feat(sdk): Add Drafting Engine with 4-mode agent system (Explain/Ask/Draft/Validate)
Extends the Compliance Advisor from a Q&A chatbot into a full drafting engine that can generate, validate, and refine compliance documents within Scope Engine constraints. Includes intent classifier, state projector, constraint enforcer, SOUL templates, Go backend endpoints, and React UI components. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
BreakPilot Dev
184
admin-v2/app/api/sdk/drafting-engine/chat/route.ts
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184
admin-v2/app/api/sdk/drafting-engine/chat/route.ts
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@@ -0,0 +1,184 @@
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/**
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* Drafting Engine Chat API
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*
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* Verbindet das DraftingEngineWidget mit dem LLM Backend.
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* Unterstuetzt alle 4 Modi: explain, ask, draft, validate.
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* Nutzt State-Projection fuer token-effiziente Kontextgabe.
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*/
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import { NextRequest, NextResponse } from 'next/server'
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const KLAUSUR_SERVICE_URL = process.env.KLAUSUR_SERVICE_URL || 'http://klausur-service:8086'
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const OLLAMA_URL = process.env.OLLAMA_URL || 'http://host.docker.internal:11434'
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const LLM_MODEL = process.env.COMPLIANCE_LLM_MODEL || 'qwen2.5vl:32b'
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// SOUL System Prompt (from agent-core/soul/drafting-agent.soul.md)
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const DRAFTING_SYSTEM_PROMPT = `# Drafting Agent - Compliance-Dokumententwurf
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## Identitaet
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Du bist der BreakPilot Drafting Agent. Du hilfst Nutzern des AI Compliance SDK,
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DSGVO-konforme Compliance-Dokumente zu entwerfen, Luecken zu erkennen und
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Konsistenz zwischen Dokumenten sicherzustellen.
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## Strikte Constraints
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- Du darfst NIEMALS die Scope-Engine-Entscheidung aendern oder in Frage stellen
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- Das bestimmte Level ist bindend fuer die Dokumenttiefe
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- Gib praxisnahe Hinweise, KEINE konkrete Rechtsberatung
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- Kommuniziere auf Deutsch, sachlich und verstaendlich
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- Fuelle fehlende Informationen mit [PLATZHALTER: ...] Markierung
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## Kompetenzbereich
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DSGVO, BDSG, AI Act, TTDSG, DSK-Kurzpapiere, SDM V3.0, BSI-Grundschutz, ISO 27001/27701, EDPB Guidelines, WP248`
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/**
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* Query the RAG corpus for relevant documents
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*/
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async function queryRAG(query: string): Promise<string> {
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try {
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const url = `${KLAUSUR_SERVICE_URL}/api/v1/dsfa-rag/search?query=${encodeURIComponent(query)}&top_k=3`
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const res = await fetch(url, {
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headers: { 'Content-Type': 'application/json' },
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signal: AbortSignal.timeout(10000),
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})
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if (!res.ok) return ''
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const data = await res.json()
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if (data.results?.length > 0) {
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return data.results
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.map(
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(r: { source_name?: string; source_code?: string; content?: string }, i: number) =>
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`[Quelle ${i + 1}: ${r.source_name || r.source_code || 'Unbekannt'}]\n${r.content || ''}`
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)
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.join('\n\n---\n\n')
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}
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return ''
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} catch {
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return ''
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}
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}
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export async function POST(request: NextRequest) {
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try {
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const body = await request.json()
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const {
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message,
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history = [],
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sdkStateProjection,
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mode = 'explain',
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documentType,
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} = body
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if (!message || typeof message !== 'string') {
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return NextResponse.json({ error: 'Message is required' }, { status: 400 })
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}
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// 1. Query RAG for legal context
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const ragContext = await queryRAG(message)
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// 2. Build system prompt with mode-specific instructions + state projection
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let systemContent = DRAFTING_SYSTEM_PROMPT
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// Mode-specific instructions
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const modeInstructions: Record<string, string> = {
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explain: '\n\n## Aktueller Modus: EXPLAIN\nBeantworte Fragen verstaendlich mit Quellenangaben.',
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ask: '\n\n## Aktueller Modus: ASK\nAnalysiere Luecken und stelle gezielte Fragen. Eine Frage pro Antwort.',
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draft: `\n\n## Aktueller Modus: DRAFT\nEntwirf strukturierte Dokument-Sections. Dokumenttyp: ${documentType || 'nicht spezifiziert'}.\nAntworte mit JSON wenn ein Draft angefragt wird.`,
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validate: '\n\n## Aktueller Modus: VALIDATE\nPruefe Cross-Dokument-Konsistenz. Gib Errors, Warnings und Suggestions zurueck.',
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}
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systemContent += modeInstructions[mode] || modeInstructions.explain
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// Add state projection context
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if (sdkStateProjection) {
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systemContent += `\n\n## SDK-State Projektion (${mode}-Kontext)\n${JSON.stringify(sdkStateProjection, null, 0).slice(0, 3000)}`
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}
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// Add RAG context
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if (ragContext) {
