Insert points - VectorAI DB

Insert individual points or batches of points into your collection. Points can include optional payload metadata.

Before you begin, make sure you have a running VectorAI DB instance and an existing collection. Vectors must match the dimension configured for the collection. See Create a collection to set one up.

Insert a single point

The insert() method is an alias for upsert(). A new ID inserts a new point, while an existing ID updates the point with the new vector and payload.

import random
from actian_vectorai import VectorAIClient, PointStruct

DIMENSION = 128
COLLECTION = "products"

# Connect to VectorAI DB server
with VectorAIClient("localhost:6574") as client:
    # Generate vector from your embedding model
    vector = [random.gauss(0, 1) for _ in range(DIMENSION)]
    
    # Insert point with payload
    point = PointStruct(
        id=1,  # Point ID
        vector=vector,  # Vector embedding
        payload={  # Metadata (optional)
            "name": "Laptop",
            "category": "electronics",
            "price": 999.99,
            "in_stock": True
        }
    )
    
    # Upsert point to collection
    client.points.upsert(COLLECTION, [point])
    print("Point inserted successfully")

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

const DIMENSION = 128;
const COLLECTION = "products";

async function main() {
    const client = new VectorAIClient('localhost:6574');

    try {
        // Generate vector from your embedding model
        const vector = Array.from({ length: DIMENSION }, () => Math.random() * 2 - 1);

        // Insert point with payload
        await client.points.upsert(COLLECTION, [{
            id: 1,  // Point ID
            vector: vector,  // Vector embedding
            payload: {  // Metadata (optional)
                name: "Laptop",
                category: "electronics",
                price: 999.99,
                in_stock: true
            }
        }], { wait: true });

        console.log("Point inserted successfully");
    } finally {
        client.close();
    }
}

main().catch(console.error);

Payload is optional. You can insert points with only ID and vector:

point = PointStruct(id=1, vector=vector)  # No payload
client.points.upsert(COLLECTION, [point])

Batch insert points

Batch operations are significantly faster than individual inserts. Use batch sizes between one hundred and one thousand points for optimal performance. The upsert_points() method is an alias for batch_upsert().

import random
from actian_vectorai import VectorAIClient, PointStruct

DIMENSION = 128
COLLECTION = "products"

# Connect to VectorAI DB server
with VectorAIClient("localhost:6574") as client:
    random.seed(42)  # Reproducible random vectors
    
    # Prepare batch data
    products = [
        {"name": "Smartphone", "category": "electronics", "price": 699.99, "in_stock": True},
        {"name": "Tablet", "category": "electronics", "price": 499.99, "in_stock": True},
        {"name": "T-Shirt", "category": "clothing", "price": 29.99, "in_stock": True},
        {"name": "Jeans", "category": "clothing", "price": 79.99, "in_stock": False},
        {"name": "Coffee Beans", "category": "food", "price": 12.99, "in_stock": True},
        {"name": "Energy Bar", "category": "food", "price": 2.99, "in_stock": True},
        {"name": "Python Book", "category": "books", "price": 39.99, "in_stock": False},
        {"name": "AI Textbook", "category": "books", "price": 89.99, "in_stock": True},
        {"name": "Action Figure", "category": "toys", "price": 24.99, "in_stock": True},
        {"name": "Board Game", "category": "toys", "price": 49.99, "in_stock": False}
    ]
    
    # Create points with vectors and payloads
    points = [
        PointStruct(
            id=i + 1,  # Point ID
            vector=[random.gauss(0, 1) for _ in range(DIMENSION)],  # Generate vector
            payload=payload  # Attach metadata (optional)
        )
        for i, payload in enumerate(products)
    ]
    
    # Batch insert all points
    client.points.upsert(COLLECTION, points)
    
    print(f"Successfully inserted {len(points)} points")

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

const DIMENSION = 128;
const COLLECTION = "products";

async function main() {
    const client = new VectorAIClient('localhost:6574');

    try {
        // Prepare batch data
        const products = [
            { name: "Smartphone", category: "electronics", price: 699.99, in_stock: true },
            { name: "Tablet", category: "electronics", price: 499.99, in_stock: true },
            { name: "T-Shirt", category: "clothing", price: 29.99, in_stock: true },
            { name: "Jeans", category: "clothing", price: 79.99, in_stock: false },
            { name: "Coffee Beans", category: "food", price: 12.99, in_stock: true },
            { name: "Energy Bar", category: "food", price: 2.99, in_stock: true },
            { name: "Python Book", category: "books", price: 39.99, in_stock: false },
            { name: "AI Textbook", category: "books", price: 89.99, in_stock: true },
            { name: "Action Figure", category: "toys", price: 24.99, in_stock: true },
            { name: "Board Game", category: "toys", price: 49.99, in_stock: false }
        ];

        // Create points with vectors and payloads
        const points = products.map((payload, i) => ({
            id: i + 1,  // Point ID
            vector: Array.from({ length: DIMENSION }, () => Math.random() * 2 - 1),  // Generate vector
            payload: payload  // Attach metadata (optional)
        }));

        // Batch insert all points
        await client.points.upsert(COLLECTION, points, { wait: true });

        console.log(`Successfully inserted ${points.length} points`);
    } finally {
        client.close();
    }
}

main().catch(console.error);