FaissFlatIndexer

class lightning_ir.retrieve.faiss.faiss_indexer.FaissFlatIndexer(index_dir: Path, index_config: FaissFlatIndexConfig, module: BiEncoderModule, verbose: bool = False)

Bases: FaissIndexer

FAISS Flat Indexer for exact nearest neighbor search using FAISS.

__init__(index_dir: Path, index_config: FaissFlatIndexConfig, module: BiEncoderModule, verbose: bool = False) → None

Initialize the FaissFlatIndexer.

Parameters:

• index_dir (Path) – Directory where the index will be stored.

• index_config (FaissFlatIndexConfig) – Configuration for the FAISS flat index.

• module (BiEncoderModule) – The BiEncoderModule to use for indexing.

• verbose (bool) – Whether to enable verbose output. Defaults to False.

Methods

__init__(index_dir, index_config, module[, ...])	Initialize the FaissFlatIndexer.
to_cpu()	Move the FAISS flat index to CPU.
to_gpu()	Move the FAISS flat index to GPU.

Attributes

INDEX_FACTORY

add(index_batch: IndexBatch, output: BiEncoderOutput) → None

Add embeddings to the FAISS index.

Parameters:

• index_batch (IndexBatch) – The batch containing document indices and embeddings.

• output (BiEncoderOutput) – The output from the bi-encoder module containing document embeddings.

Raises:

ValueError – If the document embeddings are not present in the output.

process_embeddings(embeddings: Tensor) → Tensor

Process embeddings before adding them to the FAISS index.

Parameters:

embeddings (torch.Tensor) – The embeddings to process.

Returns:

The processed embeddings.

Return type:

torch.Tensor

save() → None

Save the FAISS index to disk.

Raises:

ValueError – If the number of embeddings does not match the index’s total number of entries.

set_verbosity(verbose: bool | None = None) → None

Set the verbosity of the FAISS index.

Parameters:

verbose (bool | None) – Whether to enable verbose output. If None, uses the index’s current verbosity setting. Defaults to None.

to_cpu() → None

Move the FAISS flat index to CPU.

to_gpu() → None

Move the FAISS flat index to GPU.