Getter Functions

is_trained

Returns True only when training is complete, enabling efficient approximate nearest neighbor search. An untrained index defaults to exhaustive search.

def is_trained(self) -> bool

Returns

bool: True only when the index is fully trained; otherwise, False.

is_trained() returns False while a (re)train rebuild is in progress. During that window, queries transparently fall back to the untrained (exhaustive) search path. To distinguish “untrained” from “currently (re)training”, use is_training() or training_state().

Example Usage

# 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().")

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is_training

Returns True while a (re)train rebuild is in progress. While this is True, is_trained() returns False and queries fall back to exhaustive search.

def is_training(self) -> bool

Returns

bool: True if a training rebuild is currently running; otherwise, False.

Example Usage

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

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training_state

Returns the current training state of the index as a string.

def training_state(self) -> str

Returns

str: One of "untrained", "training", or "trained".

Example Usage

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

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index_name

Retrieves the name of the current index.

def index_name(self) -> str

Returns

str: Name of the currently loaded or created index.

Example Usage

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

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index_type

Returns the type of the current index. CyborgDB indexes are DiskIVF indexes, so this returns "disk_ivf".

def index_type(self) -> str

Returns

Return Type	Description
str	Type of the current index ("disk_ivf").

Example Usage

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

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index_config

Retrieves the configuration details of the current index.

def index_config(self) -> dict

Returns

dict: A dictionary containing the configuration of the index with the following keys:

• dimension: The dimensionality of the vectors
• metric: The distance metric used (e.g., 'euclidean', 'cosine')
• index_type: The type of the index ('disk_ivf')
• n_lists: The number of inverted lists in the index

Example Usage

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

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n_lists

Returns the number of inverted lists in the index. This property is set during training and initially defaults to 1.

@property
def n_lists(self) -> int

Returns

int: The number of inverted lists in the index.

Example Usage

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

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dimension

Returns the dimensionality of the vectors in the index.

@property
def dimension(self) -> int

Returns

int: The dimensionality of the vectors.

Example Usage

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

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metric

Returns the distance metric used by the index.

@property
def metric(self) -> str

Returns

str: The distance metric used (e.g., "euclidean", "cosine", "squared_euclidean").

Example Usage

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

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get_num_vectors

Returns the number of vectors currently stored in the index.

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

Returns

int: The number of vectors in the index.

The simple call reuses the key supplied at create_index() / load_index(). Pass index_key= (and user_id= for an RBAC user) to override the per-operation key in stateless/service deployments.

Example Usage

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

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list_ids

Lists all item IDs currently stored in the index.

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

Returns

List[str]: A list containing all item IDs in the index.

The simple call reuses the key supplied at create_index() / load_index(). Pass index_key= (and user_id= for an RBAC user) to override the per-operation key in stateless/service deployments.

Exceptions

ValueError

• Throws if the index was not created or loaded yet.
• Throws if an error occurs during retrieval.

Example Usage

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

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