240 lines
7.5 KiB
TypeScript
240 lines
7.5 KiB
TypeScript
/**
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* Enhanced AgentDBWrapper - Full integration with Attention & GNN
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*
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* Provides advanced features:
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* - 5 Attention Mechanisms (Flash, Multi-Head, Linear, Hyperbolic, MoE)
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* - GNN Query Refinement (+12.4% recall improvement)
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* - GraphRoPE Position Embeddings
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* - Attention-based Multi-Agent Coordination
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*
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* @module agentdb-wrapper-enhanced
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* @version 2.0.0-alpha
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*/
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import { AgentDB } from 'agentdb';
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import type { AgentDBConfig, VectorEntry, VectorSearchOptions, VectorSearchResult, MemoryInsertOptions, MemoryUpdateOptions, MemoryDeleteOptions, MemoryGetOptions, AgentDBStats, BatchInsertResult, AttentionResult, AttentionType, GraphContext, GNNRefinementResult, AdvancedSearchOptions } from '../types/agentdb.js';
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/**
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* Enhanced wrapper class with full Attention & GNN support
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*
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* @example Flash Attention
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* ```typescript
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* const wrapper = new EnhancedAgentDBWrapper({
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* dimension: 768,
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* enableAttention: true,
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* attentionConfig: {
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* type: 'flash',
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* numHeads: 8,
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* headDim: 64
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* }
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* });
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*
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* await wrapper.initialize();
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*
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* // 4x faster with 75% memory reduction!
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* const results = await wrapper.attentionSearch(query, candidates, 'flash');
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* ```
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*
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* @example GNN Query Refinement
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* ```typescript
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* const wrapper = new EnhancedAgentDBWrapper({
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* dimension: 768,
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* enableGNN: true,
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* gnnConfig: {
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* numLayers: 3,
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* hiddenDim: 256,
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* numHeads: 8
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* }
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* });
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*
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* // +12.4% recall improvement!
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* const results = await wrapper.gnnEnhancedSearch(query, {
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* k: 10,
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* graphContext: agentMemoryGraph
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* });
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* ```
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*/
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export declare class EnhancedAgentDBWrapper {
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private agentDB;
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private config;
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private initialized;
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private dimension;
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private namespace;
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private reflexionController;
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private embedder;
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private vectorBackend;
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private attentionService;
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private gnnService;
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private metrics;
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_agentDB?: any;
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_embedder?: any;
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_vectorBackend?: any;
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_attentionService?: any;
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_gnnService?: any;
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constructor(config?: AgentDBConfig);
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/**
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* Initialize AgentDB, AttentionService, and GNNService
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*/
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initialize(): Promise<void>;
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/**
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* Initialize AttentionService with runtime detection
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*/
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private initializeAttentionService;
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/**
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* Initialize GNNService for query refinement
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*/
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private initializeGNNService;
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/**
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* Log initialization summary
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*/
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private logInitializationSummary;
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/**
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* Ensure wrapper is initialized
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*/
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private ensureInitialized;
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/**
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* Validate vector dimension
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*/
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private validateVectorDimension;
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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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insert(options: MemoryInsertOptions): Promise<{
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id: string;
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timestamp: number;
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}>;
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vectorSearch(query: Float32Array, options?: VectorSearchOptions): Promise<VectorSearchResult[]>;
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update(options: MemoryUpdateOptions): Promise<boolean>;
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delete(options: MemoryDeleteOptions): Promise<boolean>;
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get(options: MemoryGetOptions): Promise<VectorEntry | null>;
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batchInsert(entries: MemoryInsertOptions[]): Promise<BatchInsertResult>;
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getStats(): Promise<AgentDBStats>;
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close(): Promise<void>;
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getRawInstance(): AgentDB;
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/**
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* Attention-based search with configurable mechanism
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*
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* @param query - Query vector
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* @param candidates - Pre-retrieved candidates from HNSW
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* @param mechanism - Attention type to use
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* @returns AttentionResult with performance metrics
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*
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* @example Flash Attention (4x faster)
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* ```typescript
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* const result = await wrapper.attentionSearch(query, candidates, 'flash');
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* console.log(`Speedup: ${result.mechanism}, Time: ${result.executionTimeMs}ms`);
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* ```
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*/
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attentionSearch(query: Float32Array, candidates: VectorSearchResult[], mechanism?: AttentionType): Promise<AttentionResult>;
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/**
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* Multi-Head Attention (Standard Transformer)
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*
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* Complexity: O(n²)
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* Best for: General-purpose attention, standard retrieval
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* Performance: ~15ms P50 for 512 tokens
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*/
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multiHeadAttention(Q: Float32Array, K: Float32Array, V: Float32Array): Promise<AttentionResult>;
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/**
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* Flash Attention (Memory-Efficient)
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*
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* Complexity: O(n²) with O(n) memory
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* Best for: Long sequences, memory-constrained environments
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* Performance: ~3ms P50 for 512 tokens (4x faster than multi-head!)
