Benjamin Sutter
b2530f9e20
Reale Jahresbelastung (Change 6): - FitOutCostPanel zeigt Jahresmiete + amortisierte Ausbaukosten für alle fitOut-Werte - FULL/PREMIUM: Ausbau CHF 0 (bezugsfertig), SHELL/BASIC: CRB/BKP-Richtwerte amortisiert über 5 J. - Match-Card-Chip zeigt geschätzte Investition (orange wenn >100k) - FitOutInvestment-Typ + calcFitOutInvestment() in fitOutUtils.ts - BackendAIService + MockAIService mit generateFitOutAdvice (IAIService-Interface) Must-have Kriterien (Nicht prüfbar Fix): - ÖV-Anbindung: Minutengrenze aus Freitext extrahiert, gegen publicTransportMinutes geprüft - Mindestfläche: m²-Wert aus Freitext extrahiert, gegen areaSqm geprüft - Ausbaugrad: neues Keyword-Rule für FULL/PREMIUM fitOut-Datenpflege: - fitOut-Werte zu 32 fehlenden Properties ergänzt (Logistik=SHELL, Standard=BASIC, Modern=FULL) - MAB-Werte (200/150/250 CHF/m²) zu 3 BASIC-Objekten hinzugefügt Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
672 lines
32 KiB
TypeScript
672 lines
32 KiB
TypeScript
/**
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* Backend AI Service — PowerOn Proxy
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*
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* Routes all LLM calls through the PowerOn backend. The LLM provider API key
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* is stored ONLY server-side and never reaches the browser bundle.
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*
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* ┌─────────────────────────────────────────────────────────────────────────┐
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* │ PowerOn Backend Contract │
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* │ │
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* │ Endpoint: POST /api/ai/chat/completions │
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* │ Headers: Content-Type: application/json │
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* │ (session auth cookie handled by backend — no API key here) │
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* │ │
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* │ Request body: │
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* │ { │
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* │ messages: { role: 'system' | 'user'; content: string }[] │
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* │ } │
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* │ │
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* │ Response (OpenAI-compatible): │
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* │ { │
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* │ choices: [{ message: { content: string } }] │
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* │ } │
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* │ │
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* │ The backend adds: │
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* │ - Authorization: Bearer <OPENROUTER_API_KEY> (server-side env var) │
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* │ - Model selection / routing │
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* │ - Rate limiting & audit logging │
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* └─────────────────────────────────────────────────────────────────────────┘
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*
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* Dev setup — add to vite.config.ts:
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* server: { proxy: { '/api': process.env.AI_BACKEND_URL ?? 'http://localhost:3001' } }
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*
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* Every method follows this contract:
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* 1. HTTP error → error log + MockAIService fallback
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* 2. JSON parse fail → warn + MockAIService fallback
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* 3. Zod schema fail → warn + MockAIService fallback
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* 4. Success → AI response, source: 'ai', validationPassed: true
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*/
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import type { CreateNeedInput } from '../../../domain/need'
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import type { ParseNeedResult, ParsedNeedCriteria, FollowUpQuestion } from '../../../domain/needBuilder'
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import type { UnifiedMatchResult } from '../../../domain/unifiedResult'
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import type { AssetType } from '../../../domain/enums'
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import type {
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IAIService,
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AIResponse,
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AIProvenance,
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DecisionBrief,
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ComparisonSummary,
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CriteriaExtractionResult,
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OfferEmailPayload,
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MatchExplanationInput,
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MatchExplanation,
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TradeOffInput,
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TradeOffSummary,
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DataQualityInput,
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DataQualitySummary,
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MarketSignalClassification,
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FitOutAdviceInput,
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FitOutAdvice,
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} from '../IAIService'
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import { ServiceErrorCode } from '../../types'
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import { AppError } from '../../errors'
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import { aiTraceStore, provenanceToStatus } from '../tracing'
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import type { AITraceErrorType, AITraceValidationStatus } from '../tracing'
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import {
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NeedParsingResponseSchema,
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FollowUpQuestionsResponseSchema,
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TradeOffSummaryResponseSchema,
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CompareSummaryResponseSchema,
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DecisionBriefResponseSchema,
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DataQualitySummaryResponseSchema,
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MarketSignalClassificationResponseSchema,
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OfferEmailResponseSchema,
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validateAIResponse,
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} from '../schemas'
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import { buildNeedParsingPrompt } from '../prompts/needParsingPrompt'
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import { buildFollowUpQuestionsPrompt } from '../prompts/followUpQuestionsPrompt'
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import { buildMatchExplanationPrompt } from '../prompts/matchExplanationPrompt'
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import { buildTradeOffPrompt } from '../prompts/tradeOffPrompt'
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import { buildCompareSummaryPrompt } from '../prompts/compareSummaryPrompt'
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import { buildDecisionBriefPrompt } from '../prompts/decisionBriefPrompt'
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import { buildDataQualityPrompt } from '../prompts/dataQualityPrompt'
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import { buildMarketSignalPrompt } from '../prompts/marketSignalPrompt'
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import { MockAIService } from '../mock/MockAIService'
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// ── Config ────────────────────────────────────────────────────────────────────
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/** Relative URL — resolved by Vite proxy in dev, by the same-origin backend in prod. */
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const API_BASE = '/api/ai'
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/**
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* Placeholder recorded in traces. The actual model is backend-controlled;
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* PowerOn may return it in a response extension field in future.
