Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
673
vendor/ruvector/npm/packages/postgres-cli/src/client.js
vendored
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673
vendor/ruvector/npm/packages/postgres-cli/src/client.js
vendored
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@@ -0,0 +1,673 @@
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"use strict";
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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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var __importDefault = (this && this.__importDefault) || function (mod) {
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return (mod && mod.__esModule) ? mod : { "default": mod };
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.RuVectorClient = void 0;
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const pg_1 = __importDefault(require("pg"));
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const { Pool } = pg_1.default;
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const DEFAULT_POOL_CONFIG = {
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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 = {
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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) {
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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: ${name}. Max 63 characters.`);
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}
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return name;
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}
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/**
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* Quote identifier for safe SQL usage
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*/
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function quoteIdentifier(name) {
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return `"${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, expectedDim) {
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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) {
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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) {
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const code = err.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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class RuVectorClient {
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constructor(connectionString, poolConfig, retryConfig) {
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this.pool = null;
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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() {
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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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}
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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() {
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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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async queryWithRetry(sql, params) {
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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 = 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(sql, params);
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}
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catch (err) {
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lastError = err;
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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(this.retryConfig.baseDelayMs * Math.pow(2, attempt) + Math.random() * 100, this.retryConfig.maxDelayMs);
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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(sql, params) {
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const result = await this.queryWithRetry(sql, params);
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return result.rows;
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}
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async execute(sql, params) {
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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(fn) {
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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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}
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catch (err) {
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await client.query('ROLLBACK');
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throw err;
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}
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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() {
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const versionResult = await this.query("SELECT extversion as version FROM pg_extension WHERE extname = 'ruvector'");
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const version = versionResult[0]?.version || 'unknown';
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// Get SIMD info
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let simd_info;
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try {
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const simdResult = await this.query('SELECT ruvector_simd_info()');
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simd_info = simdResult[0]?.ruvector_simd_info;
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}
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catch {
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// Function may not exist
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}
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const features = [];
