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Александр Зимин a34c6d4c82 feat(topbar): «Что нового» button + 2026-05-12 state.md update

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Design: AI-assisted Schema Authoring

Author: zimin.an Date: 2026-05-12 Status: PROPOSED v2 (post /office-hours) Supersedes: v1 (2026-05-12 — LLM-only "Approach B", before office-hours premise validation revealed Approach C hybrid) Scope: ДЗЗ domain only (ground segment + orbital, не general-purpose AI) Sprint estimate: ~3-4 days CC для Approach C hybrid (revised down from 5-7d v1) Blockers: none architectural. P5 measurement step (см. Premises) — soft prereq

TL;DR

Admin Ordinis сейчас создаёт справочник вручную — пишет JSON Schema (поля, типы, x-references, x-localized, validations), пробрасывает локализации, продумывает bitemporal flags. Это 15-30 минут per dictionary для опытного админа, час+ для нового.

Предложение (v2 — Hybrid): Admin starts с curated template (e.g. «спутник», «наземная станция»), затем при добавлении каждого нового поля кликает «AI suggest field» → LLM предлагает structure (type/format/x-localized/x-references) для одного поля на базе schema name + existing fields context. Admin reviews → accept или edit → continue.

v2 vs v1: Vместо whole-schema generation одним LLM call'ом — template skeleton + per-field LLM fill-in. Smaller LLM context, более reliable, lower GPU bar (7B sufficient instead of 32B), graceful degradation (templates работают без LLM).

Win: time-to-first-schema 30мин → 5-8мин (template + 5-10 fields, каждое 30 сек AI suggest). Compounds with marketplace: templates → bundles directly.

Risk: LLM hallucinations contained per-field (smaller blast radius vs whole schema). Templates always work even если LLM down.

Premises (validated через `/office-hours` 2026-05-12)

P1. AI = time-saver, не replacement. Schema всегда проходит human review через existing draft workflow. No auto-publish. Value = drop time-to-first-schema 30мин → 5-8мин.

P2. Local LLM (vLLM/Ollama) non-negotiable для гос-клиентов — data не покидает perimeter. External API disabled by default, feature-flag-only для corp non-classified. GPU verification = hard prereq перед implementation.

P3. Scope = ДЗЗ domain only. Few-shot training на ЦУОД bundle (5 примеров). Non-ДЗЗ customer → manually extends few-shot в свой bundle.

P4. AI predicts STRUCTURE, не SEMANTICS. Type, format, nullability, FK ref structure, locales — да. Validation rules, GOST codes, business constraints — NET. System prompt explicit: «если не уверен — оставь пустым».

P5. Demand evidence пока нулевая — founder-driven feature, не customer-pulled. Prerequisite measurement step: перед implementation добавить metric admin_schema_authoring_time_seconds (start = first draft creation, end = first review submission). Собрать 2 weeks baseline на v2.14.0 prod. Если P50 < 10 мин — отложить feature (no real pain). Если P50 > 20 мин — confirms hypothesis, proceed.

P6. Cross-model second opinion (Phase 3.5) — skipped в этом office-hours session, premises clear enough.

Architecture (Approach C — Hybrid Template + Per-Field LLM)

High-level flow

Admin opens DictionaryEditorDialog
  ↓
[Templates panel: spacecraft / ground-station / antenna / ...]
  ↓ pick "spacecraft"
  ↓
Template loaded: skeleton schema c 4 base fields (code, name, type, country)
Admin sees Monaco editor pre-filled
  ↓
Admin clicks [+ AI suggest field]
  ↓
Dialog: "Опиши поле в одну фразу"
"Орбита — апогей, перигей, наклонение"
  ↓
POST /api/v1/ai/suggest-field
  request: { schemaContext: {...}, prompt: "Орбита: апогей..." }
  ↓
LLM call (single field, small context ~500 tokens)
  ↓
Response: {
  fieldName: "orbit",
  schema: {
    type: "object",
    properties: {
      apogeeKm: {type: "number"},
      perigeeKm: {type: "number"},
      inclinationDeg: {type: "number", minimum: 0, maximum: 180}
    }
  }
}
  ↓
Admin sees diff в Monaco (current vs +suggested field)
Admin: Accept / Edit / Reject
  ↓
Existing draft workflow (no change downstream)