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*/
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const BACKEND_MODEL_PLACEHOLDER = 'backend-controlled'
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const PROMPT_VERSION = 'v1.1'
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const SCHEMA_VERSION = 'v1.0'
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// ── Provenance ────────────────────────────────────────────────────────────────
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function makeProvenance(
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source: AIProvenance['source'],
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fallbackUsed: boolean,
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validationPassed: boolean,
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extras: { fallbackReason?: string } = {},
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): AIProvenance {
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return {
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provider: 'backend',
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model: BACKEND_MODEL_PLACEHOLDER,
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generatedAt: new Date().toISOString(),
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promptVersion: PROMPT_VERSION,
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schemaVersion: SCHEMA_VERSION,
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source,
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fallbackUsed,
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validationPassed,
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traceId: crypto.randomUUID(),
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fallbackReason: extras.fallbackReason,
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}
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}
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// ── HTTP helper ───────────────────────────────────────────────────────────────
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async function chat(system: string, user: string): Promise<string> {
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const res = await fetch(`${API_BASE}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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// No Authorization header — the API key lives server-side only.
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},
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body: JSON.stringify({
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messages: [
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{ role: 'system', content: system },
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{ role: 'user', content: user },
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],
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}),
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})
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if (!res.ok) {
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const body = await res.text()
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throw new AppError({
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code: ServiceErrorCode.AI_GENERATION_FAILED,
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// Truncate to avoid leaking full backend error detail to the console.
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message: `Backend AI error ${res.status}: ${body.slice(0, 200)}`,
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})
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}
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const json = await res.json() as { choices: Array<{ message: { content: string } }> }
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return json.choices[0]?.message?.content ?? ''
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}
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// ── JSON extraction ───────────────────────────────────────────────────────────
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function extractJSON<T>(raw: string): T | null {
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const fenced = raw.match(/```(?:json)?\s*\n?([\s\S]*?)\n?```/)
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const candidate = fenced ? fenced[1] : raw.match(/([\[{][\s\S]*[\]}])/)?.[1] ?? raw
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try {
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return JSON.parse(candidate) as T
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} catch {
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return null
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}
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}
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// ── ParseNeed helpers ─────────────────────────────────────────────────────────
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type RawNeedParseAI = {
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assetType?: string | null
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areaRange?: { min: number; max: number } | null
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preferredLocations?: string[]
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budgetRange?: { maxPerSqm: number; currency: string } | null
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timing?: { earliestMoveIn: string; latestMoveIn?: string; flexibleTiming: boolean } | null
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mustHaveCriteria?: string[]
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missingFields?: string[]
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assumptions?: string[]
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}
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function followUpForField(field: string): string {
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const MAP: Record<string, string> = {
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assetType: 'Welchen Nutzungstyp suchen Sie (Büro, Retail, Logistik, Produktion)?',
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areaRange: 'Welche Fläche benötigen Sie (min–max in m²)?',
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preferredLocations: 'In welchen Städten oder Regionen suchen Sie?',
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budgetRange: 'Was ist Ihr maximales Budget pro m² und Jahr?',
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timing: 'Wann möchten Sie spätestens einziehen?',
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mustHaveCriteria: 'Haben Sie zwingende Anforderungen (ÖV-Anbindung, Parkplätze, Laderampe)?',
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}
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return MAP[field] ?? `Können Sie "${field}" präzisieren?`
