Quickstart - VectorAI DB

This short guide shows you how to create a collection, insert vectors, and perform similarity search.

VectorAI DB Required: This quickstart requires VectorAI DB running in a Docker container. Docker deployment is the only supported version at the moment. Set up VectorAI DB locally

Python
JavaScript

Prerequisites

To use the Python SDK, make sure you have:

Python 3.10 or later
numpy 1.26 or later
grpcio 1.80 or later
pydantic 2.10 or later

Follow these steps to install and begin using the SDK:

Step 1: Install the SDK

Install the VectorAI DB Python SDK using pip.

pip install actian-vectorai-client

Step 2: Run the Docker container

Download and run the VectorAI DB Docker container.

docker pull actian/vectorai:latest
docker run -d --name vectorai \
  -v ./local_data:/var/lib/actian-vectorai \
  -p 6573-6575:6573-6575 \
  -e ACTIAN_VECTORAI_ACCEPT_EULA=YES \
  actian/vectorai:latest

Port 6573 is for the RESTful API, 6574 for gRPC, and 6575 for Local UI. The /var/lib/actian-vectorai directory is the container’s location for storing Actian VectorAI DB and should be volume-mounted to persist data.

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Step 3: Create a collection

Connect to the VectorAI server and create a collection named products with dimension 128 and cosine distance metric.

Synchronous
Asynchronous

from actian_vectorai import VectorAIClient, VectorParams, Distance

with VectorAIClient("localhost:6574") as client:
    info = client.health_check()
    print(f"Connected to {info['title']} v{info['version']}")

    client.collections.create(
        "products",
        vectors_config=VectorParams(size=128, distance=Distance.Cosine)
    )
    print("Collection 'products' created successfully")

import asyncio
from actian_vectorai import AsyncVectorAIClient, VectorParams, Distance

async def main():
    async with AsyncVectorAIClient("localhost:6574") as client:
        info = await client.health_check()
        print(f"Connected to {info['title']} v{info['version']}")

        await client.collections.create(
            "products",
            vectors_config=VectorParams(size=128, distance=Distance.Cosine)
        )
        print("Collection 'products' created successfully")

asyncio.run(main())

Step 4: Insert vectors

Generate sample product vectors and insert them into the collection.

import random
from typing import List
from actian_vectorai import VectorAIClient, PointStruct

NUM_VECTORS = 100
DIMENSION = 128

def generate_sample_products(
    num_products: int = 100,
    dimension: int = 128,
    base_price: float = 10.0,
    price_variance: float = 100.0,
    seed: int = None
) -> List[PointStruct]:
    if seed is not None:
        random.seed(seed)

    categories = ["electronics", "clothing", "food"]
    points = []

    for i in range(num_products):
        category = categories[i % 3]
        price = float(i * base_price + random.random() * price_variance)
        in_stock = (i % 2 == 0)

        points.append(
            PointStruct(
                id=i,
                vector=[random.gauss(0, 1) for _ in range(dimension)],
                payload={
                    "id": i,
                    "category": category,
                    "price": round(price, 2),
                    "in_stock": in_stock,
                }
            )
        )

    return points

with VectorAIClient("localhost:6574") as client:
    print(f"Inserting {NUM_VECTORS} vectors...")

    points = generate_sample_products(NUM_VECTORS, DIMENSION, seed=42)

    client.points.upsert("products", points)
    print(f"Inserted {NUM_VECTORS} vectors")

    count = client.points.count("products")
    print(f"Vector count: {count}")

Step 5: Search for similar vectors

Perform similarity search to find the top five most similar vectors.

from actian_vectorai import VectorAIClient
import random

DIMENSION = 128
COLLECTION = "products"

with VectorAIClient("localhost:6574") as client:
    print("Searching for similar vectors...")
    query = [random.gauss(0, 1) for _ in range(DIMENSION)]
    results = client.points.search(COLLECTION, vector=query, limit=5)

    print(f"Found {len(results)} results:")
    for i, result in enumerate(results):
        print(f"[{i+1}] ID: {result.id}, Score: {result.score:.4f}")

    print("\nRetrieving vector details...")
    retrieved = client.points.get(COLLECTION, ids=[results[0].id])
    print(f"Top result payload: {retrieved[0].payload}")

If the search succeeds, the output displays the matched results ranked by similarity score.

Searching for similar vectors...
Found 5 results:
[1] ID: 39, Score: 29.2119
[2] ID: 54, Score: 27.3639
[3] ID: 76, Score: 23.6023
[4] ID: 31, Score: 21.2087
[5] ID: 22, Score: 17.9858

Retrieving vector details...
Top result payload: {'price': 451.6, 'id': 39, 'in_stock': False, 'category': 'electronics'}

Step 6: Delete collection

When you are done, clean up by deleting the collection.

from actian_vectorai import VectorAIClient

with VectorAIClient("localhost:6574") as client:
    client.collections.delete("products")
    print("Collection 'products' deleted successfully")

Prerequisites

To use the JavaScript SDK, make sure you have:

Node.js 18 or later
npm 9 or later

Step 1: Install the SDK

Install the VectorAI DB JavaScript SDK using npm.

npm install @actian/vectorai-client

Step 2: Run the Docker container

Download and run the VectorAI DB Docker container.

docker pull actian/vectorai:latest
docker run -d --name vectorai \
  -v ./local_data:/var/lib/actian-vectorai \
  -p 6573-6575:6573-6575 \
  -e ACTIAN_VECTORAI_ACCEPT_EULA=YES \
  actian/vectorai:latest

The gRPC server is available at localhost:6574. The Local UI is available at localhost:6575.

