feat: Reale Jahresbelastung, must-have fixes, fitOut data coverage
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>
This commit is contained in:
Benjamin Sutter
@@ -7,7 +7,7 @@ import type { UnifiedMatchResult } from '../../domain/unifiedResult'
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export interface AIProvenance {
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/** Which AI provider produced this response */
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provider: 'openrouter' | 'mock'
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provider: 'openrouter' | 'mock' | 'backend'
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/** Exact model ID (e.g. 'anthropic/claude-3-5-haiku') or 'mock' */
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model: string
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/** ISO-8601 timestamp of generation */
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@@ -157,6 +157,25 @@ export interface DataQualityInput {
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warnings: string[]
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}
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// ── Fit-out advice ────────────────────────────────────────────────────────────
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export interface FitOutAdviceInput {
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fitOut: string
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areaSqm: number
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mabPerSqm: number
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requiredFitOut?: string
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tenantBudgetPerSqm?: number
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monthlyRentPerSqm: number
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}
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export interface FitOutAdvice {
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recommendation: 'MIETERAUSBAU' | 'BKZ' | 'MAB_AMORTISATION'
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headline: string
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explanation: string
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negotiationTip: string
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estimatedNetInvestment: string
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}
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// ── Legacy types (kept for backward compatibility) ────────────────────────────
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export interface CriteriaExtractionResult {
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@@ -198,6 +217,9 @@ export interface IAIService {
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// Offer email (supply side)
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generateOfferEmail(payload: OfferEmailPayload): Promise<AIResponse<{ subject: string; body: string }>>
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// Fit-out investment advice (demand side)
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generateFitOutAdvice(input: FitOutAdviceInput): Promise<AIResponse<FitOutAdvice>>
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// Legacy methods
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extractCriteria(input: string): Promise<AIResponse<CriteriaExtractionResult>>
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generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<AIResponse<string[]>>
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@@ -0,0 +1,671 @@
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/**
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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',
|
||||
suggestedAnswerOptions: q.suggestedAnswerOptions,
|
||||
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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||||
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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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||||
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||||
if (!summary) {
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console.warn('[BackendAIService] generateMatchExplanation: empty response — using mock fallback')
|
||||
const fb = await MockAIService.generateMatchExplanation(input)
|
||||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: 'empty_response' }) }
|
||||
}
|
||||
const scoreLabel = input.matchScore >= 78 ? 'Starkes' : input.matchScore >= 52 ? 'Gutes' : 'Schwaches'
|
||||
return {
|
||||
data: {
|
||||
headline: `${scoreLabel} Match — ${input.propertyTitle} (${input.matchScore}/100)`,
|
||||
summary,
|
||||
keyReasons: [
|
||||
...input.positiveFactors.slice(0, 2).map(f => `+ ${f.explanation}`),
|
||||
...input.negativeFactors.slice(0, 1).map(f => `− ${f.explanation}`),
|
||||
],
|
||||
},
|
||||
provenance: makeProvenance('ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateMatchExplanation(input))
|
||||
},
|
||||
|
||||
// ── summarizeTradeOffs ──────────────────────────────────────────────────────
|
||||
summarizeTradeOffs(tradeoffs: TradeOffInput[]): Promise<AIResponse<TradeOffSummary>> {
|
||||
return withFallback('summarizeTradeOffs', async () => {
|
||||
const { system, user } = buildTradeOffPrompt(tradeoffs, 'Objekt')
|
||||
const raw = await chat(system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(TradeOffSummaryResponseSchema, json, 'summarizeTradeOffs') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[BackendAIService] summarizeTradeOffs: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.summarizeTradeOffs(tradeoffs)
|
||||
return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
return {
|
||||
data: {
|
||||
headline: ai.headline,
|
||||
items: ai.items.map(item => ({
|
||||
concern: item.concern,
|
||||
severity: item.severity,
|
||||
mitigation: item.mitigation,
|
||||
})),
|
||||
overallRisk: ai.overallRisk,
|
||||
},
|
||||
provenance: makeProvenance('ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.summarizeTradeOffs(tradeoffs))
|
||||
},
|
||||
|
||||
// ── summarizeComparison ─────────────────────────────────────────────────────
|
||||
summarizeComparison(items: UnifiedMatchResult[]): Promise<AIResponse<ComparisonSummary>> {
|
||||
return withFallback('summarizeComparison', async () => {
|
||||
type ItemWithProp = UnifiedMatchResult & {
|
||||
property?: { title?: string; location?: { city?: string }; rentPricePerSqm?: number }
|
||||
}
|
||||
const properties = (items as ItemWithProp[])
|
||||
.filter(i => i.resultType !== 'FUTURE_AVAILABILITY')
|
||||
.map(i => ({
|
||||
title: i.property?.title ?? `Match ${i.matchScore}`,
|
||||
matchScore: i.matchScore,
|
||||
city: i.property?.location?.city ?? '–',
|
||||
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))
|
||||
},
|
||||
}
|
||||
+28
-25
@@ -1,44 +1,47 @@
|
||||
/**
|
||||
* AI Service Factory
|
||||
*
|
||||
* Provider selection (priority order):
|
||||
* 1. VITE_AI_PROVIDER=openrouter → OpenRouterAIService (requires VITE_OPENROUTER_API_KEY)
|
||||
* 2. VITE_AI_PROVIDER=mock → MockAIService (deterministic, no API key required)
|
||||
* 3. VITE_USE_REAL_AI=true → OpenRouterAIService (legacy flag, requires VITE_OPENROUTER_API_KEY)
|
||||
* 4. (default) → MockAIService
|
||||
* Provider selection via VITE_AI_PROVIDER:
|
||||
*
|
||||
* If VITE_AI_PROVIDER=openrouter but VITE_OPENROUTER_API_KEY is missing, the factory
|
||||
* logs a warning and falls back to MockAIService — never silently fails.
