7 Commits

Author SHA1 Message Date
77b3e917db fix: fetch Lead first to resolve patientId before appointments query
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The build() method previously fetched Lead and Appointments in parallel.
When the input patientId was empty (outbound dial, first-time linkage),
the appointments query was skipped even though the Lead record in the DB
had a valid patientId. Now fetches Lead first, reads its patientId, then
fetches appointments/calls/activities in parallel with the correct ID.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 16:23:48 +05:30
68ba3e135d fix: remove example from schema description — AI was copying it verbatim
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 11:58:59 +05:30
e1babb30e5 fix: AI message formatting — plain text sentences, no markdown/data dump
Schema description reinforced: brief 2-3 sentence natural language only.
Prompt template updated with example output and explicit ban on markdown
headers, bold, bullet lists, and raw field labels in the message field.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 11:46:44 +05:30
ae360a183d feat: enforce structured JSON output via AI SDK Output.object
- ai-response-schema.ts: Zod schema for { message, suggestions[] }
- ai-chat.controller.ts: Output.object({ schema }) on streamText
  forces the LLM to return valid JSON matching the schema instead
  of free-form prose. Supervisor mode excluded (uses tools, not schema).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 11:40:25 +05:30
e03b1e6235 feat: structured JSON output + suggestion rules in AI system prompt
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 11:11:13 +05:30
2d18110786 feat: suggestion rules engine + caller context evaluation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 11:09:47 +05:30
a576552f8a feat: pre-fetched caller context replaces tool-based patient lookups
- CallerContextService: fetches lead profile, appointments, call history,
  activities in parallel. Caches in Redis (5 min TTL). Renders as
  human-readable KB section — no UUIDs exposed to the LLM.
- Caller resolution controller: prewarms context cache on resolve
  (fire-and-forget) so the AI stream has a cache hit.
- AI chat stream: injects caller context into system prompt KB instead
  of raw Lead ID. LLM answers patient questions from context, no tool
  calls needed for current caller data.
- Eliminates UUID hallucination: LLM never sees leadId or patientId,
  can't pass wrong ID to wrong tool parameter.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 09:56:18 +05:30
7 changed files with 454 additions and 12 deletions

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@@ -1,11 +1,13 @@
import { Controller, Post, Body, Headers, Req, Res, HttpException, Logger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import type { Request, Response } from 'express';
import { generateText, streamText, tool, stepCountIs } from 'ai';
import { generateText, streamText, Output, tool, stepCountIs } from 'ai';
import type { LanguageModel } from 'ai';
import { aiResponseSchema } from './ai-response-schema';
import { z } from 'zod';
import { PlatformGraphqlService } from '../platform/platform-graphql.service';
import { CallerResolutionService } from '../caller/caller-resolution.service';
import { CallerContextService } from '../caller/caller-context.service';
import { createAiModel, isAiConfigured } from './ai-provider';
import { AiConfigService } from '../config/ai-config.service';
import { DOCTOR_VISIT_SLOTS_FRAGMENT, normalizeDoctors } from '../shared/doctor-utils';
@@ -28,6 +30,7 @@ export class AiChatController {
private platform: PlatformGraphqlService,
private aiConfig: AiConfigService,
private caller: CallerResolutionService,
private callerContext: CallerContextService,
) {
const cfg = aiConfig.getConfig();
this.aiModel = createAiModel({
@@ -96,15 +99,19 @@ export class AiChatController {
const kb = await this.buildKnowledgeBase(auth);
systemPrompt = this.buildSystemPrompt(kb);
// Inject caller context so the AI knows who is selected
if (ctx) {
const parts: string[] = [];
if (ctx.leadName) parts.push(`Currently viewing/talking to: ${ctx.leadName}`);
if (ctx.callerPhone) parts.push(`Phone: ${ctx.callerPhone}`);
if (ctx.leadId) parts.push(`Lead ID: ${ctx.leadId}`);
if (parts.length) {
systemPrompt += `\n\nCURRENT CONTEXT:\n${parts.join('\n')}\nUse this context to answer questions about "this patient" or "this caller" without asking for their name.`;
// Inject pre-fetched caller context (appointments, call history,
// activities, AI summary) so the LLM can answer from the KB
// without tool calls. No UUIDs exposed — only human-readable data.
if (ctx?.leadId) {
const callerCtx = await this.callerContext.getOrBuild(ctx.leadId, '', auth);
if (callerCtx) {
systemPrompt += `\n\n${this.callerContext.renderForPrompt(callerCtx)}`;
if (callerCtx.suggestionTriggers?.length) {
systemPrompt += this.callerContext.renderSuggestionsForPrompt(callerCtx.suggestionTriggers);
}
}
} else if (ctx?.callerPhone) {
systemPrompt += `\n\nCURRENT CONTEXT:\nCaller phone: ${ctx.callerPhone}\nNew caller — no prior records.`;
}
}
@@ -623,6 +630,7 @@ export class AiChatController {
messages,
stopWhen: stepCountIs(5),
tools: isSupervisor ? supervisorTools : agentTools,
...(isSupervisor ? {} : { output: Output.object({ schema: aiResponseSchema }) }),
});
const response = result.toTextStreamResponse();

