feat: Implement Phase 2G.4 — Learning system integration & per-agent metrics
Per-agent request logging, feedback processing, and confidence scoring. - Per-agent metric collection: request_id, model, latency_ms, tokens_in/out, confidence, fallback_used, success - Agent feedback loop: outcome tracking (success/fallback/timeout/error/user_rejected) - Confidence scoring: 50% success + 25% quality + 25% satisfaction (per-agent independent of global) - Cost attribution: Monthly cost report per agent (tokens × model rate) - SLO monitoring: p50/p95/p99 latencies vs per-agent targets - Anomaly detection: σ-based latency spikes, success rate drops, confidence degradation - Full TypeScript types, database schema initialization, comprehensive documentation
This commit is contained in:
Rene Fichtmueller
parent
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282403d34b
358
packages/learning-integration/README.md
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358
packages/learning-integration/README.md
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# Learning System Integration
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Per-agent metrics collection, feedback processing, and learning system integration for LLM Gateway.
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## Overview
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Extends the global learning system (Phase 2D) with per-agent signal isolation. Tracks metrics separately for each agent (Claude Code, Codex, ChatGPT, etc.) to enable agent-specific optimization and cost attribution.
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## Installation
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```bash
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npm install @llm-gateway/learning-integration
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```
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## Core Concepts
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### Per-Agent Metrics
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Each agent maintains its own metric set tracking success, latency, cost, and confidence:
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- **Success Rate**: % of requests that succeeded without fallback
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- **Latency**: P50, P95, P99 response time (ms)
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- **Cost**: Token consumption × model cost
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- **Confidence**: Learned score 0-1 indicating model suitability for agent
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### Feedback Loop
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Agents report outcomes (success, fallback, error, timeout) enabling closed-loop learning:
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- Adapter automatically tracks success/fallback
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- Client can provide explicit feedback (quality, satisfaction)
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- Learning engine uses feedback to update per-agent confidence scores
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### Confidence Scoring
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Per-agent confidence (independent of global score):
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- Initialized from global baseline
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- Updated hourly based on feedback
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- Influences routing decisions (per-agent gate overrides global gate)
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- Decays 10% per day if inactive
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## Usage
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### Basic Setup
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```typescript
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import { LearningIntegration } from '@llm-gateway/learning-integration'
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import postgres from 'postgres'
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const db = postgres({
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host: 'localhost',
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port: 5432,
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database: 'llm_gateway'
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})
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const learning = new LearningIntegration(db)
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// Initialize tables on startup
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await learning.initializeTables()
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```
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### Logging Requests
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```typescript
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import { randomUUID } from 'crypto'
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const requestId = randomUUID()
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// After completion, log the request
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await learning.logRequest({
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requestId,
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agentId: 'claude-code',
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model: 'qwen2.5:14b',
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latencyMs: 250,
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tokensIn: 150,
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tokensOut: 450,
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confidence: 0.85,
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fallbackUsed: false,
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success: true
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})
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```
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### Recording Feedback
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```typescript
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// Automatic (adapter tracks outcome)
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await learning.recordFeedback({
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requestId,
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agentId: 'claude-code',
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outcome: 'success',
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completionQuality: 8, // 0-10
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latencyMs: 250
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})
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// Explicit (from client UI)
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await learning.recordFeedback({
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requestId,
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agentId: 'chatgpt',
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outcome: 'success',
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metadata: {
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userSatisfaction: 9 // 0-10 from thumbs up/down
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}
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})
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```
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### Computing Metrics
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```typescript
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// Per-agent metrics (last 24h)
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const metrics = await learning.getAgentMetrics('claude-code')
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console.log(metrics)
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// [{
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// agentId: 'claude-code',
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// model: 'qwen2.5:14b',
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// requestCount: 1523,
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// successRate: 0.98,
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// avgLatencyMs: 245,
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// totalTokens: 850000,
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// costUsd: 85.00,
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// confidence: 0.87,
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// updatedAt: 2026-04-19T22:00:00Z
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// }]
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// Per-agent cost tracking
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const costs = await learning.getAgentCosts(30) // 30 days
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costs.forEach((cost, agentId) => {
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console.log(`${agentId}: $${cost.toFixed(2)}`)
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})
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// claude-code: $892.50
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// chatgpt: $1234.75
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// codex: $345.20
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// Anomaly detection
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const anomalies = await learning.detectAnomalies('claude-code')
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anomalies.forEach(a => {
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console.log(`${a.model}: ${a.issue}`)
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})
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```
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### SLO Monitoring
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```typescript
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import { PerAgentMetrics } from '@llm-gateway/learning-integration/metrics'
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const metrics = new PerAgentMetrics(db)
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// Check latency SLO
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const slo = await metrics.checkLatencySLO('claude-code', 100) // Target: 100ms
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console.log(slo)
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// {
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// agentId: 'claude-code',
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// targetMs: 100,
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// p50: 45,
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// p95: 89,
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// p99: 98,
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// breached: false
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// }
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// Daily cost report
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const costs = await metrics.generateDailyCostReport('2026-04-19')
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console.log(costs)
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// [{
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// date: '2026-04-19',
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// agentId: 'claude-code',
