feat(ai-diagnostics): add error clustering types and cosmos containers [1.1.1-1.1.2]
This commit is contained in:
saravanakumardb1
parent
e98380003b
commit
4de01269b5
@ -76,6 +76,12 @@ const CONTAINER_DEFS: Record<string, ContainerConfig> = {
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debug_traces: { partitionKeyPath: '/pk', defaultTtl: 7 * 86400 },
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debug_logs: { partitionKeyPath: '/pk', defaultTtl: 3 * 86400 },
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debug_screenshots: { partitionKeyPath: '/sessionId', defaultTtl: 7 * 86400 },
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// AI Diagnostics (see docs/roadmaps/AI_DIAGNOSTIC_ASSISTANT_ROADMAP.md)
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error_clusters: { partitionKeyPath: '/productId', defaultTtl: 90 * 86400 },
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error_fingerprints: { partitionKeyPath: '/fingerprintHash' },
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diagnostic_insights: { partitionKeyPath: '/clusterId', defaultTtl: 90 * 86400 },
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diagnostic_queries: { partitionKeyPath: '/userId', defaultTtl: 30 * 86400 },
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proactive_alerts: { partitionKeyPath: '/productId', defaultTtl: 30 * 86400 },
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// Broadcast Messaging & Surveys (see docs/roadmaps/not-started/platform_BROADCAST_SURVEY_ROADMAP.md)
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broadcasts: { partitionKeyPath: '/productId' },
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broadcast_deliveries: { partitionKeyPath: '/userId', defaultTtl: 90 * 86400 },
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422
services/platform-service/src/modules/ab-testing/types.ts
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422
services/platform-service/src/modules/ab-testing/types.ts
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@ -0,0 +1,422 @@
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/**
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* Intelligent A/B Testing — Extended types and schemas.
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* Bayesian statistics, Thompson sampling, early stopping, AI hypothesis generation.
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*/
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import { z } from 'zod';
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// ─────────────────────────────────────────────────────────────────────────────
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// Enums and Constants
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// ─────────────────────────────────────────────────────────────────────────────
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export type ExperimentStatus = 'draft' | 'running' | 'paused' | 'stopped' | 'completed';
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export type AllocationStrategy = 'random' | 'thompson' | 'epsilon_greedy' | 'ucb';
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export type MetricType = 'conversion' | 'count' | 'duration' | 'revenue' | 'custom';
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export type MetricAggregation = 'sum' | 'mean' | 'count' | 'unique';
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export type MetricDirection = 'increase' | 'decrease';
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// ─────────────────────────────────────────────────────────────────────────────
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// Core Types
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// ─────────────────────────────────────────────────────────────────────────────
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export interface ExperimentVariant {
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id: string; // var_<uuid>
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key: string; // e.g., 'control', 'variant_a'
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name: string;
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description?: string;
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isControl: boolean;
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flagConfig: Record<string, unknown>; // Arbitrary config payload
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currentAllocationPercent: number; // 0–100%, dynamic for bandit
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}
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export interface PrimaryMetric {
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name: string;
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type: MetricType;
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eventName: string; // Telemetry event to track
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aggregation: MetricAggregation;
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direction: MetricDirection; // Is higher better?
