Getter Functions

def is_trained(self) -> bool: ...

# Check if index is trained
if index.is_trained():
    print("The index is ready for efficient querying.")
else:
    print("The index is not trained; consider calling train().")

def is_training(self) -> bool: ...

if index.is_training():
    print("A training rebuild is in progress; queries use exhaustive search for now.")

def training_state(self) -> str: ...

state = index.training_state()
print(f"Training state: {state}")  # "untrained" | "training" | "trained"

def index_name(self) -> str: ...

# Retrieve the index name
print("Current index name:", index.index_name())

def index_type(self) -> str: ...

# Retrieve the type of index
print("Index type:", index.index_type())

def index_config(self) -> dict: ...

# Retrieve index config
config = index.index_config()
print(f"Dimension: {config['dimension']}")
print(f"Metric: {config['metric']}")
print(f"Index type: {config['index_type']}")

@property
def n_lists(self) -> int: ...

# Access the n_lists property
num_lists = index.n_lists
print(f"Index has {num_lists} inverted lists")

@property
def dimension(self) -> int: ...

# Access the dimension property
vector_dim = index.dimension
print(f"Vector dimension: {vector_dim}")

@property
def metric(self) -> str: ...

# Access the metric property
dist_metric = index.metric
print(f"Distance metric: {dist_metric}")

def get_num_vectors(self,
                    *,
                    index_key: bytes,
                    user_id: bytes = None) -> int: ...

# Get the number of vectors in the index
num_vectors = index.get_num_vectors(index_key=index_key)
print(f"The index contains {num_vectors} vectors.")

def list_ids(self,
             *,
             index_key: bytes,
             user_id: bytes = None) -> List[str]: ...

# List all IDs in the index
all_ids = index.list_ids(index_key=index_key)
print(f"Total items in index: {len(all_ids)}")

# Print first 10 IDs
for id in all_ids[:10]:
    print(f"ID: {id}")