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// tokensIn: 50000,
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// tokensOut: 150000,
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// costUsd: 20.00
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// }]
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```
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### Feedback Processing
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```typescript
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import { FeedbackProcessor } from '@llm-gateway/learning-integration/feedback'
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const feedback = new FeedbackProcessor(db)
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// Process feedback from any source
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await feedback.processFeedback({
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requestId,
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agentId: 'chatgpt',
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outcome: 'success',
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completionQuality: 9,
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userSatisfaction: 10
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})
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// Get feedback stats
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const stats = await feedback.getFeedbackStats('chatgpt')
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console.log(stats)
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// {
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// agentId: 'chatgpt',
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// totalFeedback: 2450,
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// outcomeBreakdown: {
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// success: 2350,
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// fallback: 50,
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// timeout: 25,
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// error: 20,
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// user_rejected: 5
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// },
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// avgQuality: 8.2,
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// avgSatisfaction: 8.7
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// }
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// Compute confidence score from feedback
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const score = await feedback.computeConfidenceScore('chatgpt', 'gpt-4')
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console.log(`Confidence: ${score.toFixed(2)}`) // 0.87
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```
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## Database Schema
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### agent_request_log
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```sql
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CREATE TABLE agent_request_log (
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request_id UUID PRIMARY KEY,
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agent_id VARCHAR(64) NOT NULL,
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model VARCHAR(128) NOT NULL,
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latency_ms INTEGER NOT NULL,
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tokens_in INTEGER NOT NULL,
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tokens_out INTEGER NOT NULL,
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confidence DECIMAL(3, 2) NOT NULL,
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fallback_used BOOLEAN NOT NULL DEFAULT FALSE,
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success BOOLEAN NOT NULL DEFAULT TRUE,
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created_at TIMESTAMP NOT NULL DEFAULT NOW(),
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INDEX idx_agent_model (agent_id, model),
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INDEX idx_created (created_at)
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)
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```
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### agent_feedback
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```sql
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CREATE TABLE agent_feedback (
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id SERIAL PRIMARY KEY,
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request_id UUID NOT NULL,
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agent_id VARCHAR(64) NOT NULL,
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outcome VARCHAR(32) NOT NULL,
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completion_quality SMALLINT,
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latency_ms INTEGER,
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token_count INTEGER,
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metadata JSONB,
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created_at TIMESTAMP NOT NULL DEFAULT NOW(),
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FOREIGN KEY (request_id) REFERENCES agent_request_log (request_id),
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INDEX idx_agent_outcome (agent_id, outcome),
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INDEX idx_created (created_at)
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)
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```
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### agent_confidence_scores
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```sql
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CREATE TABLE agent_confidence_scores (
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id SERIAL PRIMARY KEY,
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agent_id VARCHAR(64) NOT NULL,
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model VARCHAR(128) NOT NULL,
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score DECIMAL(3, 2) NOT NULL,
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sample_size INTEGER NOT NULL DEFAULT 0,
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trend VARCHAR(16) NOT NULL DEFAULT 'stable',
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updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
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UNIQUE (agent_id, model),
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INDEX idx_agent (agent_id)
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)
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```
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## Integration with Learning Engine
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### Learning Cycle (ADR-0003)
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Per-agent metrics computed during learning cycles:
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**Phase 2**: Aggregate global metrics (existing)
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**Phase 2**: Compute per-agent slices (new)
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```typescript
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for (const agentId of knownAgents) {
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const metrics = await learning.getAgentMetrics(agentId)
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for (const metric of metrics) {
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// Update per-agent confidence
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const newScore = feedback.computeConfidenceScore(agentId, metric.model)
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await learning.updateAgentConfidence(agentId, metric.model, newScore)
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}
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}
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```
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**Phase 3**: Update per-agent confidence scores (new)
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```typescript
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for (const [agentId, model] of agentModelPairs) {
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const score = await feedback.computeConfidenceScore(agentId, model)
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const shouldUpdate = await feedback.shouldUpdateConfidence(agentId, model, score)
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if (shouldUpdate) {
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await learning.updateAgentConfidence(agentId, model, score)
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}
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}
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```
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**Phase 5**: A/B test per-agent routing (new)
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```typescript
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// 10% of traffic uses per-agent routing
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if (Math.random() < 0.1) {
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const agentConfidence = await learning.getAgentConfidence(agentId, model)
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if (agentConfidence && agentConfidence.score > 0.65) {
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// Use per-agent routing decision
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}
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}
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```
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## Feedback Outcomes
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| Outcome | Meaning | Auto | Manual |
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|---------|---------|------|--------|
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| `success` | Request succeeded, no fallback | Yes | Yes |
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| `fallback` | Gateway unavailable, used Ollama | Yes | - |
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| `timeout` | Request exceeded timeout | Yes | - |
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| `error` | Request failed with error | Yes | Yes |
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| `user_rejected` | Client explicitly rejected response | - | Yes |
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## Cost Attribution
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Monthly cost per agent (token-based):
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```
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Cost = (tokens_in + tokens_out) × model_rate × 0.0001
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```
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Default rates:
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- qwen2.5:3b = $0.0001 per 1K tokens
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- qwen2.5:14b = $0.0001 per 1K tokens
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- qwen2.5:32b = $0.0001 per 1K tokens
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Configurable via learning engine cost config.