Step 3: Create a collection

Connect to the VectorAI server and create a collection named products with dimension 128 and cosine distance metric. Save each step below to a single file called quickstart.ts and run it with npx tsx quickstart.ts.

import { VectorAIClient } from '@actian/vectorai-client';

const DIMENSION = 128;
const COLLECTION = 'products';

const client = new VectorAIClient('localhost:6574');

const info = await client.healthCheck();
console.log(`Connected to ${info.title} v${info.version}`);

await client.collections.create(COLLECTION, {
    dimension: DIMENSION,
    distanceMetric: 'COSINE',
});
console.log(`Collection '${COLLECTION}' created successfully`);

Step 4: Generate sample data

Create a helper function to generate sample product vectors with metadata.

function generateSampleProducts(numProducts: number, dimension: number) {
    const categories = ['electronics', 'clothing', 'food'];
    return Array.from({ length: numProducts }, (_, i) => ({
        id: i,
        vector: Array.from({ length: dimension }, () => Math.random() * 2 - 1),
        payload: {
            id: i,
            category: categories[i % 3],
            price: parseFloat((i * 10 + Math.random() * 100).toFixed(2)),
            in_stock: i % 2 === 0,
        },
    }));
}

Step 5: Insert vectors

Generate and insert vectors into the collection.

const NUM_VECTORS = 100;

console.log(`Inserting ${NUM_VECTORS} vectors...`);
const points = generateSampleProducts(NUM_VECTORS, DIMENSION);

await client.points.upsert(COLLECTION, points, { wait: true });
console.log(`Inserted ${NUM_VECTORS} vectors`);

const count = await client.points.count(COLLECTION);
console.log(`Vector count: ${count}`);

Step 6: Search for similar vectors

Perform similarity search to find the top five most similar vectors.

console.log('\nSearching for similar vectors...');
const query = Array.from({ length: DIMENSION }, () => Math.random() * 2 - 1);
const results = await client.points.search(COLLECTION, query, { limit: 5 });

console.log(`Found ${results.length} results:`);
for (const [i, result] of results.entries()) {
    console.log(`[${i + 1}] ID: ${result.id}, Score: ${result.score.toFixed(4)}`);
}

console.log('\nRetrieving vector details...');
const retrieved = await client.points.get(COLLECTION, [results[0].id]);
console.log(`Top result payload: ${JSON.stringify(retrieved[0].payload)}`);

If the search succeeds, the output displays the matched results ranked by similarity score.

Searching for similar vectors...
Found 5 results:
[1] ID: 39, Score: 29.2119
[2] ID: 54, Score: 27.3639
[3] ID: 76, Score: 23.6023
[4] ID: 31, Score: 21.2087
[5] ID: 22, Score: 17.9858

Retrieving vector details...
Top result payload: {"price":451.6,"id":39,"in_stock":false,"category":"electronics"}

Step 7: Delete collection

When you are done, clean up by deleting the collection and closing the connection.

await client.collections.delete(COLLECTION);
console.log(`Collection '${COLLECTION}' deleted successfully`);

client.close();

Next steps

Now that you have completed the quickstart, explore these resources to build further.

Fundamentals

Learn collections, points, vectors, search, and filtering

Python reference

Review Python SDK namespaces and configuration

JavaScript reference

Review JavaScript SDK namespaces and configuration

Integrations

Connect with LangChain and LlamaIndex

​Prerequisites

​Step 1: Install the SDK

​Step 2: Run the Docker container

​Step 3: Create a collection

​Step 4: Insert vectors

​Step 5: Search for similar vectors

​Step 6: Delete collection

​Prerequisites

​Step 1: Install the SDK

​Step 2: Run the Docker container

​Step 3: Create a collection

​Step 4: Generate sample data

​Step 5: Insert vectors

​Step 6: Search for similar vectors

​Step 7: Delete collection

​Next steps

Fundamentals

Python reference

JavaScript reference

Integrations

Prerequisites

Step 1: Install the SDK

Step 2: Run the Docker container

Step 3: Create a collection

Step 4: Insert vectors

Step 5: Search for similar vectors

Step 6: Delete collection

Prerequisites

Step 1: Install the SDK

Step 2: Run the Docker container

Step 3: Create a collection

Step 4: Generate sample data

Step 5: Insert vectors

Step 6: Search for similar vectors

Step 7: Delete collection

Next steps