feat: team module, multi-stage Dockerfile, doctor utils, AI config overhaul

- Team module: POST /api/team/members (in-place employee creation with
  temp password + Redis cache), PUT /api/team/members/:id, GET temp
  password endpoint. Uses signUpInWorkspace — no email invites.
- Dockerfile: rewritten as multi-stage build (builder + runtime) so
  native modules compile for target arch. Fixes darwin→linux crash.
- .dockerignore: exclude dist, node_modules, .env, .git, data/
- package-lock.json: regenerated against public npmjs.org (was
  pointing at localhost:4873 Verdaccio — broke docker builds)
- Doctor utils: shared DOCTOR_VISIT_SLOTS_FRAGMENT + normalizeDoctors
  helper for visit-slot-aware queries across 6 consumers
- AI config: full admin CRUD (GET/PUT/POST reset), workspace-scoped
  setup-state with workspace ID isolation, AI prompt defaults overhaul
- Agent config: camelCase field fix for SDK-synced workspaces
- Session service: workspace-scoped Redis key prefixing for setup state
- Recordings/supervisor/widget services: updated to use doctor-utils
  shared fragments instead of inline visitingHours queries

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/call-assist/call-assist.service.ts

@@ -5,6 +5,7 @@ import { PlatformGraphqlService } from '../platform/platform-graphql.service';
 import { createAiModel } from '../ai/ai-provider';
 import type { LanguageModel } from 'ai';
 import { AiConfigService } from '../config/ai-config.service';
+import { DOCTOR_VISIT_SLOTS_FRAGMENT, normalizeDoctors } from '../shared/doctor-utils';
 
 @Injectable()
 export class CallAssistService {
@@ -81,16 +82,24 @@ export class CallAssistService {
 
             const docResult = await this.platform.queryWithAuth<any>(
                 `{ doctors(first: 20) { edges { node {
-                    fullName { firstName lastName } department specialty clinic { clinicName }
+                    id fullName { firstName lastName } department specialty
+                    ${DOCTOR_VISIT_SLOTS_FRAGMENT}
                 } } } }`,
                 undefined, authHeader,
             );
-            const docs = docResult.doctors.edges.map((e: any) => e.node);
+            const docs = normalizeDoctors(docResult.doctors.edges.map((e: any) => e.node));
             if (docs.length > 0) {
                 parts.push('\nAVAILABLE DOCTORS:');
                 for (const d of docs) {
                     const name = d.fullName ? `Dr. ${d.fullName.firstName} ${d.fullName.lastName}`.trim() : 'Unknown';
-                    parts.push(`- ${name} — ${d.department ?? '?'} — ${d.clinic?.clinicName ?? '?'}`);
+                    // Show all clinics the doctor visits, joined with
+                    // " / " — call assist context is read by the AI
+                    // whisperer so multi-clinic doctors don't get
+                    // truncated to their first location.
+                    const clinicLabel = d.clinics.length > 0
+                        ? d.clinics.map((c) => c.clinicName).join(' / ')
+                        : '?';
+                    parts.push(`- ${name} — ${d.department ?? '?'} — ${clinicLabel}`);
                 }
             }
 
@@ -107,18 +116,10 @@ export class CallAssistService {
         try {
             const { text } = await generateText({
                 model: this.aiModel,
-                system: `You are a real-time call assistant for Global Hospital Bangalore.
-You listen to the customer's words and provide brief, actionable suggestions for the CC agent.
-
-${context}
-
-RULES:
-- Keep suggestions under 2 sentences
-- Focus on actionable next steps the agent should take NOW
-- If customer mentions a doctor or department, suggest available slots
-- If customer wants to cancel or reschedule, note relevant appointment details
-- If customer sounds upset, suggest empathetic response
-- Do NOT repeat what the agent already knows`,
+                system: this.aiConfig.renderPrompt('callAssist', {
+                    hospitalName: process.env.HOSPITAL_NAME ?? 'the hospital',
+                    context,
+                }),
                 prompt: `Conversation transcript so far:\n${transcript}\n\nProvide a brief suggestion for the agent based on what was just said.`,
                 maxOutputTokens: 150,
             });