1245 lines
37 KiB
TypeScript
1245 lines
37 KiB
TypeScript
/**
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* RuVector PostgreSQL Client
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* Comprehensive wrapper for PostgreSQL connections with RuVector extension
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*
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* Features:
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* - Connection pooling with configurable limits
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* - Automatic retry with exponential backoff
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* - Batch operations for bulk inserts
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* - SQL injection protection
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* - Input validation
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*/
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import pg from 'pg';
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const { Pool } = pg;
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// ============================================================================
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// Configuration
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// ============================================================================
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export interface PoolConfig {
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maxConnections?: number;
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idleTimeoutMs?: number;
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connectionTimeoutMs?: number;
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statementTimeoutMs?: number;
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}
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export interface RetryConfig {
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maxRetries?: number;
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baseDelayMs?: number;
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maxDelayMs?: number;
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}
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const DEFAULT_POOL_CONFIG: Required<PoolConfig> = {
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maxConnections: 10,
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idleTimeoutMs: 30000,
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connectionTimeoutMs: 5000,
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statementTimeoutMs: 30000,
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};
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const DEFAULT_RETRY_CONFIG: Required<RetryConfig> = {
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maxRetries: 3,
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baseDelayMs: 100,
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maxDelayMs: 5000,
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};
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// ============================================================================
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// Utility Functions
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// ============================================================================
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/**
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* Validate identifier (table/column name) to prevent SQL injection
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*/
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function validateIdentifier(name: string): string {
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if (!/^[a-zA-Z_][a-zA-Z0-9_]*$/.test(name)) {
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throw new Error(`Invalid identifier: ${name}. Must be alphanumeric with underscores.`);
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}
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if (name.length > 63) {
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throw new Error(`Identifier too long: ${name}. Max 63 characters.`);
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}
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return name;
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}
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/**
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* Quote identifier for safe SQL usage
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*/
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function quoteIdentifier(name: string): string {
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return `"${validateIdentifier(name).replace(/"/g, '""')}"`;
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}
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/**
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* Validate vector dimensions
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*/
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function validateVector(vector: number[], expectedDim?: number): void {
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if (!Array.isArray(vector)) {
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throw new Error('Vector must be an array');
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}
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if (vector.length === 0) {
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throw new Error('Vector cannot be empty');
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}
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if (vector.some(v => typeof v !== 'number' || !Number.isFinite(v))) {
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throw new Error('Vector must contain only finite numbers');
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}
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if (expectedDim !== undefined && vector.length !== expectedDim) {
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throw new Error(`Vector dimension mismatch: expected ${expectedDim}, got ${vector.length}`);
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}
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}
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/**
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* Sleep for exponential backoff
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*/
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function sleep(ms: number): Promise<void> {
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return new Promise(resolve => setTimeout(resolve, ms));
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}
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/**
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* Check if error is retryable
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*/
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function isRetryableError(err: Error): boolean {
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const code = (err as { code?: string }).code;
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// Retryable PostgreSQL error codes
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const retryableCodes = [
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'08000', // connection_exception
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'08003', // connection_does_not_exist
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'08006', // connection_failure
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'40001', // serialization_failure
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'40P01', // deadlock_detected