|
||||
* backend (default) → BackendAIService
|
||||
* Calls POST /api/ai/chat/completions on the PowerOn backend.
|
||||
* The LLM API key is stored server-side only — not in this bundle.
|
||||
* In development: configure a Vite proxy (see vite.config.ts).
|
||||
*
|
||||
* Optional: VITE_OPENROUTER_MODEL controls which model OpenRouter uses.
|
||||
* Default: anthropic/claude-3-5-haiku
|
||||
* mock → MockAIService
|
||||
* Deterministic responses, no network calls.
|
||||
* Use for local dev without a backend, or in CI.
|
||||
*
|
||||
* REMOVED: VITE_OPENROUTER_API_KEY and VITE_OPENROUTER_MODEL.
|
||||
* The OpenRouter key is now a server-side secret in PowerOn.
|
||||
*/
|
||||
import { MockAIService } from './mock/MockAIService'
|
||||
import { OpenRouterAIService } from './openrouter/OpenRouterAIService'
|
||||
import { BackendAIService } from './backend/BackendAIService'
|
||||
import type { IAIService } from './IAIService'
|
||||
|
||||
function resolveProvider(): IAIService {
|
||||
const provider = import.meta.env.VITE_AI_PROVIDER as string | undefined
|
||||
const legacyRealAI = import.meta.env.VITE_USE_REAL_AI === 'true'
|
||||
const apiKey = import.meta.env.VITE_OPENROUTER_API_KEY as string | undefined
|
||||
|
||||
const wantsOpenRouter = provider === 'openrouter' || (legacyRealAI && !provider)
|
||||
|
||||
if (wantsOpenRouter) {
|
||||
if (!apiKey) {
|
||||
console.warn(
|
||||
'[aiService] OpenRouter selected but VITE_OPENROUTER_API_KEY is missing — falling back to MockAIService.',
|
||||
'Set VITE_AI_PROVIDER=mock to suppress this warning.',
|
||||
)
|
||||
return MockAIService
|
||||
}
|
||||
return OpenRouterAIService
|
||||
if (provider === 'mock') {
|
||||
return MockAIService
|
||||
}
|
||||
|
||||
return MockAIService
|
||||
if (provider === 'openrouter') {
|
||||
console.warn(
|
||||
'[aiService] VITE_AI_PROVIDER=openrouter is no longer supported. ' +
|
||||
'Direct OpenRouter calls have been removed from the frontend. ' +
|
||||
'Using BackendAIService (POST /api/ai/chat/completions) instead. ' +
|
||||
'Set VITE_AI_PROVIDER=backend or remove the variable to suppress this warning.',
|
||||
)
|
||||
}
|
||||
|
||||
// Default: backend proxy. Falls back to mock automatically on network/HTTP errors.
|
||||
return BackendAIService
|
||||
}
|
||||
|
||||
export const aiService: IAIService = resolveProvider()
|
||||
|
||||
export { MockAIService, OpenRouterAIService }
|
||||
export { MockAIService, BackendAIService }
|
||||
// Compatibility alias for any code that still imports OpenRouterAIService by name.
|
||||
export { OpenRouterAIService } from './openrouter/OpenRouterAIService'
|
||||
export type { IAIService }
|
||||
|
||||
@@ -10,6 +10,8 @@ import type {
|
||||
TradeOffSummary,
|
||||
DataQualityInput,
|
||||
MarketSignalClassification,
|
||||
FitOutAdviceInput,
|
||||
FitOutAdvice,
|
||||
} from '../IAIService'
|
||||
import { mockProvenance } from '../IAIService'
|
||||
import { aiTraceStore } from '../tracing'
|
||||
@@ -294,6 +296,48 @@ export const MockAIService: IAIService = {
|
||||
}
|
||||
}),
|
||||
|
||||
generateFitOutAdvice: (input: FitOutAdviceInput) =>
|
||||
traceMock('generateFitOutAdvice', async () => {
|
||||
await delay(SIMULATED_DELAY.medium)
|
||||
const mab = input.mabPerSqm
|
||||
const fitOut = input.fitOut
|
||||
|
||||
let recommendation: FitOutAdvice['recommendation']
|
||||
let headline: string
|
||||
let explanation: string
|
||||
let negotiationTip: string
|
||||
|
||||
if (fitOut === 'SHELL') {
|
||||
if (mab >= 300) {
|
||||
recommendation = 'MAB_AMORTISATION'
|
||||
headline = 'MAB-Amortisation empfohlen — Vermieter trägt Grossteil der Ausbaukosten'
|
||||
explanation = `Mit CHF ${mab}/m² MAB übernimmt der Vermieter einen erheblichen Teil der Ausbauinvestition. Die verbleibende Nettoinvestition wird über die Vertragslaufzeit amortisiert. Für ${input.areaSqm.toLocaleString('de-CH')} m² Rohbaufläche ist dies die kosteneffizienteste Lösung.`
|
||||
negotiationTip = 'Verhandeln Sie eine höhere MAB-Rate gegen eine längere Mietvertragslaufzeit (min. 5 Jahre).'