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minimumDetectableEffect: number; // % change we want to detect
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}
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export interface SecondaryMetric {
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name: string;
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type: MetricType;
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eventName: string;
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}
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export interface TargetingConfig {
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platforms?: string[]; // ios, android, web
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appVersions?: { min: string; max?: string };
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regions?: string[];
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userSegments?: string[]; // pro, free, enterprise
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userProperties?: Record<string, string | number | boolean>;
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}
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export interface GuardrailsConfig {
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minSampleSizePerVariant: number; // Default: 100
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maxDurationDays: number; // Safety limit, default: 30
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autoStopEnabled: boolean;
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winnerThreshold: number; // % probability to auto-stop, default: 95
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requireApprovalFor: 'none' | 'revenue' | 'all';
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// Document Types (Cosmos DB)
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// ─────────────────────────────────────────────────────────────────────────────
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export interface ExperimentDoc {
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id: string; // exp_<uuid>
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productId: string; // partition key
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// Experiment definition
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name: string;
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description: string;
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hypothesis: string;
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aiGeneratedHypothesis?: boolean;
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// Status lifecycle
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status: ExperimentStatus;
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// Variants
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controlVariantId: string;
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variantIds: string[];
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// Configuration
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allocationStrategy: AllocationStrategy;
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targetPercent: number; // % of eligible traffic
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// Audience targeting
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targeting: TargetingConfig;
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// Metrics
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primaryMetric: PrimaryMetric;
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secondaryMetrics: SecondaryMetric[];
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// Guardrails
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guardrails: GuardrailsConfig;
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// Scheduling
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startAt?: string;
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endAt?: string;
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// Stats (denormalized for fast reads)
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totalParticipants: number;
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totalEvents: number;
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// Timestamps
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createdAt: string;
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updatedAt: string;
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startedAt?: string;
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completedAt?: string;
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// TTL for completed experiments (2 years)
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ttl?: number;
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}
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export interface VariantDoc {
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id: string; // var_<uuid>
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experimentId: string; // partition key
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// Variant definition
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name: string;
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description?: string;
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isControl: boolean;
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// Feature flag configuration
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flagConfig: Record<string, unknown>;
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// Traffic allocation (dynamic for bandit)
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currentAllocationPercent: number;
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// Statistics (real-time computed)
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stats: {
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participants: number;
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events: number;
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primaryMetricValue: number;
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primaryMetricStdDev?: number;
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conversions?: number;
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conversionRate?: number;
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// Bayesian posterior parameters
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betaAlpha?: number;
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betaBeta?: number;
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gammaShape?: number;
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gammaScale?: number;
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};
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// Bayesian results
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bayesianResults?: {
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probabilityBeatsControl: number;
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probabilityBeatsAll: number;
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expectedLiftPercent: number;
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expectedLoss: number;
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credibleInterval: {
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lower: number;
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mean: number;
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upper: number;
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};
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};
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createdAt: string;
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updatedAt: string;
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}
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export interface ExperimentAssignmentDoc {
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id: string; // ea_<uuid>
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userId: string; // partition key
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experimentId: string;
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variantId: string;
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// Assignment metadata
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assignedAt: string;
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firstExposedAt?: string;
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// Context at assignment
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assignmentContext: {
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platform: string;
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appVersion: string;
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osVersion: string;
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deviceModel?: string;
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region?: string;
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};
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// Events attributed
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eventCount: number;
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lastEventAt?: string;
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// TTL: Remove after experiment completes + analysis period
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ttl?: number;
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}
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export interface ExperimentEventDoc {
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id: string; // ee_<uuid>
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experimentId: string; // partition key
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timestamp: string; // Sort key for time-series
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// Attribution
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userId: string;
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variantId: string;
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assignmentId: string;
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// Event details
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metricName: string;
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metricType: MetricType;
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value: number;
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// Conversion tracking
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converted: boolean;
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// Context
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eventMetadata?: Record<string, unknown>;
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// Denormalized for filtering
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platform: string;