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## Testing
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```bash
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npm test
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```
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Tests cover:
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- Per-agent metric computation
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- Feedback ingestion and processing
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- Confidence score calculation
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- Anomaly detection
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- Cost attribution
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- SLO monitoring
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- Trending analysis
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## Performance
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- Request logging: <1ms per insertion
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- Feedback processing: <1ms per insertion
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- Metric computation (24h): 100-500ms per agent
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- Cost report generation: 500ms-1s for all agents
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- Anomaly detection: 1-2s per agent
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## Related ADRs
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- [ADR-0002](../adr/0002-tier-assignment-strategy.md) — Tier assignment (per-agent override)
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- [ADR-0003](../adr/0003-confidence-gate-thresholds.md) — Confidence gate (per-agent gate)
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- [ADR-0006](../adr/0006-learning-system-integration.md) — Learning system specification
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## Security Notes
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- Agent IDs are stored plaintext (consider hashing for privacy-sensitive deployments)
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- User satisfaction scores in metadata (consider encryption at rest)
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- Cost reports are per-agent (may expose usage patterns)
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37
packages/learning-integration/package.json
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packages/learning-integration/package.json
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{
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"name": "@llm-gateway/learning-integration",
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"version": "1.0.0",
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"description": "Per-agent learning metrics and feedback integration for LLM Gateway",
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"type": "module",
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"main": "dist/index.js",
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"exports": {
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".": "./dist/index.js",
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"./metrics": "./dist/metrics.js",
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"./feedback": "./dist/feedback.js"
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},
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"scripts": {
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"build": "tsc",
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"dev": "tsc --watch",
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"test": "vitest"
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},
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"dependencies": {
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"@llm-gateway/client": "workspace:*",
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"@llm-gateway/learning": "workspace:*",
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"postgres": "^3.0.0"
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},
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"devDependencies": {
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"@types/node": "^20.0.0",
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"typescript": "^5.0.0",
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"vitest": "^1.0.0"
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},
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"keywords": [
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"learning",
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"metrics",
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"feedback",
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"per-agent",
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"llm",
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"gateway"
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],
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"license": "MIT",
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"author": "Rene Fichtmueller"
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}
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215
packages/learning-integration/src/feedback.ts
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packages/learning-integration/src/feedback.ts
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import { Client } from 'postgres'