|
||||
} else {
|
||||
recommendation = 'BKZ'
|
||||
headline = 'Baukostenzuschuss (BKZ) verhandeln — Vermieter zahlt Ausbaupauschale'
|
||||
explanation = `Bei SHELL-Übergabe ohne wesentlichem MAB ist ein Baukostenzuschuss (BKZ) die effektivste Option. Der Vermieter zahlt einen einmaligen Betrag, den Sie für den Innenausbau nutzen. Typisch sind CHF 200–400/m² als BKZ.`
|
||||
negotiationTip = `Fordern Sie CHF ${Math.round(300 * input.areaSqm / 1000) * 1000}.– als BKZ-Pauschale. Reichen Sie Ausbauofferten von 2 Generalunternehmern vor der Unterzeichnung ein.`
|
||||
}
|
||||
} else {
|
||||
recommendation = 'MIETERAUSBAU'
|
||||
headline = 'Mieterausbau auf eigene Rechnung — geringe Restinvestition'
|
||||
explanation = `${input.fitOut === 'BASIC' ? 'Basisausbau' : 'Vollausbau'} erfordert nur noch Anpassungen nach Ihren Bedürfnissen. Die Investition ist überschaubar und amortisiert sich bei einer Mietdauer von 3+ Jahren.`
|
||||
negotiationTip = 'Lassen Sie eine Ausbauklausel im Mietvertrag festhalten: Entfernung von Mieterausbauten bei Auszug nur auf explizite Anforderung des Vermieters.'
|
||||
}
|
||||
|
||||
const grossMin = (fitOut === 'SHELL' ? 800 : 400) - mab
|
||||
const grossMax = (fitOut === 'SHELL' ? 1500 : 800) - mab
|
||||
const netMin = Math.max(0, grossMin)
|
||||
const netMax = Math.max(0, grossMax)
|
||||
const estimatedNetInvestment = netMax <= 0
|
||||
? 'Vollständig durch MAB gedeckt'
|
||||
: `CHF ${Math.round(netMin * input.areaSqm / 1000) * 1000}–${Math.round(netMax * input.areaSqm / 1000) * 1000}.–`
|
||||
|
||||
const data: FitOutAdvice = { recommendation, headline, explanation, negotiationTip, estimatedNetInvestment }
|
||||
return { data, provenance: mockProvenance() }
|
||||
}),
|
||||
|
||||
// Legacy methods
|
||||
extractCriteria: (_input: string) =>
|
||||
traceMock('extractCriteria', async () => ({
|
||||
|
||||
@@ -198,6 +198,13 @@ export function mockParseNeed(input: string): ParseNeedResult {
|
||||
: lower.includes('basisausbau') || lower.includes('rohbau') || lower.includes('einfach') ? 'BASIC'
|
||||
: undefined
|
||||
|
||||
// Search radius: "innerhalb von 30 km", "30km Umkreis", "im Umkreis von 50 km", "radius 20km"
|
||||
const radiusMatch =
|
||||
input.match(/(?:innerhalb\s+(?:von\s+)?|im\s+umkreis\s+(?:von\s+)?|radius\s+(?:von\s+)?)(\d+)\s*km/i) ??
|
||||
input.match(/(\d+)\s*km\s*(?:umkreis|radius|entfernung)/i) ??
|
||||
input.match(/(\d+)\s*km/i)
|
||||
const searchRadius = radiusMatch ? Math.min(100, Math.max(1, parseInt(radiusMatch[1]))) : undefined
|
||||
|
||||
// Contract duration: "7-jähriger Vertrag", "Laufzeit 7 Jahre", standalone "7 Jahre" at sentence start
|
||||
// Exclude "in X Jahren", "X Jahre im Geschäft", "X Jahre Erfahrung" etc.
|
||||
const contractMatch = input.match(/(\d+)[- ]?j[aä]hrige?(?:r)?\s+(?:vertrag|mietvertrag|laufzeit)/i)
|
||||
@@ -244,6 +251,7 @@ export function mockParseNeed(input: string): ParseNeedResult {
|
||||
budgetRange: budgetConfidence,
|
||||
timing: timingConfidence,
|
||||
mustHaveCriteria: mustHaveCriteria.length > 0 ? 0.85 : 0.10,
|
||||
searchRadius: searchRadius ? 0.90 : 0.20,
|
||||
prestigeImportance: prestigeImportance ? 0.80 : 0.20,
|
||||
parkingNeed: parkingNeed ? 0.90 : 0.30,
|
||||
}
|
||||
@@ -362,6 +370,7 @@ export function mockParseNeed(input: string): ParseNeedResult {
|
||||
requiredFitOut: fitOutStr,
|
||||
minCeilingHeightM,
|
||||
minContractDurationMonths,
|
||||
searchRadius,
|
||||
notes,
|
||||
},
|
||||
confidenceByField,
|
||||
|
||||
@@ -1,650 +1,12 @@
|
||||
/**
|
||||
* OpenRouter AI Service
|
||||
* @deprecated Direct OpenRouter calls from the frontend have been removed.
|
||||
*
|
||||
* Activation:
|
||||
* VITE_AI_PROVIDER=openrouter
|
||||
* VITE_OPENROUTER_API_KEY=<your-key>
|
||||
* VITE_OPENROUTER_MODEL=anthropic/claude-3-5-haiku (optional, default shown)
|
||||
* All LLM requests now go through the PowerOn backend proxy at /api/ai/chat/completions
|
||||
* so that the API key never appears in the browser bundle.
|
||||
*
|
||||
* Every method follows this contract:
|
||||
* 1. No API key → warn + MockAIService fallback (fallbackUsed: true)
|
||||
* 2. HTTP error → error log + MockAIService fallback
|
||||
* 3. JSON parse fail → warn + MockAIService fallback
|
||||
* 4. Zod schema fail → warn + MockAIService fallback ← NEW
|
||||
* 5. Success (full AI) → AI response, source: 'ai', validationPassed: true
|
||||
* 6. Hybrid → source: 'hybrid', documented per-method
|
||||
* This file is kept as a compatibility re-export so that any existing imports
|
||||
* of `OpenRouterAIService` continue to compile without changes.
|
||||
*
|
||||
* No invalid data ever reaches the UI.