View File

@@ -0,0 +1,14 @@
import { z } from 'zod';
export const aiResponseSchema = z.object({
message: z.string().describe('Brief 2-3 sentence summary in plain conversational sentences. NEVER include suggestions, bullet lists, markdown, headers, or field labels here — those belong in the suggestions array only.'),
suggestions: z.array(z.object({
id: z.string().describe('Unique suggestion ID like s1, s2'),
type: z.enum(['upsell', 'crosssell', 'retention', 'operational']),
title: z.string().describe('Short title for the suggestion pill'),
script: z.string().describe('2-3 sentence script the agent can read aloud to the caller'),
priority: z.enum(['high', 'medium', 'low']),
})).describe('0-4 contextual suggestions based on business rules. Include on first response, update on subsequent.'),
});
export type AiResponse = z.infer<typeof aiResponseSchema>;

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@@ -0,0 +1,242 @@
import { Injectable, Logger } from '@nestjs/common';
import { PlatformGraphqlService } from '../platform/platform-graphql.service';
import { SessionService } from '../auth/session.service';
import { evaluateSuggestionRules, type SuggestionTrigger } from '../rules-engine/suggestion-rules';
export type CallerContext = {
leadId: string;
patientId: string;
name: string;
phone: string;
isNew: boolean;
// Lead profile
leadSource: string | null;
leadStatus: string | null;
interestedService: string | null;
aiSummary: string | null;
contactAttempts: number;
lastContacted: string | null;
utmCampaign: string | null;
// Appointments
appointments: Array<{
scheduledAt: string;
status: string;
doctorName: string;
department: string;
reasonForVisit: string | null;
}>;
// Recent call history
calls: Array<{
startedAt: string;
direction: string;
duration: number | null;
disposition: string | null;
agentName: string | null;
}>;
// Lead activities
activities: Array<{
activityType: string;
summary: string | null;
occurredAt: string;
outcome: string | null;
}>;
// Rule-driven suggestion triggers
suggestionTriggers: SuggestionTrigger[];
};
const CACHE_KEY_PREFIX = 'caller:context:';
const CACHE_TTL = 300; // 5 minutes — covers the call duration
@Injectable()
export class CallerContextService {
private readonly logger = new Logger(CallerContextService.name);
constructor(
private readonly platform: PlatformGraphqlService,
private readonly session: SessionService,
) {}
async getOrBuild(leadId: string, patientId: string, auth: string): Promise<CallerContext | null> {
if (!leadId) return null;
// Check cache first
const cacheKey = `${CACHE_KEY_PREFIX}${leadId}`;
try {
const cached = await this.session.getCache(cacheKey);
if (cached) {
this.logger.log(`[CALLER-CTX] Cache hit for ${leadId}`);
return JSON.parse(cached);
}
} catch {}
// Build fresh
this.logger.log(`[CALLER-CTX] Building context for lead=${leadId} patient=${patientId}`);
const ctx = await this.build(leadId, patientId, auth);
if (ctx) {
this.session.setCache(cacheKey, JSON.stringify(ctx), CACHE_TTL).catch(() => {});
}
return ctx;
}
// Fire-and-forget pre-warm — called from caller resolution
