from vecx.vectorx import VectorX
 
# Production with specific region
vx = VectorX(token="your-token-here")

# List all indexes
indexes = vx.list_indexes()
 
# Create an index with custom parameters
vx.create_index(
    name="my_custom_index",
    dimension=768,
    key=encryption_key,
    space_type="cosine",
    M=16,             # Graph connectivity parameter (default = 16)
    ef_con=128,       # Construction-time parameter (default = 128)
    use_fp16=True     # Use half-precision for storage optimization (default = True)
)
 
# Delete an index
vx.delete_index("my_custom_index")

# Get index reference
index = vx.get_index(name="my_custom_index", key=encryption_key)
 
# Insert multiple vectors in a batch
index.upsert([
    {
        "id": "vec1",
        "vector": [...],  # Your vector
        "meta": {"title": "First document", "tags": ["important"]}
    },
    {
        "id": "vec2",
        "vector": [...],  # Another vector
        "meta": {"title": "second document", "tags": ["important"]}
        "filter": {"visibility": "public"}  # Optional filter values
    }
])
 
# Query with custom parameters
results = index.query(
    vector=[...],      # Query vector
    top_k=5,           # Number of results to return
    filter= {"visibility":{"eq":"public"}},   # Filter for matching
    ef=128,            # Runtime parameter for search quality
    include_vectors=True  # Include vector data in results
)
 
# Delete vectors
index.delete_vector("vec1")
index.delete_with_filter({"visibility":{"eq":"public"}})
 
# Get a specific vector
vector = index.get_vector("vec1")