|
||||
* → Implementation moved to: src/services/ai/backend/BackendAIService.ts
|
||||
*/
|
||||
import type { CreateNeedInput } from '../../../domain/need'
|
||||
import type { ParseNeedResult, ParsedNeedCriteria, FollowUpQuestion } from '../../../domain/needBuilder'
|
||||
import type { UnifiedMatchResult } from '../../../domain/unifiedResult'
|
||||
import type { AssetType } from '../../../domain/enums'
|
||||
import type {
|
||||
IAIService,
|
||||
AIResponse,
|
||||
AIProvenance,
|
||||
DecisionBrief,
|
||||
ComparisonSummary,
|
||||
CriteriaExtractionResult,
|
||||
OfferEmailPayload,
|
||||
MatchExplanationInput,
|
||||
MatchExplanation,
|
||||
TradeOffInput,
|
||||
TradeOffSummary,
|
||||
DataQualityInput,
|
||||
DataQualitySummary,
|
||||
MarketSignalClassification,
|
||||
} from '../IAIService'
|
||||
import { ServiceErrorCode } from '../../types'
|
||||
import { AppError } from '../../errors'
|
||||
import { aiTraceStore, provenanceToStatus } from '../tracing'
|
||||
import type { AITraceErrorType, AITraceValidationStatus } from '../tracing'
|
||||
import {
|
||||
NeedParsingResponseSchema,
|
||||
FollowUpQuestionsResponseSchema,
|
||||
TradeOffSummaryResponseSchema,
|
||||
CompareSummaryResponseSchema,
|
||||
DecisionBriefResponseSchema,
|
||||
DataQualitySummaryResponseSchema,
|
||||
MarketSignalClassificationResponseSchema,
|
||||
OfferEmailResponseSchema,
|
||||
validateAIResponse,
|
||||
} from '../schemas'
|
||||
import { buildNeedParsingPrompt } from '../prompts/needParsingPrompt'
|
||||
import { buildFollowUpQuestionsPrompt } from '../prompts/followUpQuestionsPrompt'
|
||||
import { buildMatchExplanationPrompt } from '../prompts/matchExplanationPrompt'
|
||||
import { buildTradeOffPrompt } from '../prompts/tradeOffPrompt'
|
||||
import { buildCompareSummaryPrompt } from '../prompts/compareSummaryPrompt'
|
||||
import { buildDecisionBriefPrompt } from '../prompts/decisionBriefPrompt'
|
||||
import { buildDataQualityPrompt } from '../prompts/dataQualityPrompt'
|
||||
import { buildMarketSignalPrompt } from '../prompts/marketSignalPrompt'
|
||||
import { MockAIService } from '../mock/MockAIService'
|
||||
|
||||
// ── Config ────────────────────────────────────────────────────────────────────
|
||||
|
||||
const API_BASE = 'https://openrouter.ai/api/v1'
|
||||
const DEFAULT_MODEL = 'anthropic/claude-3-5-haiku'
|
||||
const PROMPT_VERSION = 'v1.1'
|
||||
const SCHEMA_VERSION = 'v1.0'
|
||||
|
||||
interface OpenRouterConfig {
|
||||
apiKey: string
|
||||
model: string
|
||||
}
|
||||
|
||||
function getConfig(): OpenRouterConfig | null {
|
||||
const apiKey = import.meta.env.VITE_OPENROUTER_API_KEY as string | undefined
|
||||
if (!apiKey) return null
|
||||
return {
|
||||
apiKey,
|
||||
model: (import.meta.env.VITE_OPENROUTER_MODEL as string | undefined) ?? DEFAULT_MODEL,
|
||||
}
|
||||
}
|
||||
|
||||
function makeProvenance(
|
||||
config: OpenRouterConfig,
|
||||
source: AIProvenance['source'],
|
||||
fallbackUsed: boolean,
|
||||
validationPassed: boolean,
|
||||
extras: { fallbackReason?: string } = {},
|
||||
): AIProvenance {
|
||||
return {
|
||||
provider: 'openrouter',
|
||||
model: config.model,
|
||||
generatedAt: new Date().toISOString(),
|
||||
promptVersion: PROMPT_VERSION,
|
||||
schemaVersion: SCHEMA_VERSION,
|
||||
source,
|
||||
fallbackUsed,
|
||||
validationPassed,
|
||||
traceId: crypto.randomUUID(),
|
||||
fallbackReason: extras.fallbackReason,
|
||||
}
|
||||
}
|
||||
|
||||
// ── HTTP helper ───────────────────────────────────────────────────────────────
|
||||
|
||||
async function chat(config: OpenRouterConfig, system: string, user: string): Promise<string> {
|
||||
const res = await fetch(`${API_BASE}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${config.apiKey}`,
|
||||
'Content-Type': 'application/json',
|
||||
'HTTP-Referer': window.location.origin,
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: config.model,
|
||||
messages: [
|
||||
{ role: 'system', content: system },
|
||||
{ role: 'user', content: user },
|
||||
],
|
||||
}),
|
||||
})
|
||||
if (!res.ok) {
|
||||
const body = await res.text()
|
||||
throw new AppError({
|
||||
code: ServiceErrorCode.AI_GENERATION_FAILED,
|
||||
message: `OpenRouter error ${res.status}: ${body}`,
|
||||
})
|
||||
}
|
||||
const json = await res.json() as { choices: Array<{ message: { content: string } }> }
|
||||
return json.choices[0]?.message?.content ?? ''
|
||||
}
|
||||
|
||||
// ── JSON extraction ───────────────────────────────────────────────────────────
|
||||
|
||||
function extractJSON<T>(raw: string): T | null {
|
||||
const fenced = raw.match(/```(?:json)?\s*\n?([\s\S]*?)\n?```/)
|
||||
const candidate = fenced ? fenced[1] : raw.match(/([\[{][\s\S]*[\]}])/)?.[1] ?? raw
|
||||
try {
|
||||