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}
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function defaultSuggestedWeights(): Record<string, number> {
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return {
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area: 0.25, location: 0.20, budget: 0.20, timing: 0.15,
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prestige: 0.05, accessibility: 0.05, expansionPotential: 0.02,
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flexibility: 0.02, visibility: 0.02, footfall: 0.01, talentAccess: 0.01,
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esg: 0.01, taxEnvironment: 0.01,
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}
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}
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// ── Fallback wrapper ──────────────────────────────────────────────────────────
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type FallbackFn<T> = () => Promise<AIResponse<T>>
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async function withFallback<T>(
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label: string,
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fn: () => Promise<AIResponse<T>>,
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fallback: FallbackFn<T>,
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inputSizeChars?: number,
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): Promise<AIResponse<T>> {
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const startMs = Date.now()
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const callId = crypto.randomUUID()
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try {
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const result = await fn()
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const latencyMs = Date.now() - startMs
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const prov = result.provenance
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const provenance: AIProvenance = {
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...prov,
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traceId: callId,
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latencyMs,
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schemaVersion: SCHEMA_VERSION,
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}
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aiTraceStore.add({
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id: callId,
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method: label,
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provider: prov.provider,
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model: prov.model,
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promptVersion: prov.promptVersion,
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latencyMs,
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fallbackUsed: prov.fallbackUsed,
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validationPassed: prov.validationPassed,
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responseValidationStatus: provenanceToStatus(prov.fallbackUsed, prov.source, prov.fallbackReason),
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fallbackReason: prov.fallbackReason,
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source: prov.source,
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createdAt: prov.generatedAt,
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inputSizeChars,
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})
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return { ...result, provenance }
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} catch (err) {
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console.error(`[BackendAIService] ${label} failed:`, err)
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const result = await fallback()
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const latencyMs = Date.now() - startMs
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const errorType: AITraceErrorType =
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err instanceof AppError && err.code === ServiceErrorCode.AI_GENERATION_FAILED
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? 'api_error'
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: err instanceof TypeError
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? 'network'
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: 'unknown'
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const responseValidationStatus: AITraceValidationStatus =
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err instanceof AppError && err.code === ServiceErrorCode.AI_GENERATION_FAILED
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? 'api_error'
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: 'network_error'
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const fallbackReason = `${errorType}: ${err instanceof Error ? err.message.slice(0, 100) : 'unknown error'}`
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const provenance: AIProvenance = {
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...result.provenance,
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fallbackUsed: true,
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traceId: callId,
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fallbackReason,
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schemaVersion: SCHEMA_VERSION,
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latencyMs,
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}
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aiTraceStore.add({
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id: callId,
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method: label,
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provider: 'backend',
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model: BACKEND_MODEL_PLACEHOLDER,
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promptVersion: PROMPT_VERSION,
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latencyMs,
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fallbackUsed: true,
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validationPassed: false,
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responseValidationStatus,
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errorType,
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fallbackReason,
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source: 'mock',
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createdAt: new Date().toISOString(),
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inputSizeChars,
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})
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return { ...result, provenance }