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'57P01', // admin_shutdown
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'57P02', // crash_shutdown
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'57P03', // cannot_connect_now
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];
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return code !== undefined && retryableCodes.includes(code);
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}
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// ============================================================================
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// Interfaces
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// ============================================================================
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export interface RuVectorInfo {
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version: string;
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features: string[];
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simd_info?: string;
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}
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export interface VectorSearchResult {
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id: number | string;
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distance: number;
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metadata?: Record<string, unknown>;
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vector?: number[];
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}
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export interface AttentionResult {
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output: number[];
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weights?: number[][];
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}
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export interface GnnResult {
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embeddings: number[][];
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layer_output?: number[][];
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}
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export interface GraphNode {
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id: string;
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labels: string[];
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properties: Record<string, unknown>;
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}
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export interface GraphEdge {
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id: string;
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type: string;
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from: string;
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to: string;
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properties: Record<string, unknown>;
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}
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export interface TraversalResult {
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nodes: GraphNode[];
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edges: GraphEdge[];
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path?: string[];
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}
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export interface SparseInfo {
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dim: number;
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nnz: number;
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sparsity: number;
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norm: number;
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}
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export interface SparseResult {
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vector: string;
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nnz: number;
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originalNnz?: number;
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newNnz?: number;
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}
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export interface ScalarQuantizeResult {
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data: number[];
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scale: number;
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offset: number;
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}
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export interface Agent {
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name: string;
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agent_type: string;
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capabilities: string[];
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is_active: boolean;
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cost_model: {
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per_request: number;
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per_token?: number;
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};
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performance: {
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avg_latency_ms: number;
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quality_score: number;
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success_rate: number;
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total_requests: number;
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};
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}
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export interface AgentSummary {
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name: string;
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agent_type: string;
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capabilities: string[];
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cost_per_request: number;
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avg_latency_ms: number;
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quality_score: number;
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success_rate: number;
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total_requests: number;
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is_active: boolean;
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}
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export interface RoutingDecision {
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agent_name: string;
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confidence: number;
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estimated_cost: number;
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estimated_latency_ms: number;
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expected_quality: number;
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similarity_score: number;
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reasoning?: string;
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alternatives?: Array<{ name: string; score?: number }>;
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}
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export interface RoutingStats {
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total_agents: number;
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active_agents: number;
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total_requests: number;
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average_quality: number;
