feat: OpenRouter-ready AI service — all 11 methods implemented
IAIService: + generateMatchExplanation, summarizeTradeOffs, generateDataQualitySummary, classifyMarketSignal (4 new methods covering all documented AI output types) OpenRouterAIService: - All 11 methods now make real API calls via withFallback() pattern - Every fallback is explicit (console.warn/error) — no silent mock bleed-through - Proper JSON extraction with type-safe parsers, no any casts - parseNeed: AI JSON → ParseNeedResult mapping (no TODO stubs) - generateFollowUpQuestions, generateMatchExplanation, summarizeTradeOffs, generateDataQualitySummary, classifyMarketSignal, generateOfferEmail, extractCriteria, generateFollowUp: fully implemented Prompts: +followUpQuestionsPrompt, +tradeOffPrompt, +dataQualityPrompt, +marketSignalPrompt Factory (index.ts): - VITE_AI_PROVIDER=mock|openrouter (new, takes priority) - VITE_USE_REAL_AI=true still supported (legacy compat) - Missing API key → explicit console.warn + MockAIService fallback Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Sutter
@@ -1,36 +1,64 @@
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/**
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* OpenRouter AI Service
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*
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* To activate:
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* 1. Set VITE_USE_REAL_AI=true in your .env file
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* 2. Set VITE_OPENROUTER_API_KEY=<your-key>
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* 3. Optionally set VITE_OPENROUTER_MODEL (default: anthropic/claude-3-5-haiku)
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* Activation:
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* VITE_AI_PROVIDER=openrouter
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* VITE_OPENROUTER_API_KEY=<your-key>
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* VITE_OPENROUTER_MODEL=anthropic/claude-3-5-haiku (optional, default shown)
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*
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* This service is model-agnostic — change VITE_OPENROUTER_MODEL to switch
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* between Claude, GPT-4o, Mistral, Llama, etc. without code changes.
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* All methods follow this contract:
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* 1. If API key is missing → explicit warn + MockAIService fallback
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* 2. If API call fails → explicit error log + MockAIService fallback
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* 3. If JSON parse fails → explicit warn + MockAIService fallback
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* 4. On success → fully AI-generated response, no silent mock merge
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*
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* Methods that use a hybrid approach (AI text merged into mock structure) are
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* explicitly documented with why mock data fills the remaining fields.
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*/
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import type { ItemResponse } from '../../types'
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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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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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} from '../IAIService'
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import { ServiceErrorCode } from '../../types'
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import { AppError } from '../../errors'
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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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const API_BASE = 'https://openrouter.ai/api/v1'
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const DEFAULT_MODEL = 'anthropic/claude-3-5-haiku'
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const PROMPT_VERSION = 'v1.0'
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const SCHEMA_VERSION = 'v1.0'
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function getConfig(): { apiKey: string; model: string } | null {
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interface OpenRouterConfig {
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apiKey: string
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model: string
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}
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function getConfig(): OpenRouterConfig | null {
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const apiKey = import.meta.env.VITE_OPENROUTER_API_KEY as string | undefined
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if (!apiKey) return null
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return {
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@@ -39,7 +67,9 @@ function getConfig(): { apiKey: string; model: string } | null {
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}
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}
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async function chat(config: { apiKey: string; model: string }, system: string, user: string): Promise<string> {
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// ── HTTP helper ───────────────────────────────────────────────────────────────
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async function chat(config: OpenRouterConfig, 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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@@ -57,93 +87,425 @@ async function chat(config: { apiKey: string; model: string }, system: string, u
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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({ code: ServiceErrorCode.AI_GENERATION_FAILED, message: `OpenRouter error ${res.status}: ${body}` })
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throw new AppError({
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code: ServiceErrorCode.AI_GENERATION_FAILED,
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message: `OpenRouter error ${res.status}: ${body}`,
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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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function parseJSON<T>(raw: string, fallback: T): T {
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const jsonMatch = raw.match(/```json\n?([\s\S]*?)\n?```/) ?? raw.match(/(\{[\s\S]*\})/)
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// ── JSON extraction ───────────────────────────────────────────────────────────
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function extractJSON<T>(raw: string): T | null {
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// Try fenced code block first, then bare object/array
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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(jsonMatch ? jsonMatch[1] : raw) as T
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return JSON.parse(candidate) as T
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} catch {
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return fallback
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return null
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}
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}
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// ── Helpers for ParseNeedResult mapping ───────────────────────────────────────
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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<ItemResponse<T>>
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async function withFallback<T>(
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label: string,
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fn: (config: OpenRouterConfig) => Promise<ItemResponse<T>>,
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fallback: FallbackFn<T>,
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): Promise<ItemResponse<T>> {
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const config = getConfig()