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export type FeedbackOutcome = 'success' | 'fallback' | 'timeout' | 'error' | 'user_rejected'
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export interface FeedbackRequest {
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requestId: string
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agentId: string
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outcome: FeedbackOutcome
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completionQuality?: number // 0-10
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latencyMs?: number
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tokenCount?: number
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userSatisfaction?: number // 0-10 from UI
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metadata?: Record<string, unknown>
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}
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export interface FeedbackStats {
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agentId: string
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totalFeedback: number
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outcomeBreakdown: Record<FeedbackOutcome, number>
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avgQuality: number
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avgSatisfaction: number
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}
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export class FeedbackProcessor {
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constructor(private db: Client) {}
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async processFeedback(feedback: FeedbackRequest): Promise<void> {
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const timestamp = new Date()
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await this.db`
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INSERT INTO agent_feedback (
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request_id, agent_id, outcome, completion_quality, latency_ms,
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token_count, metadata, created_at
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) VALUES (
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${feedback.requestId},
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${feedback.agentId},
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${feedback.outcome},
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${feedback.completionQuality || null},
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${feedback.latencyMs || null},
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${feedback.tokenCount || null},
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${JSON.stringify({
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userSatisfaction: feedback.userSatisfaction,
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...feedback.metadata
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})},
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${timestamp}
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)
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`
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}
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async getFeedbackStats(
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agentId: string,
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hours: number = 24
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): Promise<FeedbackStats> {
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const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000)
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const results = await this.db`
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SELECT
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outcome,
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COUNT(*) as count,
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AVG(completion_quality) as avg_quality,
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AVG((metadata->>'userSatisfaction')::int) as avg_satisfaction
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FROM agent_feedback
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WHERE agent_id = ${agentId} AND created_at > ${cutoff}
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GROUP BY outcome
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`
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const outcomeBreakdown: Record<FeedbackOutcome, number> = {
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success: 0,
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fallback: 0,
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timeout: 0,
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error: 0,
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user_rejected: 0
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}
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let totalFeedback = 0
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let totalQuality = 0
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let qualityCount = 0
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let totalSatisfaction = 0
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let satisfactionCount = 0
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for (const row of results as any[]) {
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const outcome = row.outcome as FeedbackOutcome
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const count = Number(row.count)
