git.stella-ops.org/docs/modules/advisory-ai/architecture.md

Advisory AI architecture

Captures the retrieval, guardrail, and inference packaging requirements defined in the Advisory AI implementation plan and related module guides.

1) Goals

• Summarise advisories/VEX evidence into operator-ready briefs with citations.
• Explain conflicting statements with provenance and trust weights (using VEX Lens & Excititor data).
• Suggest remediation plans aligned with Offline Kit deployment models and scheduler follow-ups.
• Operate deterministically where possible; cache generated artefacts with digests for audit.

2) Pipeline overview

                       +---------------------+
   Concelier/VEX Lens  |  Evidence Retriever |
   Policy Engine ----> |  (vector + keyword) | ---> Context Pack (JSON)
   Zastava runtime     +---------------------+
                               |
                               v
                        +-------------+
                        | Prompt      |
                        | Assembler   |
                        +-------------+
                               |
                               v
                        +-------------+
                        | Guarded LLM |
                        | (local/host)|
                        +-------------+
                               |
                               v
                        +-----------------+
                        | Citation &     |
                        | Validation      |
                        +-----------------+
                               |
                               v
                        +----------------+
                        | Output cache   |
                        | (hash, bundle) |
                        +----------------+

3) Retrieval & context

• Hybrid search: vector embeddings (SBERT-compatible) + keyword filters for advisory IDs, PURLs, CVEs.

• Context packs include:

  • Advisory raw excerpts with highlighted sections and source URLs.
  • VEX statements (normalized tuples + trust metadata).
  • Policy explain traces for the affected finding.
  • Runtime/impact hints from Zastava (exposure, entrypoints).
  • Export-ready remediation data (fixed versions, patches).

• SBOM context retriever (AIAI-31-002) hydrates:

  • Version timelines (first/last observed, status, fix availability).
  • Dependency paths (runtime vs build/test, deduped by coordinate chain).
  • Tenant environment flags (prod/stage toggles) with optional blast radius summary.
  • Service-side clamps: max 500 timeline entries, 200 dependency paths, with client-provided toggles for env/blast data.
  • AddSbomContextHttpClient(...) registers the typed HTTP client that calls /v1/sbom/context, while NullSbomContextClient remains the safe default for environments that have not yet exposed the SBOM service.

  Sample configuration (wire real SBOM base URL + API key):

  services.AddSbomContextHttpClient(options =>
  {
      options.BaseAddress = new Uri("https://sbom-service.internal");
      options.Endpoint = "/v1/sbom/context";
      options.ApiKey = configuration["SBOM_SERVICE_API_KEY"];
      options.UserAgent = "stellaops-advisoryai/1.0";
      options.Tenant = configuration["TENANT_ID"];
  });
  
  services.AddAdvisoryPipeline();
  

  After configuration, issue a smoke request (e.g., ISbomContextRetriever.RetrieveAsync) during deployment validation to confirm end-to-end connectivity and credentials before enabling Advisory AI endpoints.

Retriever requests and results are trimmed/normalized before hashing; metadata (counts, provenance keys) is returned for downstream guardrails. Unit coverage ensures deterministic ordering and flag handling.

All context references include content_hash and source_id enabling verifiable citations.

4) Guardrails

• Prompt templates enforce structure: summary, conflicts, remediation, references.
• Response validator ensures:
  • No hallucinated advisories (every fact must map to input context).
  • Citations follow [n] indexing referencing actual sources.
  • Remediation suggestions only cite policy-approved sources (fixed versions, vendor hotfixes).
• Moderation/PII filters prevent leaking secrets; responses failing validation are rejected and logged.
• Pre-flight guardrails redact secrets (AWS keys, generic API tokens, PEM blobs), block "ignore previous instructions"-style prompt injection attempts, enforce citation presence, and cap prompt payload length (default 16 kB). Guardrail outcomes and redaction counts surface via advisory_guardrail_blocks / advisory_outputs_stored metrics.

5) Deterministic tooling

• Version comparators — offline semantic version + RPM EVR parsers with range evaluators. Supports chained constraints (>=, <=, !=) used by remediation advice and blast radius calcs.
  • Registered via AddAdvisoryDeterministicToolset for reuse across orchestrator, CLI, and services.
• Orchestration pipeline — see orchestration-pipeline.md for prerequisites, task breakdown, and cross-guild responsibilities before wiring the execution flows.
• Planned extensions — NEVRA/EVR comparators, ecosystem-specific normalisers, dependency chain scorers (AIAI-31-003 scope).
• Exposed via internal interfaces to allow orchestrator/toolchain reuse; all helpers stay side-effect free and deterministic for golden testing.

6) Output persistence

• Cached artefacts stored in advisory_ai_outputs with fields:
  • output_hash (sha256 of JSON response).
  • input_digest (hash of context pack).
  • summary, conflicts, remediation, citations.
  • generated_at, model_id, profile (Sovereign/FIPS etc.).
  • signatures (optional DSSE if run in deterministic mode).
• Offline bundle format contains summary.md, citations.json, context_manifest.json, signatures/.