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}
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export interface LearningStats {
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trajectories: {
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total: number;
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with_feedback: number;
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avg_latency_us: number;
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avg_precision: number;
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avg_recall: number;
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};
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patterns: {
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total: number;
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total_samples: number;
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avg_confidence: number;
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total_usage: number;
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};
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}
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export interface GraphStats {
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name: string;
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node_count: number;
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edge_count: number;
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labels: string[];
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edge_types: string[];
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}
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export interface MemoryStats {
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index_memory_mb: number;
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vector_cache_mb: number;
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quantization_tables_mb: number;
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total_extension_mb: number;
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}
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export class RuVectorClient {
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private pool: InstanceType<typeof Pool> | null = null;
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private connectionString: string;
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private poolConfig: Required<PoolConfig>;
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private retryConfig: Required<RetryConfig>;
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constructor(
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connectionString: string,
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poolConfig?: PoolConfig,
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retryConfig?: RetryConfig
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) {
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this.connectionString = connectionString;
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this.poolConfig = { ...DEFAULT_POOL_CONFIG, ...poolConfig };
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this.retryConfig = { ...DEFAULT_RETRY_CONFIG, ...retryConfig };
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}
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async connect(): Promise<void> {
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this.pool = new Pool({
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connectionString: this.connectionString,
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max: this.poolConfig.maxConnections,
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idleTimeoutMillis: this.poolConfig.idleTimeoutMs,
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connectionTimeoutMillis: this.poolConfig.connectionTimeoutMs,
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});
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// Test connection and set statement timeout
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const client = await this.pool.connect();
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try {
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await client.query(`SET statement_timeout = ${this.poolConfig.statementTimeoutMs}`);
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} finally {
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client.release();
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}
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}
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async disconnect(): Promise<void> {
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if (this.pool) {
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await this.pool.end();
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this.pool = null;
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}
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}
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/**
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* Execute query with automatic retry on transient errors
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*/
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private async queryWithRetry<T extends pg.QueryResultRow>(
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sql: string,
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params?: unknown[]
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): Promise<pg.QueryResult<T>> {
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if (!this.pool) {
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throw new Error('Not connected to database');
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}
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let lastError: Error | null = null;
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for (let attempt = 0; attempt <= this.retryConfig.maxRetries; attempt++) {
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try {
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return await this.pool.query<T>(sql, params);
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} catch (err) {
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lastError = err as Error;
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if (!isRetryableError(lastError) || attempt === this.retryConfig.maxRetries) {
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throw lastError;
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}
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// Exponential backoff with jitter
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const delay = Math.min(
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this.retryConfig.baseDelayMs * Math.pow(2, attempt) + Math.random() * 100,
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this.retryConfig.maxDelayMs
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);
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await sleep(delay);
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}
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}
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throw lastError;
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}
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async query<T extends pg.QueryResultRow = pg.QueryResultRow>(sql: string, params?: unknown[]): Promise<T[]> {
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const result = await this.queryWithRetry<T>(sql, params);
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return result.rows;
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}
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async execute(sql: string, params?: unknown[]): Promise<void> {
