> ## 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.

# Search with payload filters

> Apply must, should, and must-not filter conditions to payload fields during vector search in VectorAI DB to return results that match both similarity and metadata criteria.

Apply filters to payload fields during search to combine semantic similarity with business rules. VectorAI DB returns only results whose payloads match the filter conditions. If no points match your filters, you receive an empty response.

<Note>
  Before you begin, make sure you have an existing collection with payload-bearing points and a query vector that matches the collection's dimension.
</Note>

## Search with payload filters

The following example finds semantically similar items that match specific criteria.

<CodeGroup>
  ```python Python theme={null}
  import random
  from actian_vectorai import VectorAIClient, FilterBuilder, Field

  DIMENSION = 128
  COLLECTION = "products"

  # Connect to VectorAI DB server
  with VectorAIClient("localhost:6574") as client:
      # Search for electronics under $600 with similarity
      query_vector = [random.gauss(0, 1) for _ in range(DIMENSION)]
      
      # Build filter for electronics under $600
      filter = FilterBuilder()\
          .must(Field("category").eq("electronics"))\
          .must(Field("price").lt(600.0))\
          .build()
      
      # Search with filter
      results = client.points.search(
          COLLECTION,  # Collection name
          vector=query_vector,  # Query vector
          limit=5,  # Maximum results
          filter=filter  # Apply filter
      )
      
      # Display results
      print("Search results (electronics under $600):")
      for result in results:
          print(f"  - {result.payload} (score: {result.score:.4f})")
  ```

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

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

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

    // Search for electronics under $600 with similarity
    const queryVector = Array.from({ length: DIMENSION }, () => Math.random() * 2 - 1);

    // Build filter for electronics under $600
    const filter = new Field('category').eq('electronics').and(
      new Field('price').lt(600.0)
    );

    // Search with filter
    const results = await client.points.search(COLLECTION, queryVector, {
      limit: 5,  // Maximum results
      filter: filter,  // Apply filter
      withPayload: true,
    });

    // Display results
    console.log('Search results (electronics under $600):');
    for (const result of results) {
      console.log(`  - ${JSON.stringify(result.payload)} (score: ${result.score.toFixed(4)})`);
    }
  }

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

<Tip>
  For more complex filter operations, see the [filtering documentation](/docs/fundamentals/filtering/filtering) which covers must, should, and must-not filter types.
</Tip>

Each result includes these fields:

* `id`: The unique identifier of the matching point.
* `score`: Similarity score for points that passed the filter.
* `payload`: Full metadata dictionary showing filtered product attributes.