feat: member portal Sprint 9 — Ask AI (RAG over message archive)

- AskAIQuery model + migration (logs question/answer/citations per member) - AskAIService: RAG pipeline — retrieve via tenant-isolated SearchService (Meili), ground LLM in top-8 snippets with inline [n] citations, degrade gracefully when OPENROUTER_API_KEY absent or no context (snippet fallback) - Shared api/common/llm-client.ts (mirrors worker's OpenRouter client) - SearchModule now exports SearchService - Member endpoints: GET/POST /my/ask (history + ask) - /my/ask page + AskChat client component: question box, answer cards with sources - Ask AI nav item (top of member nav) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 17:03:01 +05:30
parent 6d89d8a609
commit f61c070428
12 changed files with 378 additions and 1 deletions
@@ -0,0 +1,16 @@
+CREATE TABLE "AskAIQuery" (
+    "id" TEXT NOT NULL,
+    "tenantId" TEXT NOT NULL,
+    "userId" TEXT NOT NULL,
+    "question" TEXT NOT NULL,
+    "answer" TEXT NOT NULL,
+    "citations" JSONB NOT NULL DEFAULT '[]',
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    CONSTRAINT "AskAIQuery_pkey" PRIMARY KEY ("id")
+);
+
+CREATE INDEX "AskAIQuery_tenantId_createdAt_idx" ON "AskAIQuery"("tenantId", "createdAt");
+CREATE INDEX "AskAIQuery_userId_createdAt_idx" ON "AskAIQuery"("userId", "createdAt");
+
+ALTER TABLE "AskAIQuery" ADD CONSTRAINT "AskAIQuery_tenantId_fkey" FOREIGN KEY ("tenantId") REFERENCES "Tenant"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
+ALTER TABLE "AskAIQuery" ADD CONSTRAINT "AskAIQuery_userId_fkey" FOREIGN KEY ("userId") REFERENCES "TowerUser"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
@@ -95,6 +95,7 @@ model Tenant {
  sevaOpportunities  SevaOpportunity[]
  gamificationEvents GamificationEvent[]
  circles            Circle[]
+  askAIQueries       AskAIQuery[]
 }

 enum AdminRole {
@@ -422,6 +423,7 @@ model TowerUser {
  sevaEntries        SevaEntry[]
  gamificationEvents GamificationEvent[]
  circleMemberships  CircleMembership[]
+  askAIQueries       AskAIQuery[]

