@xiaohui-wang/mcpadvisor
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MCP Advisor & Installation - Find the right MCP server for your needs
112 lines (111 loc) • 3.98 kB
JavaScript
import { pipeline, env } from '@xenova/transformers';
// Configure transformers environment to avoid native module issues
try {
// @ts-ignore - Disable sharp by preventing image processor usage
if (typeof window === 'undefined') {
// In Node.js environment, disable features that require native modules
env.allowLocalModels = false;
env.useFS = false;
env.useBrowserCache = false;
}
}
catch (e) {
// Ignore errors during initial setup
}
import { v4 as uuidv4 } from 'uuid';
import logger from '../../../utils/logger.js';
export class MemoryVectorDB {
documents = [];
metadatas = [];
embeddings = [];
embedder;
numDimensions;
isEmbedderReady = false;
constructor({ numDimensions = 384, clearOnStart = true } = {}) {
this.numDimensions = numDimensions;
this.initializeEmbedder();
}
async initializeEmbedder() {
try {
// @ts-ignore - Set mirror for users in China
env.remoteHost = 'https://hf-mirror.com';
// @ts-ignore - Disable node backend and image processing to avoid native module issues
env.allowLocalModels = false;
env.backends.onnx.wasm.numThreads = 1;
env.backends.onnx.wasm.wasmPaths = 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.14.0/dist/';
// @ts-ignore - Disable image processing to avoid sharp dependency
env.useFS = false;
this.embedder = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
this.isEmbedderReady = true;
logger.info('Vector embedder initialized successfully');
}
catch (error) {
logger.error('Failed to initialize vector embedder:', error);
throw error;
}
}
async getEmbedding(text) {
if (!this.isEmbedderReady) {
await new Promise(resolve => setTimeout(resolve, 100));
return this.getEmbedding(text);
}
try {
const output = await this.embedder(text, { pooling: 'mean', normalize: true });
return Array.from(output.data);
}
catch (error) {
logger.error('Error generating embedding:', { error, text });
throw error;
}
}
async add(text, metadata = { id: uuidv4() }) {
const embedding = await this.getEmbedding(text);
this.documents.push(text);
this.metadatas.push({ id: uuidv4(), ...metadata, text });
this.embeddings.push(embedding);
}
async search(query, k = 5) {
if (this.documents.length === 0)
return [];
const queryEmbedding = await this.getEmbedding(query);
const results = [];
for (let i = 0; i < this.documents.length; i++) {
const dist = this.cosineSimilarity(queryEmbedding, this.embeddings[i]);
results.push({
text: this.documents[i],
metadata: this.metadatas[i],
distance: dist
});
}
// Sort by distance (higher is better for cosine similarity)
return results
.sort((a, b) => b.distance - a.distance)
.slice(0, k);
}
cosineSimilarity(a, b) {
if (a.length !== b.length)
return 0;
let dotProduct = 0;
let normA = 0;
let normB = 0;
for (let i = 0; i < a.length; i++) {
dotProduct += a[i] * b[i];
normA += a[i] * a[i];
normB += b[i] * b[i];
}
return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}
getCount() {
return this.documents.length;
}
save() {
// In-memory implementation doesn't need to save to disk
logger.debug('Vector database state saved (in-memory)');
}
clear() {
this.documents = [];
this.metadatas = [];
this.embeddings = [];
logger.info('Vector database cleared');
}
}