Initial commit: breakpilot-compliance - Compliance SDK Platform
Services: Admin-Compliance, Backend-Compliance, AI-Compliance-SDK, Consent-SDK, Developer-Portal, PCA-Platform, DSMS Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Boenisch
184
admin-compliance/app/api/sdk/drafting-engine/chat/route.ts
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184
admin-compliance/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-compliance/app/api/sdk/drafting-engine/draft/route.ts
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168
admin-compliance/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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|
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if (!ollamaResponse.ok) {
|
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return NextResponse.json(
|
||||
{ error: `LLM nicht erreichbar (Status ${ollamaResponse.status})` },
|
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{ status: 502 }
|
||||
)
|
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}
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const result = await ollamaResponse.json()
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const content = result.message?.content || ''
|
||||
|
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// 5. Parse JSON response
|
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let sections: DraftSection[] = []
|
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try {
|
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const parsed = JSON.parse(content)
|
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sections = (parsed.sections || []).map((s: Record<string, unknown>, i: number) => ({
|
||||
id: String(s.id || `section-${i}`),
|
||||
title: String(s.title || ''),
|
||||
content: String(s.content || ''),
|
||||
schemaField: s.schemaField ? String(s.schemaField) : undefined,
|
||||
}))
|
||||
} catch {
|
||||
// If not JSON, wrap raw content as single section
|
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sections = [{
|
||||
id: 'raw',
|
||||
title: 'Entwurf',
|
||||
content: content,
|
||||
}]
|
||||
}
|
||||
|
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const draft: DraftRevision = {
|
||||
id: `draft-${Date.now()}`,
|
||||
content: sections.map(s => `## ${s.title}\n\n${s.content}`).join('\n\n'),
|
||||
sections,
|
||||
createdAt: new Date().toISOString(),
|
||||
instruction: instructions,
|
||||
}
|
||||
|
||||
const response: DraftResponse = {
|
||||
draft,
|
||||
constraintCheck,
|
||||
tokensUsed: result.eval_count || 0,
|
||||
}
|
||||
|
||||
return NextResponse.json(response)
|
||||
} catch (error) {
|
||||
console.error('Draft generation error:', error)
|
||||
return NextResponse.json(
|
||||
{ error: 'Draft-Generierung fehlgeschlagen.' },
|
||||
{ status: 503 }
|
||||
)
|
||||
}
|
||||
}
|
||||
188
admin-compliance/app/api/sdk/drafting-engine/validate/route.ts
Normal file
188
admin-compliance/app/api/sdk/drafting-engine/validate/route.ts
Normal file
@@ -0,0 +1,188 @@
|
||||
/**
|
||||
* Drafting Engine - Validate API
|
||||
*
|
||||
* Stufe 1: Deterministische Pruefung gegen DOCUMENT_SCOPE_MATRIX
|
||||
* Stufe 2: LLM Cross-Consistency Check
|
||||
*/
|
||||
|
||||
import { NextRequest, NextResponse } from 'next/server'
|
||||
import { DOCUMENT_SCOPE_MATRIX, DOCUMENT_TYPE_LABELS, getDepthLevelNumeric } from '@/lib/sdk/compliance-scope-types'
|
||||
import type { ScopeDocumentType, ComplianceDepthLevel } from '@/lib/sdk/compliance-scope-types'
|
||||
import type { ValidationContext, ValidationResult, ValidationFinding } from '@/lib/sdk/drafting-engine/types'
|
||||
import { buildCrossCheckPrompt } from '@/lib/sdk/drafting-engine/prompts/validate-cross-check'
|
||||
|
||||
const OLLAMA_URL = process.env.OLLAMA_URL || 'http://host.docker.internal:11434'
|
||||
const LLM_MODEL = process.env.COMPLIANCE_LLM_MODEL || 'qwen2.5vl:32b'
|
||||
|
||||
/**
|
||||
* Stufe 1: Deterministische Pruefung
|
||||
*/
|
||||
function deterministicCheck(
|
||||
documentType: ScopeDocumentType,
|
||||
validationContext: ValidationContext
|
||||
): ValidationFinding[] {
|
||||
const findings: ValidationFinding[] = []
|
||||
const level = validationContext.scopeLevel
|
||||
const levelNumeric = getDepthLevelNumeric(level)
|
||||
const req = DOCUMENT_SCOPE_MATRIX[documentType]?.[level]
|
||||
|
||||
// Check 1: Ist das Dokument auf diesem Level erforderlich?
|
||||
if (req && !req.required && levelNumeric < 3) {
|
||||
findings.push({
|
||||
id: `DET-OPT-${documentType}`,
|
||||
severity: 'suggestion',
|
||||
category: 'scope_violation',
|
||||
title: `${DOCUMENT_TYPE_LABELS[documentType] ?? documentType} ist optional`,
|
||||
description: `Auf Level ${level} ist dieses Dokument nicht verpflichtend.`,
|
||||
documentType,
|
||||
})
|
||||
}
|
||||
|
||||
// Check 2: VVT vorhanden wenn erforderlich?
|
||||
const vvtReq = DOCUMENT_SCOPE_MATRIX.vvt[level]
|
||||
if (vvtReq.required && validationContext.crossReferences.vvtCategories.length === 0) {
|
||||
findings.push({
|
||||
id: 'DET-VVT-MISSING',
|
||||
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