mirror of
https://dev.azure.com/globalhealthx/EMR/_git/helix-engage
synced 2026-04-11 18:28:15 +00:00
feat: add AI flow test script — validates auth, lead lookup, patient, doctor, appointments, calls, activities as Rekha
This commit is contained in:
252
scripts/test-ai-flow.ts
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252
scripts/test-ai-flow.ts
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/**
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* Test script — simulates a CC agent (Rekha) receiving a call and using the AI assistant.
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* Tests: auth → lead lookup → patient data → doctor data → appointments → calls → activities → AI response
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*
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* Run: cd helix-engage && npx tsx scripts/test-ai-flow.ts
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*/
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const GQL = 'http://localhost:4000/graphql';
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const SUB = 'fortytwo-dev';
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const ORIGIN = 'http://fortytwo-dev.localhost:4010';
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// Rekha's credentials
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const AGENT_EMAIL = 'rekha.cc@globalhospital.com';
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const AGENT_PASSWORD = 'Global@123';
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// Simulated incoming call — Priya's phone
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const CALLER_PHONE = '9949879837';
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let token = '';
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let failures = 0;
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async function gql(query: string, variables?: any) {
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const h: Record<string, string> = { 'Content-Type': 'application/json', 'X-Workspace-Subdomain': SUB };
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if (token) h['Authorization'] = `Bearer ${token}`;
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const r = await fetch(GQL, { method: 'POST', headers: h, body: JSON.stringify({ query, variables }) });
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const d: any = await r.json();
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if (d.errors) throw new Error(d.errors[0].message);
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return d.data;
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}
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function assert(condition: boolean, label: string, detail?: string) {
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if (condition) {
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console.log(` ✅ ${label}${detail ? ` — ${detail}` : ''}`);
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} else {
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console.log(` ❌ ${label}${detail ? ` — ${detail}` : ''}`);
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failures++;
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}
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}
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async function main() {
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console.log('🧪 Helix Engage — AI Flow Test\n');
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console.log(`Agent: ${AGENT_EMAIL}`);
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console.log(`Incoming call from: ${CALLER_PHONE}\n`);
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// ═══════════════════════════════════════
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// STEP 1: Login as Rekha
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// ═══════════════════════════════════════
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console.log('1️⃣ LOGIN');
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const d1 = await gql(`mutation { getLoginTokenFromCredentials(email: "${AGENT_EMAIL}", password: "${AGENT_PASSWORD}", origin: "${ORIGIN}") { loginToken { token } } }`);
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const lt = d1.getLoginTokenFromCredentials.loginToken.token;
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const d2 = await gql(`mutation { getAuthTokensFromLoginToken(loginToken: "${lt}", origin: "${ORIGIN}") { tokens { accessOrWorkspaceAgnosticToken { token } } } }`);
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token = d2.getAuthTokensFromLoginToken.tokens.accessOrWorkspaceAgnosticToken.token;
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assert(!!token, 'Authenticated as Rekha');
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// Verify role
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const profile = await gql('{ currentUser { email workspaceMember { name { firstName lastName } roles { label } } } }');
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const roles = profile.currentUser.workspaceMember.roles.map((r: any) => r.label);
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const name = `${profile.currentUser.workspaceMember.name.firstName} ${profile.currentUser.workspaceMember.name.lastName}`;
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assert(roles.includes('HelixEngage User'), `Role: ${roles.join(', ')}`);
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assert(name.includes('Rekha'), `Name: ${name}`);
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// ═══════════════════════════════════════
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// STEP 2: Look up caller by phone number
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// ═══════════════════════════════════════
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console.log('\n2️⃣ LEAD LOOKUP (by phone: 9949879837)');
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const leadsData = await gql(`{ leads(first: 50) { edges { node {
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id name contactName { firstName lastName }
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contactPhone { primaryPhoneNumber }
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contactEmail { primaryEmail }
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source status interestedService assignedAgent
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leadScore contactAttempts firstContacted lastContacted
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aiSummary aiSuggestedAction
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patientId campaignId
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} } } }`);
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const leads = leadsData.leads.edges.map((e: any) => e.node);
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assert(leads.length > 0, `Found ${leads.length} leads total`);
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// Match by phone
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const matchedLead = leads.find((l: any) => {
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const phone = l.contactPhone?.primaryPhoneNumber ?? '';
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return phone.includes(CALLER_PHONE) || CALLER_PHONE.includes(phone);
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});
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assert(!!matchedLead, 'Matched lead by phone', matchedLead?.name);
