mdcms/neuraldb-docs/pages/sdk-javascript.md

---
title: JavaScript SDK
sort: 110
section-id: client-sdks
keywords: JavaScript, TypeScript, SDK, Node.js, browser, npm, client
description: The NeuralDB JavaScript/TypeScript SDK for Node.js and browser environments
language: en
---

# JavaScript SDK

## Installation

```bash
npm install @neuraldb/client
```

## Basic Setup

```typescript
import { NeuralDB } from '@neuraldb/client';

const client = new NeuralDB({
  connectionString: process.env.NEURALDB_URL!,
  ssl: { rejectUnauthorized: true },
});
await client.connect();
```

### Connection Pool

```typescript
import { NeuralDBPool } from '@neuraldb/client';

const pool = new NeuralDBPool({
  connectionString: process.env.NEURALDB_URL!,
  max: 20,
  idleTimeoutMillis: 30000,
});
```

## Vector Operations

```typescript
import { toVector } from '@neuraldb/client';

await client.query(
  'INSERT INTO documents (content, embedding) VALUES ($1, $2)',
  ['My document content', toVector([0.023, -0.187, 0.412])]
);

async function semanticSearch(query: string, limit = 10) {
  const embeddingResponse = await openai.embeddings.create({
    model: 'text-embedding-3-small', input: query,
  });
  const queryVector = embeddingResponse.data[0].embedding;
  const { rows } = await client.query<{ id: string; content: string; similarity: number }>(
    `SELECT id, content, 1 - (embedding <=> $1) AS similarity
     FROM documents WHERE embedding IS NOT NULL
     ORDER BY embedding <=> $1 LIMIT $2`,
    [toVector(queryVector), limit]
  );
  return rows;
}
```

## High-Level Document API

```typescript
import { DocumentStore } from '@neuraldb/client';

const store = new DocumentStore(client, {
  table: 'documents',
  embeddingColumn: 'embedding',
  embeddingModel: { provider: 'openai', model: 'text-embedding-3-small', apiKey: process.env.OPENAI_API_KEY! },
});

await store.add([{ content: 'First document', metadata: { source: 'web' } }]);
const results = await store.search('query text', { limit: 10, filter: { source: 'web' } });
```