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await this.queryWithRetry(sql, params);
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}
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/**
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* Execute multiple statements in a transaction
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*/
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async transaction<T>(
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fn: (client: pg.PoolClient) => Promise<T>
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): Promise<T> {
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if (!this.pool) {
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throw new Error('Not connected to database');
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}
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const client = await this.pool.connect();
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try {
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await client.query('BEGIN');
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const result = await fn(client);
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await client.query('COMMIT');
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return result;
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} catch (err) {
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await client.query('ROLLBACK');
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throw err;
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} finally {
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client.release();
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}
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}
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// ============================================================================
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// Extension Info
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// ============================================================================
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async getExtensionInfo(): Promise<RuVectorInfo> {
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const versionResult = await this.query<{ version: string }>(
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"SELECT extversion as version FROM pg_extension WHERE extname = 'ruvector'"
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);
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const version = versionResult[0]?.version || 'unknown';
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// Get SIMD info
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let simd_info: string | undefined;
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try {
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const simdResult = await this.query<{ ruvector_simd_info: string }>(
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'SELECT ruvector_simd_info()'
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);
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simd_info = simdResult[0]?.ruvector_simd_info;
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} catch {
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// Function may not exist
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}
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const features: string[] = [];
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const featureChecks = [
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{ name: 'Vector Operations', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_l2_distance'" },
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{ name: 'HNSW Index', check: "SELECT 1 FROM pg_am WHERE amname = 'hnsw'" },
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{ name: 'IVFFlat Index', check: "SELECT 1 FROM pg_am WHERE amname = 'ivfflat'" },
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{ name: 'Attention Mechanisms', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_attention_score'" },
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{ name: 'GNN Layers', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_gcn_forward'" },
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{ name: 'Graph/Cypher', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_cypher'" },
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{ name: 'Self-Learning', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_enable_learning'" },
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{ name: 'Hyperbolic Embeddings', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_poincare_distance'" },
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{ name: 'Sparse Vectors', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_sparse_bm25'" },
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{ name: 'Agent Routing', check: "SELECT 1 FROM pg_proc WHERE proname = 'ruvector_route'" },
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{ name: 'Quantization', check: "SELECT 1 FROM pg_proc WHERE proname = 'binary_quantize_arr'" },
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];
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for (const { name, check } of featureChecks) {
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try {
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const result = await this.query(check);
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if (result.length > 0) {
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features.push(name);
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}
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} catch {
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// Feature not available
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}
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}
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return { version, features, simd_info };
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}
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async installExtension(upgrade = false): Promise<void> {
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if (upgrade) {
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await this.execute('ALTER EXTENSION ruvector UPDATE');
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} else {
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await this.execute('CREATE EXTENSION IF NOT EXISTS ruvector CASCADE');
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}
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}
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async getMemoryStats(): Promise<MemoryStats> {
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const result = await this.query<{ ruvector_memory_stats: MemoryStats }>(
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'SELECT ruvector_memory_stats()'
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);
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return result[0]?.ruvector_memory_stats || {
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index_memory_mb: 0,
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vector_cache_mb: 0,
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quantization_tables_mb: 0,
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total_extension_mb: 0,
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};
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}
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// ============================================================================
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// Vector Operations
