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Use this page to search a collection with a query vector and retrieve the most similar points.
Before you begin, make sure you have a running VectorAI DB instance and an existing collection with inserted points. The example below uses random vectors for demonstration. In production, generate vectors from an embedding model.

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Search a collection with a query vector

The search() method returns results ranked by similarity score. Score interpretation depends on your chosen distance metric. 
import random
from actian_vectorai import VectorAIClient, PointStruct

DIMENSION = 384
COLLECTION = "documents"

# Connect to VectorAI DB server
with VectorAIClient("localhost:50051") as client:
    # Batch insert multiple document vectors
    payloads = [
        {"text": "Machine learning fundamentals", "category": "education"},
        {"text": "Neural networks explained", "category": "education"},
        {"text": "Cooking recipes collection", "category": "lifestyle"},
        {"text": "Travel guide to Europe", "category": "travel"},
        {"text": "Deep learning architectures", "category": "education"}
    ]
    
    # Create points with vectors and metadata
    points = [
        PointStruct(
            id=i + 1,  # Point ID
            vector=[random.gauss(0, 1) for _ in range(DIMENSION)],  # Generate vector
            payload=payload  # Attach metadata (optional)
        )
        for i, payload in enumerate(payloads)
    ]
    
    # Batch upsert points
    client.points.upsert(COLLECTION, points)
    
    # Search for similar documents using a query vector
    query_vector = [random.gauss(0, 1) for _ in range(DIMENSION)]
    
    # Perform similarity search
    results = client.points.search(
        COLLECTION,  # Collection name
        vector=query_vector,  # Query vector
        limit=3  # Top 3 results
    )
    
    # Display results
    print("Top 3 similar documents:")
    for result in results:
        print(f"  - {result.payload['text']} (score: {result.score:.4f})")
Each result includes these fields:
  • id: The unique identifier of the matching point.
  • score: Similarity score based on the collection’s distance metric.
  • payload: Metadata dictionary containing document information.
  • vector: Vector embedding (only if with_vectors=True).