  @@unique([tenantId, phoneHash])
  @@index([phoneHash])
@@ -730,3 +732,22 @@ model CircleMembership {
  @@unique([circleId, userId])
  @@index([userId])
 }
+
+// ============================================================================
+// Ask AI (RAG over the message archive)
+// ============================================================================
+
+model AskAIQuery {
+  id        String   @id @default(cuid())
+  tenantId  String
+  tenant    Tenant   @relation(fields: [tenantId], references: [id])
+  userId    String
+  user      TowerUser @relation(fields: [userId], references: [id])
+  question  String
+  answer    String
+  citations Json     @default("[]")
+  createdAt DateTime @default(now())
+
+  @@index([tenantId, createdAt])
+  @@index([userId, createdAt])
+}
@@ -0,0 +1,37 @@
+const OPENROUTER_BASE = 'https://openrouter.ai/api/v1';
+const DEFAULT_MODEL = 'google/gemini-3.5-flash';
+
+/**
+ * Minimal OpenRouter chat completion. Mirrors the worker's llm-client so digest
+ * and Ask AI share one calling convention. Returns the assistant message text.
+ */
+export async function callLLM(
+  prompt: string,
+  apiKey: string,
+  model = DEFAULT_MODEL,
+): Promise<string> {
+  const res = await fetch(`${OPENROUTER_BASE}/chat/completions`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      Authorization: `Bearer ${apiKey}`,
+      'HTTP-Referer': 'https://tower.insignia.app',
+      'X-Title': 'TOWER Community Platform',
+    },
+    body: JSON.stringify({
+      model,
+      messages: [{ role: 'user', content: prompt }],
+      temperature: 0.1,
+    }),
+  });
+
+  if (!res.ok) {
+    const body = await res.text().catch(() => '');
+    throw new Error(`OpenRouter error ${res.status}: ${body}`);
+  }
+
+  const data = (await res.json()) as { choices: Array<{ message: { content: string } }> };
+  const content = data.choices?.[0]?.message?.content;
+  if (!content) throw new Error('OpenRouter returned empty content');
+  return content;
+}
@@ -0,0 +1,10 @@
+import { Module } from '@nestjs/common';
+import { AskAIService } from './ask-ai.service';
+import { SearchModule } from '../search/search.module';
+
+@Module({
+  imports: [SearchModule],
+  providers: [AskAIService],
+  exports: [AskAIService],
+})
+export class AskAIModule {}
@@ -0,0 +1,107 @@
+import { Injectable, Logger } from '@nestjs/common';
+import { ConfigService } from '@nestjs/config';
+import { PrismaService } from '../../prisma/prisma.service';
+import { SearchService } from '../search/search.service';
+import { callLLM } from '../../common/llm-client';
+
+export interface Citation {
+  messageId: string;
+  snippet: string;
+  senderName: string;
+  sourceGroupName: string;
+  approvedAt: number;
+}
+
+@Injectable()
+export class AskAIService {
+  private readonly logger = new Logger(AskAIService.name);
+
+  constructor(
+    private readonly prisma: PrismaService,
+    private readonly search: SearchService,
+    private readonly config: ConfigService,
+  ) {}
+
+  async ask(userId: string, tenantId: string, question: string) {
+    // 1. Retrieve: pull the most relevant approved messages from the tenant's index.
+    const { hits } = await this.search.search(tenantId, question, undefined, undefined, 1, 8);
+
+    const citations: Citation[] = hits.map((h) => ({
+      messageId: h.id,
+      snippet: h.content.slice(0, 240),
+      senderName: h.senderName || 'Unknown',
+      sourceGroupName: h.sourceGroupName,
+      approvedAt: h.approvedAt,
+    }));
+
+    // 2. Generate: ground the LLM in the retrieved snippets. Degrade gracefully
+    //    if no AI key is configured or no context was found.
+    const apiKey = this.config.get<string>('OPENROUTER_API_KEY');
+    let answer: string;
+
+    if (citations.length === 0) {
+      answer = "I couldn't find anything in your community's messages about that yet. Try rephrasing, or ask an admin.";
+    } else if (!apiKey) {
+      answer = this.fallbackAnswer(citations);
+    } else {
+      try {
+        answer = await callLLM(this.buildPrompt(question, citations), apiKey);
+      } catch (err) {
+        this.logger.warn({ err }, 'Ask AI LLM call failed — returning snippet fallback');
+        answer = this.fallbackAnswer(citations);
+      }
+    }
+
+    // 3. Log the query for history + future tuning.
+    const record = await this.prisma.askAIQuery.create({
+      data: { tenantId, userId, question, answer, citations: citations as unknown as object },
+    });
+
+    return {
+      id: record.id,
+      question,
+      answer,
+      citations,
+      createdAt: record.createdAt.toISOString(),
+    };
+  }
+
+  async history(userId: string, tenantId: string) {
+    const queries = await this.prisma.askAIQuery.findMany({
+      where: { userId, tenantId },
+      orderBy: { createdAt: 'desc' },
+      take: 20,
+    });
+    return queries.map((q) => ({
+      id: q.id,
+      question: q.question,
+      answer: q.answer,
+      citations: q.citations as unknown as Citation[],
+      createdAt: q.createdAt.toISOString(),
+    }));
+  }
+
+  private buildPrompt(question: string, citations: Citation[]): string {
+    const context = citations
+      .map((c, i) => `[${i + 1}] (${c.sourceGroupName}, by ${c.senderName}): ${c.snippet}`)
+      .join('\n');
+    return [
+      'You are a helpful assistant for a community group. Answer the member\'s question',
+      'using ONLY the message excerpts below. If the excerpts do not contain the answer,',
+      'say you don\'t have that information. Cite sources inline as [1], [2], etc.',
+      'Keep the answer concise and friendly.',
+      '',
+      'Message excerpts:',
+      context,
+      '',
+      `Question: ${question}`,
+      '',
+      'Answer:',
+    ].join('\n');
+  }
+
+  private fallbackAnswer(citations: Citation[]): string {
+    const top = citations.slice(0, 3).map((c, i) => `[${i + 1}] ${c.snippet}`).join('\n\n');
+    return `Here are the most relevant messages I found:\n\n${top}`;
+  }
+}
@@ -2,6 +2,7 @@ import { Body, Controller, Delete, Get, Param, Patch, Post, Query } from '@nestj
 import { MyService } from './my.service';
 import { SevaService } from '../seva/seva.service';
 import { CirclesService } from '../circles/circles.service';
+import { AskAIService } from '../ask-ai/ask-ai.service';
 import { MemberAuth } from '../auth/member-auth.decorator';
 import { CurrentMember } from '../auth/current-member.decorator';
 import type { MemberJwtPayload } from '@tower/types';
@@ -28,8 +29,19 @@ export class MyController {
    private readonly service: MyService,
    private readonly seva: SevaService,
    private readonly circles: CirclesService,
+    private readonly askAI: AskAIService,
  ) {}

+  @Get('ask')
+  askHistory(@CurrentMember() member: MemberJwtPayload) {
+    return this.askAI.history(member.sub, member.tenantId);
+  }
+
+  @Post('ask')
+  ask(@CurrentMember() member: MemberJwtPayload, @Body() body: { question: string }) {
+    return this.askAI.ask(member.sub, member.tenantId, body.question);
+  }
+
  @Get('seva')
  sevaList(@CurrentMember() member: MemberJwtPayload) {
    return this.seva.listForMember(member.sub, member.tenantId);
@@ -5,9 +5,10 @@ import { AuthModule } from '../auth/auth.module';
 import { SevaModule } from '../seva/seva.module';
 import { GamificationModule } from '../gamification/gamification.module';
 import { CirclesModule } from '../circles/circles.module';
+import { AskAIModule } from '../ask-ai/ask-ai.module';

@Module({
-  imports: [AuthModule, SevaModule, GamificationModule, CirclesModule],
+  imports: [AuthModule, SevaModule, GamificationModule, CirclesModule, AskAIModule],
  controllers: [MyController],
  providers: [MyService],
 })
@@ -5,5 +5,6 @@ import { SearchService } from './search.service';
@Module({
  controllers: [SearchController],
  providers: [SearchService],
+  exports: [SearchService],
 })
 export class SearchModule {}