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Add Ruby language analyzer and related functionality

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- Introduced global usings for Ruby analyzer.
- Implemented RubyLockData, RubyLockEntry, and RubyLockParser for handling Gemfile.lock files.
- Created RubyPackage and RubyPackageCollector to manage Ruby packages and vendor cache.
- Developed RubyAnalyzerPlugin and RubyLanguageAnalyzer for analyzing Ruby projects.
- Added tests for Ruby language analyzer with sample Gemfile.lock and expected output.
- Included necessary project files and references for the Ruby analyzer.
- Added third-party licenses for tree-sitter dependencies.
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2025-11-03 01:15:43 +02:00
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docs/modules/advisory-ai/architecture.md
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# 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).
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.
## 5) 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/`.
## 6) 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.
## 7) APIs
- `POST /v1/advisory-ai/summaries` — generate (or retrieve cached) summary for `{advisoryKey, artifactId, policyVersion}`.
- `POST /v1/advisory-ai/conflicts` — explain conflicting VEX statements with trust ranking.
- `POST /v1/advisory-ai/remediation` — fetch remediation plan with target fix versions, prerequisites, verification steps.
- `GET /v1/advisory-ai/outputs/{hash}` — retrieve cached artefact (used by CLI/Console/Export Center).
All endpoints accept `profile` parameter (default `fips-local`) and return `output_hash`, `input_digest`, and `citations` for verification.
## 8) 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.
## 9) 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.
# 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.
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.
## 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 /v1/advisory-ai/summaries` — generate (or retrieve cached) summary for `{advisoryKey, artifactId, policyVersion}`.
- `POST /v1/advisory-ai/conflicts` — explain conflicting VEX statements with trust ranking.
- `POST /v1/advisory-ai/remediation` — fetch remediation plan with target fix versions, prerequisites, verification steps.
- `GET /v1/advisory-ai/outputs/{hash}` — retrieve cached artefact (used by CLI/Console/Export Center).
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.

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# Advisory AI Orchestration Pipeline (Planning Notes)
> **Status:** Draft – prerequisite design for AIAI-31-004 integration work.
> **Audience:** Advisory AI guild, WebService/Worker guilds, CLI guild, Docs/QA support teams.
## 1. Goal
Wire the deterministic pipeline (Summary / Conflict / Remediation flows) into the Advisory AI service, workers, and CLI with deterministic caching, prompt preparation, and guardrail fallback. This document captures the pre-integration checklist and task breakdown so each guild understands their responsibilities before coding begins.
## 2. Prerequisites
| Area | Requirement | Owner | Status |
|------|-------------|-------|--------|
| **Toolset** | Deterministic comparators, dependency analyzer (`IDeterministicToolset`, `AdvisoryPipelineOrchestrator`) | Advisory AI | ✅ landed (AIAI-31-003) |
| **SBOM context** | Real SBOM context client delivering timelines + dependency paths | SBOM Service Guild | ⏳ pending (AIAI-31-002) |
| **Prompt artifacts** | Liquid/Handlebars prompt templates for summary/conflict/remediation | Advisory AI Docs Guild | ⏳ authoring needed |
| **Cache strategy** | Decision on DSSE or hash-only cache entries, TTLs, and eviction policy | Advisory AI + Platform | 🔲 define |
| **Auth scopes** | Confirm service account scopes for new API endpoints/worker-to-service calls | Authority Guild | 🔲 define |
**Blocking risk:** SBOM client and prompt templates must exist (even stubbed) before the orchestrator can produce stable plans.
## 3. Integration plan (high-level)
1. **Service layer (WebService / Worker)**
- Inject `IAdvisoryPipelineOrchestrator` via `AddAdvisoryPipeline`.
- Define REST endpoint `POST /v1/advisories/{key}/pipeline/{task}` (task ∈ summary/conflict/remediation).
- Worker consumes queue messages (`advisory.pipeline.execute`) -> fetches plan -> executes prompt -> persists output & provenance.
- Add metrics: `advisory_pipeline_requests_total`, `advisory_pipeline_plan_cache_hits_total`, `advisory_pipeline_latency_seconds`.
2. **CLI**
- New command `stella advise run <task>` with flags for artifact id, profile, policy version, `--force-refresh`.
- Render JSON/Markdown outputs; handle caching hints (print cache key, status).
3. **Caching / storage**
- Choose storage (Mongo collection vs existing DSSE output store).
- Persist `AdvisoryTaskPlan` metadata + generated output keyed by cache key + policy version.
- Expose TTL/force-refresh semantics.
4. **Docs & QA**
- Publish API spec (`docs/advisory-ai/api.md`) + CLI docs.
- Add golden outputs for deterministic runs; property tests for cache key stability.
## 4. Task Breakdown
### AIAI-31-004A (Service orchestration wiring)
- **Scope:** WebService/Worker injection, REST/queue plumbing, metrics counters, basic cache stub.
- **Dependencies:** `AddAdvisoryPipeline`, SBOM client stub.
- **Exit:** API responds with plan metadata + queue message; worker logs execution attempt; metrics emitted.
### AIAI-31-004B (Prompt assembly & cache persistence)
- **Scope:** Implement prompt assembler, connect to guardrails, persist cache entries w/ DSSE metadata.
- **Dependencies:** Prompt templates, cache storage decision, guardrail interface.
- **Exit:** Deterministic outputs stored; force-refresh honoured; tests cover prompt assembly + caching.
### AIAI-31-004C (CLI integration & docs)
- **Scope:** CLI command + output renderer, docs updates, CLI tests (golden outputs).
- **Dependencies:** Service endpoints stable, caching semantics documented.
- **Exit:** CLI command produces deterministic output, docs updated, smoke tests recorded.
### Supporting tasks (other guilds)
- **AUTH-AIAI-31-004** – Update scopes and DSSE policy (Authority guild).
- **DOCS-AIAI-31-003** – Publish API documentation, CLI guide updates (Docs guild).
- **QA-AIAI-31-004** – Golden/properties/perf suite for pipeline (QA guild).
## 5. Acceptance checklist (per task)
| Item | Notes |
|------|-------|
| Cache key stability | `AdvisoryPipelineOrchestrator` hash must remain stable under re-run of identical inputs. |
| Metrics & logging | Request id, cache key, task type, profile, latency; guardrail results logged without sensitive prompt data. |
| Offline readiness | All prompt templates bundled with Offline Kit; CLI works in air-gapped mode with cached data. |
| Policy awareness | Plans encode policy version used; outputs reference policy digest for audit. |
| Testing | Unit tests (plan generation, cache keys, DI), integration (service endpoint, worker, CLI), deterministic golden outputs. |
## 6. Next steps
1. Finalize SBOM context client (AIAI-31-002) and prompt templates.
2. Create queue schema spec (`docs/modules/advisory-ai/queue-contracts.md`) if not already available.
3. Schedule cross-guild kickoff to agree on cache store & DSSE policy.
_Last updated: 2025-11-02_