feat: Phase 2 — RAG integration in Requirements + DSFA Draft

Add legal context enrichment from Qdrant vector corpus to the two
highest-priority modules (Requirements AI assistant and DSFA drafting
engine).

Go SDK:
- Add SearchCollection() with collection override + whitelist validation
- Refactor Search() to delegate to shared searchInternal()

Python backend:
- New ComplianceRAGClient proxying POST /sdk/v1/rag/search (error-tolerant)
- AI assistant: enrich interpret_requirement() and suggest_controls() with RAG
- Requirements API: add ?include_legal_context=true query parameter

Admin (Next.js):
- Extract shared queryRAG() utility from chat route
- Inject RAG legal context into v1 and v2 draft pipelines

Tests for all three layers (Go, Python, TypeScript shared utility).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

admin-compliance/app/api/sdk/drafting-engine/chat/route.ts

@@ -7,8 +7,8 @@
  */
 
 import { NextRequest, NextResponse } from 'next/server'
+import { queryRAG } from '@/lib/sdk/drafting-engine/rag-query'
 
-const KLAUSUR_SERVICE_URL = process.env.KLAUSUR_SERVICE_URL || 'http://klausur-service:8086'
 const OLLAMA_URL = process.env.OLLAMA_URL || 'http://host.docker.internal:11434'
 const LLM_MODEL = process.env.COMPLIANCE_LLM_MODEL || 'qwen2.5vl:32b'
 
@@ -30,34 +30,6 @@ Konsistenz zwischen Dokumenten sicherzustellen.
 ## Kompetenzbereich
 DSGVO, BDSG, AI Act, TTDSG, DSK-Kurzpapiere, SDM V3.0, BSI-Grundschutz, ISO 27001/27701, EDPB Guidelines, WP248`
 
-/**
- * Query the RAG corpus for relevant documents
- */
-async function queryRAG(query: string): Promise<string> {
-  try {
-    const url = `${KLAUSUR_SERVICE_URL}/api/v1/dsfa-rag/search?query=${encodeURIComponent(query)}&top_k=3`
-    const res = await fetch(url, {
-      headers: { 'Content-Type': 'application/json' },
-      signal: AbortSignal.timeout(10000),
-    })
-
-    if (!res.ok) return ''
-
-    const data = await res.json()
-    if (data.results?.length > 0) {
-      return data.results
-        .map(
-          (r: { source_name?: string; source_code?: string; content?: string }, i: number) =>
-            `[Quelle ${i + 1}: ${r.source_name || r.source_code || 'Unbekannt'}]\n${r.content || ''}`
-        )
-        .join('\n\n---\n\n')
-    }
-    return ''
-  } catch {
-    return ''
-  }
-}
-
 export async function POST(request: NextRequest) {
   try {
     const body = await request.json()