return JSON.parse(candidate) as T
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
// ── ParseNeed helpers ─────────────────────────────────────────────────────────
|
||||
|
||||
type RawNeedParseAI = {
|
||||
assetType?: string | null
|
||||
areaRange?: { min: number; max: number } | null
|
||||
preferredLocations?: string[]
|
||||
budgetRange?: { maxPerSqm: number; currency: string } | null
|
||||
timing?: { earliestMoveIn: string; latestMoveIn?: string; flexibleTiming: boolean } | null
|
||||
mustHaveCriteria?: string[]
|
||||
missingFields?: string[]
|
||||
assumptions?: string[]
|
||||
}
|
||||
|
||||
function followUpForField(field: string): string {
|
||||
const MAP: Record<string, string> = {
|
||||
assetType: 'Welchen Nutzungstyp suchen Sie (Büro, Retail, Logistik, Produktion)?',
|
||||
areaRange: 'Welche Fläche benötigen Sie (min–max in m²)?',
|
||||
preferredLocations: 'In welchen Städten oder Regionen suchen Sie?',
|
||||
budgetRange: 'Was ist Ihr maximales Budget pro m² und Jahr?',
|
||||
timing: 'Wann möchten Sie spätestens einziehen?',
|
||||
mustHaveCriteria: 'Haben Sie zwingende Anforderungen (ÖV-Anbindung, Parkplätze, Laderampe)?',
|
||||
}
|
||||
return MAP[field] ?? `Können Sie "${field}" präzisieren?`
|
||||
}
|
||||
|
||||
function defaultSuggestedWeights(): Record<string, number> {
|
||||
return {
|
||||
area: 0.25, location: 0.20, budget: 0.20, timing: 0.15,
|
||||
prestige: 0.05, accessibility: 0.05, expansionPotential: 0.02,
|
||||
flexibility: 0.02, visibility: 0.02, footfall: 0.01, talentAccess: 0.01,
|
||||
esg: 0.01, taxEnvironment: 0.01,
|
||||
}
|
||||
}
|
||||
|
||||
// ── Fallback wrapper ──────────────────────────────────────────────────────────
|
||||
|
||||
type FallbackFn<T> = () => Promise<AIResponse<T>>
|
||||
|
||||
async function withFallback<T>(
|
||||
label: string,
|
||||
fn: (config: OpenRouterConfig) => Promise<AIResponse<T>>,
|
||||
fallback: FallbackFn<T>,
|
||||
inputSizeChars?: number,
|
||||
): Promise<AIResponse<T>> {
|
||||
const config = getConfig()
|
||||
const startMs = Date.now()
|
||||
const callId = crypto.randomUUID()
|
||||
|
||||
if (!config) {
|
||||
console.warn(`[OpenRouterAIService] ${label}: no API key — using MockAIService`)
|
||||
const result = await fallback()
|
||||
const latencyMs = Date.now() - startMs
|
||||
const provenance: AIProvenance = {
|
||||
...result.provenance,
|
||||
fallbackUsed: true,
|
||||
traceId: callId,
|
||||
fallbackReason: 'no_api_key',
|
||||
schemaVersion: SCHEMA_VERSION,
|
||||
latencyMs,
|
||||
}
|
||||
aiTraceStore.add({
|
||||
id: callId,
|
||||
method: label,
|
||||
provider: 'openrouter',
|
||||
model: DEFAULT_MODEL,
|
||||
promptVersion: PROMPT_VERSION,
|
||||
latencyMs,
|
||||
fallbackUsed: true,
|
||||
validationPassed: false,
|
||||
responseValidationStatus: 'fallback',
|
||||
errorType: 'no_api_key',
|
||||
fallbackReason: 'no_api_key',
|
||||
source: 'mock',
|
||||
createdAt: new Date().toISOString(),
|
||||
inputSizeChars,
|
||||
})
|
||||
return { ...result, provenance }
|
||||
}
|
||||
|
||||
try {
|
||||
const result = await fn(config)
|
||||
const latencyMs = Date.now() - startMs
|
||||
const prov = result.provenance
|
||||
const provenance: AIProvenance = {
|
||||
...prov,
|
||||
traceId: callId,
|
||||
latencyMs,
|
||||
schemaVersion: SCHEMA_VERSION,
|
||||
}
|
||||
aiTraceStore.add({
|
||||
id: callId,
|
||||
method: label,
|
||||
provider: prov.provider,
|
||||
model: prov.model,
|
||||
promptVersion: prov.promptVersion,
|
||||
latencyMs,
|
||||
fallbackUsed: prov.fallbackUsed,
|
||||
validationPassed: prov.validationPassed,
|
||||
responseValidationStatus: provenanceToStatus(prov.fallbackUsed, prov.source, prov.fallbackReason),
|
||||
fallbackReason: prov.fallbackReason,
|
||||
source: prov.source,
|
||||
createdAt: prov.generatedAt,
|
||||
inputSizeChars,
|
||||
})
|
||||
return { ...result, provenance }
|
||||
} catch (err) {
|
||||
console.error(`[OpenRouterAIService] ${label} failed:`, err)
|
||||
const result = await fallback()
|
||||
const latencyMs = Date.now() - startMs
|
||||
const errorType: AITraceErrorType =
|
||||
err instanceof AppError && err.code === ServiceErrorCode.AI_GENERATION_FAILED
|
||||
? 'api_error'
|
||||
: err instanceof TypeError
|
||||
? 'network'
|
||||
: 'unknown'
|
||||
const responseValidationStatus: AITraceValidationStatus =
|
||||
err instanceof AppError && err.code === ServiceErrorCode.AI_GENERATION_FAILED
|
||||
? 'api_error'
|
||||
: 'network_error'
|
||||
const fallbackReason = `${errorType}: ${err instanceof Error ? err.message.slice(0, 100) : 'unknown error'}`
|
||||
const provenance: AIProvenance = {
|
||||
...result.provenance,
|
||||
fallbackUsed: true,
|
||||
traceId: callId,
|
||||
fallbackReason,
|
||||
schemaVersion: SCHEMA_VERSION,
|
