282403d34b
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
38 lines
827 B
JSON
38 lines
827 B
JSON
{
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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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