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Documentation Index

Fetch the complete documentation index at: https://docs.vectoraidb.actian.com/llms.txt

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Euclidean distance measures straight-line distance between points. It is well suited for image embeddings and spatial data where the absolute distance between vectors represents meaningful differences.
Before you begin, make sure you have a running VectorAI DB instance. The SDK references this metric as Distance.Euclid.
This example creates a collection named image_embeddings configured to use Euclidean distance. 
from actian_vectorai import VectorAIClient, VectorParams, Distance

# Connect to VectorAI DB server
with VectorAIClient("localhost:6574") as client:
    # Create collection with Euclidean distance
    client.collections.create(
        "image_embeddings",  # Collection name
        vectors_config=VectorParams(size=128, distance=Distance.Euclid)  # Euclidean metric
    )
To confirm the collection was created successfully, call client.collections.get_info("image_embeddings") and check that the status field returns Ready.