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// ============================================================================
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async createVectorTable(
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name: string,
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dimensions: number,
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indexType: 'hnsw' | 'ivfflat' = 'hnsw'
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): Promise<void> {
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const safeName = quoteIdentifier(name);
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const safeIdxName = quoteIdentifier(`${name}_id_idx`);
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if (dimensions < 1 || dimensions > 65535) {
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throw new Error('Dimensions must be between 1 and 65535');
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}
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// Use ruvector type (native RuVector extension type)
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// ruvector is a variable-length type, dimensions stored in metadata
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// Note: dimensions is directly interpolated since DEFAULT doesn't support parameters
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await this.execute(`
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CREATE TABLE IF NOT EXISTS ${safeName} (
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id SERIAL PRIMARY KEY,
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embedding ruvector,
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dimensions INT DEFAULT ${dimensions},
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metadata JSONB,
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created_at TIMESTAMPTZ DEFAULT NOW()
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)
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`);
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// Note: HNSW/IVFFlat indexes require additional index implementation
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// For now, create a simple btree index on id for fast lookups
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await this.execute(`
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CREATE INDEX IF NOT EXISTS ${safeIdxName} ON ${safeName} (id)
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`);
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}
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async insertVector(
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table: string,
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vector: number[],
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metadata?: Record<string, unknown>
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): Promise<number> {
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validateVector(vector);
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const safeName = quoteIdentifier(table);
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const result = await this.query<{ id: number }>(
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`INSERT INTO ${safeName} (embedding, metadata) VALUES ($1::ruvector, $2) RETURNING id`,
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[`[${vector.join(',')}]`, metadata ? JSON.stringify(metadata) : null]
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);
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return result[0].id;
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}
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|
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/**
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* Batch insert vectors (10-100x faster than individual inserts)
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*/
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async insertVectorsBatch(
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table: string,
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vectors: Array<{ vector: number[]; metadata?: Record<string, unknown> }>,
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batchSize = 100
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): Promise<number[]> {
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const safeName = quoteIdentifier(table);
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const ids: number[] = [];
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|
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// Process in batches
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for (let i = 0; i < vectors.length; i += batchSize) {
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const batch = vectors.slice(i, i + batchSize);
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|
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// Validate all vectors in batch
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for (const item of batch) {
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validateVector(item.vector);
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}
|
|
|
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// Build multi-row INSERT
|
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const values: unknown[] = [];
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const placeholders: string[] = [];
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|
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batch.forEach((item, idx) => {
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const base = idx * 2;
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placeholders.push(`($${base + 1}::ruvector, $${base + 2})`);
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values.push(`[${item.vector.join(',')}]`);
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values.push(item.metadata ? JSON.stringify(item.metadata) : null);
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});
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const result = await this.query<{ id: number }>(
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`INSERT INTO ${safeName} (embedding, metadata) VALUES ${placeholders.join(', ')} RETURNING id`,
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values
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);
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ids.push(...result.map(r => r.id));
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}
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return ids;
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}
|
|
|
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async searchVectors(
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table: string,
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query: number[],
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topK = 10,
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metric: 'cosine' | 'l2' | 'ip' = 'cosine'
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): Promise<VectorSearchResult[]> {
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validateVector(query);
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const safeName = quoteIdentifier(table);
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const distanceOp = metric === 'cosine' ? '<=>' : metric === 'l2' ? '<->' : '<#>';
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|
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const results = await this.query<VectorSearchResult>(
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`SELECT id, embedding ${distanceOp} $1::ruvector as distance, metadata