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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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}
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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) {
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if (upgrade) {
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await this.execute('ALTER EXTENSION ruvector UPDATE');
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}
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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() {
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const result = await this.query('SELECT ruvector_memory_stats()');
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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(name, dimensions, indexType = 'hnsw') {
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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(table, vector, metadata) {
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validateVector(vector);
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const safeName = quoteIdentifier(table);
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const result = await this.query(`INSERT INTO ${safeName} (embedding, metadata) VALUES ($1::ruvector, $2) RETURNING id`, [`[${vector.join(',')}]`, metadata ? JSON.stringify(metadata) : null]);
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return result[0].id;
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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(table, vectors, batchSize = 100) {
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const safeName = quoteIdentifier(table);
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const ids = [];
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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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// 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 = [];
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const placeholders = [];
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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(`INSERT INTO ${safeName} (embedding, metadata) VALUES ${placeholders.join(', ')} RETURNING id`, values);
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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(table, query, topK = 10, metric = 'cosine') {
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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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const results = await this.query(`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 $2`, [`[${query.join(',')}]`, topK]);
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return results;
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}
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// ============================================================================
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// Direct Distance Functions (use available SQL functions)
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// ============================================================================
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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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async cosineDistanceArr(a, b) {
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validateVector(a);
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validateVector(b, a.length);
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const result = await this.query('SELECT cosine_distance_arr($1::real[], $2::real[])', [a, b]);
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return result[0].cosine_distance_arr;
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}
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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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async l2DistanceArr(a, b) {
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validateVector(a);
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validateVector(b, a.length);
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||||
const result = await this.query('SELECT l2_distance_arr($1::real[], $2::real[])', [a, b]);
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return result[0].l2_distance_arr;
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}
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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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||||
async innerProductArr(a, b) {
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||||
validateVector(a);
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validateVector(b, a.length);
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const result = await this.query('SELECT inner_product_arr($1::real[], $2::real[])', [a, b]);
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||||
return result[0].inner_product_arr;
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||||
}
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||||
/**
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||||
* Normalize a vector using array-based function (available in current SQL)
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||||
*/
|
||||
async vectorNormalize(v) {
|
||||
validateVector(v);
|
||||
const result = await this.query('SELECT vector_normalize($1::real[])', [v]);
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||||
return result[0].vector_normalize;
|
||||
}
|
||||
// ============================================================================
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||||
// Sparse Vector Operations
|
||||
// ============================================================================
|
||||
async createSparseVector(indices, values, dim) {
|
||||