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assert(matchedLead?.contactName?.firstName === 'Priya', `First name: ${matchedLead?.contactName?.firstName}`);
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assert(matchedLead?.status === 'APPOINTMENT_SET', `Status: ${matchedLead?.status}`);
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assert(!!matchedLead?.aiSummary, 'Has AI summary', matchedLead?.aiSummary?.substring(0, 80) + '...');
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assert(!!matchedLead?.aiSuggestedAction, 'Has AI suggested action', matchedLead?.aiSuggestedAction);
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assert(!!matchedLead?.patientId, 'Linked to patient', matchedLead?.patientId);
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assert(!!matchedLead?.campaignId, 'Linked to campaign', matchedLead?.campaignId);
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const leadId = matchedLead?.id;
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const patientId = matchedLead?.patientId;
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// ═══════════════════════════════════════
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// STEP 3: Fetch patient record
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// ═══════════════════════════════════════
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console.log('\n3️⃣ PATIENT RECORD');
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if (patientId) {
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const patientData = await gql(`{ patients(first: 1, filter: { id: { eq: "${patientId}" } }) { edges { node {
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id name fullName { firstName lastName }
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phones { primaryPhoneNumber }
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emails { primaryEmail }
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dateOfBirth gender patientType
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} } } }`);
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const patient = patientData.patients.edges[0]?.node;
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assert(!!patient, 'Patient record found', patient?.name);
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assert(patient?.gender === 'FEMALE', `Gender: ${patient?.gender}`);
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assert(patient?.patientType === 'RETURNING', `Type: ${patient?.patientType}`);
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assert(!!patient?.dateOfBirth, `DOB: ${patient?.dateOfBirth}`);
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} else {
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assert(false, 'No patient ID to look up');
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}
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// ═══════════════════════════════════════
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// STEP 4: Fetch appointments for this patient
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// ═══════════════════════════════════════
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console.log('\n4️⃣ APPOINTMENTS');
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if (patientId) {
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const apptData = await gql(`{ appointments(first: 20, filter: { patientId: { eq: "${patientId}" } }) { edges { node {
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id name scheduledAt durationMin appointmentType status
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doctorName department reasonForVisit
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doctorId
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} } } }`);
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const appts = apptData.appointments.edges.map((e: any) => e.node);
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assert(appts.length >= 2, `Found ${appts.length} appointments`);
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const completed = appts.filter((a: any) => a.status === 'COMPLETED');
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const scheduled = appts.filter((a: any) => a.status === 'SCHEDULED');
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assert(completed.length >= 1, `${completed.length} completed`);
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assert(scheduled.length >= 1, `${scheduled.length} upcoming`);
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for (const a of appts) {
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const status = a.status === 'COMPLETED' ? '✓' : '📅';
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assert(!!a.doctorId, `${status} ${a.name} — doctor linked`, a.doctorId);
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}
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}
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// ═══════════════════════════════════════
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// STEP 5: Fetch doctor record (Dr. Patel)
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// ═══════════════════════════════════════
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console.log('\n5️⃣ DOCTOR LOOKUP');
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const doctorsData = await gql(`{ doctors(first: 10) { edges { node {
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id name fullName { firstName lastName }
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department specialty qualifications yearsOfExperience
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branchClinic visitingHours
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consultationFeeNew { amountMicros currencyCode }
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consultationFeeFollowUp { amountMicros currencyCode }
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active registrationNumber
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portalUserId
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} } } }`);
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const doctors = doctorsData.doctors.edges.map((e: any) => e.node);
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assert(doctors.length === 5, `Found ${doctors.length} doctors`);
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const drPatel = doctors.find((d: any) => d.name?.includes('Patel'));
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assert(!!drPatel, 'Found Dr. Patel', drPatel?.specialty);
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assert(!!drPatel?.visitingHours, `Hours: ${drPatel?.visitingHours}`);
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assert(!!drPatel?.consultationFeeNew, `Fee (new): ₹${(drPatel?.consultationFeeNew?.amountMicros ?? 0) / 1_000_000}`);
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assert(!!drPatel?.portalUserId, 'Linked to workspace member', drPatel?.portalUserId);
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const drSharma = doctors.find((d: any) => d.name?.includes('Sharma'));
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assert(!!drSharma, 'Found Dr. Sharma', drSharma?.specialty);
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// ═══════════════════════════════════════
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// STEP 6: Fetch call history for this lead
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// ═══════════════════════════════════════
