors STELLAOPS_ADVISORYAI_URL when set; otherwise the CLI reuses the backend URL and scopes requests via X-StellaOps-Scopes.

Epic alignment

  • Epic 8: Advisory AI Assistant.
  • DOCS-AI stories to be tracked in ../../TASKS.md.