Components

                  Admin clicks [+ AI suggest field]
                          │
                          ▼
       ┌──────────────────────────────────────┐
       │  ordinis-admin-ui                    │
       │  TemplatePicker.tsx (NEW)            │
       │  AiFieldSuggestPanel.tsx (NEW)       │
       │  Monaco editor (existing)            │
       └──────────────────┬───────────────────┘
                          │ POST /api/v1/ai/suggest-field
                          ▼
       ┌──────────────────────────────────────┐
       │  ordinis-rest-api                    │
       │  AiSchemaController (NEW)            │
       │  AiSchemaService (NEW)               │
       │    ├─ template loader                │
       │    ├─ per-field prompt builder       │
       │    │   (existing schema → context)   │
       │    ├─ LLM adapter call (OpenAI-compat│
       │    │     HTTP, vLLM/Ollama, 7B)      │
       │    ├─ response parser (JSON extract) │
       │    └─ SchemaValidator (existing)     │
       └──────────────────┬───────────────────┘
                          │ field schema or 422
                          ▼
                  Frontend Monaco preview (diff view)
                          │
                          ▼
                  Standard DraftService flow

Template registry

ordinis-cuod-bundle/src/main/resources/templates/:

spacecraft.template.json       # 4 base fields: code, name, type, country
ground-station.template.json   # координаты + оператор
antenna.template.json          # диаметр + диапазоны
frequency-band.template.json   # min/max МГц + band code
operator.template.json         # имя + страна + контакт
ALL.template.json              # empty starter с x-id-source hint

Каждый — JSON Schema fragment с metadata header:

{
  "$comment": "Template: spacecraft (ЦУОД)",
  "x-template-name": "Космический аппарат",
  "x-template-description": "Скелет для справочника КА — добавь fields через AI suggest или вручную",
  "type": "object",
  "x-id-source": "code",
  "required": ["code"],
  "properties": {
    "code": {"type": "string", "x-unique": true, "description": "уникальный код КА"},
    "name": {"type": "object", "x-localized": true},
    "type": {"type": "string", "x-references": "satellite_type.code"},
    "country": {"type": "string", "x-references": "country.code"}
  }
}

`AiSchemaService.suggestField(existingSchema, prompt)`

System prompt fragment:

Ты помогаешь админу справочников ДЗЗ строить JSON Schema fields.
Получаешь: existing schema (текущие поля), prompt (описание нового поля).
Возвращаешь: ОДНО поле — fieldName + schema.

Используй:
- x-localized: true для локализованных текстов
- x-references: "dict.field" для FK на другой справочник
- x-unique: true для бизнес-ключей
- format: date / date-time / email / uri где уместно

НЕ ИЗОБРЕТАЙ:
- GOST коды или иные standardized codes
- Бизнес-validations (только structural type/format/min/max)
- Имена существующих справочников (если не уверен в имени target dict — verbal hint без x-references)

Few-shot examples (3-5 пар):

Existing: {code, name, country}
Prompt: "Орбита — апогей, перигей, наклонение"
Output: {
  "fieldName": "orbit",
  "schema": {
    "type": "object",
    "properties": {
      "apogeeKm": {"type": "number", "description": "Апогей, км"},
      "perigeeKm": {"type": "number", "description": "Перигей, км"},
      "inclinationDeg": {"type": "number", "minimum": 0, "maximum": 180}
    }
  }
}

LLM stack

Tier	Model	Hardware	Cost/month
Prod (recommended)	`Qwen2.5-Coder-7B-Instruct` (or `Qwen2.5-7B-Instruct`)	1 × A10 24GB (or shared с другими тенантами в vLLM)	~$200 (cloud) или existing internal GPU
Dev/Staging	`qwen2.5-coder:7b-instruct` via Ollama	Dev laptop or CPU server	$0
Fallback (off by default)	OpenAI GPT-4o-mini	External API	~$0.002 per request × 100/day = $6/mo per customer

Per-field calls типичны ~500 tokens prompt + ~200 tokens response = ~700 tokens total. На 7B model и A10 — sub-second latency.

