Search with filters

Combine vector similarity with metadata conditions.

Filtered search combines vector similarity with metadata conditions. Results must match both your query similarity and filter criteria.

The filter conditions evaluate alongside vector similarity, not instead of it. VectorAI DB finds similar vectors that also meet your criteria. Use filtered search to combine semantic search with business rules like:

• Price ranges
• Availability status
• Category restrictions

Python

import asyncio
from actian_vectorai import AsyncVectorAIClient, FilterBuilder, Field
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)]
        
        # Search with metadata filters
        filter = FilterBuilder()\
            .must(Field("category").eq("electronics"))\
            .must(Field("price").lt(500.0))\
            .build()
        
        # Search with filter
        results = await client.points.search(
            "my_collection",  # Collection name
            vector=query_vector,  # Query vector
            limit=10,  # Number of results
            filter=filter  # Apply filter
        )
        
        # Display results
        for result in results:
            print(f"Product: {result.payload['name']}")
            print(f"Price: ${result.payload['price']}")
            print(f"Score: {result.score}")

asyncio.run(main())

Learn more about filter syntax and operators in the filtering documentation.

Each result includes these fields:

• id: The unique identifier of the matching point.
• score: Similarity score for points that passed the filter.
• payload: Metadata dictionary showing filtered attributes.