Search with vectors

Search a collection in VectorAI DB using a query vector to retrieve the most semantically similar points, ranked by similarity score.

Use this page to search a collection with a query vector and retrieve the most similar points.

You need an existing collection with inserted points. The example below uses random vectors for demonstration. In production, generate vectors from an embedding model.

Search a collection with a query vector

The search() method returns results ranked by similarity score. Score interpretation depends on your chosen distance metric.

Python

import random
from actian_vectorai import VectorAIClient, PointStruct, VectorParams, Distance

DIMENSION = 384
COLLECTION = "documents"

# Connect to VectorAI DB server
with VectorAIClient("localhost:6574") as client:
    # Create the collection
    client.collections.create(COLLECTION, vectors_config=VectorParams(size=DIMENSION, distance=Distance.Cosine))
    
    # 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).