Skip to main content

​
is_trained

Returns True if the index has been trained, enabling efficient approximate nearest neighbor search. An untrained index will default to exhaustive search. 
def is_trained(self) -> bool

​
Returns

bool: True if the index has been trained; otherwise, False.

​
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().")

​
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())

​
index_type

Returns the type of the current index (e.g., ivf, ivfpq, ivfflat). 
def index_type(self) -> str

​
Returns

Return TypeDescription
strType of the current index.

​
Example Usage

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

​
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 (e.g., ‘ivf’, ‘ivfpq’, ‘ivfflat’)
  • n_lists: The number of inverted lists in the index
  • pq_dim: The PQ dimension (if applicable)
  • pq_bits: The PQ bits (if applicable)

​
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']}")

​
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")

​
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}")

​
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}")

​
get_num_vectors

Returns the number of vectors currently stored in the index. 
def get_num_vectors(self) -> int

​
Returns

int: The number of vectors in the index.

​
Example Usage

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

​
list_ids

Lists all item IDs currently stored in the index. 
def list_ids(self) -> List[str]

​
Returns

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

​
Exceptions

  • 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}")