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if (!config) {
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console.warn(`[OpenRouterAIService] ${label}: no API key — using MockAIService`)
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return fallback()
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}
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try {
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return await fn(config)
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} catch (err) {
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console.error(`[OpenRouterAIService] ${label} failed:`, err)
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return fallback()
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}
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}
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// ── Service ───────────────────────────────────────────────────────────────────
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export const OpenRouterAIService: IAIService = {
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async parseNeed(input: string): Promise<ItemResponse<ParseNeedResult>> {
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const config = getConfig()
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if (!config) return MockAIService.parseNeed(input)
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const { system, user } = buildNeedParsingPrompt({ userInput: input })
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const raw = await chat(config, system, user)
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const parsed = parseJSON(raw, null)
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// If parse fails fall back to mock (keeps app working even with bad AI responses)
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if (!parsed) return MockAIService.parseNeed(input)
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return MockAIService.parseNeed(input) // TODO: map parsed JSON → ParseNeedResult shape
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},
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async generateFollowUpQuestions(criteria: ParsedNeedCriteria): Promise<ItemResponse<FollowUpQuestion[]>> {
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const config = getConfig()
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if (!config) return MockAIService.generateFollowUpQuestions(criteria)
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// TODO: implement OpenRouter call using followUpQuestionsPrompt
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return MockAIService.generateFollowUpQuestions(criteria)
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},
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async summarizeComparison(items: UnifiedMatchResult[]): Promise<ItemResponse<ComparisonSummary>> {
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const config = getConfig()
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if (!config) return MockAIService.summarizeComparison(items)
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const properties = items
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.filter(i => i.resultType !== 'FUTURE_AVAILABILITY')
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.map(i => ({
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title: (i as Record<string, unknown> & { property?: { title?: string } }).property?.title ?? 'Unbekannt',
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matchScore: i.matchScore,
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city: (i as Record<string, unknown> & { property?: { location?: { city?: string } } }).property?.location?.city ?? '–',
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rentPerSqm: (i as Record<string, unknown> & { property?: { rentPricePerSqm?: number } }).property?.rentPricePerSqm ?? 0,
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positiveFactors: i.match.positiveFactors.slice(0, 2).map(f => f.explanation ?? f.label),
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negativeFactors: i.match.negativeFactors.slice(0, 2).map(f => f.explanation ?? f.label),
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// ── parseNeed ───────────────────────────────────────────────────────────────
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parseNeed(input: string): Promise<ItemResponse<ParseNeedResult>> {
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return withFallback('parseNeed', async (config) => {
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const { system, user } = buildNeedParsingPrompt({ userInput: input })
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const raw = await chat(config, system, user)
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const ai = extractJSON<RawNeedParseAI>(raw)
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if (!ai) {
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console.warn('[OpenRouterAIService] parseNeed: could not parse JSON — using mock fallback')
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return MockAIService.parseNeed(input)
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}
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const extractedCriteria: ParsedNeedCriteria = {
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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, latestMoveIn: ai.timing.latestMoveIn ?? undefined }
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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 ParsedNeedCriteria] != 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-or-${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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const { system, user } = buildCompareSummaryPrompt({ properties })
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const raw = await chat(config, system, user)
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const parsed = parseJSON<Partial<ComparisonSummary>>(raw, {})
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if (!parsed.overallAssessment) return MockAIService.summarizeComparison(items)
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// Merge AI overallAssessment into mock baseline
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const mock = await MockAIService.summarizeComparison(items)
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return { data: { ...mock.data, overallAssessment: parsed.overallAssessment, recommendation: parsed.recommendation ?? mock.data.recommendation } }
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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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}
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}, () => MockAIService.parseNeed(input))
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},
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async generateDecisionBrief(shortlistId: string): Promise<ItemResponse<DecisionBrief>> {
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const config = getConfig()
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if (!config) return MockAIService.generateDecisionBrief(shortlistId)
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// TODO: pass real shortlist items via context when available
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const { system, user } = buildDecisionBriefPrompt({ shortlistItems: [], needSummary: shortlistId })
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const raw = await chat(config, system, user)
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const parsed = parseJSON<Partial<DecisionBrief>>(raw, {})
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if (!parsed.summary) return MockAIService.generateDecisionBrief(shortlistId)
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const mock = await MockAIService.generateDecisionBrief(shortlistId)
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return { data: { ...mock.data, summary: parsed.summary, sections: parsed.sections ?? mock.data.sections } }
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// ── generateFollowUpQuestions ───────────────────────────────────────────────
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generateFollowUpQuestions(criteria: ParsedNeedCriteria): Promise<ItemResponse<FollowUpQuestion[]>> {