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}
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}
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// ── Service ───────────────────────────────────────────────────────────────────
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export const BackendAIService: IAIService = {
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// ── parseNeed ───────────────────────────────────────────────────────────────
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parseNeed(input: string): Promise<AIResponse<ParseNeedResult>> {
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return withFallback('parseNeed', async () => {
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const { system, user } = buildNeedParsingPrompt({ userInput: input })
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const raw = await chat(system, user)
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const json = extractJSON<RawNeedParseAI>(raw)
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const ai = json ? validateAIResponse(NeedParsingResponseSchema, json, 'parseNeed') : null
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if (!ai) {
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console.warn('[BackendAIService] parseNeed: invalid response — using mock fallback')
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const fb = await MockAIService.parseNeed(input)
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return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
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}
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const extractedCriteria: ParseNeedResult['extractedCriteria'] = {
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assetType: (ai.assetType ?? undefined) as AssetType | undefined,
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areaRange: ai.areaRange ?? undefined,
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preferredLocations: ai.preferredLocations,
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budgetRange: ai.budgetRange ?? undefined,
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timing: ai.timing
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? { ...ai.timing, earliestMoveIn: ai.timing.earliestMoveIn ?? '', flexibleTiming: ai.timing.flexibleTiming ?? false }
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: undefined,
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mustHaveCriteria: ai.mustHaveCriteria,
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}
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const missingFields = ai.missingFields ?? []
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const confidenceByField: Record<string, number> = {}
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Object.keys(extractedCriteria).forEach(k => {
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confidenceByField[k] = extractedCriteria[k as keyof typeof extractedCriteria] != null ? 0.85 : 0
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})
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missingFields.forEach(f => { confidenceByField[f] = 0 })
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const followUpQuestionCandidates: FollowUpQuestion[] = missingFields.map((field, i) => ({
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id: `fq-be-${i}`,
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questionText: followUpForField(field),
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targetField: field,
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reason: `Feld "${field}" nicht im Text erkannt`,
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importance: 'recommended' as const,
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}))
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return {
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data: {
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extractedCriteria,
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confidenceByField,
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missingFields,
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assumptions: ai.assumptions ?? [],
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suggestedWeights: defaultSuggestedWeights(),
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followUpQuestionCandidates,
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rawSummary: raw.substring(0, 500),
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promptVersion: PROMPT_VERSION,
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schemaVersion: SCHEMA_VERSION,
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},
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provenance: makeProvenance('ai', false, true),
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}
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}, () => MockAIService.parseNeed(input), input.length)
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},
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// ── generateFollowUpQuestions ───────────────────────────────────────────────
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generateFollowUpQuestions(criteria: ParsedNeedCriteria): Promise<AIResponse<FollowUpQuestion[]>> {
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return withFallback('generateFollowUpQuestions', async () => {
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const missingFields = [
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...(!criteria.assetType ? ['assetType'] : []),
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...(!criteria.areaRange || (criteria.areaRange.min <= 0 && criteria.areaRange.max <= 0) ? ['areaRange'] : []),
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...(!criteria.preferredLocations?.length ? ['preferredLocations'] : []),
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...(!criteria.budgetRange ? ['budgetRange'] : []),
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...(!criteria.timing ? ['timing'] : []),
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...(!criteria.mustHaveCriteria?.length ? ['mustHaveCriteria'] : []),
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]
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const { system, user } = buildFollowUpQuestionsPrompt({ criteria, missingFields })
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const raw = await chat(system, user)
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const json = extractJSON<unknown[]>(raw)
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const ai = json ? validateAIResponse(FollowUpQuestionsResponseSchema, json, 'generateFollowUpQuestions') : null
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if (!ai?.length) {