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console.log('\n6️⃣ CALL HISTORY');
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if (leadId) {
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const callsData = await gql(`{ calls(first: 20, filter: { leadId: { eq: "${leadId}" } }) { edges { node {
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id name direction callStatus agentName
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startedAt durationSec disposition
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} } } }`);
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const calls = callsData.calls.edges.map((e: any) => e.node);
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assert(calls.length >= 3, `Found ${calls.length} calls for Priya`);
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for (const c of calls) {
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console.log(` ${c.direction} | ${c.disposition ?? c.callStatus} | ${c.name}`);
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}
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}
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// ═══════════════════════════════════════
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// STEP 7: Fetch lead activities
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// ═══════════════════════════════════════
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console.log('\n7️⃣ LEAD ACTIVITIES');
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if (leadId) {
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const activitiesData = await gql(`{ leadActivities(first: 30, filter: { leadId: { eq: "${leadId}" } }, orderBy: [{ occurredAt: DescNullsLast }]) { edges { node {
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id activityType summary occurredAt performedBy channel
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} } } }`);
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const activities = activitiesData.leadActivities.edges.map((e: any) => e.node);
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assert(activities.length >= 8, `Found ${activities.length} activities for Priya`);
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for (const a of activities.slice(0, 5)) {
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console.log(` ${a.activityType} | ${a.summary}`);
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}
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if (activities.length > 5) console.log(` ... and ${activities.length - 5} more`);
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}
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// ═══════════════════════════════════════
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// STEP 8: Simulate AI context assembly
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// ═══════════════════════════════════════
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console.log('\n8️⃣ AI CONTEXT ASSEMBLY');
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console.log(' What the AI assistant would receive:\n');
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console.log(` Caller: ${matchedLead?.contactName?.firstName} ${matchedLead?.contactName?.lastName}`);
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console.log(` Phone: ${CALLER_PHONE}`);
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console.log(` Status: ${matchedLead?.status}`);
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console.log(` Service: ${matchedLead?.interestedService}`);
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console.log(` AI Summary: ${matchedLead?.aiSummary}`);
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console.log(` Suggested Action: ${matchedLead?.aiSuggestedAction}`);
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console.log(` Doctor: Dr. Patel — ${drPatel?.specialty}`);
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console.log(` Next Appointment: ${drPatel?.visitingHours} at ${drPatel?.branchClinic}`);
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console.log(` Fee: ₹${(drPatel?.consultationFeeNew?.amountMicros ?? 0) / 1_000_000} (new) / ₹${(drPatel?.consultationFeeFollowUp?.amountMicros ?? 0) / 1_000_000} (follow-up)`);
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// ═══════════════════════════════════════
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// STEP 9: Test AI chat endpoint (if sidecar is running)
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// ═══════════════════════════════════════
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console.log('\n9️⃣ AI CHAT (via sidecar — skipped if not running)');
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try {
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const aiRes = await fetch('http://localhost:4100/api/ai/chat', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${token}` },
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body: JSON.stringify({
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message: 'Tell me about the patient on the line. When is their next appointment?',
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context: { callerPhone: CALLER_PHONE, leadId, leadName: `${matchedLead?.contactName?.firstName} ${matchedLead?.contactName?.lastName}` },
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}),
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signal: AbortSignal.timeout(15000),
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});
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if (aiRes.ok) {
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const aiData = await aiRes.json();
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assert(!!aiData.reply, 'AI responded');
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console.log(`\n 🤖 AI says:\n "${aiData.reply}"\n`);
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assert(aiData.sources?.length > 0, `Sources: ${aiData.sources?.join(', ')}`);
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} else {
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console.log(` ⏭️ Sidecar returned ${aiRes.status} — skipping AI test`);
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}
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} catch {
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console.log(' ⏭️ Sidecar not running — AI chat test skipped');
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}
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// ═══════════════════════════════════════
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// RESULTS
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// ═══════════════════════════════════════
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console.log('\n' + '═'.repeat(50));
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if (failures === 0) {
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console.log('🎉 ALL TESTS PASSED');
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} else {
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console.log(`⚠️ ${failures} test(s) failed`);
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}
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console.log('═'.repeat(50));
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}
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main().catch(e => { console.error('💥', e.message); process.exit(1); });
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