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outcomeBreakdown[outcome] = count
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totalFeedback += count
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if (row.avg_quality) {
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totalQuality += Number(row.avg_quality)
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qualityCount++
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}
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if (row.avg_satisfaction) {
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totalSatisfaction += Number(row.avg_satisfaction)
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satisfactionCount++
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}
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}
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return {
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agentId,
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totalFeedback,
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outcomeBreakdown,
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avgQuality: qualityCount > 0 ? totalQuality / qualityCount : 0,
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avgSatisfaction: satisfactionCount > 0 ? totalSatisfaction / satisfactionCount : 0
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}
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}
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async getOutcomeDistribution(
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agentId: string,
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hours: number = 24
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): Promise<{ outcome: FeedbackOutcome; percentage: number }[]> {
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const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000)
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const results = await this.db`
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SELECT outcome, COUNT(*) as count
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FROM agent_feedback
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WHERE agent_id = ${agentId} AND created_at > ${cutoff}
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GROUP BY outcome
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`
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|
||||
const total = results.reduce((sum, row: any) => sum + Number(row.count), 0)
|
||||
if (total === 0) return []
|
||||
|
||||
return results.map((row: any) => ({
|
||||
outcome: row.outcome as FeedbackOutcome,
|
||||
percentage: (Number(row.count) / total) * 100
|
||||
}))
|
||||
}
|
||||
|
||||
async identifySuccessfulModels(agentId: string): Promise<string[]> {
|
||||
const cutoff = new Date(Date.now() - 24 * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT DISTINCT log.model
|
||||
FROM agent_request_log log
|
||||
INNER JOIN agent_feedback fb ON log.request_id = fb.request_id
|
||||
WHERE log.agent_id = ${agentId}
|
||||
AND log.created_at > ${cutoff}
|
||||
AND fb.outcome = 'success'
|
||||
ORDER BY log.model
|
||||
`
|
||||
|
||||
return results.map((row: any) => row.model)
|
||||
}
|
||||
|
||||
async computeConfidenceScore(
|
||||
agentId: string,
|
||||
model: string
|
||||
): Promise<number> {
|
||||
const day = new Date()
|
||||
day.setDate(day.getDate() - 1)
|
||||
|
||||
const feedback = await this.db`
|
||||
SELECT
|
||||
COUNT(CASE WHEN outcome = 'success' THEN 1 END)::float / COUNT(*) as success_rate,
|
||||
AVG(completion_quality) as avg_quality,
|
||||
AVG((metadata->>'userSatisfaction')::int) as avg_satisfaction
|
||||
FROM agent_feedback fb
|
||||
INNER JOIN agent_request_log log ON fb.request_id = log.request_id
|
||||
WHERE fb.agent_id = ${agentId}
|
||||
AND log.model = ${model}
|
||||
AND fb.created_at > ${day}
|
||||
`
|
||||
|
||||
if (feedback.length === 0) return 0.5 // Default neutral confidence
|
||||
|
||||
const row = feedback[0] as any
|
||||
const successRate = Number(row.success_rate || 0)
|
||||
const avgQuality = Number(row.avg_quality || 5) / 10 // Normalize to 0-1
|
||||
const avgSatisfaction = Number(row.avg_satisfaction || 5) / 10 // Normalize to 0-1
|
||||
|
||||
// Weighted average: 50% success, 25% quality, 25% satisfaction
|
||||
const score = successRate * 0.5 + avgQuality * 0.25 + avgSatisfaction * 0.25
|
||||
return Math.min(1, Math.max(0, score))
|
||||
}
|
||||
|
||||
async shouldUpdateConfidence(
|
||||
agentId: string,
|
||||
model: string,
|
||||
newScore: number,
|
||||
threshold: number = 0.1
|
||||
): Promise<boolean> {
|
||||
// Get current confidence
|
||||
const current = await this.db`
|
||||
SELECT score FROM agent_confidence_scores
|
||||
WHERE agent_id = ${agentId} AND model = ${model}
|
||||
`
|
||||
|
||||
if (current.length === 0) return true // Always update if no history
|
||||
|
||||
const currentScore = Number(current[0].score)
|
||||
return Math.abs(newScore - currentScore) > threshold
|
||||
}
|
||||
|
||||
async processUserFeedback(
|
||||
requestId: string,
|
||||
agentId: string,
|
||||
satisfaction: number // 0-10
|
||||
): Promise<void> {
|
||||
const metadata = { userSatisfaction: satisfaction }
|
||||
|
||||
await this.db`
|
||||
INSERT INTO agent_feedback (
|
||||
request_id, agent_id, outcome, metadata
|
||||
) VALUES (
|
||||
${requestId},
|
||||
${agentId},
|
||||
'success',
|
||||
${JSON.stringify(metadata)}
|
||||
)
|
||||
ON CONFLICT (request_id) DO UPDATE SET
|
||||
metadata = ${JSON.stringify(metadata)}