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console.warn('[BackendAIService] generateFollowUpQuestions: invalid response — using mock fallback')
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const fb = await MockAIService.generateFollowUpQuestions(criteria)
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return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
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}
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return {
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data: ai.map((q, i) => ({
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id: `fq-be-${i}`,
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questionText: q.questionText,
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targetField: q.targetField,
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reason: q.reason ?? 'AI-generiert',
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suggestedAnswerOptions: q.suggestedAnswerOptions,
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importance: (q.importance ?? 'recommended') as FollowUpQuestion['importance'],
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})),
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provenance: makeProvenance('ai', false, true),
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}
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}, () => MockAIService.generateFollowUpQuestions(criteria))
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},
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// ── generateMatchExplanation ────────────────────────────────────────────────
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generateMatchExplanation(input: MatchExplanationInput): Promise<AIResponse<MatchExplanation>> {
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return withFallback('generateMatchExplanation', async () => {
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const { system, user } = buildMatchExplanationPrompt(input)
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const raw = await chat(system, user)
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const summary = raw.trim()
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if (!summary) {
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console.warn('[BackendAIService] generateMatchExplanation: empty response — using mock fallback')
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const fb = await MockAIService.generateMatchExplanation(input)
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return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: 'empty_response' }) }
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}
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const scoreLabel = input.matchScore >= 78 ? 'Starkes' : input.matchScore >= 52 ? 'Gutes' : 'Schwaches'
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return {
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data: {
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headline: `${scoreLabel} Match — ${input.propertyTitle} (${input.matchScore}/100)`,
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summary,
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keyReasons: [
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...input.positiveFactors.slice(0, 2).map(f => `+ ${f.explanation}`),
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...input.negativeFactors.slice(0, 1).map(f => `− ${f.explanation}`),
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],
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},
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provenance: makeProvenance('ai', false, true),
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}
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}, () => MockAIService.generateMatchExplanation(input))
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},
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// ── summarizeTradeOffs ──────────────────────────────────────────────────────
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summarizeTradeOffs(tradeoffs: TradeOffInput[]): Promise<AIResponse<TradeOffSummary>> {
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return withFallback('summarizeTradeOffs', async () => {
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const { system, user } = buildTradeOffPrompt(tradeoffs, 'Objekt')
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const raw = await chat(system, user)
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const json = extractJSON<unknown>(raw)
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const ai = json ? validateAIResponse(TradeOffSummaryResponseSchema, json, 'summarizeTradeOffs') : null
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if (!ai) {
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console.warn('[BackendAIService] summarizeTradeOffs: invalid response — using mock fallback')
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const fb = await MockAIService.summarizeTradeOffs(tradeoffs)
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return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
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}
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return {
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data: {
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headline: ai.headline,
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items: ai.items.map(item => ({
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concern: item.concern,
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severity: item.severity,
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mitigation: item.mitigation,
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})),
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overallRisk: ai.overallRisk,
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},
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provenance: makeProvenance('ai', false, true),
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}
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}, () => MockAIService.summarizeTradeOffs(tradeoffs))
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},
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// ── summarizeComparison ─────────────────────────────────────────────────────
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summarizeComparison(items: UnifiedMatchResult[]): Promise<AIResponse<ComparisonSummary>> {
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return withFallback('summarizeComparison', async () => {
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type ItemWithProp = UnifiedMatchResult & {
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property?: { title?: string; location?: { city?: string }; rentPricePerSqm?: number }
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}