||||
latencyMs,
|
||||
}
|
||||
aiTraceStore.add({
|
||||
id: callId,
|
||||
method: label,
|
||||
provider: 'openrouter',
|
||||
model: config.model,
|
||||
promptVersion: PROMPT_VERSION,
|
||||
latencyMs,
|
||||
fallbackUsed: true,
|
||||
validationPassed: false,
|
||||
responseValidationStatus,
|
||||
errorType,
|
||||
fallbackReason,
|
||||
source: 'mock',
|
||||
createdAt: new Date().toISOString(),
|
||||
inputSizeChars,
|
||||
})
|
||||
return { ...result, provenance }
|
||||
}
|
||||
}
|
||||
|
||||
// ── Service ───────────────────────────────────────────────────────────────────
|
||||
|
||||
export const OpenRouterAIService: IAIService = {
|
||||
|
||||
// ── parseNeed ───────────────────────────────────────────────────────────────
|
||||
parseNeed(input: string): Promise<AIResponse<ParseNeedResult>> {
|
||||
return withFallback('parseNeed', async (config) => {
|
||||
const { system, user } = buildNeedParsingPrompt({ userInput: input })
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<RawNeedParseAI>(raw)
|
||||
const ai = json ? validateAIResponse(NeedParsingResponseSchema, json, 'parseNeed') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] parseNeed: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.parseNeed(input)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
|
||||
const extractedCriteria: ParsedNeedCriteria = {
|
||||
assetType: (ai.assetType ?? undefined) as AssetType | undefined,
|
||||
areaRange: ai.areaRange ?? undefined,
|
||||
preferredLocations: ai.preferredLocations,
|
||||
budgetRange: ai.budgetRange ?? undefined,
|
||||
timing: ai.timing
|
||||
? { ...ai.timing, earliestMoveIn: ai.timing.earliestMoveIn ?? '', flexibleTiming: ai.timing.flexibleTiming ?? false }
|
||||
: undefined,
|
||||
mustHaveCriteria: ai.mustHaveCriteria,
|
||||
}
|
||||
const missingFields = ai.missingFields ?? []
|
||||
const confidenceByField: Record<string, number> = {}
|
||||
Object.keys(extractedCriteria).forEach(k => {
|
||||
confidenceByField[k] = extractedCriteria[k as keyof ParsedNeedCriteria] != null ? 0.85 : 0
|
||||
})
|
||||
missingFields.forEach(f => { confidenceByField[f] = 0 })
|
||||
const followUpQuestionCandidates: FollowUpQuestion[] = missingFields.map((field, i) => ({
|
||||
id: `fq-or-${i}`,
|
||||
questionText: followUpForField(field),
|
||||
targetField: field,
|
||||
reason: `Feld "${field}" nicht im Text erkannt`,
|
||||
importance: 'recommended' as const,
|
||||
}))
|
||||
return {
|
||||
data: {
|
||||
extractedCriteria,
|
||||
confidenceByField,
|
||||
missingFields,
|
||||
assumptions: ai.assumptions ?? [],
|
||||
suggestedWeights: defaultSuggestedWeights(),
|
||||
followUpQuestionCandidates,
|
||||
rawSummary: raw.substring(0, 500),
|
||||
promptVersion: PROMPT_VERSION,
|
||||
schemaVersion: SCHEMA_VERSION,
|
||||
},
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.parseNeed(input), input.length)
|
||||
},
|
||||
|
||||
// ── generateFollowUpQuestions ───────────────────────────────────────────────
|
||||
generateFollowUpQuestions(criteria: ParsedNeedCriteria): Promise<AIResponse<FollowUpQuestion[]>> {
|
||||
return withFallback('generateFollowUpQuestions', async (config) => {
|
||||
const missingFields = [
|
||||
...(!criteria.assetType ? ['assetType'] : []),
|
||||
...(!criteria.areaRange || (criteria.areaRange.min <= 0 && criteria.areaRange.max <= 0) ? ['areaRange'] : []),
|
||||
...(!criteria.preferredLocations?.length ? ['preferredLocations'] : []),
|
||||
...(!criteria.budgetRange ? ['budgetRange'] : []),
|
||||
...(!criteria.timing ? ['timing'] : []),
|
||||
...(!criteria.mustHaveCriteria?.length ? ['mustHaveCriteria'] : []),
|
||||
]
|
||||
const { system, user } = buildFollowUpQuestionsPrompt({ criteria, missingFields })
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown[]>(raw)
|
||||
const ai = json ? validateAIResponse(FollowUpQuestionsResponseSchema, json, 'generateFollowUpQuestions') : null
|
||||
|
||||
if (!ai?.length) {
|
||||
console.warn('[OpenRouterAIService] generateFollowUpQuestions: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.generateFollowUpQuestions(criteria)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
return {
|
||||
data: ai.map((q, i) => ({
|
||||
id: `fq-or-${i}`,
|
||||
questionText: q.questionText,
|
||||
targetField: q.targetField,
|
||||
reason: q.reason ?? 'AI-generiert',
|
||||
suggestedAnswerOptions: q.suggestedAnswerOptions,
|
||||
importance: (q.importance ?? 'recommended') as FollowUpQuestion['importance'],
|
||||
})),
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateFollowUpQuestions(criteria))
|
||||
},
|
||||
|
||||
// ── generateMatchExplanation ────────────────────────────────────────────────
|
||||
// Plain-text response — no JSON schema to validate, but non-empty check enforced.