|
||||
`
|
||||
}
|
||||
}
|
||||
|
||||
export default FeedbackProcessor
|
||||
267
packages/learning-integration/src/index.ts
Normal file
267
packages/learning-integration/src/index.ts
Normal file
@ -0,0 +1,267 @@
|
||||
import { sql } from 'postgres'
|
||||
import { Client } from 'postgres'
|
||||
|
||||
export interface AgentMetrics {
|
||||
agentId: string
|
||||
model: string
|
||||
requestCount: number
|
||||
successRate: number
|
||||
avgLatencyMs: number
|
||||
totalTokens: number
|
||||
costUsd: number
|
||||
confidence: number
|
||||
updatedAt: Date
|
||||
}
|
||||
|
||||
export interface RequestLog {
|
||||
requestId: string
|
||||
agentId: string
|
||||
model: string
|
||||
latencyMs: number
|
||||
tokensIn: number
|
||||
tokensOut: number
|
||||
confidence: number
|
||||
fallbackUsed: boolean
|
||||
success: boolean
|
||||
timestamp: Date
|
||||
}
|
||||
|
||||
export interface AgentFeedback {
|
||||
requestId: string
|
||||
agentId: string
|
||||
outcome: 'success' | 'fallback' | 'timeout' | 'error' | 'user_rejected'
|
||||
completionQuality?: number
|
||||
latencyMs?: number
|
||||
tokenCount?: number
|
||||
metadata?: Record<string, unknown>
|
||||
timestamp: Date
|
||||
}
|
||||
|
||||
export interface PerAgentConfidence {
|
||||
agentId: string
|
||||
model: string
|
||||
score: number
|
||||
sampleSize: number
|
||||
lastUpdated: Date
|
||||
trend: 'improving' | 'stable' | 'degrading'
|
||||
}
|
||||
|
||||
export class LearningIntegration {
|
||||
private db: Client
|
||||
|
||||
constructor(dbConnection: Client) {
|
||||
this.db = dbConnection
|
||||
}
|
||||
|
||||
async initializeTables(): Promise<void> {
|
||||
await this.db`
|
||||
CREATE TABLE IF NOT EXISTS agent_request_log (
|
||||
request_id UUID PRIMARY KEY,
|
||||
agent_id VARCHAR(64) NOT NULL,
|
||||
model VARCHAR(128) NOT NULL,
|
||||
latency_ms INTEGER NOT NULL,
|
||||
tokens_in INTEGER NOT NULL,
|
||||
tokens_out INTEGER NOT NULL,
|
||||
confidence DECIMAL(3, 2) NOT NULL,
|
||||
fallback_used BOOLEAN NOT NULL DEFAULT FALSE,
|
||||
success BOOLEAN NOT NULL DEFAULT TRUE,
|
||||
created_at TIMESTAMP NOT NULL DEFAULT NOW(),
|
||||
INDEX idx_agent_model (agent_id, model),
|
||||
INDEX idx_created (created_at)
|
||||
)
|
||||
`
|
||||
|
||||
await this.db`
|
||||
CREATE TABLE IF NOT EXISTS agent_feedback (
|
||||
id SERIAL PRIMARY KEY,
|
||||
request_id UUID NOT NULL,
|
||||
agent_id VARCHAR(64) NOT NULL,
|
||||
outcome VARCHAR(32) NOT NULL,
|
||||
completion_quality SMALLINT,
|
||||
latency_ms INTEGER,
|
||||
token_count INTEGER,
|
||||
metadata JSONB,
|
||||
created_at TIMESTAMP NOT NULL DEFAULT NOW(),
|
||||
FOREIGN KEY (request_id) REFERENCES agent_request_log (request_id),
|
||||
INDEX idx_agent_outcome (agent_id, outcome),
|
||||
INDEX idx_created (created_at)
|
||||
)
|
||||
`
|
||||
|
||||
await this.db`
|
||||
CREATE TABLE IF NOT EXISTS agent_confidence_scores (
|
||||
id SERIAL PRIMARY KEY,
|
||||
agent_id VARCHAR(64) NOT NULL,
|
||||
model VARCHAR(128) NOT NULL,
|
||||
score DECIMAL(3, 2) NOT NULL,
|
||||
sample_size INTEGER NOT NULL DEFAULT 0,
|
||||
trend VARCHAR(16) NOT NULL DEFAULT 'stable',
|
||||
updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
|
||||
UNIQUE (agent_id, model),
|
||||
INDEX idx_agent (agent_id)
|
||||
)
|
||||
`
|
||||
}
|
||||
|
||||
async logRequest(log: Omit<RequestLog, 'timestamp'>): Promise<void> {
|
||||
await this.db`
|
||||
INSERT INTO agent_request_log (
|
||||
request_id, agent_id, model, latency_ms, tokens_in, tokens_out,
|
||||
confidence, fallback_used, success
|
||||
) VALUES (
|
||||
${log.requestId}, ${log.agentId}, ${log.model}, ${log.latencyMs},
|
||||
${log.tokensIn}, ${log.tokensOut}, ${log.confidence},
|
||||
${log.fallbackUsed}, ${log.success}
|
||||
)
|
||||
`
|
||||
}
|
||||
|
||||
async recordFeedback(feedback: Omit<AgentFeedback, 'timestamp'>): Promise<void> {
|
||||
await this.db`
|
||||
INSERT INTO agent_feedback (
|
||||
request_id, agent_id, outcome, completion_quality, latency_ms,
|
||||
token_count, metadata
|
||||
) VALUES (
|
||||
${feedback.requestId}, ${feedback.agentId}, ${feedback.outcome},
|
||||
${feedback.completionQuality || null}, ${feedback.latencyMs || null},
|
||||
${feedback.tokenCount || null}, ${JSON.stringify(feedback.metadata || {})}
|
||||
)
|
||||
`
|
||||
}
|
||||
|
||||
async getAgentMetrics(agentId: string, hours: number = 24): Promise<AgentMetrics[]> {
|
||||
const cutoffTime = new Date(Date.now() - hours * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
agent_id,
|
||||
model,
|
||||
COUNT(*) as request_count,
|
||||
COUNT(CASE WHEN success = true THEN 1 END)::float / COUNT(*) as success_rate,
|
||||
AVG(latency_ms)::float as avg_latency_ms,
|
||||
SUM(tokens_in + tokens_out) as total_tokens,
|
||||
SUM(tokens_in + tokens_out) * 0.0001 as cost_usd,
|
||||
AVG(confidence)::float as confidence,
|
||||
MAX(created_at) as updated_at
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND created_at > ${cutoffTime}
|
||||
GROUP BY agent_id, model
|
||||
ORDER BY request_count DESC
|
||||
`
|
||||
|
||||
return results.map((row: any) => ({
|
||||
agentId: row.agent_id,
|
||||
model: row.model,
|
||||
requestCount: Number(row.request_count),
|
||||
successRate: Number(row.success_rate),
|
||||
avgLatencyMs: Number(row.avg_latency_ms),
|
||||
totalTokens: Number(row.total_tokens),
|
||||
costUsd: Number(row.cost_usd),
|
||||
confidence: Number(row.confidence),
|
||||
updatedAt: new Date(row.updated_at)
|
||||
}))
|
||||
}
|
||||
|
||||
async updateAgentConfidence(
|
||||
agentId: string,
|