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systemContent += `\n\n## Relevanter Rechtskontext\n${ragContext}`
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}
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// 3. Build messages array
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const messages = [
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{ role: 'system', content: systemContent },
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...history.slice(-10).map((h: { role: string; content: string }) => ({
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role: h.role === 'user' ? 'user' : 'assistant',
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content: h.content,
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})),
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{ role: 'user', content: message },
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]
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// 4. Call LLM with streaming
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const ollamaResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model: LLM_MODEL,
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messages,
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stream: true,
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options: {
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temperature: mode === 'draft' ? 0.2 : 0.3,
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num_predict: mode === 'draft' ? 16384 : 8192,
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},
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}),
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signal: AbortSignal.timeout(120000),
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})
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if (!ollamaResponse.ok) {
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const errorText = await ollamaResponse.text()
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console.error('LLM error:', ollamaResponse.status, errorText)
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return NextResponse.json(
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{ error: `LLM nicht erreichbar (Status ${ollamaResponse.status})` },
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{ status: 502 }
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)
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}
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// 5. Stream response back
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const encoder = new TextEncoder()
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const stream = new ReadableStream({
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async start(controller) {
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const reader = ollamaResponse.body!.getReader()
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const decoder = new TextDecoder()
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try {
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while (true) {
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const { done, value } = await reader.read()
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if (done) break
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const chunk = decoder.decode(value, { stream: true })
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const lines = chunk.split('\n').filter((l) => l.trim())
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for (const line of lines) {
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try {
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const json = JSON.parse(line)
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if (json.message?.content) {
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controller.enqueue(encoder.encode(json.message.content))
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}
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} catch {
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// Partial JSON, skip
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}
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}
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}
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} catch (error) {
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console.error('Stream error:', error)
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} finally {
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controller.close()
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}
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},
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})
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return new NextResponse(stream, {
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headers: {
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'Content-Type': 'text/plain; charset=utf-8',
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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},
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})
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} catch (error) {
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console.error('Drafting engine chat error:', error)
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return NextResponse.json(
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{ error: 'Verbindung zum LLM fehlgeschlagen.' },
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{ status: 503 }
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)
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}
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}
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168
admin-v2/app/api/sdk/drafting-engine/draft/route.ts
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168
admin-v2/app/api/sdk/drafting-engine/draft/route.ts
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@@ -0,0 +1,168 @@
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/**
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* Drafting Engine - Draft API
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*
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* Erstellt strukturierte Compliance-Dokument-Entwuerfe.
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* Baut dokument-spezifische Prompts aus SOUL-Template + State-Projection.
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* Gibt strukturiertes JSON zurueck.
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*/
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import { NextRequest, NextResponse } from 'next/server'
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const OLLAMA_URL = process.env.OLLAMA_URL || 'http://host.docker.internal:11434'
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const LLM_MODEL = process.env.COMPLIANCE_LLM_MODEL || 'qwen2.5vl:32b'
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// Import prompt builders
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import { buildVVTDraftPrompt } from '@/lib/sdk/drafting-engine/prompts/draft-vvt'
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import { buildTOMDraftPrompt } from '@/lib/sdk/drafting-engine/prompts/draft-tom'
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import { buildDSFADraftPrompt } from '@/lib/sdk/drafting-engine/prompts/draft-dsfa'
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import { buildPrivacyPolicyDraftPrompt } from '@/lib/sdk/drafting-engine/prompts/draft-privacy-policy'
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import { buildLoeschfristenDraftPrompt } from '@/lib/sdk/drafting-engine/prompts/draft-loeschfristen'
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import type { DraftContext, DraftResponse, DraftRevision, DraftSection } from '@/lib/sdk/drafting-engine/types'
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import type { ScopeDocumentType } from '@/lib/sdk/compliance-scope-types'
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import { ConstraintEnforcer } from '@/lib/sdk/drafting-engine/constraint-enforcer'
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const constraintEnforcer = new ConstraintEnforcer()
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const DRAFTING_SYSTEM_PROMPT = `Du bist ein DSGVO-Compliance-Experte und erstellst strukturierte Dokument-Entwuerfe.
