> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectoraidb.actian.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Search with vectors

> Include vector embeddings in search results.

By default, search excludes vector data from results. To include the actual vector embeddings, set `with_vectors=True`.

Include vectors when you:

* Need embeddings for additional processing.
* Want to perform secondary similarity calculations.
* Require raw embedding data in your app.

Including vectors significantly increases response size, so only request them when necessary.

<CodeGroup>
  ```python Python theme={null}
  import asyncio
  from actian_vectorai import AsyncVectorAIClient
  import random

  async def main():
      # Connect to VectorAI DB server
      async with AsyncVectorAIClient("localhost:6574") as client:
          # Generate query vector
          query_vector = [random.gauss(0, 1) for _ in range(128)]
          
          # Include both payload and vector data
          results = await client.points.search(
              "my_collection",  # Collection name
              vector=query_vector,  # Query vector
              limit=10,  # Number of results
              with_payload=True,  # Include payload metadata
              with_vectors=True  # Include vector embeddings
          )
          
          # Display results
          for result in results:
              print(f"ID: {result.id}, Score: {result.score}")
              print(f"Payload: {result.payload}")
              if result.vectors:
                  print(f"Vector dimensions: {len(result.vectors)}")

  asyncio.run(main())
  ```

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

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

      try {
          // Generate query vector
          const queryVector = Array.from({ length: 128 }, () => Math.random() * 2 - 1);

          // Include both payload and vector data
          const results = await client.points.search(
              'my_collection',  // Collection name
              queryVector,      // Query vector
              {
                  limit: 10,          // Number of results
                  withPayload: true,  // Include payload metadata
                  withVectors: true   // Include vector embeddings
              }
          );

          // Display results
          for (const result of results) {
              console.log(`ID: ${result.id}, Score: ${result.score}`);
              console.log(`Payload: ${JSON.stringify(result.payload)}`);
              if (result.vector) {
                  console.log(`Vector dimensions: ${result.vector.length}`);
              }
          }
      } finally {
          client.close();
      }
  }

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

Each result includes these fields:

* `id`: The unique identifier of the matching point.
* `score`: Similarity score based on distance metric.
* `payload`: Full metadata dictionary for the point.
* `vector`: The complete vector embedding array.