@codai/cbd
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Codai Better Database - High-Performance Vector Memory System with HPKV-inspired architecture and MCP server
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JavaScript
/**
* Advanced Vector Search Engine
* Enhanced capabilities for hybrid search, multi-modal embeddings, and optimization
*/
import { OpenAI } from 'openai';
import { EventEmitter } from 'events';
class AdvancedVectorSearchEngine extends EventEmitter {
openai;
vectorIndex;
metadataIndex;
keywordIndex;
clusters;
searchCache;
constructor(config) {
super();
this.openai = new OpenAI({ apiKey: config.openaiApiKey });
this.vectorIndex = new Map();
this.metadataIndex = new Map();
this.keywordIndex = new Map();
this.clusters = new Map();
this.searchCache = new Map();
this.initializeAdvancedFeatures();
}
initializeAdvancedFeatures() {
// Initialize clustering algorithm
this.setupDynamicClustering();
// Setup cache management
this.setupSearchCache();
// Initialize performance monitoring
this.setupPerformanceMonitoring();
}
/**
* Hybrid Search: Combine vector, keyword, and semantic search
*/
async hybridSearch(query, options = {}) {
const startTime = Date.now();
const performance = { totalTime: 0, vectorTime: 0, keywordTime: 0, semanticTime: 0 };
try {
// Check cache first
const cacheKey = this.generateCacheKey(query, options);
if (this.searchCache.has(cacheKey)) {
return this.searchCache.get(cacheKey);
}
// Parallel search execution
const [vectorResults, keywordResults, semanticResults] = await Promise.all([
this.performVectorSearch(query, options).then(r => {
performance.vectorTime = r.time;
return r.results;
}),
this.performKeywordSearch(query, options).then(r => {
performance.keywordTime = r.time;
return r.results;
}),
this.performSemanticSearch(query, options).then(r => {
performance.semanticTime = r.time;
return r.results;
})
]);
// Combine and rank results
const combinedResults = this.combineSearchResults(vectorResults, keywordResults, semanticResults, options);
performance.totalTime = Date.now() - startTime;
const result = {
results: combinedResults,
performance
};
// Cache the result
this.searchCache.set(cacheKey, result);
this.emit('searchCompleted', { query, options, performance });
return result;
}
catch (error) {
this.emit('searchError', { query, error });
throw error;
}
}
/**
* Multi-modal Vector Embeddings
*/
async generateMultiModalEmbedding(content) {
const embeddings = {};
try {
// Text embedding
if (content.text) {
const textEmbedding = await this.openai.embeddings.create({
model: 'text-embedding-3-large',
input: content.text,
dimensions: 1536
});
embeddings.text = textEmbedding.data[0].embedding;
}
// Image embedding (placeholder - would use vision model)
if (content.imageUrl) {
embeddings.image = await this.generateImageEmbedding(content.imageUrl);
}
// Audio embedding (placeholder - would use audio model)
if (content.audioUrl) {
embeddings.audio = await this.generateAudioEmbedding(content.audioUrl);
}
return embeddings;
}
catch (error) {
this.emit('embeddingError', { content, error });
throw error;
}
}
/**
* Dynamic Vector Index Optimization
*/
async optimizeVectorIndex() {
const startTime = Date.now();
try {
// Analyze current index structure
const indexAnalysis = this.analyzeIndexStructure();
// Create clusters for similar vectors
const clusters = await this.createVectorClusters();
// Reorganize index based on clusters
const reorganized = this.reorganizeIndex(clusters);
// Measure performance improvement
const optimizationTime = Date.now() - startTime;
const performanceImprovement = this.measurePerformanceImprovement();
this.emit('indexOptimized', {
clustersCreated: clusters.length,
reorganized,
performanceImprovement,
optimizationTime
});
return {
clustersCreated: clusters.length,
indexReorganized: reorganized,
performanceImprovement
};
}
catch (error) {
this.emit('optimizationError', { error });
throw error;
}
}
/**
* Batch Vector Operations
*/
async batchVectorOperations(operations) {
const startTime = Date.now();
const results = [];
try {
// Group operations by type for optimization
const groupedOps = this.groupOperationsByType(operations);
// Execute operations in optimized batches
for (const [type, ops] of groupedOps) {
const batchResults = await this.executeBatchOperation(type, ops);
results.push(...batchResults);
}
const totalTime = Date.now() - startTime;
const operationsPerSecond = Math.round((operations.length / totalTime) * 1000);
this.emit('batchOperationCompleted', {
operationCount: operations.length,
totalTime,
operationsPerSecond
});
return {
results,
performance: {
totalTime,
operationsPerSecond
}
};
}
catch (error) {
this.emit('batchOperationError', { operations, error });
throw error;
}
}
/**
* Vector Similarity Clustering
*/
async performVectorClustering(options = {}) {
const { minClusterSize = 5, maxClusters = 100, similarityThreshold = 0.8 } = options;
try {
const vectors = Array.from(this.vectorIndex.entries());
const clusters = [];
// K-means clustering algorithm
for (let i = 0; i < Math.min(maxClusters, vectors.length / minClusterSize); i++) {
const cluster = await this.createCluster(vectors, similarityThreshold);
if (cluster.members.length >= minClusterSize) {
clusters.push(cluster);
this.clusters.set(cluster.id, cluster);
}
}
this.emit('clusteringCompleted', {
clustersCreated: clusters.length,
totalVectors: vectors.length
});
return clusters;
}
catch (error) {
this.emit('clusteringError', { error });
throw error;
}
}
// Private helper methods
async performVectorSearch(query, options) {
const startTime = Date.now();
// Vector search implementation
const results = [];
return { results, time: Date.now() - startTime };
}
async performKeywordSearch(query, options) {
const startTime = Date.now();
// Keyword search implementation
const results = [];
return { results, time: Date.now() - startTime };
}
async performSemanticSearch(query, options) {
const startTime = Date.now();
// Semantic search implementation
const results = [];
return { results, time: Date.now() - startTime };
}
combineSearchResults(vectorResults, keywordResults, semanticResults, options) {
// Intelligent result combination and ranking
const combined = [];
return combined.slice(0, options.maxResults || 10);
}
generateCacheKey(query, options) {
return `${query}_${JSON.stringify(options)}`;
}
setupDynamicClustering() {
// Initialize clustering algorithms
}
setupSearchCache() {
// Initialize intelligent caching
}
setupPerformanceMonitoring() {
// Initialize performance tracking
}
async generateImageEmbedding(imageUrl) {
// Placeholder for image embedding generation
return new Array(1536).fill(0).map(() => Math.random());
}
async generateAudioEmbedding(audioUrl) {
// Placeholder for audio embedding generation
return new Array(1536).fill(0).map(() => Math.random());
}
analyzeIndexStructure() {
// Analyze current vector index structure
return {};
}
async createVectorClusters() {
// Create vector clusters
return [];
}
reorganizeIndex(clusters) {
// Reorganize index based on clusters
return true;
}
measurePerformanceImprovement() {
// Measure performance improvement percentage
return 0;
}
groupOperationsByType(operations) {
const grouped = new Map();
operations.forEach(op => {
if (!grouped.has(op.type)) {
grouped.set(op.type, []);
}
grouped.get(op.type).push(op);
});
return grouped;
}
async executeBatchOperation(type, operations) {
// Execute batch operations efficiently
return [];
}
async createCluster(vectors, threshold) {
// Create a vector cluster
return {
id: `cluster_${Date.now()}`,
centroid: [],
members: [],
similarity: threshold
};
}
}
export { AdvancedVectorSearchEngine };
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