|
||||
generateMatchExplanation(input: MatchExplanationInput): Promise<AIResponse<MatchExplanation>> {
|
||||
return withFallback('generateMatchExplanation', async (config) => {
|
||||
const { system, user } = buildMatchExplanationPrompt(input)
|
||||
const raw = await chat(config, system, user)
|
||||
const summary = raw.trim()
|
||||
|
||||
if (!summary) {
|
||||
console.warn('[OpenRouterAIService] generateMatchExplanation: empty response — using mock fallback')
|
||||
const fb = await MockAIService.generateMatchExplanation(input)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: 'empty_response' }) }
|
||||
}
|
||||
const scoreLabel = input.matchScore >= 78 ? 'Starkes' : input.matchScore >= 52 ? 'Gutes' : 'Schwaches'
|
||||
return {
|
||||
data: {
|
||||
headline: `${scoreLabel} Match — ${input.propertyTitle} (${input.matchScore}/100)`,
|
||||
summary,
|
||||
keyReasons: [
|
||||
...input.positiveFactors.slice(0, 2).map(f => `+ ${f.explanation}`),
|
||||
...input.negativeFactors.slice(0, 1).map(f => `− ${f.explanation}`),
|
||||
],
|
||||
},
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateMatchExplanation(input))
|
||||
},
|
||||
|
||||
// ── summarizeTradeOffs ──────────────────────────────────────────────────────
|
||||
summarizeTradeOffs(tradeoffs: TradeOffInput[]): Promise<AIResponse<TradeOffSummary>> {
|
||||
return withFallback('summarizeTradeOffs', async (config) => {
|
||||
const { system, user } = buildTradeOffPrompt(tradeoffs, 'Objekt')
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(TradeOffSummaryResponseSchema, json, 'summarizeTradeOffs') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] summarizeTradeOffs: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.summarizeTradeOffs(tradeoffs)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
return {
|
||||
data: {
|
||||
headline: ai.headline,
|
||||
items: ai.items.map(item => ({
|
||||
concern: item.concern,
|
||||
severity: item.severity,
|
||||
mitigation: item.mitigation,
|
||||
})),
|
||||
overallRisk: ai.overallRisk,
|
||||
},
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.summarizeTradeOffs(tradeoffs))
|
||||
},
|
||||
|
||||
// ── summarizeComparison ─────────────────────────────────────────────────────
|
||||
// Hybrid: AI provides narrative text; mock provides structural per-property data.
|
||||
// source: 'hybrid' — both are labeled in provenance.
|
||||
summarizeComparison(items: UnifiedMatchResult[]): Promise<AIResponse<ComparisonSummary>> {
|
||||
return withFallback('summarizeComparison', async (config) => {
|
||||
type ItemWithProp = UnifiedMatchResult & {
|
||||
property?: { title?: string; location?: { city?: string }; rentPricePerSqm?: number }
|
||||
}
|
||||
const properties = (items as ItemWithProp[])
|
||||
.filter(i => i.resultType !== 'FUTURE_AVAILABILITY')
|
||||
.map(i => ({
|
||||
title: i.property?.title ?? `Match ${i.matchScore}`,
|
||||
matchScore: i.matchScore,
|
||||
city: i.property?.location?.city ?? '–',
|
||||
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(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(CompareSummaryResponseSchema, json, 'summarizeComparison') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] summarizeComparison: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.summarizeComparison(items)
|
||||
return { ...fb, provenance: makeProvenance(config, '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(config, 'hybrid', false, true),
|
||||
}
|
||||
}, () => MockAIService.summarizeComparison(items))
|
||||
},
|
||||
|
||||
// ── generateDecisionBrief ───────────────────────────────────────────────────
|
||||
// Hybrid: AI generates narrative summary + sections; mock fills structural metadata.
|
||||
generateDecisionBrief(shortlistId: string): Promise<AIResponse<DecisionBrief>> {
|
||||
return withFallback('generateDecisionBrief', async (config) => {
|
||||
const { system, user } = buildDecisionBriefPrompt({ shortlistItems: [], needSummary: shortlistId })
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(DecisionBriefResponseSchema, json, 'generateDecisionBrief') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] generateDecisionBrief: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.generateDecisionBrief(shortlistId)
|
||||
return { ...fb, provenance: makeProvenance(config, '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(config, 'hybrid', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateDecisionBrief(shortlistId))
|
||||
},
|
||||
|
||||
// ── generateDataQualitySummary ──────────────────────────────────────────────
|
||||
generateDataQualitySummary(propertyId: string, quality: DataQualityInput): Promise<AIResponse<DataQualitySummary>> {
|
||||
return withFallback('generateDataQualitySummary', async (config) => {
|
||||
const { system, user } = buildDataQualityPrompt(propertyId, quality)
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(DataQualitySummaryResponseSchema, json, 'generateDataQualitySummary') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] generateDataQualitySummary: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.generateDataQualitySummary(propertyId, quality)
|
||||
return { ...fb, provenance: makeProvenance(config, '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(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateDataQualitySummary(propertyId, quality))
|
||||
},
|
||||
|
||||
// ── classifyMarketSignal ────────────────────────────────────────────────────
|
||||
classifyMarketSignal(signalText: string): Promise<AIResponse<MarketSignalClassification>> {
|
||||
return withFallback('classifyMarketSignal', async (config) => {
|
||||
const { system, user } = buildMarketSignalPrompt(signalText)
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json
|
||||
? validateAIResponse(MarketSignalClassificationResponseSchema, json, 'classifyMarketSignal')