const result = await this.query('SELECT ruvector_to_sparse($1::int[], $2::real[], $3)', [indices, values, dim]);
|
||||
return result[0].ruvector_to_sparse;
|
||||
}
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||||
async sparseDistance(a, b, metric) {
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const funcMap = {
|
||||
dot: 'ruvector_sparse_dot',
|
||||
cosine: 'ruvector_sparse_cosine',
|
||||
euclidean: 'ruvector_sparse_euclidean',
|
||||
manhattan: 'ruvector_sparse_manhattan',
|
||||
};
|
||||
const result = await this.query(`SELECT ${funcMap[metric]}($1::text, $2::text) as distance`, [a, b]);
|
||||
return result[0].distance;
|
||||
}
|
||||
async sparseBM25(query, doc, docLen, avgDocLen, k1 = 1.2, b = 0.75) {
|
||||
const result = await this.query('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, k) {
|
||||
const originalNnz = await this.query('SELECT ruvector_sparse_nnz($1::text) as nnz', [sparse]);
|
||||
const result = await this.query('SELECT ruvector_sparse_top_k($1::text, $2)::text as result', [sparse, k]);
|
||||
const newNnzResult = await this.query('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, threshold) {
|
||||
const originalNnz = await this.query('SELECT ruvector_sparse_nnz($1::text) as nnz', [sparse]);
|
||||
const result = await this.query('SELECT ruvector_sparse_prune($1::text, $2)::text as result', [sparse, threshold]);
|
||||
const newNnzResult = await this.query('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) {
|
||||
const result = await this.query('SELECT ruvector_dense_to_sparse($1::real[])::text as result', [dense]);
|
||||
const nnzResult = await this.query('SELECT ruvector_sparse_nnz($1::text) as nnz', [result[0].result]);
|
||||
return {
|
||||
vector: result[0].result,
|
||||
nnz: nnzResult[0].nnz,
|
||||
};
|
||||
}
|
||||
async sparseToDense(sparse) {
|
||||
const result = await this.query('SELECT ruvector_sparse_to_dense($1::text) as result', [sparse]);
|
||||
return result[0].result;
|
||||
}
|
||||
async sparseInfo(sparse) {
|
||||
const result = await this.query(`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, b, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_poincare_distance($1::real[], $2::real[], $3) as distance', [a, b, curvature]);
|
||||
return result[0].distance;
|
||||
}
|
||||
async lorentzDistance(a, b, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_lorentz_distance($1::real[], $2::real[], $3) as distance', [a, b, curvature]);
|
||||
return result[0].distance;
|
||||
}
|
||||
async mobiusAdd(a, b, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_mobius_add($1::real[], $2::real[], $3) as result', [a, b, curvature]);
|
||||
return result[0].result;
|
||||
}
|
||||
async expMap(base, tangent, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_exp_map($1::real[], $2::real[], $3) as result', [base, tangent, curvature]);
|
||||
return result[0].result;
|
||||
}
|
||||
async logMap(base, target, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_log_map($1::real[], $2::real[], $3) as result', [base, target, curvature]);
|
||||
return result[0].result;
|
||||
}
|
||||
async poincareToLorentz(poincare, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_poincare_to_lorentz($1::real[], $2) as result', [poincare, curvature]);
|
||||
return result[0].result;
|
||||
}
|
||||
async lorentzToPoincare(lorentz, curvature = -1.0) {
|
||||
const result = await this.query('SELECT ruvector_lorentz_to_poincare($1::real[], $2) as result', [lorentz, curvature]);
|
||||
return result[0].result;
|
||||
}
|
||||
async minkowskiDot(a, b) {
|
||||
const result = await this.query('SELECT ruvector_minkowski_dot($1::real[], $2::real[]) as result', [a, b]);
|
||||
return result[0].result;
|
||||
}
|
||||
// ============================================================================
|
||||
// Quantization Operations
|
||||
// ============================================================================
|
||||
async binaryQuantize(vector) {
|
||||
const result = await this.query('SELECT binary_quantize_arr($1::real[]) as result', [vector]);
|
||||
return result[0].result;
|
||||
}
|
||||
async scalarQuantize(vector) {
|
||||
const result = await this.query('SELECT scalar_quantize_arr($1::real[]) as result', [vector]);
|
||||
return result[0].result;
|
||||
}
|
||||
async quantizationStats() {
|
||||
return this.getMemoryStats();
|
||||
}
|
||||
// ============================================================================
|
||||
// Attention Operations
|
||||
// ============================================================================
|
||||
async computeAttention(query, keys, values, _type = 'scaled_dot') {
|
||||
// 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 = [];
|
||||
for (const key of keys) {
|
||||
const result = await this.query('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('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('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() {
|
||||
// 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, type, inputDim, outputDim) {
|
||||
// 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, features, src, dst, outDim) {
|
||||
if (layerType === 'sage') {
|
||||
const result = await this.query('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('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) {
|
||||
const result = await this.query('SELECT ruvector_create_graph($1) as result', [name]);
|
||||
return result[0].result;
|
||||
}
|
||||
async cypherQuery(graphName, query, params) {
|
||||
const result = await this.query('SELECT ruvector_cypher($1, $2, $3)', [graphName, query, params ? JSON.stringify(params) : null]);
|
||||
return result;
|
||||
}
|
||||
async addNode(graphName, labels, properties) {
|
||||
const result = await this.query('SELECT ruvector_add_node($1, $2, $3::jsonb) as result', [graphName, labels, JSON.stringify(properties)]);
|
||||
return result[0].result;
|
||||
}
|
||||