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appVersion: string;
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// TTL
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ttl?: number;
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}
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export interface ExperimentMetricDoc {
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id: string; // em_<uuid>
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experimentId: string; // partition key
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metricName: string;
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variantId: string;
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// Aggregated values
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count: number;
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sum: number;
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mean: number;
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stdDev: number;
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min: number;
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max: number;
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// Conversion-specific
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conversions: number;
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conversionRate: number;
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// Last updated
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updatedAt: string;
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}
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export interface ExperimentResult {
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experimentId: string;
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status: 'in_progress' | 'winner_found' | 'no_winner' | 'stopped';
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// Overall stats
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totalParticipants: number;
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totalEvents: number;
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daysRunning: number;
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// Winner info
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winnerVariantId?: string;
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winnerProbability?: number;
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// Variant results
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variantResults: Array<{
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variantId: string;
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variantName: string;
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isControl: boolean;
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participants: number;
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primaryMetricValue: number;
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probabilityBeatsControl: number;
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expectedLiftPercent: number;
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credibleInterval: { lower: number; mean: number; upper: number };
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}>;
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// Statistical summary
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statisticalSummary: {
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probabilityAnyBeatsControl: number;
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expectedLossIfShipped: number;
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recommendedAction: 'ship' | 'rollback' | 'continue' | 'stop';
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};
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// Early stopping
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earlyStopped: boolean;
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stopReason?: string;
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generatedAt: string;
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// AI Hypothesis Types
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// ─────────────────────────────────────────────────────────────────────────────
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export interface HypothesisInput {
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featureName: string;
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adoptionRate: number;
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baselineRate: number;
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segmentData: Record<string, number>;
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feedbackSamples: string[];
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competitorFeatures?: string[];
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}
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export interface GeneratedHypothesis {
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primary: string;
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alternatives: string[];
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expectedEffectSize: number;
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successMetric: string;
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riskAssessment: 'low' | 'medium' | 'high';
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impactScore: number; // 0–100
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difficultyScore: number; // 0–100
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powerPrediction: number; // Statistical power 0–100
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}
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export interface ExperimentSuggestion {
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id: string;
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hypothesis: GeneratedHypothesis;
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suggestedVariants: Array<{ name: string; description: string }>;
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suggestedMetrics: PrimaryMetric[];
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suggestedDuration: number;
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suggestedSampleSize: number;
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priority: number;
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aiGenerated: boolean;
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createdAt: string;
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// Zod Schemas
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// ─────────────────────────────────────────────────────────────────────────────
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const VariantInputSchema = z.object({
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key: z
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.string()
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.min(1)
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.regex(/^[a-z0-9_]+$/),
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name: z.string().min(1).max(200),
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description: z.string().default(''),
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isControl: z.boolean().default(false),
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flagConfig: z.record(z.unknown()).default({}),
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});
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const PrimaryMetricSchema = z.object({
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name: z.string().min(1),
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type: z.enum(['conversion', 'count', 'duration', 'revenue', 'custom']),
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eventName: z.string().min(1),
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aggregation: z.enum(['sum', 'mean', 'count', 'unique']),
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direction: z.enum(['increase', 'decrease']),
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minimumDetectableEffect: z.number().min(0.01).max(100).default(5),
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});
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const SecondaryMetricSchema = z.object({
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name: z.string().min(1),
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type: z.enum(['conversion', 'count', 'duration', 'revenue', 'custom']),
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eventName: z.string().min(1),
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});
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const TargetingSchema = z.object({
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platforms: z.array(z.enum(['ios', 'android', 'web'])).optional(),
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appVersions: z
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.object({
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min: z.string(),
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max: z.string().optional(),
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})
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.optional(),
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regions: z.array(z.string()).optional(),
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userSegments: z.array(z.string()).optional(),
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userProperties: z.record(z.union([z.string(), z.number(), z.boolean()])).optional(),
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});
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const GuardrailsSchema = z.object({
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minSampleSizePerVariant: z.number().int().min(10).default(100),
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maxDurationDays: z.number().int().min(1).max(90).default(30),
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autoStopEnabled: z.boolean().default(true),
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winnerThreshold: z.number().int().min(80).max(99).default(95),
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requireApprovalFor: z.enum(['none', 'revenue', 'all']).default('none'),
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});
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export const CreateExperimentSchema = z.object({
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name: z.string().min(1).max(200),
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description: z.string().default(''),
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hypothesis: z.string().min(1),
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variants: z.array(VariantInputSchema).min(2).max(10),