// so the cache is hot when the AI stream fires seconds later.
prewarm(leadId: string, patientId: string, auth: string): void {
if (!leadId) return;
this.getOrBuild(leadId, patientId, auth).catch(err => {
this.logger.warn(`[CALLER-CTX] Prewarm failed: ${err.message}`);
});
}
private async build(leadId: string, patientId: string, auth: string): Promise<CallerContext | null> {
try {
// Step 1: Fetch lead first to get the authoritative patientId
const leadData = await this.platform.queryWithAuth<any>(
`{ lead(filter: { id: { eq: "${leadId}" } }) {
id contactName { firstName lastName }
contactPhone { primaryPhoneNumber }
source status interestedService
aiSummary contactAttempts lastContacted
utmCampaign patientId
} }`,
undefined, auth,
);
const lead = leadData?.lead;
if (!lead) return null;
// Use Lead's patientId as authoritative source — the input
// param may be empty if caller resolution just linked them.
const resolvedPatientId = patientId || lead.patientId || '';
this.logger.log(`[CALLER-CTX] Resolved patientId=${resolvedPatientId} (input=${patientId}, lead=${lead.patientId ?? '∅'})`);
const firstName = lead.contactName?.firstName ?? '';
const lastName = lead.contactName?.lastName ?? '';
// Step 2: Fetch appointments, calls, activities in parallel
// using the resolved patientId from the Lead record.
const [appointmentsData, callsData, activitiesData] = await Promise.all([
resolvedPatientId ? this.platform.queryWithAuth<any>(
`{ appointments(first: 10, filter: { patientId: { eq: "${resolvedPatientId}" } }, orderBy: [{ scheduledAt: DescNullsLast }]) { edges { node {
scheduledAt status doctorName department reasonForVisit
} } } }`,
undefined, auth,
) : Promise.resolve(null),
this.platform.queryWithAuth<any>(
`{ calls(first: 10, filter: { leadId: { eq: "${leadId}" } }, orderBy: [{ startedAt: DescNullsLast }]) { edges { node {
startedAt direction durationSec disposition agentName
} } } }`,
undefined, auth,
),
this.platform.queryWithAuth<any>(
`{ leadActivities(first: 10, filter: { leadId: { eq: "${leadId}" } }, orderBy: [{ occurredAt: DescNullsLast }]) { edges { node {
activityType summary occurredAt outcome
} } } }`,
undefined, auth,
),
]);
const appointments = (appointmentsData?.appointments?.edges ?? []).map((e: any) => e.node);
const calls = (callsData?.calls?.edges ?? []).map((e: any) => ({
startedAt: e.node.startedAt,
direction: e.node.direction,
duration: e.node.durationSec,
disposition: e.node.disposition,
agentName: e.node.agentName,
}));
const suggestionTriggers = evaluateSuggestionRules({
isNew: false,
interestedService: lead.interestedService ?? null,
leadStatus: lead.status ?? null,
contactAttempts: lead.contactAttempts ?? 0,
appointments,
calls: calls.map((c: any) => ({ direction: c.direction, disposition: c.disposition, startedAt: c.startedAt })),
utmCampaign: lead.utmCampaign ?? null,
leadSource: lead.source ?? null,
});
return {
leadId,
patientId: resolvedPatientId,
name: `${firstName} ${lastName}`.trim() || 'Unknown',
phone: lead.contactPhone?.primaryPhoneNumber ?? '',
isNew: false,