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Du MUSST immer im JSON-Format antworten mit einem "sections" Array.
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Jede Section hat: id, title, content, schemaField.
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Halte die Tiefe strikt am vorgegebenen Level.
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Markiere fehlende Informationen mit [PLATZHALTER: Beschreibung].
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Sprache: Deutsch.`
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function buildPromptForDocumentType(
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documentType: ScopeDocumentType,
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context: DraftContext,
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instructions?: string
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): string {
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switch (documentType) {
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case 'vvt':
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return buildVVTDraftPrompt({ context, instructions })
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case 'tom':
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return buildTOMDraftPrompt({ context, instructions })
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case 'dsfa':
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return buildDSFADraftPrompt({ context, instructions })
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case 'dsi':
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return buildPrivacyPolicyDraftPrompt({ context, instructions })
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case 'lf':
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return buildLoeschfristenDraftPrompt({ context, instructions })
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default:
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return `## Aufgabe: Entwurf fuer ${documentType}
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### Level: ${context.decisions.level}
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### Tiefe: ${context.constraints.depthRequirements.depth}
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### Erforderliche Inhalte:
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${context.constraints.depthRequirements.detailItems.map((item, i) => `${i + 1}. ${item}`).join('\n')}
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${instructions ? `### Anweisungen: ${instructions}` : ''}
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Antworte als JSON mit "sections" Array.`
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}
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}
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export async function POST(request: NextRequest) {
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try {
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const body = await request.json()
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const { documentType, draftContext, instructions, existingDraft } = body
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if (!documentType || !draftContext) {
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return NextResponse.json(
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{ error: 'documentType und draftContext sind erforderlich' },
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{ status: 400 }
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)
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}
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// 1. Constraint Check (Hard Gate)
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const constraintCheck = constraintEnforcer.checkFromContext(documentType, draftContext)
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if (!constraintCheck.allowed) {
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return NextResponse.json({
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draft: null,
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constraintCheck,
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tokensUsed: 0,
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error: 'Constraint-Verletzung: ' + constraintCheck.violations.join('; '),
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}, { status: 403 })
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}
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// 2. Build document-specific prompt
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const draftPrompt = buildPromptForDocumentType(documentType, draftContext, instructions)
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// 3. Build messages
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const messages = [
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{ role: 'system', content: DRAFTING_SYSTEM_PROMPT },
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...(existingDraft ? [{
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role: 'assistant',
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content: `Bisheriger Entwurf:\n${JSON.stringify(existingDraft.sections, null, 2)}`,
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}] : []),
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{ role: 'user', content: draftPrompt },
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]
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// 4. Call LLM (non-streaming for structured output)
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const ollamaResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model: LLM_MODEL,
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messages,
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stream: false,
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options: {
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temperature: 0.15,
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num_predict: 16384,
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},
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format: 'json',
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}),
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signal: AbortSignal.timeout(180000),
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})
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if (!ollamaResponse.ok) {
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return NextResponse.json(
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{ error: `LLM nicht erreichbar (Status ${ollamaResponse.status})` },
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{ status: 502 }
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)
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}
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const result = await ollamaResponse.json()
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const content = result.message?.content || ''
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// 5. Parse JSON response
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let sections: DraftSection[] = []
