# 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:
```json
{
  "$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:**
1. **Solves P2 (GPU constraint):** 7B model on A10 (or shared instance) practical для гос-клиентов с modest GPU budget
2. **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
3. **Engineering preference «minimal diff»:** Start narrow, expand if used
4. **Compounds with marketplace:** Templates already structured for bundle export
5. **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:
```java
// 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

1. **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.**

2. **Где сидит vLLM?** Existing GPU node в k8s? Если нет — defer полностью или provision new node. **Verify в P0 prereq.**

3. **Rate limit per user vs per tenant?** Per user 30/min для v1. Per tenant aggregation — v2 multi-tenant.

4. **Audit log marking «AI assisted»?** Yes — add `_meta.aiAssisted: true` flag в schema metadata. Compliance trail.

5. **Localized prompts (ru vs en)?** Admin types на ru typically. System prompt expects ru. v2: detect language, adapt few-shot.

6. **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:**
```yaml
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.

---

## See also

- Companion: `dictionary-marketplace.md` — bundle catalog (next /office-hours candidate, compounds с AI assist)
- v1 supersededy: this same path was «whole-schema LLM» originally, downgraded to «hybrid template + per-field» после Phase 4 alternatives generation
- Office-hours design doc: `~/.gstack/projects/claude/zimin-main-design-ai-schema-assist-20260512.md` (generated separately)