leadSource: lead.source ?? null,
leadStatus: lead.status ?? null,
interestedService: lead.interestedService ?? null,
aiSummary: lead.aiSummary ?? null,
contactAttempts: lead.contactAttempts ?? 0,
lastContacted: lead.lastContacted ?? null,
utmCampaign: lead.utmCampaign ?? null,
appointments,
calls,
activities: (activitiesData?.leadActivities?.edges ?? []).map((e: any) => e.node),
suggestionTriggers,
};
} catch (err: any) {
this.logger.warn(`[CALLER-CTX] Build failed: ${err.message}`);
return null;
}
}
renderSuggestionsForPrompt(triggers: SuggestionTrigger[]): string {
if (triggers.length === 0) return '';
const lines = [
'',
'SUGGESTION RULES (from business configuration):',
'Based on this caller\'s profile, the following suggestions should be offered.',
'Generate a natural, conversational script for each that the agent can read aloud.',
'Return them in the `suggestions` array of your JSON response.',
'',
];
triggers.forEach((t, i) => {
lines.push(`${i + 1}. [${t.type}/${t.priority}] ${t.title}${t.reason}`);
});
return lines.join('\n');
}
renderForPrompt(ctx: CallerContext): string {
const lines: string[] = [];
lines.push(`## CURRENT CALLER: ${ctx.name}`);
lines.push(`Phone: ${ctx.phone}`);
if (ctx.leadSource) lines.push(`Source: ${ctx.leadSource}`);
if (ctx.leadStatus) lines.push(`Status: ${ctx.leadStatus}`);
if (ctx.interestedService) lines.push(`Interested in: ${ctx.interestedService}`);
if (ctx.utmCampaign) lines.push(`Campaign: ${ctx.utmCampaign}`);
if (ctx.contactAttempts > 0) lines.push(`Contact attempts: ${ctx.contactAttempts}`);
if (ctx.lastContacted) lines.push(`Last contacted: ${ctx.lastContacted}`);
if (ctx.aiSummary) {
lines.push(`\nAI Summary: ${ctx.aiSummary}`);
}
if (ctx.appointments.length > 0) {
lines.push(`\n### Appointments (${ctx.appointments.length})`);
for (const a of ctx.appointments) {
const date = a.scheduledAt ? new Date(a.scheduledAt).toLocaleDateString('en-IN', { day: 'numeric', month: 'short', year: 'numeric' }) : '?';
lines.push(`- ${date} | ${a.doctorName ?? '?'} (${a.department ?? '?'}) | ${a.status}${a.reasonForVisit ? ` | ${a.reasonForVisit}` : ''}`);
}
} else {
lines.push('\nNo appointments on record.');
}
if (ctx.calls.length > 0) {
lines.push(`\n### Call History (last ${ctx.calls.length})`);
for (const c of ctx.calls) {
const date = c.startedAt ? new Date(c.startedAt).toLocaleDateString('en-IN', { day: 'numeric', month: 'short', year: 'numeric' }) : '?';
const dur = c.duration ? `${Math.floor(c.duration / 60)}m${c.duration % 60}s` : '?';
lines.push(`- ${date} | ${c.direction ?? '?'} | ${dur} | ${c.disposition ?? 'No disposition'}${c.agentName ? ` | Agent: ${c.agentName}` : ''}`);
}
}
if (ctx.activities.length > 0) {
lines.push(`\n### Recent Activity (last ${ctx.activities.length})`);
for (const a of ctx.activities) {
const date = a.occurredAt ? new Date(a.occurredAt).toLocaleDateString('en-IN', { day: 'numeric', month: 'short', year: 'numeric' }) : '?';
lines.push(`- ${date} | ${a.activityType}${a.summary ? `: ${a.summary}` : ''}${a.outcome ? `${a.outcome}` : ''}`);
}
}
return lines.join('\n');
}
}

