Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
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ruv
264
vendor/ruvector/npm/packages/postgres-cli/src/client.d.ts
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264
vendor/ruvector/npm/packages/postgres-cli/src/client.d.ts
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/**
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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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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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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<{
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name: string;
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score?: number;
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}>;
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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 declare class RuVectorClient {
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private pool;
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private connectionString;
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private poolConfig;
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private retryConfig;
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constructor(connectionString: string, poolConfig?: PoolConfig, retryConfig?: RetryConfig);
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connect(): Promise<void>;
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disconnect(): Promise<void>;
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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 queryWithRetry;
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query<T extends pg.QueryResultRow = pg.QueryResultRow>(sql: string, params?: unknown[]): Promise<T[]>;
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execute(sql: string, params?: unknown[]): Promise<void>;
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/**
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* Execute multiple statements in a transaction
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*/
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transaction<T>(fn: (client: pg.PoolClient) => Promise<T>): Promise<T>;
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getExtensionInfo(): Promise<RuVectorInfo>;
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installExtension(upgrade?: boolean): Promise<void>;
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getMemoryStats(): Promise<MemoryStats>;
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createVectorTable(name: string, dimensions: number, indexType?: 'hnsw' | 'ivfflat'): Promise<void>;
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insertVector(table: string, vector: number[], metadata?: Record<string, unknown>): Promise<number>;
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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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insertVectorsBatch(table: string, vectors: Array<{
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vector: number[];
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metadata?: Record<string, unknown>;
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}>, batchSize?: number): Promise<number[]>;
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searchVectors(table: string, query: number[], topK?: number, metric?: 'cosine' | 'l2' | 'ip'): Promise<VectorSearchResult[]>;
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/**
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* Compute cosine distance using array-based function (available in current SQL)
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*/
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cosineDistanceArr(a: number[], b: number[]): Promise<number>;
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/**
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* Compute L2 distance using array-based function (available in current SQL)
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*/
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l2DistanceArr(a: number[], b: number[]): Promise<number>;
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/**
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* Compute inner product using array-based function (available in current SQL)
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*/
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innerProductArr(a: number[], b: number[]): Promise<number>;
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/**
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* Normalize a vector using array-based function (available in current SQL)
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*/
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vectorNormalize(v: number[]): Promise<number[]>;
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createSparseVector(indices: number[], values: number[], dim: number): Promise<string>;
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sparseDistance(a: string, b: string, metric: 'dot' | 'cosine' | 'euclidean' | 'manhattan'): Promise<number>;
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sparseBM25(query: string, doc: string, docLen: number, avgDocLen: number, k1?: number, b?: number): Promise<number>;
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sparseTopK(sparse: string, k: number): Promise<SparseResult>;
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sparsePrune(sparse: string, threshold: number): Promise<SparseResult>;
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denseToSparse(dense: number[]): Promise<SparseResult>;
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sparseToDense(sparse: string): Promise<number[]>;
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sparseInfo(sparse: string): Promise<SparseInfo>;
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poincareDistance(a: number[], b: number[], curvature?: number): Promise<number>;
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lorentzDistance(a: number[], b: number[], curvature?: number): Promise<number>;
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mobiusAdd(a: number[], b: number[], curvature?: number): Promise<number[]>;
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expMap(base: number[], tangent: number[], curvature?: number): Promise<number[]>;
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logMap(base: number[], target: number[], curvature?: number): Promise<number[]>;
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poincareToLorentz(poincare: number[], curvature?: number): Promise<number[]>;
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lorentzToPoincare(lorentz: number[], curvature?: number): Promise<number[]>;
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minkowskiDot(a: number[], b: number[]): Promise<number>;
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binaryQuantize(vector: number[]): Promise<number[]>;
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scalarQuantize(vector: number[]): Promise<ScalarQuantizeResult>;
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quantizationStats(): Promise<MemoryStats>;
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computeAttention(query: number[], keys: number[][], values: number[][], _type?: 'scaled_dot' | 'multi_head' | 'flash'): Promise<AttentionResult>;
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listAttentionTypes(): Promise<string[]>;
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createGnnLayer(name: string, type: 'gcn' | 'graphsage' | 'gat' | 'gin', inputDim: number, outputDim: number): Promise<void>;
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gnnForward(layerType: 'gcn' | 'sage', features: number[][], src: number[], dst: number[], outDim: number): Promise<number[][]>;
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createGraph(name: string): Promise<boolean>;
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cypherQuery(graphName: string, query: string, params?: Record<string, unknown>): Promise<unknown[]>;
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addNode(graphName: string, labels: string[], properties: Record<string, unknown>): Promise<number>;
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addEdge(graphName: string, sourceId: number, targetId: number, edgeType: string, properties: Record<string, unknown>): Promise<number>;
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shortestPath(graphName: string, startId: number, endId: number, maxHops: number): Promise<{
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nodes: number[];
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edges: number[];
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length: number;
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cost: number;
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}>;
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graphStats(graphName: string): Promise<GraphStats>;
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listGraphs(): Promise<string[]>;
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deleteGraph(graphName: string): Promise<boolean>;
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registerAgent(name: string, agentType: string, capabilities: string[], costPerRequest: number, avgLatencyMs: number, qualityScore: number): Promise<boolean>;
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registerAgentFull(config: Record<string, unknown>): Promise<boolean>;
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updateAgentMetrics(name: string, latencyMs: number, success: boolean, quality?: number): Promise<boolean>;
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removeAgent(name: string): Promise<boolean>;
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setAgentActive(name: string, isActive: boolean): Promise<boolean>;
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route(embedding: number[], optimizeFor?: string, constraints?: Record<string, unknown>): Promise<RoutingDecision>;
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listAgents(): Promise<AgentSummary[]>;
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getAgent(name: string): Promise<Agent>;
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findAgentsByCapability(capability: string, limit?: number): Promise<AgentSummary[]>;
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routingStats(): Promise<RoutingStats>;
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clearAgents(): Promise<boolean>;
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enableLearning(tableName: string, config?: Record<string, unknown>): Promise<string>;
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recordFeedback(tableName: string, queryVector: number[], relevantIds: number[], irrelevantIds: number[]): Promise<string>;
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learningStats(tableName: string): Promise<LearningStats>;
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autoTune(tableName: string, optimizeFor?: string, sampleQueries?: number[][]): Promise<Record<string, unknown>>;
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extractPatterns(tableName: string, numClusters?: number): Promise<string>;
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getSearchParams(tableName: string, queryVector: number[]): Promise<{
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ef_search: number;
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probes: number;
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confidence: number;
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}>;
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clearLearning(tableName: string): Promise<string>;
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trainFromTrajectories(data: Record<string, unknown>[], epochs?: number): Promise<{
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loss: number;
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accuracy: number;
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}>;
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predict(input: number[]): Promise<number[]>;
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runBenchmark(type: 'vector' | 'attention' | 'gnn' | 'all', size: number, dimensions: number): Promise<Record<string, unknown>>;
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}
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export default RuVectorClient;
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//# sourceMappingURL=client.d.ts.map
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