> ## 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.

# Filter with must conditions

> Apply AND logic to require all conditions match.

Must filters require all specified conditions to be true. Use must filters when you need strict requirements, such as filtering by user permissions, availability status, or regulatory constraints.

<Note>
  This page uses a `products` collection with 128-dimensional vectors and payload fields: `category` (string) and `price` (float).
</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)]
      
      # Filter: category must equal "electronics" AND price < $100
      filter = FilterBuilder()\
          .must(Field("category").eq("electronics"))\
          .must(Field("price").lt(100.0))\
          .build()
      
      # Search with filter
      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"ID: {result.id}, Score: {result.score}")
          print(f"Category: {result.payload['category']}")
          print(f"Price: ${result.payload['price']}")
  ```

  ```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);

      // Filter: category must equal "electronics" AND price < $100
      const filter = new FilterBuilder()
          .must(new Field('category').eq('electronics'))
          .must(new Field('price').lt(100.0))
          .build();

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

      // Display results
      for (const result of results) {
          console.log(`ID: ${result.id}, Score: ${result.score}`);
          console.log(`Category: ${result.payload.category}`);
          console.log(`Price: $${result.payload.price}`);
      }
  }

  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.