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allocationStrategy: z.enum(['random', 'thompson', 'epsilon_greedy', 'ucb']).default('random'),
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targetPercent: z.number().int().min(1).max(100).default(100),
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targeting: TargetingSchema.default({}),
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primaryMetric: PrimaryMetricSchema,
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secondaryMetrics: z.array(SecondaryMetricSchema).default([]),
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guardrails: GuardrailsSchema.default({}),
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startAt: z.string().datetime().optional(),
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});
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export const UpdateExperimentSchema = z.object({
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name: z.string().min(1).max(200).optional(),
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description: z.string().optional(),
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hypothesis: z.string().optional(),
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status: z.enum(['draft', 'running', 'paused', 'stopped', 'completed']).optional(),
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targetPercent: z.number().int().min(1).max(100).optional(),
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targeting: TargetingSchema.optional(),
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guardrails: GuardrailsSchema.optional(),
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});
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export const TrackEventSchema = z.object({
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experimentId: z.string(),
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userId: z.string(),
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metricName: z.string(),
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metricType: z.enum(['conversion', 'count', 'duration', 'revenue', 'custom']),
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value: z.number(),
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converted: z.boolean().default(true),
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eventMetadata: z.record(z.unknown()).optional(),
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platform: z.string().default('unknown'),
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appVersion: z.string().default('unknown'),
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});
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export const AdjustAllocationSchema = z.object({
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variantId: z.string(),
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newAllocationPercent: z.number().int().min(0).max(100),
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});
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// ─────────────────────────────────────────────────────────────────────────────
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// Type Exports
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// ─────────────────────────────────────────────────────────────────────────────
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export type CreateExperimentInput = z.infer<typeof CreateExperimentSchema>;
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export type UpdateExperimentInput = z.infer<typeof UpdateExperimentSchema>;
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export type TrackEventInput = z.infer<typeof TrackEventSchema>;
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export type AdjustAllocationInput = z.infer<typeof AdjustAllocationSchema>;
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411
services/platform-service/src/modules/ai-diagnostics/types.ts
Normal file
411
services/platform-service/src/modules/ai-diagnostics/types.ts
Normal file
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import { z } from 'zod';
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// ============================================================================
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// Error Fingerprinting Types
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// ============================================================================
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export const ErrorFingerprintSchema = z.object({
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id: z.string(), // ef_<uuid>
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productId: z.string(),
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fingerprintHash: z.string(), // SHA-256 of normalized error
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// Normalized error signature
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errorType: z.string(), // Exception class/name
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messageTemplate: z.string(), // Normalized message with placeholders
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stackSignature: z.string(), // Normalized stack frames
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// Source location (normalized)
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sourceLocation: z
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.object({
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file: z.string().optional(),
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function: z.string().optional(),
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line: z.number().optional(),
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})
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.optional(),
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// Metadata
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createdAt: z.string(),
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updatedAt: z.string(),
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occurrenceCount: z.number().default(1),
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uniqueUsers: z.number().default(1),
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// TTL for cleanup
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ttl: z.number().default(90 * 86400), // 90 days
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});
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export type ErrorFingerprint = z.infer<typeof ErrorFingerprintSchema>;
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// ============================================================================
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// Error Cluster Types
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// ============================================================================
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export const ClusterStatusSchema = z.enum(['active', 'investigating', 'resolved', 'ignored']);
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export type ClusterStatus = z.infer<typeof ClusterStatusSchema>;
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export const CommonContextSchema = z.object({
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osVersions: z.array(z.object({ version: z.string(), count: z.number() })),
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appVersions: z.array(z.object({ version: z.string(), count: z.number() })),
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deviceModels: z.array(z.object({ model: z.string(), count: z.number() })),
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screenContexts: z.array(z.object({ screen: z.string(), count: z.number() })),
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});
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export type CommonContext = z.infer<typeof CommonContextSchema>;
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export const ErrorClusterDocSchema = z.object({
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id: z.string(), // ec_<uuid>
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productId: z.string(), // partition key
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fingerprintHash: z.string(),
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// Cluster metadata
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firstSeenAt: z.string(), // ISO 8601
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lastSeenAt: z.string(),
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occurrenceCount: z.number(), // Total occurrences
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uniqueUsers: z.number(), // Affected user count
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// Error signature
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errorType: z.string(), // Exception class/name
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messageTemplate: z.string(), // Normalized message with placeholders
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stackSignature: z.string(), // Normalized stack frames
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// Vector embedding for semantic search
|
||||
embedding: z.array(z.number()).optional(), // 1536-dim from text-embedding-3-small
|
||||
embeddingVersion: z.string().optional(), // Model version for re-embedding
|
||||
|
||||
// Context patterns (auto-extracted)
|
||||
commonContext: CommonContextSchema.optional(),
|
||||
|
||||
// Related data
|
||||
relatedClusterIds: z.array(z.string()).default([]), // Similar clusters (vector similarity)
|
||||
mergedIntoClusterId: z.string().optional(), // If deduplicated
|
||||
|
||||
// Resolution tracking
|
||||
status: ClusterStatusSchema.default('active'),
|
||||
resolvedAt: z.string().optional(),
|
||||
resolutionCommit: z.string().optional(), // Link to fix
|
||||
|
||||
// Analysis metadata
|
||||
lastAnalyzedAt: z.string().optional(),
|
||||
insightId: z.string().optional(), // Link to latest diagnostic insight
|
||||
|
||||
// Timestamps
|
||||
createdAt: z.string(),
|
||||
updatedAt: z.string(),
|
||||
ttl: z.number().default(90 * 86400), // 90 days
|
||||
});
|
||||
|
||||
export type ErrorClusterDoc = z.infer<typeof ErrorClusterDocSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Cluster Analysis Types
|
||||
// ============================================================================
|
||||
|
||||
export const ClusterAnalysisSchema = z.object({
|
||||
id: z.string(), // ca_<uuid>
|
||||
clusterId: z.string(),
|
||||
productId: z.string(),
|
||||
|
||||
// Analysis metadata
|
||||
analyzedAt: z.string(),
|
||||
analysisType: z.enum(['root_cause', 'pattern', 'comparison', 'trend']),
|
||||
|
||||
// AI-generated summary
|
||||
summary: z.string(), // "This appears to be a memory leak in the image cache..."
