> ## Documentation Index
> Fetch the complete documentation index at: https://actianvectorai-docs-low-effort-fixes.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Update points

> Modify existing points with new vector and payload data.

Update existing points by providing a new vector and optionally new payload data. The upsert operation replaces the point components you provide.

## Update a single point

The update operation replaces the vector and optionally the payload. When updating a point, you must provide the vector, but payload is optional.

To update only specific payload fields, first retrieve the existing point, modify the payload, then upsert it back.

<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 new vector
      updated_vector = [random.gauss(0, 1) for _ in range(DIMENSION)]
      
      # Update point with new data
      point = PointStruct(
          id=1,  # Point ID to update
          vector=updated_vector,  # New vector embedding
          payload={  # New metadata (optional)
              "name": "Gaming Laptop",
              "category": "electronics",
              "price": 1499.99,
              "updated": True
          }
      )
      
      # Upsert updated point
      client.points.upsert(COLLECTION, [point])
      
      print("Point updated 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');

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

          // Update point with new data
          await client.points.upsert(COLLECTION, [{
              id: 1,  // Point ID to update
              vector: updatedVector,  // New vector embedding
              payload: {  // New metadata (optional)
                  name: "Gaming Laptop",
                  category: "electronics",
                  price: 1499.99,
                  updated: true
              }
          }], { wait: true });

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

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

## Update multiple points

The same replacement rules from single-point updates apply here. You must provide the vector, and payload is optional. Partial payload updates are not supported.

<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:
      # Prepare update data
      updates = [
          {"id": 2, "name": "5G Smartphone", "category": "electronics", "price": 899.99, "updated": True},
          {"id": 3, "name": "Pro Tablet", "category": "electronics", "price": 799.99, "updated": True},
          {"id": 4, "name": "Premium T-Shirt", "category": "clothing", "price": 49.99, "updated": True}
      ]
      
      # Create points with new data
      points = [
          PointStruct(
              id=item["id"],  # Point ID to update
              vector=[random.gauss(0, 1) for _ in range(DIMENSION)],  # New vector
              payload={k: v for k, v in item.items() if k != "id"}  # Extract payload fields (optional)
          )
          for item in updates
      ]
      
      # Batch update all points
      client.points.upsert(COLLECTION, points)
      
      print(f"Updated {len(points)} points")
  ```

  ```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');

      try {
          // Prepare update data
          const updates = [
              { id: 2, name: "5G Smartphone", category: "electronics", price: 899.99, updated: true },
              { id: 3, name: "Pro Tablet", category: "electronics", price: 799.99, updated: true },
              { id: 4, name: "Premium T-Shirt", category: "clothing", price: 49.99, updated: true }
          ];

          // Create points with new data
          const points = updates.map(item => ({
              id: item.id,  // Point ID to update
              vector: Array.from({ length: DIMENSION }, () => Math.random() * 2 - 1),  // New vector
              payload: {  // Extract payload fields (optional)
                  name: item.name,
                  category: item.category,
                  price: item.price,
                  updated: item.updated
              }
          }));

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

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

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