from vecx.vectorx import VectorX
 
# Initialize client with your API token
vx = VectorX(token="your-token-here")
 
# Generate a secure encryption key
encryption_key = vx.generate_key()
 
 
# Create a new index
vx.create_index(
    name="my_index",
    dimension=768,  # Your vector dimension
    key=encryption_key,  # Encryption key
    space_type="cosine"  # Distance metric (cosine, l2, ip)
)
 
# Get index reference
index = vx.get_index(name="my_index", key=encryption_key)
 
# Insert vectors
index.upsert([
    {
        "id": "doc1",
        "vector": [0.1, 0.2, 0.3, ...],  # Your vector data
        "meta": {"text": "Example document"}
        "filter":{"category": "reference"} # Optional filter
    }
])
 
# Query similar vectors
results = index.query(
    vector=[0.2, 0.3, 0.4, ...],  # Query vector
    top_k=10,
    filter={"category": {"eq":"reference"}}  # Optional filter
)
 
# Process results
for item in results:
    print(f"ID: {item['id']}, Similarity: {item['similarity']}")
    print(f"Metadata: {item['meta']}")