|
||||
patternDescription: z.string(), // Detailed pattern analysis
|
||||
|
||||
// Key observations
|
||||
keyObservations: z.array(
|
||||
z.object({
|
||||
observation: z.string(),
|
||||
evidence: z.string(),
|
||||
confidence: z.enum(['high', 'medium', 'low']),
|
||||
})
|
||||
),
|
||||
|
||||
// Related clusters
|
||||
similarClusters: z.array(
|
||||
z.object({
|
||||
clusterId: z.string(),
|
||||
similarityScore: z.number(),
|
||||
reason: z.string(),
|
||||
})
|
||||
),
|
||||
|
||||
// Timestamps
|
||||
createdAt: z.string(),
|
||||
updatedAt: z.string(),
|
||||
ttl: z.number().default(90 * 86400),
|
||||
});
|
||||
|
||||
export type ClusterAnalysis = z.infer<typeof ClusterAnalysisSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Diagnostic Insight Types
|
||||
// ============================================================================
|
||||
|
||||
export const RootCauseCategorySchema = z.enum([
|
||||
'config',
|
||||
'dependency',
|
||||
'logic',
|
||||
'resource',
|
||||
'external',
|
||||
'unknown',
|
||||
]);
|
||||
export type RootCauseCategory = z.infer<typeof RootCauseCategorySchema>;
|
||||
|
||||
export const EvidenceTypeSchema = z.enum([
|
||||
'stack_trace',
|
||||
'telemetry_pattern',
|
||||
'device_correlation',
|
||||
'api_failure',
|
||||
'similar_issue',
|
||||
'config_mismatch',
|
||||
'version_regression',
|
||||
]);
|
||||
export type EvidenceType = z.infer<typeof EvidenceTypeSchema>;
|
||||
|
||||
export const EvidenceSchema = z.object({
|
||||
type: EvidenceTypeSchema,
|
||||
description: z.string(),
|
||||
strength: z.enum(['strong', 'moderate', 'weak']),
|
||||
data: z.record(z.unknown()).optional(),
|
||||
});
|
||||
|
||||
export type Evidence = z.infer<typeof EvidenceSchema>;
|
||||
|
||||
export const SimilarResolvedIssueSchema = z.object({
|
||||
clusterId: z.string(),
|
||||
resolution: z.string(),
|
||||
confidence: z.number(), // 0.0-1.0
|
||||
});
|
||||
|
||||
export type SimilarResolvedIssue = z.infer<typeof SimilarResolvedIssueSchema>;
|
||||
|
||||
export const FeedbackStatsSchema = z.object({
|
||||
helpful: z.number().default(0),
|
||||
notHelpful: z.number().default(0),
|
||||
engineerNotes: z.array(z.string()).default([]),
|
||||
});
|
||||
|
||||
export type FeedbackStats = z.infer<typeof FeedbackStatsSchema>;
|
||||
|
||||
export const DiagnosticInsightDocSchema = z.object({
|
||||
id: z.string(), // di_<uuid>
|
||||
clusterId: z.string(), // partition key (with productId)
|
||||
productId: z.string(),
|
||||
|
||||
// AI-generated analysis
|
||||
analysisType: z.enum(['root_cause', 'pattern', 'comparison', 'trend']),
|
||||
generatedAt: z.string(),
|
||||
|
||||
// LLM output
|
||||
rootCauseCategory: RootCauseCategorySchema,
|
||||
hypothesis: z.string(), // Natural language explanation
|
||||
reasoning: z.string(), // Why LLM thinks this
|
||||
confidence: z.enum(['high', 'medium', 'low']),
|
||||
confidenceScore: z.number(), // 0.0-1.0
|
||||
|
||||
// Evidence
|
||||
evidence: z.array(EvidenceSchema),
|
||||
|
||||
// Suggested actions
|
||||
suggestedInvestigation: z.array(z.string()),
|
||||
potentialFixDirection: z.string().optional(),
|
||||
similarResolvedIssues: z.array(SimilarResolvedIssueSchema).optional(),
|
||||
|
||||
// Feedback
|
||||
feedbackStats: FeedbackStatsSchema.default({ helpful: 0, notHelpful: 0, engineerNotes: [] }),
|
||||
|
||||
// LLM metadata
|
||||
modelUsed: z.string(), // gpt-4o, gpt-4o-mini
|
||||
promptTokens: z.number(),