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const properties = (items as ItemWithProp[])
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.filter(i => i.resultType !== 'FUTURE_AVAILABILITY')
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.map(i => ({
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title: i.property?.title ?? `Match ${i.matchScore}`,
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matchScore: i.matchScore,
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city: i.property?.location?.city ?? '–',
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rentPerSqm: i.property?.rentPricePerSqm ?? 0,
|
||
positiveFactors: i.match.positiveFactors.slice(0, 2).map(f => f.explanation ?? f.criterion),
|
||
negativeFactors: i.match.negativeFactors.slice(0, 2).map(f => f.explanation ?? f.criterion),
|
||
}))
|
||
const { system, user } = buildCompareSummaryPrompt({ properties })
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown>(raw)
|
||
const ai = json ? validateAIResponse(CompareSummaryResponseSchema, json, 'summarizeComparison') : null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] summarizeComparison: invalid response — using mock fallback')
|
||
const fb = await MockAIService.summarizeComparison(items)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
const mock = await MockAIService.summarizeComparison(items)
|
||
return {
|
||
data: {
|
||
...mock.data,
|
||
overallAssessment: ai.overallAssessment,
|
||
recommendation: ai.recommendation ?? mock.data.recommendation,
|
||
},
|
||
provenance: makeProvenance('hybrid', false, true),
|
||
}
|
||
}, () => MockAIService.summarizeComparison(items))
|
||
},
|
||
|
||
// ── generateDecisionBrief ───────────────────────────────────────────────────
|
||
generateDecisionBrief(shortlistId: string): Promise<AIResponse<DecisionBrief>> {
|
||
return withFallback('generateDecisionBrief', async () => {
|
||
const { system, user } = buildDecisionBriefPrompt({ shortlistItems: [], needSummary: shortlistId })
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown>(raw)
|
||
const ai = json ? validateAIResponse(DecisionBriefResponseSchema, json, 'generateDecisionBrief') : null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] generateDecisionBrief: invalid response — using mock fallback')
|
||
const fb = await MockAIService.generateDecisionBrief(shortlistId)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
const mock = await MockAIService.generateDecisionBrief(shortlistId)
|
||
return {
|
||
data: {
|
||
...mock.data,
|
||
summary: ai.summary,
|
||
sections: ai.sections.map(s => ({ title: s.title, body: s.body })),
|
||
},
|
||
provenance: makeProvenance('hybrid', false, true),
|
||
}
|
||
}, () => MockAIService.generateDecisionBrief(shortlistId))
|
||
},
|
||
|
||
// ── generateDataQualitySummary ──────────────────────────────────────────────
|
||
generateDataQualitySummary(propertyId: string, quality: DataQualityInput): Promise<AIResponse<DataQualitySummary>> {
|
||
return withFallback('generateDataQualitySummary', async () => {
|
||
const { system, user } = buildDataQualityPrompt(propertyId, quality)
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown>(raw)
|
||
const ai = json ? validateAIResponse(DataQualitySummaryResponseSchema, json, 'generateDataQualitySummary') : null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] generateDataQualitySummary: invalid response — using mock fallback')
|
||
const fb = await MockAIService.generateDataQualitySummary(propertyId, quality)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: {
|
||
overallAssessment: ai.overallAssessment,
|
||
missingCriticalFields: ai.missingCriticalFields ?? quality.missingCriticalFields,
|
||
recommendation: ai.recommendation,
|
||
confidence: ai.confidence,
|
||
},
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.generateDataQualitySummary(propertyId, quality))
|
||
},
|
||
|
||
// ── classifyMarketSignal ────────────────────────────────────────────────────
|
||
classifyMarketSignal(signalText: string): Promise<AIResponse<MarketSignalClassification>> {
|
||
return withFallback('classifyMarketSignal', async () => {
|
||
const { system, user } = buildMarketSignalPrompt(signalText)
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown>(raw)
|
||
const ai = json
|
||
? validateAIResponse(MarketSignalClassificationResponseSchema, json, 'classifyMarketSignal')
|
||
: null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] classifyMarketSignal: invalid response — using mock fallback')
|
||
const fb = await MockAIService.classifyMarketSignal(signalText)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: {
|
||
signalType: ai.signalType,
|
||
probability: ai.probability,
|
||
timeHorizonMonths: ai.timeHorizonMonths ?? null,
|
||
areaSqmEstimate: ai.areaSqmEstimate ?? null,
|
||
credibility: ai.credibility,
|
||
reasoning: ai.reasoning,
|
||
},
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.classifyMarketSignal(signalText), signalText.length)
|
||
},
|
||
|
||
// ── generateOfferEmail ──────────────────────────────────────────────────────
|
||
generateOfferEmail(payload: OfferEmailPayload): Promise<AIResponse<{ subject: string; body: string }>> {
|
||
return withFallback('generateOfferEmail', async () => {
|
||
const propertyList = payload.properties
|
||
.map((p, i) => `• ${p} (Match-Score: ${payload.matchScores[i]}%)`)
|
||
.join('\n')
|
||
const system = `Du bist Immobilienmakler bei Wincasa AG. Erstelle eine professionelle, knappe Angebotsmail auf Deutsch. Antworte als JSON: { "subject": "...", "body": "..." }`
|
||
const user = `Suchanfrage: "${payload.needTitle}"\n\nObjekte:\n${propertyList}\n\nErstelle eine professionelle Angebotsmail.`
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown>(raw)
|
||
const ai = json ? validateAIResponse(OfferEmailResponseSchema, json, 'generateOfferEmail') : null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] generateOfferEmail: invalid response — using mock fallback')
|
||
const fb = await MockAIService.generateOfferEmail(payload)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: { subject: ai.subject, body: ai.body },
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.generateOfferEmail(payload))
|
||
},
|
||
|
||
// ── generateFitOutAdvice ────────────────────────────────────────────────────
|
||
generateFitOutAdvice(input: FitOutAdviceInput): Promise<AIResponse<FitOutAdvice>> {
|
||
return withFallback('generateFitOutAdvice', async () => {
|
||
const FIT_LABELS: Record<string, string> = { SHELL: 'Rohbau', BASIC: 'Basisausbau', FULL: 'Vollausbau', PREMIUM: 'Premiumausbau' }
|
||
const system = `Du bist Schweizer Gewerbeimmobilien-Experte. Bewerte die Ausbausituation und empfiehl die beste Verhandlungsoption.
|
||
Verfügbare Optionen: MIETERAUSBAU (Mieter zahlt alles), BKZ (Vermieter zahlt Einmalpauschale), MAB_AMORTISATION (MAB über Miete amortisiert).