7) Profiles & sovereignty

• Profiles: default, fips-local (FIPS-compliant local model), gost-local, cloud-openai (optional, disabled by default). Each profile defines allowed models, key management, and telemetry endpoints.
• CryptoProfile/RootPack integration: generated artefacts can be signed using configured CryptoProfile to satisfy procurement/trust requirements.

8) APIs

• POST /api/v1/advisory/{task} — executes Summary/Conflict/Remediation pipeline (task ∈ summary|conflict|remediation). Requests accept {advisoryKey, artifactId?, policyVersion?, profile, preferredSections?, forceRefresh} and return sanitized prompt payloads, citations, guardrail metadata, provenance hash, and cache hints.
• GET /api/v1/advisory/outputs/{cacheKey}?taskType=SUMMARY&profile=default — retrieves cached artefacts for downstream consumers (Console, CLI, Export Center). Guardrail state and provenance hash accompany results.

All endpoints accept profile parameter (default fips-local) and return output_hash, input_digest, and citations for verification.

9) Observability

• Metrics: advisory_ai_requests_total{profile,type}, advisory_ai_latency_seconds, advisory_ai_validation_failures_total.
• Logs: include output_hash, input_digest, profile, model_id, tenant, artifacts. Sensitive context is not logged.
• Traces: spans for retrieval, prompt assembly, model inference, validation, cache write.

10) Operational controls

• Feature flags per tenant (ai.summary.enabled, ai.remediation.enabled).
• Rate limits (per tenant, per profile) enforced by Orchestrator to prevent runaway usage.
• Offline/air-gapped deployments run local models packaged with Offline Kit; model weights validated via manifest digests.

11) Hosting surfaces

• WebService — exposes /v1/advisory-ai/pipeline/{task} to materialise plans and enqueue execution messages.
• Worker — background service draining the advisory pipeline queue (file-backed stub) pending integration with shared transport.
• Both hosts register AddAdvisoryAiCore, which wires the SBOM context client, deterministic toolset, pipeline orchestrator, and queue metrics.
• SBOM base address + tenant metadata are configured via AdvisoryAI:SbomBaseAddress and propagated through AddSbomContext.

12) QA harness & determinism (Sprint 110 refresh)

• Injection fixtures: src/AdvisoryAI/__Tests/StellaOps.AdvisoryAI.Tests/TestData/prompt-injection-fixtures.txt drives AdvisoryGuardrailInjectionTests, ensuring blocked phrases (ignore previous instructions, override the system prompt, etc.) are rejected with redaction counters, preventing prompt-injection regressions.
• Golden prompts: summary-prompt.json now pairs with conflict-prompt.json; AdvisoryPromptAssemblerTests load both to enforce deterministic JSON payloads across task types and verify vector preview truncation (600 characters + ellipsis) keeps prompts under the documented perf ceiling.
• Plan determinism: AdvisoryPipelineOrchestratorTests shuffle structured/vector/SBOM inputs and assert cache keys + metadata remain stable, proving that seeded plan caches stay deterministic even when retrievers emit out-of-order results.
• Execution telemetry: AdvisoryPipelineExecutorTests exercise partial citation coverage (target ≥0.5 when only half the structured chunks are cited) so advisory_ai_citation_coverage_ratio reflects real guardrail quality.
• Plan cache stability: AdvisoryPlanCacheTests now seed the in-memory cache with a fake time provider to confirm TTL refresh when plans are replaced, guaranteeing reproducible eviction under air-gapped runs.

13) Deployment profiles, scaling, and remote inference

• Local inference containers. advisory-ai-web exposes the API/plan cache endpoints while advisory-ai-worker drains the queue and executes prompts. Both containers mount the same RWX volume that hosts three deterministic paths: /var/lib/advisory-ai/queue, /var/lib/advisory-ai/plans, /var/lib/advisory-ai/outputs. Compose bundles create named volumes (advisory-ai-{queue,plans,outputs}) and the Helm chart mounts the stellaops-advisory-ai-data PVC so web + worker remain in lockstep.
• Remote inference toggle. Set AdvisoryAI:Inference:Mode (env: ADVISORYAI__AdvisoryAI__Inference__Mode) to Remote when you want prompts to be executed by an external inference tier. Provide AdvisoryAI:Inference:Remote:BaseAddress and, optionally, ...:ApiKey. When remote calls fail the executor falls back to the sanitized prompt and sets inference.fallback_* metadata so CLI/Console surface a warning.
• Scalability. Start with 1 web replica + 1 worker for up to ~10 requests/minute. For higher throughput, scale advisory-ai-worker horizontally; each worker is CPU-bound (2 vCPU / 4 GiB RAM recommended) while the web front end is I/O-bound (1 vCPU / 1 GiB). Because the queue/plan/output stores are content-addressed files, ensure the shared volume delivers ≥500 IOPS and <5 ms latency; otherwise queue depth will lag.
• Offline & air-gapped stance. The Compose/Helm manifests avoid external network calls by default and the Offline Kit now publishes the advisory-ai-web and advisory-ai-worker images alongside their SBOMs/provenance. Operators can rehydrate the RWX volume from the kit to pre-prime cache directories before enabling the service.