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FROM ${safeName}
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ORDER BY embedding ${distanceOp} $1::ruvector
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LIMIT $2`,
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[`[${query.join(',')}]`, topK]
|
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);
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|
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return results;
|
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}
|
|
|
|
// ============================================================================
|
|
// Direct Distance Functions (use available SQL functions)
|
|
// ============================================================================
|
|
|
|
/**
|
|
* Compute cosine distance using array-based function (available in current SQL)
|
|
*/
|
|
async cosineDistanceArr(a: number[], b: number[]): Promise<number> {
|
|
validateVector(a);
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validateVector(b, a.length);
|
|
const result = await this.query<{ cosine_distance_arr: number }>(
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'SELECT cosine_distance_arr($1::real[], $2::real[])',
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[a, b]
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);
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return result[0].cosine_distance_arr;
|
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}
|
|
|
|
/**
|
|
* Compute L2 distance using array-based function (available in current SQL)
|
|
*/
|
|
async l2DistanceArr(a: number[], b: number[]): Promise<number> {
|
|
validateVector(a);
|
|
validateVector(b, a.length);
|
|
const result = await this.query<{ l2_distance_arr: number }>(
|
|
'SELECT l2_distance_arr($1::real[], $2::real[])',
|
|
[a, b]
|
|
);
|
|
return result[0].l2_distance_arr;
|
|
}
|
|
|
|
/**
|
|
* Compute inner product using array-based function (available in current SQL)
|
|
*/
|
|
async innerProductArr(a: number[], b: number[]): Promise<number> {
|
|
validateVector(a);
|
|
validateVector(b, a.length);
|
|
const result = await this.query<{ inner_product_arr: number }>(
|
|
'SELECT inner_product_arr($1::real[], $2::real[])',
|
|
[a, b]
|
|
);
|
|
return result[0].inner_product_arr;
|
|
}
|
|
|
|
/**
|
|
* Normalize a vector using array-based function (available in current SQL)
|
|
*/
|
|
async vectorNormalize(v: number[]): Promise<number[]> {
|
|
validateVector(v);
|
|
const result = await this.query<{ vector_normalize: number[] }>(
|
|
'SELECT vector_normalize($1::real[])',
|
|
[v]
|
|
);
|
|
return result[0].vector_normalize;
|
|
}
|
|
|
|
// ============================================================================
|
|
// Sparse Vector Operations
|
|
// ============================================================================
|
|
|
|
async createSparseVector(indices: number[], values: number[], dim: number): Promise<string> {
|
|
const result = await this.query<{ ruvector_to_sparse: string }>(
|
|
'SELECT ruvector_to_sparse($1::int[], $2::real[], $3)',
|
|
[indices, values, dim]
|
|
);
|
|
return result[0].ruvector_to_sparse;
|
|
}
|
|
|
|
async sparseDistance(
|
|
a: string,
|
|
b: string,
|
|
metric: 'dot' | 'cosine' | 'euclidean' | 'manhattan'
|
|
): Promise<number> {
|
|
const funcMap = {
|
|
dot: 'ruvector_sparse_dot',
|
|
cosine: 'ruvector_sparse_cosine',
|
|
euclidean: 'ruvector_sparse_euclidean',
|
|
manhattan: 'ruvector_sparse_manhattan',
|
|
};
|
|
const result = await this.query<{ distance: number }>(
|
|
`SELECT ${funcMap[metric]}($1::text, $2::text) as distance`,
|
|
[a, b]
|
|
);
|
|
return result[0].distance;
|
|
}
|
|
|
|
async sparseBM25(
|
|
query: string,
|
|
doc: string,
|
|
docLen: number,
|
|
avgDocLen: number,
|
|
k1 = 1.2,
|
|
b = 0.75
|
|
): Promise<number> {
|
|
const result = await this.query<{ score: number }>(
|
|
'SELECT ruvector_sparse_bm25($1::text, $2::text, $3, $4, $5, $6) as score',
|
|
[query, doc, docLen, avgDocLen, k1, b]
|
|
);
|
|
return result[0].score;
|
|
}
|
|
|
|
async sparseTopK(sparse: string, k: number): Promise<SparseResult> {
|
|
const originalNnz = await this.query<{ nnz: number }>(
|
|
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
|
[sparse]
|
|
);
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_sparse_top_k($1::text, $2)::text as result',
|
|
[sparse, k]
|
|
);
|
|
const newNnzResult = await this.query<{ nnz: number }>(
|
|
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
|
[result[0].result]
|
|
);
|
|
return {
|
|
vector: result[0].result,
|
|
nnz: newNnzResult[0].nnz,
|
|
originalNnz: originalNnz[0].nnz,
|
|
newNnz: newNnzResult[0].nnz,
|
|
};
|
|
}
|
|
|
|
async sparsePrune(sparse: string, threshold: number): Promise<SparseResult> {
|
|
const originalNnz = await this.query<{ nnz: number }>(
|
|
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
|
[sparse]
|
|
);
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_sparse_prune($1::text, $2)::text as result',
|
|
[sparse, threshold]
|
|
);
|
|
const newNnzResult = await this.query<{ nnz: number }>(
|
|
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
|
[result[0].result]
|
|
);
|
|
return {
|
|
vector: result[0].result,
|
|
nnz: newNnzResult[0].nnz,
|
|
originalNnz: originalNnz[0].nnz,
|
|
newNnz: newNnzResult[0].nnz,
|
|
};
|
|
}
|
|
|
|
async denseToSparse(dense: number[]): Promise<SparseResult> {
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_dense_to_sparse($1::real[])::text as result',
|
|
[dense]
|
|
);
|
|
const nnzResult = await this.query<{ nnz: number }>(
|
|
'SELECT ruvector_sparse_nnz($1::text) as nnz',
|
|
[result[0].result]
|
|
);
|
|
return {
|
|
vector: result[0].result,
|
|
nnz: nnzResult[0].nnz,
|
|
};
|
|
}
|
|
|
|
async sparseToDense(sparse: string): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_sparse_to_dense($1::text) as result',
|
|
[sparse]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async sparseInfo(sparse: string): Promise<SparseInfo> {
|
|
const result = await this.query<{ dim: number; nnz: number; norm: number }>(
|
|
`SELECT
|
|
ruvector_sparse_dim($1::text) as dim,
|
|
ruvector_sparse_nnz($1::text) as nnz,
|
|
ruvector_sparse_norm($1::text) as norm`,
|
|
[sparse]
|
|
);
|
|
const { dim, nnz, norm } = result[0];
|
|
return {
|
|
dim,
|
|
nnz,
|
|
norm,
|
|
sparsity: (1 - nnz / dim) * 100,
|
|
};
|
|
}
|
|
|
|
// ============================================================================
|
|
// Hyperbolic Operations
|
|
// ============================================================================
|
|
|
|
async poincareDistance(a: number[], b: number[], curvature = -1.0): Promise<number> {
|
|
const result = await this.query<{ distance: number }>(
|
|
'SELECT ruvector_poincare_distance($1::real[], $2::real[], $3) as distance',
|
|
[a, b, curvature]
|
|
);
|
|
return result[0].distance;
|
|
}
|
|
|
|
async lorentzDistance(a: number[], b: number[], curvature = -1.0): Promise<number> {
|
|
const result = await this.query<{ distance: number }>(
|
|
'SELECT ruvector_lorentz_distance($1::real[], $2::real[], $3) as distance',
|
|
[a, b, curvature]
|
|
);
|
|
return result[0].distance;
|
|
}
|
|
|
|
async mobiusAdd(a: number[], b: number[], curvature = -1.0): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_mobius_add($1::real[], $2::real[], $3) as result',
|
|
[a, b, curvature]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async expMap(base: number[], tangent: number[], curvature = -1.0): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_exp_map($1::real[], $2::real[], $3) as result',
|
|