|
||||
completionTokens: z.number(),
|
||||
|
||||
// Correlation IDs for context
|
||||
correlationIds: z.array(z.string()).optional(),
|
||||
telemetryEventIds: z.array(z.string()).optional(),
|
||||
|
||||
createdAt: z.string(),
|
||||
updatedAt: z.string(),
|
||||
ttl: z.number().default(90 * 86400),
|
||||
});
|
||||
|
||||
export type DiagnosticInsightDoc = z.infer<typeof DiagnosticInsightDocSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Natural Language Query Types
|
||||
// ============================================================================
|
||||
|
||||
export const QueryIntentSchema = z.enum([
|
||||
'root_cause',
|
||||
'pattern_search',
|
||||
'comparison',
|
||||
'trend',
|
||||
'impact',
|
||||
]);
|
||||
export type QueryIntent = z.infer<typeof QueryIntentSchema>;
|
||||
|
||||
export const ExtractedEntitiesSchema = z.object({
|
||||
products: z.array(z.string()).optional(),
|
||||
timeRange: z.object({ start: z.string(), end: z.string() }).optional(),
|
||||
errorTypes: z.array(z.string()).optional(),
|
||||
platforms: z.array(z.string()).optional(),
|
||||
userSegments: z.array(z.string()).optional(),
|
||||
clusterIds: z.array(z.string()).optional(),
|
||||
});
|
||||
|
||||
export type ExtractedEntities = z.infer<typeof ExtractedEntitiesSchema>;
|
||||
|
||||
export const SupportingDataSchema = z.object({
|
||||
type: z.enum(['cluster', 'telemetry', 'session', 'insight']),
|
||||
id: z.string(),
|
||||
relevanceScore: z.number(),
|
||||
});
|
||||
|
||||
export type SupportingData = z.infer<typeof SupportingDataSchema>;
|
||||
|
||||
export const NaturalLanguageQueryDocSchema = z.object({
|
||||
id: z.string(), // nq_<uuid>
|
||||
userId: z.string(), // Admin who asked
|
||||
productId: z.string().optional(), // Optional filter
|
||||
|
||||
// Query
|
||||
rawQuery: z.string(), // "Why did iOS keyboard crash yesterday?"
|
||||
parsedIntent: QueryIntentSchema,
|
||||
extractedEntities: ExtractedEntitiesSchema,
|
||||
|
||||
// Execution
|
||||
executedQuery: z.string().optional(), // Translated Cosmos query
|
||||
dataSources: z.array(z.string()).default([]), // Clusters, telemetry, sessions accessed
|
||||
executionTimeMs: z.number().optional(),
|
||||
|
||||
// Response
|
||||
aiResponse: z.string().optional(), // Generated answer
|
||||
confidence: z.number().optional(), // Overall confidence
|
||||
supportingData: z.array(SupportingDataSchema).optional(),
|
||||
|
||||
// Feedback
|
||||
userRating: z.enum(['helpful', 'not_helpful']).optional(),
|
||||
userComment: z.string().optional(),
|
||||
|
||||
createdAt: z.string(),
|
||||
ttl: z.number().default(30 * 86400), // 30 days
|
||||
});
|
||||
|
||||
export type NaturalLanguageQueryDoc = z.infer<typeof NaturalLanguageQueryDocSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Analysis Request/Response Types
|
||||
// ============================================================================
|
||||
|
||||
export const AnalyzeClusterRequestSchema = z.object({
|
||||
clusterId: z.string(),
|
||||
analysisType: z.enum(['root_cause', 'pattern', 'comparison', 'trend']).default('root_cause'),
|
||||
modelPreference: z.enum(['gpt-4o-mini', 'gpt-4o']).default('gpt-4o-mini'),
|
||||
includeTelemetry: z.boolean().default(true),
|
||||
includeSimilarClusters: z.boolean().default(true),
|
||||
});
|
||||
|
||||
export type AnalyzeClusterRequest = z.infer<typeof AnalyzeClusterRequestSchema>;
|
||||
|
||||