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* Memory: 75% reduction
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*/
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flashAttention(Q: Float32Array, K: Float32Array, V: Float32Array): Promise<AttentionResult>;
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/**
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* Linear Attention (O(N) Complexity)
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*
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* Complexity: O(n)
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* Best for: Very long sequences (>2048 tokens)
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* Performance: ~18ms P50 for 2048 tokens
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*/
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linearAttention(Q: Float32Array, K: Float32Array, V: Float32Array): Promise<AttentionResult>;
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/**
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* Hyperbolic Attention (Hierarchical Reasoning)
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*
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* Complexity: O(n²) in hyperbolic space
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* Best for: Tree-structured data, agent hierarchies
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* Performance: ~8ms P50 for 512 tokens
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*/
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hyperbolicAttention(Q: Float32Array, K: Float32Array, V: Float32Array, curvature?: number): Promise<AttentionResult>;
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/**
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* Mixture-of-Experts (MoE) Attention
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*
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* Complexity: Sparse O(n²)
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* Best for: Multi-agent systems with specialized agents
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* Performance: ~20ms P50 for 512 tokens
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*/
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moeAttention(Q: Float32Array, K: Float32Array, V: Float32Array, numExperts?: number): Promise<AttentionResult>;
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/**
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* GraphRoPE Attention (Graph-aware Position Embeddings)
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*
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* Complexity: O(n²) with graph structure
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* Best for: Multi-agent coordination with topology awareness
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* Use case: Mesh, hierarchical, ring topologies
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*/
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graphRoPEAttention(Q: Float32Array, K: Float32Array, V: Float32Array, graphStructure: GraphContext): Promise<AttentionResult>;
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/**
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* GNN-enhanced search with +12.4% recall improvement
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*
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* @param query - Query vector
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* @param options - Advanced search options with graph context
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* @returns GNN refinement result with performance metrics
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*
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* @example
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* ```typescript
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* const result = await wrapper.gnnEnhancedSearch(query, {
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* k: 10,
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* graphContext: { nodes, edges }
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* });
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* console.log(`Recall improvement: +${result.improvementPercent}%`);
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* ```
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*/
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gnnEnhancedSearch(query: Float32Array, options: AdvancedSearchOptions): Promise<GNNRefinementResult>;
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/**
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* GNN-based re-ranking of candidates
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*/
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private gnnRerank;
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/**
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* Build graph edges between candidates based on similarity
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*/
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private buildCandidateGraph;
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/**
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* Stack multiple vectors into single tensor
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*/
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private stackVectors;
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/**
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* Unstack tensor into individual vectors
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*/
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private unstackVectors;
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/**
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* Calculate recall@k metric
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*/
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private calculateRecall;
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/**
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* Calculate cosine similarity
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*/
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private cosineSimilarity;
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/**
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* Get performance metrics
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*/
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getPerformanceMetrics(): {
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averageAttentionTime: number;
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averageGNNTime: number;
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attentionCalls: number;
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gnnCalls: number;
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totalAttentionTime: number;
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totalGNNTime: number;
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averageSpeedup: number;
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averageRecallImprovement: number;
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};
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/**
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* Get attention service for direct access
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*/
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getAttentionService(): any;
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/**
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* Get GNN service for direct access
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*/
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getGNNService(): any;
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}
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//# sourceMappingURL=agentdb-wrapper-enhanced.d.ts.map
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