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

# Combine filter types

> Build complex queries with must, should, and must-not conditions.

Combine must, should, and must-not filter types to create complex queries. This approach handles scenarios requiring strict requirements, flexible alternatives, and explicit exclusions.

<Note>
  Before you begin, make sure you have a running VectorAI DB instance and a `products` collection with 128-dimensional vectors and payload fields: `category` (string), `price` (float), `rating` (float), and `condition` (string).
</Note>

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

  # Connect to VectorAI DB server
  with VectorAIClient("localhost:6574") as client:
      # Generate query vector
      query_vector = [random.gauss(0, 1) for _ in range(128)]
      
      # Must be electronics
      # Should be either cheap OR highly rated
      # Must NOT be refurbished
      filter = FilterBuilder()\
          .must(Field("category").eq("electronics"))\
          .should(Field("price").lt(50.0))\
          .should(Field("rating").gt(4.0))\
          .must_not(Field("condition").eq("refurbished"))\
          .build()
      
      # Search with combined filters
      results = client.points.search(
          "products",  # Collection name
          vector=query_vector,  # Query vector
          limit=10,  # Maximum results
          filter=filter  # Apply filter
      )
      
      # Display results
      for result in results:
          print(f"Category: {result.payload['category']}")
          print(f"Price: ${result.payload['price']}")
          print(f"Rating: {result.payload.get('rating', 'N/A')}")
          print("-" * 50)
  ```

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

  async function main() {
      // Connect to VectorAI DB server
      const client = new VectorAIClient('localhost:6574');

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

      // Must be electronics
      // Should be either cheap OR highly rated
      // Must NOT be refurbished
      const filter = new FilterBuilder()
          .must(new Field('category').eq('electronics'))
          .should(new Field('price').lt(50.0))
          .should(new Field('rating').gt(4.0))
          .mustNot(new Field('condition').eq('refurbished'))
          .build();

      // Search with combined filters
      const results = await client.points.search('products', queryVector, {
          filter: filter,
          limit: 10
      });

      // Display results
      for (const result of results) {
          console.log(`Category: ${result.payload.category}`);
          console.log(`Price: $${result.payload.price}`);
          console.log(`Rating: ${result.payload.rating ?? 'N/A'}`);
          console.log('-'.repeat(50));
      }
  }

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

Each result includes these fields:

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