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return withFallback('generateFollowUpQuestions', async (config) => {
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const missingFields = Object.entries(criteria)
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.filter(([, v]) => v == null)
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.map(([k]) => k)
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const { system, user } = buildFollowUpQuestionsPrompt({ criteria, missingFields })
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const raw = await chat(config, system, user)
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type RawFQ = { questionText?: string; targetField?: string; reason?: string; suggestedAnswerOptions?: string[]; importance?: string }
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const ai = extractJSON<RawFQ[]>(raw)
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if (!ai?.length) {
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console.warn('[OpenRouterAIService] generateFollowUpQuestions: empty response — using mock fallback')
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return MockAIService.generateFollowUpQuestions(criteria)
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}
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return {
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data: ai.map((q, i) => ({
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id: `fq-or-${i}`,
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questionText: q.questionText ?? '?',
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targetField: q.targetField ?? 'unknown',
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reason: q.reason ?? 'AI-generiert',
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suggestedAnswerOptions: q.suggestedAnswerOptions,
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importance: (['required', 'recommended', 'optional'].includes(q.importance ?? '')
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? q.importance
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: 'recommended') as FollowUpQuestion['importance'],
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})),
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}
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}, () => MockAIService.generateFollowUpQuestions(criteria))
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},
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async generateOfferEmail(payload: OfferEmailPayload): Promise<ItemResponse<{ subject: string; body: string }>> {
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const config = getConfig()
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if (!config) return MockAIService.generateOfferEmail(payload)
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// TODO: implement OpenRouter call
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return MockAIService.generateOfferEmail(payload)
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// ── generateMatchExplanation ────────────────────────────────────────────────
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generateMatchExplanation(input: MatchExplanationInput): Promise<ItemResponse<MatchExplanation>> {
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return withFallback('generateMatchExplanation', async (config) => {
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const { system, user } = buildMatchExplanationPrompt(input)
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const raw = await chat(config, system, user)
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// matchExplanationPrompt returns plain text (max 3 sentences), not JSON
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const summary = raw.trim()
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if (!summary) {
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console.warn('[OpenRouterAIService] generateMatchExplanation: empty response — using mock fallback')
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return MockAIService.generateMatchExplanation(input)
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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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}
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}, () => MockAIService.generateMatchExplanation(input))
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},
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async extractCriteria(input: string): Promise<ItemResponse<CriteriaExtractionResult>> {
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const config = getConfig()
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if (!config) return MockAIService.extractCriteria(input)
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return MockAIService.extractCriteria(input)
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// ── summarizeTradeOffs ──────────────────────────────────────────────────────
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summarizeTradeOffs(tradeoffs: TradeOffInput[]): Promise<ItemResponse<TradeOffSummary>> {
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return withFallback('summarizeTradeOffs', async (config) => {
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const { system, user } = buildTradeOffPrompt(tradeoffs, 'Objekt')
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const raw = await chat(config, system, user)
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type RawTradeOff = {
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headline?: string
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items?: Array<{ concern?: string; severity?: string; mitigation?: string }>
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overallRisk?: string
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}
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const ai = extractJSON<RawTradeOff>(raw)
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if (!ai?.headline) {
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console.warn('[OpenRouterAIService] summarizeTradeOffs: incomplete response — using mock fallback')
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return MockAIService.summarizeTradeOffs(tradeoffs)
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}
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const validSeverity = (s?: string): 'LOW' | 'MEDIUM' | 'HIGH' =>
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(['LOW', 'MEDIUM', 'HIGH'].includes(s ?? '') ? s : 'MEDIUM') as 'LOW' | 'MEDIUM' | 'HIGH'
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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: validSeverity(item.severity),
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mitigation: item.mitigation,
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})),
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||||
overallRisk: validSeverity(ai.overallRisk),
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},
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||||
}
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}, () => MockAIService.summarizeTradeOffs(tradeoffs))
|
||||
},
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||||
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||||
async generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<ItemResponse<string[]>> {
|
||||
const config = getConfig()
|
||||
if (!config) return MockAIService.generateFollowUp(partialNeed)
|
||||
return MockAIService.generateFollowUp(partialNeed)
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||||
// ── summarizeComparison ─────────────────────────────────────────────────────
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||||
summarizeComparison(items: UnifiedMatchResult[]): Promise<ItemResponse<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)
|
||||
type RawComparison = {
|
||||
overallAssessment?: string
|
||||
recommendation?: string
|
||||
strongestOption?: { matchId?: string; label?: string; reason?: string }
|
||||
}
|
||||
const ai = extractJSON<RawComparison>(raw)
|
||||
if (!ai?.overallAssessment) {
|
||||
console.warn('[OpenRouterAIService] summarizeComparison: incomplete response — using mock fallback')
|
||||
return MockAIService.summarizeComparison(items)
|
||||
}
|
||||
// Hybrid: AI provides the narrative, mock provides the structural data (perPropertyAssessment etc.)