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@@ -1,11 +1,15 @@
import { Controller, Post, Body, Headers, HttpException, HttpStatus, Logger } from '@nestjs/common';
import { CallerResolutionService } from './caller-resolution.service';
import { CallerContextService } from './caller-context.service';
@Controller('api/caller')
export class CallerResolutionController {
private readonly logger = new Logger(CallerResolutionController.name);
constructor(private readonly resolution: CallerResolutionService) {}
constructor(
private readonly resolution: CallerResolutionService,
private readonly callerContext: CallerContextService,
) {}
@Post('resolve')
async resolve(
@@ -21,6 +25,12 @@ export class CallerResolutionController {
this.logger.log(`[RESOLVE] Resolving caller: ${phone}`);
const result = await this.resolution.resolve(phone, auth);
// Pre-warm caller context cache so the AI chat has it ready
if (result.leadId) {
this.callerContext.prewarm(result.leadId, result.patientId, auth);
}
return result;
}
}

View File

@@ -3,11 +3,12 @@ import { PlatformModule } from '../platform/platform.module';
import { AuthModule } from '../auth/auth.module';
import { CallerResolutionController } from './caller-resolution.controller';
import { CallerResolutionService } from './caller-resolution.service';
import { CallerContextService } from './caller-context.service';
@Module({
imports: [PlatformModule, forwardRef(() => AuthModule)],
controllers: [CallerResolutionController],
providers: [CallerResolutionService],
exports: [CallerResolutionService],
providers: [CallerResolutionService, CallerContextService],
exports: [CallerResolutionService, CallerContextService],
})
export class CallerResolutionModule {}

View File

@@ -125,6 +125,21 @@ RULES:
7. NEVER give medical advice, diagnosis, or treatment recommendations.
8. Format with bullet points for easy scanning.
RESPONSE FORMAT (STRICT):
You MUST respond with valid JSON in this exact format — no markdown fences, no extra text, just raw JSON:
{"message": "your response text here", "suggestions": [{"id": "s1", "type": "upsell", "title": "short title", "script": "2-3 sentence script the agent reads aloud", "priority": "high"}]}
Response format rules:
- "message" MUST be plain text sentences only. NEVER use markdown headers (###), bold (**), bullet lists (-), or field labels (Phone:, Status:). Write natural conversational sentences like you are briefing a colleague. Do NOT repeat suggestions in the message — they belong only in the suggestions array.
- "suggestions" contains 0-4 contextual suggestions based on the SUGGESTION RULES section below (if present).
- Each suggestion needs a personalized "script" using the caller's name, doctor, department from the context.
- type must be one of: upsell, crosssell, retention, operational
- priority must be one of: high, medium, low
- On the first response (patient summary), always include suggestions from the rules.
- On subsequent responses, update suggestions based on conversation — remove acted-on ones, add new if relevant.
- If no suggestion rules are provided, return an empty suggestions array.
- Do NOT repeat raw data fields in the message. The summary card already shows name, phone, appointments. Keep the message to insight and context the card doesn't show.
KNOWLEDGE BASE (this is real data from our system):
{{knowledgeBase}}`;