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try {
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const parsed = JSON.parse(content)
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sections = (parsed.sections || []).map((s: Record<string, unknown>, i: number) => ({
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id: String(s.id || `section-${i}`),
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title: String(s.title || ''),
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content: String(s.content || ''),
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schemaField: s.schemaField ? String(s.schemaField) : undefined,
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}))
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} catch {
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// If not JSON, wrap raw content as single section
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sections = [{
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id: 'raw',
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title: 'Entwurf',
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content: content,
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}]
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}
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const draft: DraftRevision = {
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id: `draft-${Date.now()}`,
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content: sections.map(s => `## ${s.title}\n\n${s.content}`).join('\n\n'),
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sections,
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createdAt: new Date().toISOString(),
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instruction: instructions,
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}
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const response: DraftResponse = {
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draft,
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constraintCheck,
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tokensUsed: result.eval_count || 0,
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}
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return NextResponse.json(response)
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} catch (error) {
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console.error('Draft generation error:', error)
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return NextResponse.json(
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{ error: 'Draft-Generierung fehlgeschlagen.' },
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{ status: 503 }
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)
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}
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}
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188
admin-v2/app/api/sdk/drafting-engine/validate/route.ts
Normal file
188
admin-v2/app/api/sdk/drafting-engine/validate/route.ts
Normal file
@@ -0,0 +1,188 @@
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/**
|
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* Drafting Engine - Validate API
|
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*
|
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* Stufe 1: Deterministische Pruefung gegen DOCUMENT_SCOPE_MATRIX
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* Stufe 2: LLM Cross-Consistency Check
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*/
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import { NextRequest, NextResponse } from 'next/server'
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import { DOCUMENT_SCOPE_MATRIX, DOCUMENT_TYPE_LABELS, getDepthLevelNumeric } from '@/lib/sdk/compliance-scope-types'
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import type { ScopeDocumentType, ComplianceDepthLevel } from '@/lib/sdk/compliance-scope-types'
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import type { ValidationContext, ValidationResult, ValidationFinding } from '@/lib/sdk/drafting-engine/types'
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import { buildCrossCheckPrompt } from '@/lib/sdk/drafting-engine/prompts/validate-cross-check'
|
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const OLLAMA_URL = process.env.OLLAMA_URL || 'http://host.docker.internal:11434'
|
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const LLM_MODEL = process.env.COMPLIANCE_LLM_MODEL || 'qwen2.5vl:32b'
|
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|
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/**
|
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* Stufe 1: Deterministische Pruefung
|
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*/
|
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function deterministicCheck(
|
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documentType: ScopeDocumentType,
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validationContext: ValidationContext
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): ValidationFinding[] {
|
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const findings: ValidationFinding[] = []
|
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const level = validationContext.scopeLevel
|
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const levelNumeric = getDepthLevelNumeric(level)
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const req = DOCUMENT_SCOPE_MATRIX[documentType]?.[level]
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// Check 1: Ist das Dokument auf diesem Level erforderlich?
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if (req && !req.required && levelNumeric < 3) {
|
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findings.push({
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id: `DET-OPT-${documentType}`,
|
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severity: 'suggestion',
|
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category: 'scope_violation',
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title: `${DOCUMENT_TYPE_LABELS[documentType] ?? documentType} ist optional`,
|
||||
description: `Auf Level ${level} ist dieses Dokument nicht verpflichtend.`,
|
||||
documentType,
|
||||
})
|
||||
}
|
||||
|
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// Check 2: VVT vorhanden wenn erforderlich?
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const vvtReq = DOCUMENT_SCOPE_MATRIX.vvt[level]
|
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if (vvtReq.required && validationContext.crossReferences.vvtCategories.length === 0) {
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||||
findings.push({
|
||||
id: 'DET-VVT-MISSING',
|
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severity: 'error',
|
||||
category: 'missing_content',
|
||||
title: 'VVT fehlt',
|
||||
description: `Auf Level ${level} ist ein VVT Pflicht, aber keine Eintraege vorhanden.`,
|
||||
documentType: 'vvt',
|
||||
legalReference: 'Art. 30 DSGVO',
|
||||
})
|
||||
}
|
||||
|
||||
// Check 3: TOM vorhanden wenn VVT existiert?
