memsearch-core

Semantic memory search core library for TypeScript/Node.js

Installation

npm install memsearch-core

Quick Start

import { MemSearch } from 'memsearch-core';

const mem = new MemSearch({
  paths: ['./memory'],
  embedding: { provider: 'openai', model: 'text-embedding-3-small' },
  milvus: { uri: '~/.memsearch/milvus.db', collection: 'memsearch_chunks' },
});

// Index markdown files
await mem.index();

// Semantic search
const results = await mem.search('Redis caching', { topK: 5 });
console.log(results[0].content, results[0].score);

// Cleanup
mem.close();

Features

• 📝 Markdown-first — Your memories are just .md files
• ⚡ Smart dedup — SHA-256 content hashing prevents duplicates
• 🔄 Live sync — File watcher auto-indexes changes
• 🔍 Hybrid search — Dense vector + BM25 + RRF reranking (Milvus backend only)
• 🎯 Type-safe — Full TypeScript support
• 🗄️ Flexible backends — Embedded LanceDB (default) or Milvus

Embedding Providers

| Provider | Model | Env Variable | | -------- | ------------------------ | ---------------- | | OpenAI | text-embedding-3-small | OPENAI_API_KEY | | Google | gemini-embedding-001 | GOOGLE_API_KEY | | Voyage | voyage-3-lite | VOYAGE_API_KEY | | Ollama | nomic-embed-text | — (local) |

Vector Store Backends

Embedded Mode (Default)

Uses LanceDB for zero-config setup. No external database required.

import { MemSearch } from 'memsearch-core';

const mem = new MemSearch({
  paths: ['./memory'],
  embedding: { provider: 'openai', model: 'text-embedding-3-small' },
  dataDir: '~/.memsearch', // Optional: defaults to ./memsearch_data
});

Limitations:

• No BM25 hybrid search - Dense vector search only
• Single-process only - No concurrent write access

Best for personal use, development, and single-agent applications.

Milvus Backend (Optional)

Enable for BM25 hybrid search and multi-process support:

Legacy format (still supported):

const mem = new MemSearch({
  paths: ['./memory'],
  embedding: { provider: 'openai', model: 'text-embedding-3-small' },
  milvus: {
    uri: 'http://localhost:19530',
    collection: 'memsearch_chunks',
  },
});

New recommended format (v1.3.0+):

const mem = new MemSearch({
  paths: ['./memory'],
  embedding: { provider: 'openai', model: 'text-embedding-3-small' },
  vectorStore: {
    provider: 'milvus',
    milvus: {
      uri: 'http://localhost:19530',
      collection: 'memsearch_chunks',
    },
  },
});

API Reference

See API.md for full documentation.

License

MIT

Repository

https://github.com/jabing/memsearch-ts