Insert points

Add individual points or batches of points to a collection.

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.

Python

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")

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().

Python

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")