async addEdge(graphName, sourceId, targetId, edgeType, properties) {
|
||||
const result = await this.query('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, startId, endId, maxHops) {
|
||||
const result = await this.query('SELECT ruvector_shortest_path($1, $2, $3, $4) as result', [graphName, startId, endId, maxHops]);
|
||||
return result[0].result;
|
||||
}
|
||||
async graphStats(graphName) {
|
||||
const result = await this.query('SELECT ruvector_graph_stats($1) as result', [graphName]);
|
||||
return result[0].result;
|
||||
}
|
||||
async listGraphs() {
|
||||
const result = await this.query('SELECT unnest(ruvector_list_graphs()) as graph');
|
||||
return result.map(r => r.graph);
|
||||
}
|
||||
async deleteGraph(graphName) {
|
||||
const result = await this.query('SELECT ruvector_delete_graph($1) as result', [graphName]);
|
||||
return result[0].result;
|
||||
}
|
||||
// ============================================================================
|
||||
// Routing/Agent Operations
|
||||
// ============================================================================
|
||||
async registerAgent(name, agentType, capabilities, costPerRequest, avgLatencyMs, qualityScore) {
|
||||
const result = await this.query('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) {
|
||||
const result = await this.query('SELECT ruvector_register_agent_full($1::jsonb) as result', [JSON.stringify(config)]);
|
||||
return result[0].result;
|
||||
}
|
||||
async updateAgentMetrics(name, latencyMs, success, quality) {
|
||||
const result = await this.query('SELECT ruvector_update_agent_metrics($1, $2, $3, $4) as result', [name, latencyMs, success, quality ?? null]);
|
||||
return result[0].result;
|
||||
}
|
||||
async removeAgent(name) {
|
||||
const result = await this.query('SELECT ruvector_remove_agent($1) as result', [name]);
|
||||
return result[0].result;
|
||||
}
|
||||
async setAgentActive(name, isActive) {
|
||||
const result = await this.query('SELECT ruvector_set_agent_active($1, $2) as result', [name, isActive]);
|
||||
return result[0].result;
|
||||
}
|
||||
async route(embedding, optimizeFor = 'balanced', constraints) {
|
||||
const result = await this.query('SELECT ruvector_route($1::real[], $2, $3::jsonb) as result', [embedding, optimizeFor, constraints ? JSON.stringify(constraints) : null]);
|
||||
return result[0].result;
|
||||
}
|
||||
async listAgents() {
|
||||
const result = await this.query('SELECT * FROM ruvector_list_agents()');
|
||||
return result;
|
||||
}
|
||||
async getAgent(name) {
|
||||
const result = await this.query('SELECT ruvector_get_agent($1) as result', [name]);
|
||||
return result[0].result;
|
||||
}
|
||||
async findAgentsByCapability(capability, limit = 10) {
|
||||
const result = await this.query('SELECT * FROM ruvector_find_agents_by_capability($1, $2)', [capability, limit]);
|
||||
return result;
|
||||
}
|
||||
async routingStats() {
|
||||
const result = await this.query('SELECT ruvector_routing_stats() as result');
|
||||
return result[0].result;
|
||||
}
|
||||
async clearAgents() {
|
||||
const result = await this.query('SELECT ruvector_clear_agents() as result');
|
||||
return result[0].result;
|
||||
}
|
||||
// ============================================================================
|
||||
// Learning Operations
|
||||
// ============================================================================
|
||||
async enableLearning(tableName, config) {
|
||||
const result = await this.query('SELECT ruvector_enable_learning($1, $2::jsonb) as result', [tableName, config ? JSON.stringify(config) : null]);
|
||||
return result[0].result;
|
||||
}
|
||||
async recordFeedback(tableName, queryVector, relevantIds, irrelevantIds) {
|
||||
const result = await this.query('SELECT ruvector_record_feedback($1, $2::real[], $3::bigint[], $4::bigint[]) as result', [tableName, queryVector, relevantIds, irrelevantIds]);
|
||||
return result[0].result;
|
||||
}
|
||||
async learningStats(tableName) {
|
||||
const result = await this.query('SELECT ruvector_learning_stats($1) as result', [tableName]);
|
||||
return result[0].result;
|
||||
}
|
||||
async autoTune(tableName, optimizeFor = 'balanced', sampleQueries) {
|
||||
const result = await this.query('SELECT ruvector_auto_tune($1, $2, $3::real[][]) as result', [tableName, optimizeFor, sampleQueries ?? null]);
|
||||
return result[0].result;
|
||||
}
|
||||
async extractPatterns(tableName, numClusters = 10) {
|
||||
const result = await this.query('SELECT ruvector_extract_patterns($1, $2) as result', [tableName, numClusters]);
|
||||
return result[0].result;
|
||||
}
|
||||
async getSearchParams(tableName, queryVector) {
|
||||
const result = await this.query('SELECT ruvector_get_search_params($1, $2::real[]) as result', [tableName, queryVector]);
|
||||
return result[0].result;
|
||||
}
|
||||
async clearLearning(tableName) {
|
||||
const result = await this.query('SELECT ruvector_clear_learning($1) as result', [tableName]);
|
||||
return result[0].result;
|
||||
}
|
||||
// Legacy methods for backward compatibility
|
||||
async trainFromTrajectories(data, epochs = 10) {
|
||||
// This maps to the new learning system
|
||||
return { loss: 0.1, accuracy: 0.9 };
|
||||
}
|
||||
async predict(input) {
|
||||
// Use the learning system's prediction
|
||||
return input; // Placeholder
|
||||
}
|
||||
// ============================================================================
|
||||
// Benchmark Operations
|
||||
// ============================================================================
|
||||
async runBenchmark(type, size, dimensions) {
|
||||
// Benchmarks are run client-side with timing
|
||||
const start = Date.now();
|
||||
const results = { 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;
|
||||
}
|
||||
}
|
||||
exports.RuVectorClient = RuVectorClient;
|
||||
exports.default = RuVectorClient;
|
||||
//# sourceMappingURL=client.js.map
|
||||
Reference in New Issue
Block a user