> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-ml-crtx-1153-academy-tutorial-rewrites.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Create payloads

> Add JSON metadata when inserting points into a collection.

Each point can carry a JSON payload with metadata fields such as categories, prices, and timestamps. These fields become available for filtering during search.

## Insert a point with payload

The payload parameter accepts any valid JSON structure. Include only the fields you need for filtering or displaying results.

To create one point with metadata:

<CodeGroup>
  ```python Python theme={null}
  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 with payload created successfully")
  ```

  ```javascript JavaScript theme={null}
  import { VectorAIClient } from '@actian/vectorai-client';

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

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

    // 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 with payload created successfully');
  }

  main().catch(console.error);
  ```
</CodeGroup>

## Insert multiple points with payloads

Batch operations are more efficient than individual inserts. Each payload in the list corresponds to the point at the same index in the IDs and vectors lists.

To create multiple points with metadata at once:

<CodeGroup>
  ```python Python theme={null}
  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
      payloads = [
          {"category": "electronics", "price": 299.99, "in_stock": True},
          {"category": "electronics", "price": 599.99, "in_stock": True},
          {"category": "clothing", "price": 49.99, "in_stock": True},
          {"category": "clothing", "price": 79.99, "in_stock": False},
          {"category": "food", "price": 9.99, "in_stock": True},
          {"category": "food", "price": 4.99, "in_stock": True},
          {"category": "books", "price": 19.99, "in_stock": False},
          {"category": "books", "price": 24.99, "in_stock": True},
          {"category": "toys", "price": 39.99, "in_stock": True},
          {"category": "toys", "price": 59.99, "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(payloads)
      ]

      # Batch insert all points
      client.points.upsert(COLLECTION, points)
      print(f"Batch inserted {len(points)} points with payloads")
  ```

  ```javascript JavaScript theme={null}
  import { VectorAIClient } from '@actian/vectorai-client';

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

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

    // Prepare batch data
    const payloads = [
      { category: 'electronics', price: 299.99, in_stock: true },
      { category: 'electronics', price: 599.99, in_stock: true },
      { category: 'clothing', price: 49.99, in_stock: true },
      { category: 'clothing', price: 79.99, in_stock: false },
      { category: 'food', price: 9.99, in_stock: true },
      { category: 'food', price: 4.99, in_stock: true },
      { category: 'books', price: 19.99, in_stock: false },
      { category: 'books', price: 24.99, in_stock: true },
      { category: 'toys', price: 39.99, in_stock: true },
      { category: 'toys', price: 59.99, in_stock: false },
    ];

    // Create points with vectors and payloads
    const points = payloads.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(`Batch inserted ${points.length} points with payloads`);
  }

  main().catch(console.error);
  ```
</CodeGroup>