leann-backend-flashlib-ivf

GPU-accelerated FlashLib IVF-Flat (inverted file) backend for LEANN - the GPU counterpart of the FAISS leann-backend-ivf backend.

FlashLib is a GPU library of classical ML operators built on Triton / CuteDSL. Its IVF-Flat index coarse-quantizes the corpus into nlist cells and, at search time, scans only the nprobe nearest cells - entirely on CUDA tensors. At a fixed (nlist, nprobe) it probes the same candidate set as a reference IVF-Flat (FAISS / cuVS), so recall is comparable; the difference is GPU vs CPU kernels.

This is registered as the flashlib_ivf backend, distinct from the exact GPU k-NN flashlib backend (which does brute-force NearestNeighbors, not IVF).

Requirements

• A CUDA GPU (required at build time for k-means training and at search time).
• pip install flashlib and torch.

Install

# from a LEANN checkout
uv sync --extra flashlib-ivf
# or
pip install leann-backend-flashlib-ivf

Usage

from leann import LeannBuilder, LeannSearcher

builder = LeannBuilder(backend_name="flashlib_ivf", nlist=1024, distance_metric="cosine")
builder.add_text("LEANN recomputes embeddings to save storage.")
builder.build_index("demo.leann")

searcher = LeannSearcher("demo.leann")
print(searcher.search("How does LEANN save storage?", top_k=3, complexity=32))  # nprobe=min(32, nlist)

Or from the example apps:

python -m apps.document_rag --query "What are the main techniques LEANN explores?" \
    --backend-name flashlib_ivf

How it works

The built IVF-Flat index is a small set of torch tensors (centroids, cell-contiguous data, row ids, CSR offsets), so this backend persists it with torch.save (<index>.flashlib_ivf.pt) plus an id map (<index>.flashlib_ivf_id_map.json) and reloads it onto the GPU at searcher start-up - no k-means re-train.

FlashLib's only distance metric is squared L2. For mips / cosine the vectors are L2-normalized at build and query time, on which squared-L2 ranking is equivalent to inner-product / cosine ranking.

Parameters