Graceful degradation

State	What happens
LLM endpoint reachable	Full AI suggest experience
LLM endpoint 503/timeout	UI shows banner "AI временно недоступен" + AI button disabled. Templates still work — admin keeps editing manually in Monaco.
Global feature flag `ordinis.ai.enabled=false`	No AI button render. Templates available.
Per-customer license disabled	Same as flag off (per-tenant in v2 multi-tenant)

Approaches Considered

Approach A — Templates only (no LLM)

Что: ~20 curated template schemas в bundle, admin picks → Monaco edit
Effort: 1-2 days
Pros: No LLM dep. Ships next week. Zero ops. Solves ~80% case (admin variation existing template)
Cons: Не differentiator. Custom non-template schemas = manual.

Approach B — LLM whole-schema gen (v1 doc)

Что: Admin types «справочник КА с типом, страной, орбитой» → LLM генерит ВСЮ schema → admin reviews/edits
Effort: 5-7 days
Pros: Demo wow. Real differentiator. Single LLM call per schema.
Cons: Whole schema in LLM context = larger prompt = 32B+ model = higher GPU bar. Hallucinations harder to localize (whole schema affected). Larger blast radius если model drifts.

Approach C — Hybrid (RECOMMENDED, v2 chosen)

Что: Template skeleton + per-field LLM fill-in
Effort: 3-4 days
Pros:
- Smaller LLM context (single field, ~500 tokens) → 7B model достаточно → consumer GPU OK
- Hallucinations localized per-field (admin reviews individually, not whole schema)
- Templates always work — graceful degradation when LLM down
- Compounds с marketplace: templates → bundles directly
- 3-4 days effort vs 5-7d for B — ships earlier, measure usage earlier
Cons:
- UX more granular (per-field clicks vs one-shot whole-schema)
- Demo less impressive (smaller per-call generation, not "magical")

Recommended Approach: C (Hybrid)

Reasoning:

Solves P2 (GPU constraint): 7B model on A10 (or shared instance) practical для гос-клиентов с modest GPU budget
Solves P5 (no demand evidence): Smaller commitment, ship in 1 sprint, measure usage before expanding. If admins не используют AI button → roll back с minimal sunk cost
Engineering preference «minimal diff»: Start narrow, expand if used
Compounds with marketplace: Templates already structured for bundle export
Risk asymmetry: If AI removed, templates still ship → graceful degradation built-in, not bolted-on

If P5 measurement confirms strong demand (P50 > 20 min author time) AND P2 GPU provisioned with capacity headroom → upgrade to Approach B as v2. Existing template + per-field UX stays as fallback.

Implementation plan

Phase 0 (prerequisite — 2 weeks)

P5 measurement. Before any code, ship metric:

// ordinis-rest-api/src/main/java/.../service/DictionaryDefinitionService.java
// New @Timed annotation на createDraft endpoint, tag schema_size_kb
@Timed(value = "admin.schema_authoring.seconds", description = "Time from first draft create to review submission per dict")

Plus admin-ui telemetry: t_dialog_open → t_submit per session.

Baseline для 2 weeks на v2.14.0 prod. Если P50 < 10 мин → defer feature. Если > 20 мин → proceed.

Phase 1 (~3-4 days, 6 steps)

Step	Effort	Notes
1. Template loader + 5 curated templates в `ordinis-cuod-bundle/templates/`	4h	JSON files, no logic
2. `TemplatePicker.tsx` — list templates, pick → load skeleton в Monaco	3h	Standard React
3. `LlmAdapter` (OpenAI-compat HTTP, circuit breaker, timeout 10s)	3h	BouncyCastle-free, simple HttpClient
4. `AiSchemaService.suggestField()` + 5 few-shot examples	4h	System prompt + parse JSON extract
5. `AiSchemaController` POST `/ai/suggest-field` + RBAC INTERNAL+ + rate limit 30/min	2h	Standard controller
6. `AiFieldSuggestPanel.tsx` — button, prompt input, diff preview в Monaco, accept/edit/reject	6h	UX core, Monaco diff integration
Tests (10 cases)	6h	testcontainers + RTL
Docs (admin guide + ops runbook GPU)	2h	docs/user-guide/ai-schema.md
Total Phase 1	~30h (3-4 days)	within ~1 sprint

