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https://dev.azure.com/globalhealthx/EMR/_git/helix-engage-server
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feat: recording analysis module with Deepgram + AI insights + Redis cache
- RecordingsModule: POST /api/recordings/analyze - Deepgram pre-recorded API: diarize, summarize, topics, sentiment, utterances - AI insights via OpenAI generateObject: call outcome, coaching, compliance, satisfaction - Redis cache: 7-day TTL per callId, check before hitting Deepgram/OpenAI - Generic getCache/setCache added to SessionService for cross-module use Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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198
src/recordings/recordings.service.ts
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198
src/recordings/recordings.service.ts
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import { Injectable, Logger } from '@nestjs/common';
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import { ConfigService } from '@nestjs/config';
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import { generateObject } from 'ai';
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import { z } from 'zod';
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import { createAiModel } from '../ai/ai-provider';
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import type { LanguageModel } from 'ai';
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const DEEPGRAM_API = 'https://api.deepgram.com/v1/listen';
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export type TranscriptWord = {
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word: string;
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start: number;
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end: number;
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speaker: number;
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confidence: number;
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};
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export type TranscriptUtterance = {
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speaker: number;
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start: number;
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end: number;
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text: string;
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};
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export type CallAnalysis = {
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transcript: TranscriptUtterance[];
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summary: string | null;
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sentiment: 'positive' | 'neutral' | 'negative' | 'mixed';
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sentimentScore: number;
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insights: {
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keyTopics: string[];
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actionItems: string[];
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coachingNotes: string[];
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complianceFlags: string[];
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patientSatisfaction: string;
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callOutcome: string;
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};
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durationSec: number;
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};
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@Injectable()
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export class RecordingsService {
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private readonly logger = new Logger(RecordingsService.name);
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private readonly deepgramApiKey: string;
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private readonly aiModel: LanguageModel | null;
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constructor(private config: ConfigService) {
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this.deepgramApiKey = process.env.DEEPGRAM_API_KEY ?? '';
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this.aiModel = createAiModel(config);
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}
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async analyzeRecording(recordingUrl: string): Promise<CallAnalysis> {
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if (!this.deepgramApiKey) throw new Error('DEEPGRAM_API_KEY not configured');
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this.logger.log(`[RECORDING] Analyzing: ${recordingUrl}`);
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// Step 1: Send to Deepgram pre-recorded API with diarization + sentiment
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const dgResponse = await fetch(DEEPGRAM_API + '?' + new URLSearchParams({
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model: 'nova-2',
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language: 'en',
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smart_format: 'true',
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diarize: 'true',
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summarize: 'v2',
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topics: 'true',
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sentiment: 'true',
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utterances: 'true',
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}), {
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method: 'POST',
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headers: {
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'Authorization': `Token ${this.deepgramApiKey}`,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({ url: recordingUrl }),
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});
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if (!dgResponse.ok) {
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const err = await dgResponse.text();
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this.logger.error(`[RECORDING] Deepgram failed: ${dgResponse.status} ${err}`);
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throw new Error(`Deepgram transcription failed: ${dgResponse.status}`);
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}
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const dgData = await dgResponse.json();
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const results = dgData.results;
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// Extract utterances (speaker-labeled segments)
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const utterances: TranscriptUtterance[] = (results?.utterances ?? []).map((u: any) => ({
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speaker: u.speaker ?? 0,
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start: u.start ?? 0,
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end: u.end ?? 0,
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text: u.transcript ?? '',
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}));
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// Extract summary
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const summary = results?.summary?.short ?? null;
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// Extract sentiment from Deepgram
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const sentiments = results?.sentiments?.segments ?? [];
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const avgSentiment = this.computeAverageSentiment(sentiments);
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// Extract topics
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const topics = results?.topics?.segments?.flatMap((s: any) =>
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(s.topics ?? []).map((t: any) => t.topic),
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) ?? [];
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const duration = results?.channels?.[0]?.alternatives?.[0]?.words?.length > 0
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? results.channels[0].alternatives[0].words.slice(-1)[0].end
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: 0;
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// Step 2: Full transcript text for AI analysis
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const fullTranscript = utterances.map(u =>
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`Speaker ${u.speaker === 0 ? 'Agent' : 'Customer'}: ${u.text}`,
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).join('\n');
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this.logger.log(`[RECORDING] Transcribed: ${utterances.length} utterances, ${Math.round(duration)}s`);
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// Step 3: AI insights
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const insights = await this.generateInsights(fullTranscript, summary, topics);
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return {
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transcript: utterances,
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summary,
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sentiment: avgSentiment.label,
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sentimentScore: avgSentiment.score,
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insights,
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durationSec: Math.round(duration),
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};
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}
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private computeAverageSentiment(segments: any[]): { label: 'positive' | 'neutral' | 'negative' | 'mixed'; score: number } {
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if (!segments?.length) return { label: 'neutral', score: 0 };
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let positive = 0, negative = 0, neutral = 0;
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for (const seg of segments) {
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const s = seg.sentiment ?? 'neutral';
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if (s === 'positive') positive++;
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else if (s === 'negative') negative++;
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else neutral++;
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}
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const total = segments.length;
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const score = (positive - negative) / total;
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if (positive > negative * 2) return { label: 'positive', score };
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if (negative > positive * 2) return { label: 'negative', score };
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if (positive > 0 && negative > 0) return { label: 'mixed', score };
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return { label: 'neutral', score };
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}
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private async generateInsights(
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transcript: string,
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summary: string | null,
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topics: string[],
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): Promise<CallAnalysis['insights']> {
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if (!this.aiModel || !transcript.trim()) {
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return {
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keyTopics: topics.slice(0, 5),
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actionItems: [],
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coachingNotes: [],
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complianceFlags: [],
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patientSatisfaction: 'Unknown',
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callOutcome: 'Unknown',
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};
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}
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try {
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const { object } = await generateObject({
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model: this.aiModel,
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schema: z.object({
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keyTopics: z.array(z.string()).describe('Main topics discussed (max 5)'),
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actionItems: z.array(z.string()).describe('Follow-up actions needed'),
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coachingNotes: z.array(z.string()).describe('Agent performance observations — what went well and what could improve'),
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complianceFlags: z.array(z.string()).describe('Any compliance concerns (HIPAA, patient safety, misinformation)'),
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patientSatisfaction: z.string().describe('One-line assessment of patient satisfaction'),
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callOutcome: z.string().describe('One-line summary of what was accomplished'),
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}),
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system: `You are a call quality analyst for Global Hospital Bangalore.
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Analyze the following call recording transcript and provide structured insights.
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Be specific, brief, and actionable. Focus on healthcare context.
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${summary ? `\nCall summary: ${summary}` : ''}
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${topics.length > 0 ? `\nDetected topics: ${topics.join(', ')}` : ''}`,
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prompt: transcript,
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maxOutputTokens: 500,
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});
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return object;
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} catch (err) {
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this.logger.error(`[RECORDING] AI insights failed: ${err}`);
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return {
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keyTopics: topics.slice(0, 5),
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actionItems: [],
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coachingNotes: [],
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complianceFlags: [],
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patientSatisfaction: 'Analysis unavailable',
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callOutcome: 'Analysis unavailable',
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};
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}
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}
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}
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