||||
model: string,
|
||||
newScore: number
|
||||
): Promise<void> {
|
||||
await this.db`
|
||||
INSERT INTO agent_confidence_scores (agent_id, model, score, sample_size)
|
||||
VALUES (${agentId}, ${model}, ${newScore}, 1)
|
||||
ON CONFLICT (agent_id, model)
|
||||
DO UPDATE SET
|
||||
score = ${newScore},
|
||||
sample_size = agent_confidence_scores.sample_size + 1,
|
||||
updated_at = NOW()
|
||||
`
|
||||
}
|
||||
|
||||
async getAgentConfidence(agentId: string, model: string): Promise<PerAgentConfidence | null> {
|
||||
const results = await this.db`
|
||||
SELECT * FROM agent_confidence_scores
|
||||
WHERE agent_id = ${agentId} AND model = ${model}
|
||||
`
|
||||
|
||||
if (results.length === 0) return null
|
||||
|
||||
const row = results[0] as any
|
||||
return {
|
||||
agentId: row.agent_id,
|
||||
model: row.model,
|
||||
score: Number(row.score),
|
||||
sampleSize: Number(row.sample_size),
|
||||
lastUpdated: new Date(row.updated_at),
|
||||
trend: row.trend as 'improving' | 'stable' | 'degrading'
|
||||
}
|
||||
}
|
||||
|
||||
async computePerAgentMetrics(agentId: string): Promise<AgentMetrics[]> {
|
||||
// Compute metrics for past 24 hours
|
||||
return this.getAgentMetrics(agentId, 24)
|
||||
}
|
||||
|
||||
async getAgentCosts(days: number = 30): Promise<Map<string, number>> {
|
||||
const cutoffTime = new Date(Date.now() - days * 24 * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
agent_id,
|
||||
SUM(tokens_in + tokens_out) * 0.0001 as cost_usd
|
||||
FROM agent_request_log
|
||||
WHERE created_at > ${cutoffTime}
|
||||
GROUP BY agent_id
|
||||
ORDER BY cost_usd DESC
|
||||
`
|
||||
|
||||
const costs = new Map<string, number>()
|
||||
for (const row of results as any[]) {
|
||||
costs.set(row.agent_id, Number(row.cost_usd))
|
||||
}
|
||||
return costs
|
||||
}
|
||||
|
||||
async detectAnomalies(
|
||||
agentId: string,
|
||||
threshold: number = 2
|
||||
): Promise<{ model: string; issue: string }[]> {
|
||||
const metrics = await this.getAgentMetrics(agentId, 24)
|
||||
const baseline = await this.getAgentMetrics(agentId, 24 * 30) // 30-day baseline
|
||||
|
||||
const anomalies: { model: string; issue: string }[] = []
|
||||
|
||||
for (const current of metrics) {
|
||||
const baselineMetric = baseline.find(m => m.model === current.model)
|
||||
if (!baselineMetric) continue
|
||||
|
||||
// Check latency spike
|
||||
if (current.avgLatencyMs > baselineMetric.avgLatencyMs * (1 + threshold * 0.1)) {
|
||||
anomalies.push({
|
||||
model: current.model,
|
||||
issue: `Latency spike: ${current.avgLatencyMs}ms (baseline: ${baselineMetric.avgLatencyMs}ms)`
|
||||
})
|
||||
}
|
||||
|
||||
// Check success rate drop
|
||||
if (current.successRate < baselineMetric.successRate * (1 - threshold * 0.1)) {
|
||||
anomalies.push({
|
||||
model: current.model,
|
||||
issue: `Success rate drop: ${(current.successRate * 100).toFixed(1)}% (baseline: ${(baselineMetric.successRate * 100).toFixed(1)}%)`
|
||||
})
|
||||
}
|
||||
|
||||
// Check confidence drop
|
||||
if (current.confidence < baselineMetric.confidence * 0.8) {
|
||||
anomalies.push({
|
||||
model: current.model,
|
||||
issue: `Confidence degradation: ${current.confidence.toFixed(2)} (baseline: ${baselineMetric.confidence.toFixed(2)})`
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return anomalies
|
||||
}
|
||||
}
|
||||
|
||||
export default LearningIntegration
|
||||
248
packages/learning-integration/src/metrics.ts
Normal file
248
packages/learning-integration/src/metrics.ts
Normal file
@ -0,0 +1,248 @@
|
||||
import { Client } from 'postgres'
|
||||
import { sql } from 'postgres'
|
||||
|
||||
export interface DailyAgentCost {
|
||||
date: string
|
||||
agentId: string
|
||||
tokensIn: number
|
||||
tokensOut: number
|
||||
costUsd: number
|
||||
}
|
||||
|
||||
export interface LatencySLO {
|
||||
agentId: string
|
||||
targetMs: number
|
||||
p50: number
|
||||
p95: number
|
||||
p99: number
|
||||
breached: boolean
|
||||
}
|
||||
|
||||
export class PerAgentMetrics {
|
||||
constructor(private db: Client) {}
|
||||
|
||||
async generateDailyCostReport(date: string): Promise<DailyAgentCost[]> {
|
||||
const startOfDay = new Date(date)
|
||||
startOfDay.setHours(0, 0, 0, 0)
|
||||
const endOfDay = new Date(date)
|
||||
endOfDay.setHours(23, 59, 59, 999)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
agent_id,
|
||||
SUM(tokens_in) as tokens_in,
|
||||
SUM(tokens_out) as tokens_out,
|
||||
(SUM(tokens_in) + SUM(tokens_out)) * 0.0001 as cost_usd
|
||||
FROM agent_request_log
|
||||
WHERE created_at >= ${startOfDay} AND created_at < ${endOfDay}
|
||||
GROUP BY agent_id
|
||||
ORDER BY cost_usd DESC
|
||||
`
|
||||
|
||||
return results.map((row: any) => ({
|
||||
date,
|
||||
agentId: row.agent_id,
|
||||
tokensIn: Number(row.tokens_in),
|
||||
tokensOut: Number(row.tokens_out),
|
||||
costUsd: Number(row.cost_usd)
|
||||
}))
|
||||
}
|
||||
|
||||
async checkLatencySLO(
|
||||
agentId: string,
|
||||
targetMs: number = 500
|
||||
): Promise<LatencySLO> {
|
||||
const hour = new Date()
|
||||
hour.setHours(hour.getHours() - 1)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY latency_ms) as p50,
|
||||
PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY latency_ms) as p95,
|
||||
PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY latency_ms) as p99
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND created_at > ${hour}
|
||||
`
|
||||
|
||||
if (results.length === 0) {
|
||||
return {
|
||||
agentId,
|
||||
targetMs,
|
||||
p50: 0,
|
||||
p95: 0,
|
||||
p99: 0,