|
||||
: null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] classifyMarketSignal: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.classifyMarketSignal(signalText)
|
||||
return { ...fb, provenance: makeProvenance(config, '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(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.classifyMarketSignal(signalText), signalText.length)
|
||||
},
|
||||
|
||||
// ── generateOfferEmail ──────────────────────────────────────────────────────
|
||||
generateOfferEmail(payload: OfferEmailPayload): Promise<AIResponse<{ subject: string; body: string }>> {
|
||||
return withFallback('generateOfferEmail', async (config) => {
|
||||
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(config, system, user)
|
||||
const json = extractJSON<unknown>(raw)
|
||||
const ai = json ? validateAIResponse(OfferEmailResponseSchema, json, 'generateOfferEmail') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] generateOfferEmail: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.generateOfferEmail(payload)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
return {
|
||||
data: { subject: ai.subject, body: ai.body },
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateOfferEmail(payload))
|
||||
},
|
||||
|
||||
// ── Legacy: extractCriteria ─────────────────────────────────────────────────
|
||||
extractCriteria(input: string): Promise<AIResponse<CriteriaExtractionResult>> {
|
||||
return withFallback('extractCriteria', async (config) => {
|
||||
const { system, user } = buildNeedParsingPrompt({ userInput: input })
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<RawNeedParseAI>(raw)
|
||||
const ai = json ? validateAIResponse(NeedParsingResponseSchema, json, 'extractCriteria') : null
|
||||
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] extractCriteria: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.extractCriteria(input)
|
||||
return { ...fb, provenance: makeProvenance(config, '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(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.extractCriteria(input))
|
||||
},
|
||||
|
||||
// ── Legacy: generateFollowUp ────────────────────────────────────────────────
|
||||
generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<AIResponse<string[]>> {
|
||||
return withFallback('generateFollowUp', async (config) => {
|
||||
const missingFields = [
|
||||
...(!partialNeed.assetType ? ['assetType'] : []),
|
||||
...(!partialNeed.preferredLocations?.length ? ['preferredLocations'] : []),
|
||||
...(!partialNeed.timing ? ['timing'] : []),
|
||||
...(!partialNeed.budgetRange ? ['budgetRange'] : []),
|
||||
]
|
||||
if (!missingFields.length) {
|
||||
return { data: [], provenance: makeProvenance(config, 'ai', false, true) }
|
||||
}
|
||||
const { system, user } = buildFollowUpQuestionsPrompt({
|
||||
criteria: partialNeed as ParsedNeedCriteria,
|
||||
missingFields,
|
||||
})
|
||||
const raw = await chat(config, system, user)
|
||||
const json = extractJSON<unknown[]>(raw)
|
||||
const ai = json ? validateAIResponse(FollowUpQuestionsResponseSchema, json, 'generateFollowUp') : null
|
||||
|
||||
if (!ai?.length) {
|
||||
console.warn('[OpenRouterAIService] generateFollowUp: invalid response — using mock fallback')
|
||||
const fb = await MockAIService.generateFollowUp(partialNeed)
|
||||
return { ...fb, provenance: makeProvenance(config, 'mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
|
||||
}
|
||||
return {
|
||||
data: ai.map(q => q.questionText).filter(Boolean),
|
||||
provenance: makeProvenance(config, 'ai', false, true),
|
||||
}
|
||||
}, () => MockAIService.generateFollowUp(partialNeed))
|
||||
},
|
||||
}
|
||||
export { BackendAIService as OpenRouterAIService } from '../backend/BackendAIService'
|
||||
|
||||
@@ -16,6 +16,10 @@ export function generateSummary(c: ParsedNeedCriteria): string {
|
||||
if (c.budgetRange?.maxPerSqm) parts.push(`Budget max. CHF ${c.budgetRange.maxPerSqm}/m²`)
|
||||
if (c.timing?.earliestMoveIn) parts.push(`ab ${c.timing.earliestMoveIn}`)
|
||||
if (c.mustHaveCriteria?.length) parts.push(`Must-haves: ${c.mustHaveCriteria.join(', ')}`)
|
||||
if (c.searchRadius) parts.push(`Radius ${c.searchRadius} km`)
|
||||
if (c.isAnonymous) parts.push('Anonyme Suche')
|
||||
if (c.requiresDivisibility && c.minDivisibleUnit) parts.push(`Teilbar ab ${c.minDivisibleUnit} m²`)
|
||||
if (c.fitOutBudgetMaxPerSqm) parts.push(`Ausbaubudget max. CHF ${c.fitOutBudgetMaxPerSqm}/m²`)
|
||||
return parts.join(', ')
|
||||
}
|
||||
|
||||
@@ -50,6 +54,11 @@ export function buildNeedInput(
|
||||
requireBarrierFree: criteria.requireBarrierFree,
|
||||
minCeilingHeightM: criteria.minCeilingHeightM,
|
||||
minContractDurationMonths: criteria.minContractDurationMonths,
|
||||
searchRadius: criteria.searchRadius,
|
||||
isAnonymous: criteria.isAnonymous,
|
||||
requiresDivisibility: criteria.requiresDivisibility,
|
||||
minDivisibleUnit: criteria.minDivisibleUnit,
|
||||
fitOutBudgetMaxPerSqm: criteria.fitOutBudgetMaxPerSqm,
|
||||
notes: criteria.notes,
|
||||
extractedFromText: undefined,
|
||||
}
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
import { MockupUnitProvider } from '../provider/MockupUnitProvider'
|
||||
import type { PropertyUnit } from '../domain/property'
|
||||
import { throwServiceError } from './errors'
|
||||
import type { ItemResponse } from './types'
|
||||
|
||||
export const unitService = {
|
||||
async update(unitId: string, data: Partial<PropertyUnit>): Promise<ItemResponse<PropertyUnit>> {
|
||||
try {
|
||||
const unit = await MockupUnitProvider.update(unitId, data)
|
||||
return { data: unit }
|
||||
} catch (err) {
|
||||
throwServiceError('unitService.update', err)
|
||||
}
|
||||
},
|
||||
}
|
||||
Reference in New Issue
Block a user