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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/**
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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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