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Python
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JavaScript
from actian_vectorai import VectorAIClient, VectorParams, Distance, PointStruct
with VectorAIClient("localhost:50051") as client:
# vectors_config defines the shape of your data: dimensions + similarity metric
client.collections.create("my_collection", vectors_config=VectorParams(size=128, distance=Distance.Cosine))
client.points.upsert("my_collection", [
PointStruct(id=1, vector=[0.1] * 128, payload={"label": "first vector"}),
])
# replace query vector with your model's output for semantic search
results = client.points.search("my_collection", vector=[0.15] * 128, limit=1)
print(results[0].payload) # {"label": "first vector"}const client = new VectorAIClient('localhost:6574');
// vectors_config defines the shape of your data: dimensions + similarity metric
await client.collections.create('my_collection', { dimension: 128, distanceMetric: 'COSINE' });
await client.points.upsert('my_collection', [
{ id: 1, vector: new Array(128).fill(0.1), payload: { label: 'first vector' } },
]);
// replace query vector with your model's output for semantic search
const results = await client.points.search('my_collection', new Array(128).fill(0.15), { limit: 1 });
console.log(results[0].payload); // { label: 'first vector' }
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