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Combine must, should, and must-not filter types to create complex queries. This approach handles scenarios requiring strict requirements, flexible alternatives, and explicit exclusions.
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).
from actian_vectorai import VectorAIClient, FilterBuilder, Field
import random

# Connect to VectorAI DB server
with VectorAIClient("localhost:50051") 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)
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.