|
||
Antworte als JSON:
|
||
{
|
||
"recommendation": "MIETERAUSBAU" | "BKZ" | "MAB_AMORTISATION",
|
||
"headline": "kurze Empfehlung (max 80 Zeichen)",
|
||
"explanation": "2-3 Sätze Begründung auf Deutsch",
|
||
"negotiationTip": "konkreter Verhandlungstipp auf Deutsch",
|
||
"estimatedNetInvestment": "CHF-Betrag als String"
|
||
}`
|
||
const user = `Übergabezustand: ${FIT_LABELS[input.fitOut] ?? input.fitOut}
|
||
Fläche: ${input.areaSqm} m²
|
||
MAB des Vermieters: CHF ${input.mabPerSqm}/m²
|
||
Monatliche Miete: CHF ${input.monthlyRentPerSqm}/m²${input.tenantBudgetPerSqm ? `\nEigenes Ausbaubudget: CHF ${input.tenantBudgetPerSqm}/m²` : ''}${input.requiredFitOut ? `\nGewünschter Zustand: ${FIT_LABELS[input.requiredFitOut] ?? input.requiredFitOut}` : ''}
|
||
|
||
Bitte analysiere die Situation und empfiehl die beste Option für den Mieter.`
|
||
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<FitOutAdvice>(raw)
|
||
|
||
if (!json || !json.recommendation || !json.headline) {
|
||
console.warn('[BackendAIService] generateFitOutAdvice: invalid response — using mock fallback')
|
||
const fb = await MockAIService.generateFitOutAdvice(input)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: {
|
||
recommendation: json.recommendation,
|
||
headline: json.headline,
|
||
explanation: json.explanation ?? '',
|
||
negotiationTip: json.negotiationTip ?? '',
|
||
estimatedNetInvestment: json.estimatedNetInvestment ?? '–',
|
||
},
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.generateFitOutAdvice(input))
|
||
},
|
||
|
||
// ── Legacy: extractCriteria ─────────────────────────────────────────────────
|
||
extractCriteria(input: string): Promise<AIResponse<CriteriaExtractionResult>> {
|
||
return withFallback('extractCriteria', async () => {
|
||
const { system, user } = buildNeedParsingPrompt({ userInput: input })
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<RawNeedParseAI>(raw)
|
||
const ai = json ? validateAIResponse(NeedParsingResponseSchema, json, 'extractCriteria') : null
|
||
|
||
if (!ai) {
|
||
console.warn('[BackendAIService] extractCriteria: invalid response — using mock fallback')
|
||
const fb = await MockAIService.extractCriteria(input)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: {
|
||
extractedCriteria: {
|
||
assetType: ai.assetType as AssetType | undefined ?? undefined,
|
||
requiredArea: ai.areaRange ?? undefined,
|
||
preferredLocations: ai.preferredLocations ?? [],
|
||
budgetRange: ai.budgetRange ?? undefined,
|
||
},
|
||
confidence: 0.80,
|
||
missingFields: ai.missingFields ?? [],
|
||
assumptions: ai.assumptions ?? [],
|
||
followUpQuestions: (ai.missingFields ?? []).map(followUpForField),
|
||
},
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.extractCriteria(input))
|
||
},
|
||
|
||
// ── Legacy: generateFollowUp ────────────────────────────────────────────────
|
||
generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<AIResponse<string[]>> {
|
||
return withFallback('generateFollowUp', async () => {
|
||
const missingFields = [
|
||
...(!partialNeed.assetType ? ['assetType'] : []),
|
||
...(!partialNeed.preferredLocations?.length ? ['preferredLocations'] : []),
|
||
...(!partialNeed.timing ? ['timing'] : []),
|
||
...(!partialNeed.budgetRange ? ['budgetRange'] : []),
|
||
]
|
||
if (!missingFields.length) {
|
||
return { data: [], provenance: makeProvenance('ai', false, true) }
|
||
}
|
||
const { system, user } = buildFollowUpQuestionsPrompt({
|
||
criteria: partialNeed as ParsedNeedCriteria,
|
||
missingFields,
|
||
})
|
||
const raw = await chat(system, user)
|
||
const json = extractJSON<unknown[]>(raw)
|
||
const ai = json ? validateAIResponse(FollowUpQuestionsResponseSchema, json, 'generateFollowUp') : null
|
||
|
||
if (!ai?.length) {
|
||
console.warn('[BackendAIService] generateFollowUp: invalid response — using mock fallback')
|
||
const fb = await MockAIService.generateFollowUp(partialNeed)
|
||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||
}
|
||
return {
|
||
data: ai.map(q => q.questionText).filter(Boolean),
|
||
provenance: makeProvenance('ai', false, true),
|
||
}
|
||
}, () => MockAIService.generateFollowUp(partialNeed))
|
||
},
|
||
}
|