This page shows how to store dense vectors with metadata in a collection and configure collections with different distance metrics.
Before you begin, make sure you have a running VectorAI DB instance and the Python client library installed (pip install actian-vectorai). The examples on this page use random vectors for demonstration. In production, generate vectors from an embedding model.
Store a dense vector
The following example creates a collection and stores a vector with metadata. In production, you generate vectors using pre-trained embedding models like Sentence Transformers, OpenAI embeddings, or custom neural networks. The embedding model determines the vector dimension and semantic properties.
import random
from actian_vectorai import VectorAIClient, VectorParams, Distance, PointStruct
DIMENSION = 384
COLLECTION = "documents"
# Connect to VectorAI DB server
with VectorAIClient("localhost:50051") as client:
# Create a collection for dense vectors
client.collections.create(
COLLECTION, # Collection name
vectors_config=VectorParams(size=DIMENSION, distance=Distance.Cosine) # Vector configuration
)
# Generate a dense vector (in practice, from an embedding model)
vector = [random.gauss(0, 1) for _ in range(DIMENSION)]
# Store the vector with metadata
point = PointStruct(
id=1, # Point ID
vector=vector, # Vector embedding
payload={ # Metadata (optional)
"text": "VectorAI DB enables semantic search",
"category": "documentation"
}
)
# Upsert point to collection
client.points.upsert(COLLECTION, [point])
print(f"Stored {DIMENSION}-dimensional dense vector")
Store vectors with different distance metrics
The distance metric you choose determines how VectorAI DB measures similarity between vectors. The following example creates two collections using different metrics and inserts test vectors into each.
import random
from actian_vectorai import VectorAIClient, VectorParams, Distance, PointStruct
DIMENSION = 128
# Connect to VectorAI DB server
with VectorAIClient("localhost:50051") as client:
# Create collections with different distance metrics
for metric in [Distance.Cosine, Distance.Euclid]:
collection_name = f"vectors_{metric.name.lower()}"
# Create collection with specific metric
client.collections.create(
collection_name, # Collection name
vectors_config=VectorParams(size=DIMENSION, distance=metric) # Vector configuration
)
# Insert test vectors
points = [
PointStruct(
id=1, # Point ID
vector=[random.gauss(0, 1) for _ in range(DIMENSION)] # Generate vector
),
PointStruct(
id=2, # Point ID
vector=[random.gauss(0, 1) for _ in range(DIMENSION)] # Generate vector
)
]
# Upsert points to collection
client.points.upsert(collection_name, points)
print(f"Collection '{collection_name}' uses {metric.name} distance")
The distance metric cannot be changed after collection creation. For details, see Vectors overview. 