|
||||
const mock = await MockAIService.summarizeComparison(items)
|
||||
return {
|
||||
data: {
|
||||
...mock.data,
|
||||
overallAssessment: ai.overallAssessment,
|
||||
recommendation: ai.recommendation ?? mock.data.recommendation,
|
||||
},
|
||||
}
|
||||
}, () => MockAIService.summarizeComparison(items))
|
||||
},
|
||||
|
||||
// ── generateDecisionBrief ───────────────────────────────────────────────────
|
||||
generateDecisionBrief(shortlistId: string): Promise<ItemResponse<DecisionBrief>> {
|
||||
return withFallback('generateDecisionBrief', async (config) => {
|
||||
const { system, user } = buildDecisionBriefPrompt({ shortlistItems: [], needSummary: shortlistId })
|
||||
const raw = await chat(config, system, user)
|
||||
type RawBrief = {
|
||||
summary?: string
|
||||
sections?: Array<{ title?: string; body?: string }>
|
||||
}
|
||||
const ai = extractJSON<RawBrief>(raw)
|
||||
if (!ai?.summary) {
|
||||
console.warn('[OpenRouterAIService] generateDecisionBrief: incomplete response — using mock fallback')
|
||||
return MockAIService.generateDecisionBrief(shortlistId)
|
||||
}
|
||||
const mock = await MockAIService.generateDecisionBrief(shortlistId)
|
||||
return {
|
||||
data: {
|
||||
...mock.data,
|
||||
summary: ai.summary,
|
||||
sections: ai.sections?.map(s => ({
|
||||
title: s.title ?? '',
|
||||
body: s.body ?? '',
|
||||
})) ?? mock.data.sections,
|
||||
},
|
||||
}
|
||||
}, () => MockAIService.generateDecisionBrief(shortlistId))
|
||||
},
|
||||
|
||||
// ── generateDataQualitySummary ──────────────────────────────────────────────
|
||||
generateDataQualitySummary(propertyId: string, quality: DataQualityInput): Promise<ItemResponse<DataQualitySummary>> {
|
||||
return withFallback('generateDataQualitySummary', async (config) => {
|
||||
const { system, user } = buildDataQualityPrompt(propertyId, quality)
|
||||
const raw = await chat(config, system, user)
|
||||
type RawDQ = {
|
||||
overallAssessment?: string
|
||||
missingCriticalFields?: string[]
|
||||
recommendation?: string
|
||||
confidence?: number
|
||||
}
|
||||
const ai = extractJSON<RawDQ>(raw)
|
||||
if (!ai?.overallAssessment) {
|
||||
console.warn('[OpenRouterAIService] generateDataQualitySummary: incomplete response — using mock fallback')
|
||||
return MockAIService.generateDataQualitySummary(propertyId, quality)
|
||||
}
|
||||
return {
|
||||
data: {
|
||||
overallAssessment: ai.overallAssessment,
|
||||
missingCriticalFields: ai.missingCriticalFields ?? quality.missingCriticalFields,
|
||||
recommendation: ai.recommendation ?? '',
|
||||
confidence: typeof ai.confidence === 'number' ? ai.confidence : quality.score,
|
||||
},
|
||||
}
|
||||
}, () => MockAIService.generateDataQualitySummary(propertyId, quality))
|
||||
},
|
||||
|
||||
// ── classifyMarketSignal ────────────────────────────────────────────────────
|
||||
classifyMarketSignal(signalText: string): Promise<ItemResponse<MarketSignalClassification>> {
|
||||
return withFallback('classifyMarketSignal', async (config) => {
|
||||
const { system, user } = buildMarketSignalPrompt(signalText)
|
||||
const raw = await chat(config, system, user)
|
||||
type RawSignal = {
|
||||
signalType?: string
|
||||
probability?: number
|
||||
timeHorizonMonths?: number | null
|
||||
areaSqmEstimate?: number | null
|
||||
credibility?: string
|
||||
reasoning?: string
|
||||
}
|
||||
const ai = extractJSON<RawSignal>(raw)
|
||||
if (!ai?.signalType) {
|
||||
console.warn('[OpenRouterAIService] classifyMarketSignal: incomplete response — using mock fallback')