Phase 2 (conditional — only if usage > 5 admin'ов/week)

Approach B upgrade — whole-schema generation в дополнение к per-field
More templates (~15-20 total covering common ДЗЗ cases)
Fine-tuning local model на ЦУОД historical schema corpus (if quality bar не достигнут few-shot'ом)

Test plan

#	Test	Type	Critical?
1	Template loader returns 5 templates with valid JSON Schema	unit	—
2	TemplatePicker → schema injected в Monaco	RTL	—
3	`suggestField` happy path: orbit prompt → valid object schema	integration	—
4	`suggestField` LLM returns invalid JSON → 422	integration	—
5	`suggestField` validates against meta-schema → 422 if invalid	integration	—
6	Rate limit 30/min per user → 429	integration	—
7	LLM timeout 10s → 504 + circuit breaker tracks failure	integration	—
8	Circuit breaker: 10 fails в minute → 503 для 5 min	integration	YES
9	LLM disabled (flag off) → endpoint 404, frontend hides AI button	integration	—
10	Graceful degradation: LLM down, templates still load and edit	integration	YES

Open questions

Какой 7B model? Qwen2.5-Coder-7B vs Qwen2.5-7B-Instruct vs Llama-3.2-7B? Quick eval нужен на 20 few-shot test cases ДЗЗ domain. Defer to GPU prereq step.
Где сидит vLLM? Existing GPU node в k8s? Если нет — defer полностью или provision new node. Verify в P0 prereq.
Rate limit per user vs per tenant? Per user 30/min для v1. Per tenant aggregation — v2 multi-tenant.
Audit log marking «AI assisted»? Yes — add _meta.aiAssisted: true flag в schema metadata. Compliance trail.
Localized prompts (ru vs en)? Admin types на ru typically. System prompt expects ru. v2: detect language, adapt few-shot.
Template versioning? Templates bundled с ordinis-cuod-bundle, semver follows bundle. Updates через bundle upgrade (см. dictionary-marketplace.md).

Distribution plan

Feature ships as part of ordinis backend + ordinis-admin-ui. No new artifact:

LlmAdapter config через env vars (ORDINIS_AI_ENDPOINT, ORDINIS_AI_MODEL, etc.)
Templates в existing ordinis-cuod-bundle.jar
Frontend новые components в existing ordinis-admin-ui chunk

Helm values addition:

ai:
  enabled: false  # gated by license + GPU availability
  endpoint: ""
  model: "qwen2.5-coder:7b-instruct"

Deployment = standard helm upgrade. No new pods (vLLM separate concern — assumed pre-existing infra).

Success criteria

Quantitative (measured Phase 0 baseline + after Phase 1 ship):

P50 admin schema authoring time: baseline → -50% (target 5-8 min)
AI suggest acceptance rate: ≥40% (admin accepts AI suggestion without edits) for v1 success
LLM error rate: <5% (parse + meta-schema failures combined)
Circuit breaker activations: ≤1/day prod

Qualitative:

Customer interview after 1 month: «использую регулярно» from ≥2 admin'ов

Failure criteria (rollback):

AI button used <1×/week per admin across 2 weeks → feature roll back
LLM cost (if external API) > $50/month per customer → flag off

What I noticed about how you think

(From this /office-hours session 2026-05-12)

Ты сразу downgrade'нул scope от «AI generates whole schema» (founder polish version) на «templates + per-field AI» (hybrid) когда я представил Approach C. Это не attachment to your earlier doc — это openness к correction. Это редко.
Когда я предложил skip Phase 3.5 (cross-model second opinion) — ты выбрал proceed без него. Premises вам clear enough, no need for ceremony. Sign of confident decision-making, не perfectionism paralysis.
«согласен с рекомендациями» vs detail-by-detail negotiation = trust в analysis или impatience? Likely former — patterns этой сессии (5 MRs shipped, prod deploy, 3 design docs) показывают delegating-when-trust-built behavior.
P5 measurement step ты accepted without pushback — это founder maturity. Большинство build first, measure later. Ты accepting measure-first приоритет = real bias toward evidence.

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