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

# Retrieve points

> Fetch points from a collection by ID or through pagination.

Retrieve specific points from your collection by their IDs. Choose to include vector data, payload metadata, or both.

## Retrieve points by ID

The `retrieve()` method is an alias for `get_many()`. Control what data is returned with these options:

* `with_vectors=False` — Reduces bandwidth when you only need payload metadata.
* `with_payload=False` — Returns only vectors, useful for computation without metadata.

<CodeGroup>
  ```python Python theme={null}
  from actian_vectorai import VectorAIClient

  COLLECTION = "products"

  # Connect to VectorAI DB server
  with VectorAIClient("localhost:6574") as client:
      # Retrieve specific points by ID
      retrieve_ids = [1, 2, 3, 5]
      results = client.points.get(COLLECTION, ids=retrieve_ids)
      
      # Display results
      for point in results:
          print(f"ID {point.id}:")
          if point.vectors:
              print(f"  Vector dimensions: {len(point.vectors)}")
          if point.payload:
              print(f"  Payload: {point.payload}")
  ```

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

  const COLLECTION = "products";

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

      try {
          // Retrieve specific points by ID
          const retrieveIds = [1, 2, 3, 5];
          const results = await client.points.get(COLLECTION, retrieveIds);

          // Display results
          for (const point of results) {
              console.log(`ID ${point.id}:`);
              if (point.vectors) {
                  console.log(`  Vector dimensions: ${point.vectors.length}`);
              }
              if (point.payload) {
                  console.log(`  Payload:`, point.payload);
              }
          }
      } finally {
          client.close();
      }
  }

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

By default, both vectors and payloads are included in the response. Each point includes these fields:

* `id`: The unique identifier of the point.
* `vector`: The vector embedding (array of floats) if requested.
* `payload`: The metadata dictionary if requested.

## Scroll through points

Scroll operations are ideal for processing large collections that do not fit in memory. The cursor tracks your position in the collection, allowing you to process data in manageable chunks.

<CodeGroup>
  ```python Python theme={null}
  from actian_vectorai import VectorAIClient

  COLLECTION = "products"

  # Connect to VectorAI DB server
  with VectorAIClient("localhost:6574") as client:
      all_points = []
      offset = None  # Start from beginning
      page_num = 1
      
      while True:
          # Get next page of results
          results, next_offset = client.points.scroll(
              COLLECTION,  # Collection name
              limit=3,  # Points per page
              offset=offset,  # Current position
              with_vectors=False,  # Exclude vectors
              with_payload=True  # Include payloads
          )
          
          # Break if no results
          if not results:
              break
          
          # Display page results
          print(f"\nPage {page_num} ({len(results)} points):")
          for point in results:
              print(f"  ID {point.id}: {point.payload}")
              all_points.append(point)
          
          # Check for more pages
          if next_offset is None:
              break
          
          # Move to next page
          offset = next_offset
          page_num += 1
      
      print(f"\nTotal points retrieved: {len(all_points)}")
  ```

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

  const COLLECTION = "products";

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

      try {
          const allPoints = [];
          let pageNum = 1;

          // Scroll through all points using scrollAll
          for await (const point of client.points.scrollAll(COLLECTION, {
              limit: 3,  // Points per page
              withPayload: true  // Include payloads
          })) {
              console.log(`  ID ${point.id}:`, point.payload);
              allPoints.push(point);
          }

          console.log(`\nTotal points retrieved: ${allPoints.length}`);
      } finally {
          client.close();
      }
  }

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

The method returns this data:

* `results`: List of points for the current page.
  * `id`: The unique identifier of the point.
  * `vector`: The vector embedding if requested.
  * `payload`: The metadata dictionary if requested.
* `next_offset`: Cursor position for the next page (None if no more pages).