fix(drafting): Drafting-Engine auf prod reparieren — RAG via ai-sdk + OVH-LLM-Kaskade
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Die Drafting-Engine (Dokument-Entwurf, v2-Pipeline, Validierung, Drafting-Chat,
Vendor-Vertragspruefung) war auf prod doppelt tot:
- RAG ueber bp-core-rag-service:8097 (existiert auf prod nicht)
- LLM ueber OLLAMA_URL/api/chat mit qwen2.5vl (prod = ollama-embed, kein Chat-Modell)

Fix (analog zum Compliance-Advisor):
- rag-query.ts -> ai-compliance-sdk /sdk/v1/rag/search (bge-m3, prod-erreichbar).
- Neue lib/sdk/drafting-engine/llm-cascade.ts: OVH/LiteLLM (gpt-oss-120b) zuerst,
  Ollama als Dev-Fallback; cascadeComplete (JSON) + cascadeStream. Das Backend nutzt
  OVH+JSON bereits erfolgreich auf prod (extract-datasheet).
- 5 Aufrufstellen (draft-helpers, draft-helpers-v2, validate, chat, vendor-review)
  auf die Kaskade umgestellt; keine direkten Ollama-Calls mehr.
- Tests: llm-cascade + rag-query aktualisiert.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
2026-06-19 10:02:06 +02:00
parent cd3e0b15ad
commit 90a70c8404
9 changed files with 398 additions and 203 deletions
@@ -10,9 +10,7 @@ import { DOCUMENT_SCOPE_MATRIX_CORE, DOCUMENT_TYPE_LABELS, getDepthLevelNumeric
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'
import { cascadeComplete } from '@/lib/sdk/drafting-engine/llm-cascade'
/**
* Anti-Fake-Evidence: Verbotene Formulierungen
@@ -244,30 +242,17 @@ export async function POST(request: NextRequest) {
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,
think: false,
options: { temperature: 0.1, num_predict: 8192, num_ctx: 8192 },
format: 'json',
}),
signal: AbortSignal.timeout(120000),
})
const llm = await cascadeComplete(
[
{ role: 'system', content: 'Du bist ein DSGVO-Compliance-Validator. Antworte NUR im JSON-Format.' },
{ role: 'user', content: crossCheckPrompt },
],
{ json: true, temperature: 0.1, maxTokens: 8192, timeoutMs: 120000 },
)
if (ollamaResponse.ok) {
const result = await ollamaResponse.json()
if (llm) {
try {
const parsed = JSON.parse(result.message?.content || '{}')
const parsed = JSON.parse(llm.content || '{}')
llmFindings = [
...(parsed.errors || []),
...(parsed.warnings || []),