export const SubmitFeedbackRequestSchema = z.object({
|
||||
insightId: z.string(),
|
||||
rating: z.enum(['helpful', 'not_helpful']),
|
||||
note: z.string().optional(),
|
||||
});
|
||||
|
||||
export type SubmitFeedbackRequest = z.infer<typeof SubmitFeedbackRequestSchema>;
|
||||
|
||||
export const QueryDiagnosticsRequestSchema = z.object({
|
||||
query: z.string(),
|
||||
productId: z.string().optional(),
|
||||
timeRange: z.object({ start: z.string(), end: z.string() }).optional(),
|
||||
});
|
||||
|
||||
export type QueryDiagnosticsRequest = z.infer<typeof QueryDiagnosticsRequestSchema>;
|
||||
|
||||
export const SearchErrorsRequestSchema = z.object({
|
||||
query: z.string(), // Semantic search query
|
||||
productId: z.string().optional(),
|
||||
limit: z.number().default(10),
|
||||
similarityThreshold: z.number().default(0.75),
|
||||
});
|
||||
|
||||
export type SearchErrorsRequest = z.infer<typeof SearchErrorsRequestSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Error Event Types (for ingestion from telemetry)
|
||||
// ============================================================================
|
||||
|
||||
export const ErrorEventSchema = z.object({
|
||||
id: z.string(),
|
||||
productId: z.string(),
|
||||
correlationId: z.string().optional(),
|
||||
|
||||
// Error details
|
||||
errorType: z.string(),
|
||||
message: z.string(),
|
||||
stackTrace: z.string().optional(),
|
||||
|
||||
// Context
|
||||
platform: z.string().optional(), // ios, android, web, desktop
|
||||
osVersion: z.string().optional(),
|
||||
appVersion: z.string().optional(),
|
||||
deviceModel: z.string().optional(),
|
||||
screen: z.string().optional(),
|
||||
userId: z.string().optional(),
|
||||
|
||||
// Telemetry linking
|
||||
precedingEvents: z.array(z.record(z.unknown())).optional(),
|
||||
|
||||
// Timestamp
|
||||
timestamp: z.string(),
|
||||
});
|
||||
|
||||
export type ErrorEvent = z.infer<typeof ErrorEventSchema>;
|
||||
|
||||
// ============================================================================
|
||||
// Proactive Alert Types
|
||||
// ============================================================================
|
||||
|
||||
export const AlertTypeSchema = z.enum([
|
||||
'new_cluster',
|
||||
'cluster_spike',
|
||||
'new_error_type',
|
||||
'regression_detected',
|
||||
'anomaly_detected',
|
||||
]);
|
||||
export type AlertType = z.infer<typeof AlertTypeSchema>;
|
||||
|
||||
export const ProactiveAlertSchema = z.object({
|
||||
id: z.string(), // pa_<uuid>
|
||||
productId: z.string(),
|
||||
alertType: AlertTypeSchema,
|
||||
|
||||
// Target
|
||||
clusterId: z.string().optional(),
|
||||
errorType: z.string().optional(),
|
||||
|
||||
// Alert details
|
||||
severity: z.enum(['critical', 'high', 'medium', 'low']),
|
||||
title: z.string(),
|
||||
description: z.string(),
|
||||
|
||||
// Metrics
|
||||
baselineCount: z.number().optional(),
|
||||
currentCount: z.number(),
|
||||
spikeRatio: z.number().optional(),
|
||||
|
||||
// AI analysis
|
||||
aiSummary: z.string().optional(),
|
||||
suggestedAction: z.string().optional(),
|
||||
|
||||
// Status
|
||||
acknowledgedAt: z.string().optional(),
|
||||
acknowledgedBy: z.string().optional(),
|
||||
resolvedAt: z.string().optional(),
|
||||
|
||||
createdAt: z.string(),
|
||||
ttl: z.number().default(30 * 86400),
|
||||
});
|
||||
|
||||
export type ProactiveAlert = z.infer<typeof ProactiveAlertSchema>;
|
||||
Loading…
Reference in New Issue
Block a user