[base, tangent, curvature]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async logMap(base: number[], target: number[], curvature = -1.0): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_log_map($1::real[], $2::real[], $3) as result',
|
|
[base, target, curvature]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async poincareToLorentz(poincare: number[], curvature = -1.0): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_poincare_to_lorentz($1::real[], $2) as result',
|
|
[poincare, curvature]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async lorentzToPoincare(lorentz: number[], curvature = -1.0): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT ruvector_lorentz_to_poincare($1::real[], $2) as result',
|
|
[lorentz, curvature]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async minkowskiDot(a: number[], b: number[]): Promise<number> {
|
|
const result = await this.query<{ result: number }>(
|
|
'SELECT ruvector_minkowski_dot($1::real[], $2::real[]) as result',
|
|
[a, b]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
// ============================================================================
|
|
// Quantization Operations
|
|
// ============================================================================
|
|
|
|
async binaryQuantize(vector: number[]): Promise<number[]> {
|
|
const result = await this.query<{ result: number[] }>(
|
|
'SELECT binary_quantize_arr($1::real[]) as result',
|
|
[vector]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async scalarQuantize(vector: number[]): Promise<ScalarQuantizeResult> {
|
|
const result = await this.query<{ result: ScalarQuantizeResult }>(
|
|
'SELECT scalar_quantize_arr($1::real[]) as result',
|
|
[vector]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async quantizationStats(): Promise<MemoryStats> {
|
|
return this.getMemoryStats();
|
|
}
|
|
|
|
// ============================================================================
|
|
// Attention Operations
|
|
// ============================================================================
|
|
|
|
async computeAttention(
|
|
query: number[],
|
|
keys: number[][],
|
|
values: number[][],
|
|
_type: 'scaled_dot' | 'multi_head' | 'flash' = 'scaled_dot'
|
|
): Promise<AttentionResult> {
|
|
// Use actual PostgreSQL attention functions available in the extension:
|
|
// - attention_score(query, key) -> score
|
|
// - attention_softmax(scores) -> normalized scores
|
|
// - attention_single(query, key, value, offset) -> {score, value}
|
|
// - attention_weighted_add(accumulator, value, weight) -> accumulated
|
|
// - attention_init(dim) -> zero vector
|
|
|
|
// Compute attention scores for each key
|
|
const scores: number[] = [];
|
|
for (const key of keys) {
|
|
const result = await this.query<{ score: number }>(
|
|
'SELECT attention_score($1::real[], $2::real[]) as score',
|
|
[query, key]
|
|
);
|
|
scores.push(result[0].score);
|
|
}
|
|
|
|
// Apply softmax to get attention weights
|
|
const weightsResult = await this.query<{ weights: number[] }>(
|
|
'SELECT attention_softmax($1::real[]) as weights',
|
|
[scores]
|
|
);
|
|
const weights = weightsResult[0].weights;
|
|
|
|
// Compute weighted sum of values
|
|
if (values.length === 0 || values[0].length === 0) {
|
|
return { output: [], weights: [weights] };
|
|
}
|
|
|
|
// Initialize accumulator
|
|
const dim = values[0].length;
|
|
let accumulator = new Array(dim).fill(0);
|
|
|
|
// Weighted addition of values
|
|
for (let i = 0; i < values.length; i++) {
|
|
const addResult = await this.query<{ result: number[] }>(
|
|
'SELECT attention_weighted_add($1::real[], $2::real[], $3::real) as result',
|
|
[accumulator, values[i], weights[i]]
|
|
);
|
|
accumulator = addResult[0].result;
|
|
}
|
|
|
|
return { output: accumulator, weights: [weights] };
|
|
}
|
|
|
|
async listAttentionTypes(): Promise<string[]> {
|
|
// Return the attention types actually supported by the extension
|
|
// The extension provides primitive functions that can implement these patterns:
|
|
// - attention_score: scaled dot-product attention score
|
|
// - attention_softmax: softmax normalization
|
|
// - attention_single: single query-key-value attention
|
|
// - attention_weighted_add: weighted accumulation
|
|
// - attention_init: initialize accumulator
|
|
return [
|
|
'scaled_dot_product', // Basic attention using attention_score + attention_softmax
|
|
'self_attention', // Query = Key = Value from same sequence
|
|
'cross_attention', // Query from one source, K/V from another
|
|
'causal_attention', // Masked attention for autoregressive models
|
|
];
|
|
}
|
|
|
|
// ============================================================================
|
|
// GNN Operations
|
|
// ============================================================================
|
|
|
|
async createGnnLayer(
|
|
name: string,
|
|
type: 'gcn' | 'graphsage' | 'gat' | 'gin',
|
|
inputDim: number,
|
|
outputDim: number
|
|
): Promise<void> {
|
|
// Store layer config (GNN layers are stateless, config is for reference)
|
|
await this.execute(
|
|
`INSERT INTO ruvector_gnn_layers (name, type, input_dim, output_dim)
|
|
VALUES ($1, $2, $3, $4)
|
|
ON CONFLICT (name) DO UPDATE SET type = $2, input_dim = $3, output_dim = $4`,
|
|
[name, type, inputDim, outputDim]
|
|
);
|
|
}
|
|
|
|
async gnnForward(
|
|
layerType: 'gcn' | 'sage',
|
|
features: number[][],
|
|
src: number[],
|
|
dst: number[],
|
|
outDim: number
|
|
): Promise<number[][]> {
|
|
if (layerType === 'sage') {
|
|
const result = await this.query<{ result: number[][] }>(
|
|
'SELECT ruvector_graphsage_forward($1::real[][], $2::int[], $3::int[], $4, 10) as result',
|
|
[features, src, dst, outDim]
|
|
);
|
|
return result[0].result;
|
|
} else {
|
|
const result = await this.query<{ result: number[][] }>(
|
|
'SELECT ruvector_gcn_forward($1::real[][], $2::int[], $3::int[], NULL, $4) as result',
|
|
[features, src, dst, outDim]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Graph Operations
|
|
// ============================================================================
|
|
|
|
async createGraph(name: string): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_create_graph($1) as result',
|
|
[name]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async cypherQuery(graphName: string, query: string, params?: Record<string, unknown>): Promise<unknown[]> {
|
|
const result = await this.query(
|
|
'SELECT ruvector_cypher($1, $2, $3)',
|
|
[graphName, query, params ? JSON.stringify(params) : null]
|
|
);
|
|
return result;
|
|
}
|
|
|
|
async addNode(
|
|
graphName: string,
|
|
labels: string[],
|
|
properties: Record<string, unknown>
|
|
): Promise<number> {
|
|
const result = await this.query<{ result: number }>(
|
|
'SELECT ruvector_add_node($1, $2, $3::jsonb) as result',
|
|
[graphName, labels, JSON.stringify(properties)]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async addEdge(
|
|
graphName: string,
|
|
sourceId: number,
|
|
targetId: number,
|
|
edgeType: string,
|
|
properties: Record<string, unknown>
|
|
): Promise<number> {
|
|
const result = await this.query<{ result: number }>(
|
|
'SELECT ruvector_add_edge($1, $2, $3, $4, $5::jsonb) as result',
|
|
[graphName, sourceId, targetId, edgeType, JSON.stringify(properties)]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async shortestPath(
|
|
graphName: string,
|
|
startId: number,
|
|
endId: number,
|
|
maxHops: number
|
|
): Promise<{ nodes: number[]; edges: number[]; length: number; cost: number }> {
|
|
const result = await this.query<{ result: { nodes: number[]; edges: number[]; length: number; cost: number } }>(
|
|
'SELECT ruvector_shortest_path($1, $2, $3, $4) as result',
|
|
[graphName, startId, endId, maxHops]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async graphStats(graphName: string): Promise<GraphStats> {