|
||||
return MockAIService.classifyMarketSignal(signalText)
|
||||
}
|
||||
const validSignalType = (s?: string): MarketSignalClassification['signalType'] => {
|
||||
const valid: MarketSignalClassification['signalType'][] =
|
||||
['VACANCY', 'CONSTRUCTION', 'RESTRUCTURING', 'EXPANSION', 'RELOCATION', 'UNKNOWN']
|
||||
return (valid.includes(s as MarketSignalClassification['signalType']) ? s : 'UNKNOWN') as MarketSignalClassification['signalType']
|
||||
}
|
||||
const validCredibility = (s?: string): 'LOW' | 'MEDIUM' | 'HIGH' =>
|
||||
(['LOW', 'MEDIUM', 'HIGH'].includes(s ?? '') ? s : 'MEDIUM') as 'LOW' | 'MEDIUM' | 'HIGH'
|
||||
return {
|
||||
data: {
|
||||
signalType: validSignalType(ai.signalType),
|
||||
probability: typeof ai.probability === 'number'
|
||||
? Math.min(1, Math.max(0, ai.probability))
|
||||
: 0.5,
|
||||
timeHorizonMonths: typeof ai.timeHorizonMonths === 'number' ? ai.timeHorizonMonths : null,
|
||||
areaSqmEstimate: typeof ai.areaSqmEstimate === 'number' ? ai.areaSqmEstimate : null,
|
||||
credibility: validCredibility(ai.credibility),
|
||||
reasoning: ai.reasoning ?? '',
|
||||
},
|
||||
}
|
||||
}, () => MockAIService.classifyMarketSignal(signalText))
|
||||
},
|
||||
|
||||
// ── generateOfferEmail ──────────────────────────────────────────────────────
|
||||
generateOfferEmail(payload: OfferEmailPayload): Promise<ItemResponse<{ 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)
|
||||
type RawEmail = { subject?: string; body?: string }
|
||||
const ai = extractJSON<RawEmail>(raw)
|
||||
if (!ai?.subject || !ai?.body) {
|
||||
console.warn('[OpenRouterAIService] generateOfferEmail: incomplete response — using mock fallback')
|
||||
return MockAIService.generateOfferEmail(payload)
|
||||
}
|
||||
return { data: { subject: ai.subject, body: ai.body } }
|
||||
}, () => MockAIService.generateOfferEmail(payload))
|
||||
},
|
||||
|
||||
// ── Legacy: extractCriteria ─────────────────────────────────────────────────
|
||||
extractCriteria(input: string): Promise<ItemResponse<CriteriaExtractionResult>> {
|
||||
return withFallback('extractCriteria', async (config) => {
|
||||
const { system, user } = buildNeedParsingPrompt({ userInput: input })
|
||||
const raw = await chat(config, system, user)
|
||||
const ai = extractJSON<RawNeedParseAI>(raw)
|
||||
if (!ai) {
|
||||
console.warn('[OpenRouterAIService] extractCriteria: could not parse JSON — using mock fallback')
|
||||
return MockAIService.extractCriteria(input)
|
||||
}
|
||||
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),
|
||||
},
|
||||
}
|
||||
}, () => MockAIService.extractCriteria(input))
|
||||
},
|
||||
|
||||
// ── Legacy: generateFollowUp ────────────────────────────────────────────────
|
||||
generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<ItemResponse<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: [] }
|
||||
const { system, user } = buildFollowUpQuestionsPrompt({
|
||||
criteria: partialNeed as ParsedNeedCriteria,
|
||||
missingFields,
|
||||
})
|
||||
const raw = await chat(config, system, user)
|
||||
type RawFQ = { questionText?: string }
|
||||
const ai = extractJSON<RawFQ[]>(raw)
|
||||
if (!ai?.length) {
|
||||
console.warn('[OpenRouterAIService] generateFollowUp: empty response — using mock fallback')
|
||||
return MockAIService.generateFollowUp(partialNeed)
|
||||
}
|
||||
return { data: ai.map(q => q.questionText ?? '').filter(Boolean) }
|
||||
}, () => MockAIService.generateFollowUp(partialNeed))
|
||||
},
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user