vectoriadb

library2.2.0jsnpmunverified

VectoriaDB is a lightweight, production-ready, in-memory vector database designed for efficient semantic search in JavaScript and TypeScript environments. It is currently at stable version 2.2.0, with frequent minor releases in the 2.x line addressing performance, security, and new features like geospatial support. The library differentiates itself by providing a complete, privacy-first solution for vector search within a single Node.js process, leveraging `transformers.js` to generate embeddings locally using a default `Xenova/all-MiniLM-L6-v2` model. It features optimized HNSW indexing for sub-millisecond search on 100k+ documents, robust TypeScript support, and minimal dependencies, making it ideal for embedded search in applications, CLIs, or desktop environments where external vector database services are undesirable or unnecessary. Unlike distributed vector databases, VectoriaDB focuses on performance and simplicity for in-memory, single-node deployments.

vector-search database llm-agents ai-ml

npm install vectoriadb

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · VECTORIADB

vectoriadb

vector-searchjavascriptv2.2.0

Install

—

Import

—

Disk

—

Pass rate

0/ 6

Env Coverage0 / 6

glibc

18–22

musl

18–22

Install & Compatibility

Where this runs

tested against v? · npm install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

node 18–226 runs

build_error

glibc

node 18–226 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

VectoriaDB

✓ import { VectoriaDB } from 'vectoriadb'

✗ const VectoriaDB = require('vectoriadb')

VectoriaDB is ESM-first and primarily used with ES module imports. Avoid CommonJS `require()` unless using dynamic imports.

SearchOptions

✓ import type { SearchOptions } from 'vectoriadb'

✗ import { SearchOptions } from 'vectoriadb'

Type import for specifying parameters when performing searches. Use `import type` for type-only imports to prevent bundling issues.

Document

✓ import type { Document } from 'vectoriadb'

Type definition for the structure of documents stored in VectoriaDB, typically used with generics for metadata.

This quickstart demonstrates how to initialize VectoriaDB, add new text documents with associated metadata, and perform a semantic search query.

import { VectoriaDB } from 'vectoriadb'; // Create and initialize the database const db = new VectoriaDB(); await db.initialize(); // Add documents await db.add('doc-1', 'How to create a user account', { id: 'doc-1', category: 'auth', author: 'Alice' }); await db.add('doc-2', 'Send email notifications to users', { id: 'doc-2', category: 'notifications', author: 'Bob' }); // Search const results = await db.search('creating new accounts'); console.log(results[0].metadata); // { id: 'doc-1', category: 'auth', ... } console.log(results[0].score); // 0.87

Debug

Known issues

gotchaVectoriaDB requires Node.js version 18 or higher and TypeScript 5.0+ (if using TypeScript). Running with older versions may lead to compatibility issues or errors, particularly due to `transformers.js` dependencies.

fix

Ensure your development and production environments use Node.js 18+ and TypeScript 5.0+.

affects: <=2.2.0

breakingVersions prior to 2.2.0 might not properly release native ONNX resources, potentially leading to resource leaks or native mutex crashes during application shutdown, especially if `EmbeddingService` was used directly.

fix

Upgrade to VectoriaDB v2.2.0 or newer. Ensure `db.close()` is called on your `VectoriaDB` instance to trigger proper resource disposal via the new `dispose` method in `EmbeddingService`.

affects: <2.2.0

securityVersion 2.1.3 included a patch for a vulnerability in the authentication mechanism. Older versions might be susceptible to security risks if authentication features were used.

fix

Upgrade to VectoriaDB v2.1.3 or newer to benefit from the patched authentication vulnerability.

affects: <2.1.3

gotchaVectoriaDB is designed for in-memory use cases and embedded search. It is not suitable for persistent storage (without external adapters), distributed architectures, or multi-million document scales requiring specialized distributed vector databases.

fix

Evaluate your use case carefully. For distributed, persistent, or extremely large-scale vector search, consider alternatives like Pinecone, Weaviate, or Milvus.

affects: >=1.0.0

Errors

Common errors & fixes

Error: Cannot find module 'vectoriadb'

CommonJS environment attempting to import an ES module-first package, or incorrect module resolution setup.

fix

Ensure your project is configured for ES modules (e.g., add `"type": "module"` to `package.json`) or use dynamic `import()` for CommonJS contexts.

TypeError: VectoriaDB is not a constructor

Attempting to instantiate `VectoriaDB` incorrectly, often due to a default import when a named import is required, or CJS `require()` syntax in an ESM context.

fix

Use the named import syntax: `import { VectoriaDB } from 'vectoriadb';`

ONNX runtime warnings regarding 'dtype'

Older versions of VectoriaDB (prior to v2.2.0) implicitly handling `dtype` for ONNX runtime, leading to warning messages.

fix

Upgrade to VectoriaDB v2.2.0 or newer, which explicitly sets `dtype` to 'fp32' in `EmbeddingService` to suppress these warnings.

Error: Node.js version X.X.X is not supported. Please upgrade to Node.js 18 or higher.

Running VectoriaDB on an unsupported Node.js version, which is a dependency requirement for `transformers.js`.

fix

Upgrade your Node.js environment to version 18 or newer.

Upgrade

Version history

2.2.0latest on npm

Audit

Dependencies

@huggingface/transformersrequiredPeer dependency required for local embedding generation using pre-trained transformer models.

Agent activity

51 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/agentfront/vectoriadb ↗

packagehttps://www.npmjs.com/package/vectoriadb ↗

homepagehttps://vectoriadb.dev ↗