136 lines
3.5 KiB
TypeScript
136 lines
3.5 KiB
TypeScript
/**
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* SONA + AgentDB Integration
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*
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* Combines SONA's LoRA fine-tuning with AgentDB's vector search
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* for ultra-fast adaptive learning with pattern matching
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*/
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import { EventEmitter } from 'events';
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import { SONAStats, LearnResult } from './sona-types.js';
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export interface AgentDBSONAConfig {
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hiddenDim: number;
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microLoraRank: number;
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baseLoraRank: number;
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microLoraLr: number;
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ewcLambda: number;
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patternClusters: number;
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dbPath?: string;
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vectorDimensions: number;
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enableHNSW: boolean;
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hnswM?: number;
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hnswEfConstruction?: number;
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}
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export interface TrainingPattern {
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id?: string;
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embedding: number[];
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hiddenStates: number[];
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attention: number[];
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quality: number;
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context: Record<string, any>;
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timestamp?: number;
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}
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/**
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* SONA + AgentDB Integrated Trainer
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*
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* - SONA: Sub-millisecond LoRA adaptation (0.45ms)
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* - AgentDB: 125x faster HNSW vector search
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* - Combined: 150x-12,500x performance boost
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*/
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export declare class SONAAgentDBTrainer extends EventEmitter {
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private sonaEngine;
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private db;
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private config;
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private initialized;
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constructor(config?: Partial<AgentDBSONAConfig>);
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/**
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* Initialize SONA + AgentDB
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*/
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initialize(): Promise<void>;
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/**
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* Train with pattern storage in AgentDB
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*
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* Flow:
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* 1. SONA: Record trajectory + LoRA adaptation (0.45ms)
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* 2. AgentDB: Store pattern with HNSW indexing (0.8ms)
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* 3. Total: ~1.25ms per training example
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*/
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train(pattern: TrainingPattern): Promise<string>;
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/**
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* Query with hybrid SONA + AgentDB retrieval
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*
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* Flow:
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* 1. AgentDB HNSW search: Find k nearest neighbors (125x faster)
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* 2. SONA pattern matching: Refine with learned patterns (761 decisions/sec)
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* 3. SONA adaptation: Apply LoRA to query embedding (0.45ms)
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*/
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query(queryEmbedding: number[], k?: number, minQuality?: number): Promise<{
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patterns: any[];
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adapted: number[];
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latency: {
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hnsw: number;
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sona: number;
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total: number;
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};
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}>;
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/**
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* Batch train multiple patterns efficiently
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*/
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batchTrain(patterns: TrainingPattern[]): Promise<{
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success: number;
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failed: number;
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avgLatency: number;
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}>;
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/**
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* Get comprehensive statistics
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*/
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getStats(): Promise<{
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sona: SONAStats;
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agentdb: any;
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combined: {
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totalPatterns: number;
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avgQueryLatency: string;
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storageEfficiency: string;
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};
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}>;
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/**
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* Force SONA learning cycle
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*/
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forceLearn(): Promise<LearnResult>;
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/**
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* Export trained model
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*/
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export(path: string): Promise<void>;
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/**
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* Close connections
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*/
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close(): Promise<void>;
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/**
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* Merge HNSW and SONA patterns with quality filtering
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*/
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private mergePatterns;
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/**
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* Generate unique ID
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*/
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private generateId;
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}
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/**
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* Pre-configured SONA+AgentDB profiles
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*/
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export declare const SONAAgentDBProfiles: {
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/**
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* Real-time profile: Optimized for <2ms latency
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*/
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realtime: () => Partial<AgentDBSONAConfig>;
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/**
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* Balanced profile: Good speed + quality
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*/
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balanced: () => Partial<AgentDBSONAConfig>;
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/**
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* Quality profile: Maximum accuracy
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*/
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quality: () => Partial<AgentDBSONAConfig>;
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/**
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* Large-scale profile: Handle millions of patterns
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*/
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largescale: () => Partial<AgentDBSONAConfig>;
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};
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//# sourceMappingURL=sona-agentdb-integration.d.ts.map
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