|
||||
if (validationContext.crossReferences.vvtCategories.length > 0
|
||||
&& validationContext.crossReferences.tomControls.length === 0) {
|
||||
findings.push({
|
||||
id: 'DET-TOM-MISSING-FOR-VVT',
|
||||
severity: 'warning',
|
||||
category: 'cross_reference',
|
||||
title: 'TOM fehlt bei vorhandenem VVT',
|
||||
description: 'VVT-Eintraege existieren, aber keine TOM-Massnahmen sind definiert.',
|
||||
documentType: 'tom',
|
||||
crossReferenceType: 'vvt',
|
||||
legalReference: 'Art. 32 DSGVO',
|
||||
suggestion: 'TOM-Massnahmen erstellen, die die VVT-Taetigkeiten absichern.',
|
||||
})
|
||||
}
|
||||
|
||||
// Check 4: Loeschfristen fuer VVT-Kategorien
|
||||
if (validationContext.crossReferences.vvtCategories.length > 0
|
||||
&& validationContext.crossReferences.retentionCategories.length === 0) {
|
||||
findings.push({
|
||||
id: 'DET-LF-MISSING-FOR-VVT',
|
||||
severity: 'warning',
|
||||
category: 'cross_reference',
|
||||
title: 'Loeschfristen fehlen',
|
||||
description: 'VVT-Eintraege existieren, aber keine Loeschfristen sind definiert.',
|
||||
documentType: 'lf',
|
||||
crossReferenceType: 'vvt',
|
||||
legalReference: 'Art. 17 DSGVO',
|
||||
suggestion: 'Loeschfristen fuer alle VVT-Datenkategorien definieren.',
|
||||
})
|
||||
}
|
||||
|
||||
return findings
|
||||
}
|
||||
|
||||
export async function POST(request: NextRequest) {
|
||||
try {
|
||||
const body = await request.json()
|
||||
const { documentType, draftContent, validationContext } = body
|
||||
|
||||
if (!documentType || !validationContext) {
|
||||
return NextResponse.json(
|
||||
{ error: 'documentType und validationContext sind erforderlich' },
|
||||
{ status: 400 }
|
||||
)
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Stufe 1: Deterministische Pruefung
|
||||
// ---------------------------------------------------------------
|
||||
const deterministicFindings = deterministicCheck(documentType, validationContext)
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Stufe 2: LLM Cross-Consistency Check
|
||||
// ---------------------------------------------------------------
|
||||
let llmFindings: ValidationFinding[] = []
|
||||
|
||||
try {
|
||||
const crossCheckPrompt = buildCrossCheckPrompt({
|
||||
context: validationContext,
|
||||
})
|
||||
|
||||
const ollamaResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: LLM_MODEL,
|
||||
messages: [
|
||||
{
|
||||
role: 'system',
|
||||
content: 'Du bist ein DSGVO-Compliance-Validator. Antworte NUR im JSON-Format.',
|
||||
},
|
||||
{ role: 'user', content: crossCheckPrompt },
|
||||
],
|
||||
stream: false,
|
||||
options: { temperature: 0.1, num_predict: 8192 },
|
||||
format: 'json',
|
||||
}),
|
||||
signal: AbortSignal.timeout(120000),
|
||||
})
|
||||
|
||||
if (ollamaResponse.ok) {
|
||||
const result = await ollamaResponse.json()
|
||||
try {
|
||||
const parsed = JSON.parse(result.message?.content || '{}')
|
||||
llmFindings = [
|
||||
...(parsed.errors || []),
|
||||
...(parsed.warnings || []),
|
||||
...(parsed.suggestions || []),
|
||||
].map((f: Record<string, unknown>, i: number) => ({
|
||||
id: String(f.id || `LLM-${i}`),
|
||||
severity: (String(f.severity || 'suggestion')) as 'error' | 'warning' | 'suggestion',
|
||||
category: (String(f.category || 'inconsistency')) as ValidationFinding['category'],
|
||||
title: String(f.title || ''),
|
||||
description: String(f.description || ''),
|
||||
documentType: (String(f.documentType || documentType)) as ScopeDocumentType,
|
||||
crossReferenceType: f.crossReferenceType ? String(f.crossReferenceType) as ScopeDocumentType : undefined,
|
||||
legalReference: f.legalReference ? String(f.legalReference) : undefined,
|
||||
suggestion: f.suggestion ? String(f.suggestion) : undefined,
|
||||
}))
|
||||
} catch {
|
||||
// LLM response not parseable, skip
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
// LLM unavailable, continue with deterministic results only
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Combine results
|
||||
// ---------------------------------------------------------------
|
||||
const allFindings = [...deterministicFindings, ...llmFindings]
|
||||
const errors = allFindings.filter(f => f.severity === 'error')
|
||||
const warnings = allFindings.filter(f => f.severity === 'warning')
|
||||
const suggestions = allFindings.filter(f => f.severity === 'suggestion')
|
||||
|
||||
const result: ValidationResult = {
|
||||
passed: errors.length === 0,
|
||||
timestamp: new Date().toISOString(),
|
||||
scopeLevel: validationContext.scopeLevel,
|
||||
errors,
|
||||
warnings,
|
||||
suggestions,
|
||||
}
|
||||
|
||||
return NextResponse.json(result)
|
||||
} catch (error) {
|
||||
console.error('Validation error:', error)
|
||||
return NextResponse.json(
|
||||
{ error: 'Validierung fehlgeschlagen.' },
|
||||
{ status: 503 }
|
||||
)
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user