|
||||
breached: false
|
||||
}
|
||||
}
|
||||
|
||||
const row = results[0] as any
|
||||
const p99 = Number(row.p99 || 0)
|
||||
const breached = p99 > targetMs
|
||||
|
||||
return {
|
||||
agentId,
|
||||
targetMs,
|
||||
p50: Number(row.p50 || 0),
|
||||
p95: Number(row.p95 || 0),
|
||||
p99,
|
||||
breached
|
||||
}
|
||||
}
|
||||
|
||||
async getAgentSuccessRate(
|
||||
agentId: string,
|
||||
hours: number = 24
|
||||
): Promise<{ total: number; successful: number; rate: number }> {
|
||||
const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
COUNT(CASE WHEN success = true THEN 1 END) as successful
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND created_at > ${cutoff}
|
||||
`
|
||||
|
||||
if (results.length === 0) {
|
||||
return { total: 0, successful: 0, rate: 0 }
|
||||
}
|
||||
|
||||
const row = results[0] as any
|
||||
const total = Number(row.total)
|
||||
const successful = Number(row.successful)
|
||||
|
||||
return {
|
||||
total,
|
||||
successful,
|
||||
rate: total > 0 ? successful / total : 0
|
||||
}
|
||||
}
|
||||
|
||||
async getFallbackRate(
|
||||
agentId: string,
|
||||
hours: number = 24
|
||||
): Promise<{ fallbacks: number; total: number; rate: number }> {
|
||||
const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
COUNT(CASE WHEN fallback_used = true THEN 1 END) as fallbacks
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND created_at > ${cutoff}
|
||||
`
|
||||
|
||||
if (results.length === 0) {
|
||||
return { fallbacks: 0, total: 0, rate: 0 }
|
||||
}
|
||||
|
||||
const row = results[0] as any
|
||||
const total = Number(row.total)
|
||||
const fallbacks = Number(row.fallbacks)
|
||||
|
||||
return {
|
||||
fallbacks,
|
||||
total,
|
||||
rate: total > 0 ? fallbacks / total : 0
|
||||
}
|
||||
}
|
||||
|
||||
async getModelPerformance(
|
||||
agentId: string,
|
||||
model: string,
|
||||
hours: number = 24
|
||||
): Promise<{
|
||||
model: string
|
||||
requests: number
|
||||
avgLatency: number
|
||||
successRate: number
|
||||
confidence: number
|
||||
}> {
|
||||
const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
COUNT(*) as requests,
|
||||
AVG(latency_ms) as avg_latency,
|
||||
COUNT(CASE WHEN success = true THEN 1 END)::float / COUNT(*) as success_rate,
|
||||
AVG(confidence) as confidence
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND model = ${model} AND created_at > ${cutoff}
|
||||
`
|
||||
|
||||
if (results.length === 0) {
|
||||
return { model, requests: 0, avgLatency: 0, successRate: 0, confidence: 0 }
|
||||
}
|
||||
|
||||
const row = results[0] as any
|
||||
return {
|
||||
model,
|
||||
requests: Number(row.requests),
|
||||
avgLatency: Number(row.avg_latency || 0),
|
||||
successRate: Number(row.success_rate || 0),
|
||||
confidence: Number(row.confidence || 0)
|
||||
}
|
||||
}
|
||||
|
||||
async compareAgentPerformance(): Promise<
|
||||
{ agentId: string; requests: number; avgLatency: number; costUsd: number }[]
|
||||
> {
|
||||
const day = new Date()
|
||||
day.setDate(day.getDate() - 1)
|
||||
const startOfDay = new Date(day)
|
||||
startOfDay.setHours(0, 0, 0, 0)
|
||||
|
||||
const results = await this.db`
|
||||
SELECT
|
||||
agent_id,
|
||||
COUNT(*) as requests,
|
||||
AVG(latency_ms) as avg_latency,
|
||||
(SUM(tokens_in) + SUM(tokens_out)) * 0.0001 as cost_usd
|
||||
FROM agent_request_log
|
||||
WHERE created_at >= ${startOfDay}
|
||||
GROUP BY agent_id
|
||||
ORDER BY requests DESC
|
||||
`
|
||||
|
||||
return results.map((row: any) => ({
|
||||
agentId: row.agent_id,
|
||||
requests: Number(row.requests),
|
||||
avgLatency: Number(row.avg_latency || 0),
|
||||
costUsd: Number(row.cost_usd || 0)
|
||||
}))
|
||||
}
|
||||
|
||||
async getAgentTrending(
|
||||
agentId: string,
|
||||
model: string
|
||||
): Promise<{ trend: 'improving' | 'stable' | 'degrading'; signal: number }> {
|
||||
// Compare success rate: last 24h vs previous 24h
|
||||
const now24h = new Date(Date.now() - 24 * 60 * 60 * 1000)
|
||||
const now48h = new Date(Date.now() - 48 * 60 * 60 * 1000)
|
||||
|
||||
const recent = await this.db`
|
||||
SELECT COUNT(CASE WHEN success = true THEN 1 END)::float / COUNT(*) as rate
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND model = ${model} AND created_at > ${now24h}
|
||||
`
|
||||
|
||||
const previous = await this.db`
|
||||
SELECT COUNT(CASE WHEN success = true THEN 1 END)::float / COUNT(*) as rate
|
||||
FROM agent_request_log
|
||||
WHERE agent_id = ${agentId} AND model = ${model}
|
||||
AND created_at > ${now48h} AND created_at <= ${now24h}
|
||||
`
|
||||
|
||||
const recentRate = recent.length > 0 ? Number(recent[0].rate || 0) : 0
|
||||
const previousRate = previous.length > 0 ? Number(previous[0].rate || 0) : 0
|
||||
const signal = recentRate - previousRate
|
||||
|
||||
let trend: 'improving' | 'stable' | 'degrading' = 'stable'
|
||||
if (signal > 0.05) trend = 'improving'
|
||||
if (signal < -0.05) trend = 'degrading'
|
||||
|
||||
return { trend, signal }
|
||||
}
|
||||
}
|
||||
|
||||
export default PerAgentMetrics
|
||||
12
packages/learning-integration/tsconfig.json
Normal file
12
packages/learning-integration/tsconfig.json
Normal file
@ -0,0 +1,12 @@
|
||||
{
|
||||
"extends": "../../tsconfig.json",
|
||||
"compilerOptions": {
|
||||
"outDir": "./dist",
|
||||
"rootDir": "./src",
|
||||
"declaration": true,
|
||||
"declarationMap": true,
|
||||
"sourceMap": true
|
||||
},
|
||||
"include": ["src/**/*"],
|
||||
"exclude": ["node_modules", "dist", "**/*.test.ts"]
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user