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@@ -0,0 +1,152 @@
export type SuggestionType = 'upsell' | 'crosssell' | 'retention' | 'operational';
export type SuggestionPriority = 'high' | 'medium' | 'low';
export type SuggestionTrigger = {
type: SuggestionType;
title: string;
reason: string;
priority: SuggestionPriority;
};
type CallerFacts = {
isNew: boolean;
interestedService: string | null;
leadStatus: string | null;
contactAttempts: number;
appointments: Array<{ status: string; department: string; doctorName: string; scheduledAt: string }>;
calls: Array<{ direction: string; disposition: string | null; startedAt: string }>;
utmCampaign: string | null;
leadSource: string | null;
};
const DEPARTMENT_PACKAGES: Record<string, { package: string; description: string }> = {
CARDIOLOGY: { package: 'Cardiac Wellness Package', description: 'ECG, stress test, lipid panel' },
ORTHOPEDICS: { package: 'Joint Care Package', description: 'X-ray, physiotherapy assessment, bone density' },
GENERAL_MEDICINE: { package: 'Full Body Checkup', description: 'Complete health screening with blood work' },
NEUROLOGY: { package: 'Neuro Wellness Package', description: 'EEG, nerve conduction, cognitive assessment' },
GYNECOLOGY: { package: 'Women\'s Health Package', description: 'Pap smear, mammogram, hormone panel' },
};
const CROSS_SELL_MAP: Record<string, { department: string; reason: string }> = {
ORTHOPEDICS: { department: 'Physiotherapy', reason: 'complement orthopedic treatment' },
CARDIOLOGY: { department: 'Dietician', reason: 'dietary guidance for heart health' },
GENERAL_MEDICINE: { department: 'Ophthalmology', reason: 'routine eye screening' },
};
export const evaluateSuggestionRules = (facts: CallerFacts): SuggestionTrigger[] => {
const triggers: SuggestionTrigger[] = [];
// Rule 1: Package upsell by department
for (const appt of facts.appointments) {
const dept = (appt.department ?? '').toUpperCase().replace(/\s+/g, '_');
const pkg = DEPARTMENT_PACKAGES[dept];
if (pkg && appt.status === 'SCHEDULED') {
triggers.push({
type: 'upsell',
title: pkg.package,
reason: `Patient has ${appt.department} appointment with ${appt.doctorName}, offer ${pkg.description}`,
priority: 'high',
});
break;
}
}
// Rule 2: Reschedule missed/cancelled appointments
const needsReschedule = facts.appointments.find(a =>
a.status === 'CANCELLED' || a.status === 'RESCHEDULED' || a.status === 'NO_SHOW'
);
if (needsReschedule) {
triggers.push({
type: 'retention',
title: 'Reschedule appointment',
reason: `Last ${needsReschedule.department} appointment was ${needsReschedule.status.toLowerCase()}, offer to rebook with ${needsReschedule.doctorName}`,
priority: 'medium',
});
}
// Rule 3: Cross-sell related department
for (const appt of facts.appointments) {
const dept = (appt.department ?? '').toUpperCase().replace(/\s+/g, '_');
const cross = CROSS_SELL_MAP[dept];
if (cross && appt.status === 'SCHEDULED') {
triggers.push({
type: 'crosssell',
title: `${cross.department} consultation`,
reason: `${cross.reason} — patient already seeing ${appt.department}`,
priority: 'low',
});
break;
}
}
// Rule 4: First-visit patient — health checkup
if (facts.isNew || facts.contactAttempts === 0) {
triggers.push({
type: 'upsell',
title: 'Welcome Health Checkup',
reason: 'First-time patient, offer introductory health screening package',
priority: 'medium',
});
}
// Rule 5: Returning patient with no recent appointment
if (!facts.isNew && facts.appointments.length === 0 && facts.contactAttempts > 2) {
triggers.push({
type: 'retention',
title: 'Re-engagement',
reason: `Returning patient with ${facts.contactAttempts} prior contacts but no active appointments`,
priority: 'high',
});
}
return triggers.slice(0, 4);
};
// For display in Settings > Automations (read-only cards)
export const SUGGESTION_RULE_DEFINITIONS = [
{
name: 'Package Upsell by Department',
category: 'upsell' as const,
description: 'Suggest department wellness package when patient has a scheduled appointment.',
trigger: 'On call connect',
condition: 'Scheduled appointment exists',
action: 'Suggest department package',
enabled: true,
},
{
name: 'Reschedule Missed Appointment',
category: 'retention' as const,
description: 'Offer to rebook when patient has a cancelled or rescheduled appointment.',
trigger: 'On call connect',
condition: 'Cancelled/Rescheduled/No-show appointment exists',
action: 'Suggest rebooking',
enabled: true,
},
{
name: 'Cross-sell Related Department',
category: 'crosssell' as const,
description: 'Suggest complementary department service based on current appointment.',
trigger: 'On call connect',
condition: 'Scheduled appointment in mapped department',
action: 'Suggest related service',
enabled: true,
},
{
name: 'First Visit Health Checkup',
category: 'upsell' as const,
description: 'Suggest introductory health screening for first-time patients.',
trigger: 'On call connect',
condition: 'New patient or zero contact attempts',
action: 'Suggest health checkup package',
enabled: true,
},
{
name: 'Returning Patient Re-engagement',
category: 'retention' as const,
description: 'Prompt re-engagement for returning patients with no active appointments.',
trigger: 'On call connect',
condition: 'Returning patient, no appointments, 3+ contacts',
action: 'Suggest booking',
enabled: true,
},
];