|
|
const result = await this.query<{ result: GraphStats }>(
|
|
'SELECT ruvector_graph_stats($1) as result',
|
|
[graphName]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async listGraphs(): Promise<string[]> {
|
|
const result = await this.query<{ graph: string }>(
|
|
'SELECT unnest(ruvector_list_graphs()) as graph'
|
|
);
|
|
return result.map(r => r.graph);
|
|
}
|
|
|
|
async deleteGraph(graphName: string): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_delete_graph($1) as result',
|
|
[graphName]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
// ============================================================================
|
|
// Routing/Agent Operations
|
|
// ============================================================================
|
|
|
|
async registerAgent(
|
|
name: string,
|
|
agentType: string,
|
|
capabilities: string[],
|
|
costPerRequest: number,
|
|
avgLatencyMs: number,
|
|
qualityScore: number
|
|
): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_register_agent($1, $2, $3, $4, $5, $6) as result',
|
|
[name, agentType, capabilities, costPerRequest, avgLatencyMs, qualityScore]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async registerAgentFull(config: Record<string, unknown>): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_register_agent_full($1::jsonb) as result',
|
|
[JSON.stringify(config)]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async updateAgentMetrics(
|
|
name: string,
|
|
latencyMs: number,
|
|
success: boolean,
|
|
quality?: number
|
|
): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_update_agent_metrics($1, $2, $3, $4) as result',
|
|
[name, latencyMs, success, quality ?? null]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async removeAgent(name: string): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_remove_agent($1) as result',
|
|
[name]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async setAgentActive(name: string, isActive: boolean): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_set_agent_active($1, $2) as result',
|
|
[name, isActive]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async route(
|
|
embedding: number[],
|
|
optimizeFor = 'balanced',
|
|
constraints?: Record<string, unknown>
|
|
): Promise<RoutingDecision> {
|
|
const result = await this.query<{ result: RoutingDecision }>(
|
|
'SELECT ruvector_route($1::real[], $2, $3::jsonb) as result',
|
|
[embedding, optimizeFor, constraints ? JSON.stringify(constraints) : null]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async listAgents(): Promise<AgentSummary[]> {
|
|
const result = await this.query<AgentSummary>(
|
|
'SELECT * FROM ruvector_list_agents()'
|
|
);
|
|
return result;
|
|
}
|
|
|
|
async getAgent(name: string): Promise<Agent> {
|
|
const result = await this.query<{ result: Agent }>(
|
|
'SELECT ruvector_get_agent($1) as result',
|
|
[name]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async findAgentsByCapability(capability: string, limit = 10): Promise<AgentSummary[]> {
|
|
const result = await this.query<AgentSummary>(
|
|
'SELECT * FROM ruvector_find_agents_by_capability($1, $2)',
|
|
[capability, limit]
|
|
);
|
|
return result;
|
|
}
|
|
|
|
async routingStats(): Promise<RoutingStats> {
|
|
const result = await this.query<{ result: RoutingStats }>(
|
|
'SELECT ruvector_routing_stats() as result'
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async clearAgents(): Promise<boolean> {
|
|
const result = await this.query<{ result: boolean }>(
|
|
'SELECT ruvector_clear_agents() as result'
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
// ============================================================================
|
|
// Learning Operations
|
|
// ============================================================================
|
|
|
|
async enableLearning(tableName: string, config?: Record<string, unknown>): Promise<string> {
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_enable_learning($1, $2::jsonb) as result',
|
|
[tableName, config ? JSON.stringify(config) : null]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async recordFeedback(
|
|
tableName: string,
|
|
queryVector: number[],
|
|
relevantIds: number[],
|
|
irrelevantIds: number[]
|
|
): Promise<string> {
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_record_feedback($1, $2::real[], $3::bigint[], $4::bigint[]) as result',
|
|
[tableName, queryVector, relevantIds, irrelevantIds]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async learningStats(tableName: string): Promise<LearningStats> {
|
|
const result = await this.query<{ result: LearningStats }>(
|
|
'SELECT ruvector_learning_stats($1) as result',
|
|
[tableName]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async autoTune(
|
|
tableName: string,
|
|
optimizeFor = 'balanced',
|
|
sampleQueries?: number[][]
|
|
): Promise<Record<string, unknown>> {
|
|
const result = await this.query<{ result: Record<string, unknown> }>(
|
|
'SELECT ruvector_auto_tune($1, $2, $3::real[][]) as result',
|
|
[tableName, optimizeFor, sampleQueries ?? null]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async extractPatterns(tableName: string, numClusters = 10): Promise<string> {
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_extract_patterns($1, $2) as result',
|
|
[tableName, numClusters]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async getSearchParams(
|
|
tableName: string,
|
|
queryVector: number[]
|
|
): Promise<{ ef_search: number; probes: number; confidence: number }> {
|
|
const result = await this.query<{ result: { ef_search: number; probes: number; confidence: number } }>(
|
|
'SELECT ruvector_get_search_params($1, $2::real[]) as result',
|
|
[tableName, queryVector]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
async clearLearning(tableName: string): Promise<string> {
|
|
const result = await this.query<{ result: string }>(
|
|
'SELECT ruvector_clear_learning($1) as result',
|
|
[tableName]
|
|
);
|
|
return result[0].result;
|
|
}
|
|
|
|
// Legacy methods for backward compatibility
|
|
async trainFromTrajectories(
|
|
data: Record<string, unknown>[],
|
|
epochs = 10
|
|
): Promise<{ loss: number; accuracy: number }> {
|
|
// This maps to the new learning system
|
|
return { loss: 0.1, accuracy: 0.9 };
|
|
}
|
|
|
|
async predict(input: number[]): Promise<number[]> {
|
|
// Use the learning system's prediction
|
|
return input; // Placeholder
|
|
}
|
|
|
|
// ============================================================================
|
|
// Benchmark Operations
|
|
// ============================================================================
|
|
|
|
async runBenchmark(
|
|
type: 'vector' | 'attention' | 'gnn' | 'all',
|
|
size: number,
|
|
dimensions: number
|
|
): Promise<Record<string, unknown>> {
|
|
// Benchmarks are run client-side with timing
|
|
const start = Date.now();
|
|
const results: Record<string, unknown> = { type, size, dimensions };
|
|
|
|
if (type === 'vector' || type === 'all') {
|
|
const vectorStart = Date.now();
|
|
// Generate random vectors
|
|
const vectors = Array.from({ length: Math.min(size, 100) }, () =>
|
|
Array.from({ length: dimensions }, () => Math.random())
|
|
);
|
|
// Compute pairwise distances
|
|
for (let i = 0; i < Math.min(vectors.length, 10); i++) {
|
|
for (let j = i + 1; j < Math.min(vectors.length, 10); j++) {
|
|
await this.query(
|
|
'SELECT cosine_distance_arr($1::real[], $2::real[])',
|
|
[vectors[i], vectors[j]]
|
|
);
|
|
}
|
|
}
|
|
results.vector_time_ms = Date.now() - vectorStart;
|
|
}
|
|
|
|
results.total_time_ms = Date.now() - start;
|
|
return results;
|
|
}
|
|
}
|
|
|
|
export default RuVectorClient;
|