tachiom 0.2.1

State-of-the-art index for late-interaction multivector retrieval

TACHIOM

TACHIOM is a fast and scalable data structure for late-interaction multi-vector retrieval, written in Rust with Python bindings. It introduces Token-Aware Clustering (TAC), which distributes the coarse-centroid budget proportionally across token types, and a hierarchical Product Quantization scheme for efficient candidate reranking.

Installation

Python

Quick start (prebuilt wheels)

For most users, this is the easiest option:

pip install tachiom

If a compatible wheel exists for your platform, pip will download and install it directly without compilation. If no compatible wheel exists, pip will automatically compile from source.

Building from source (maximum performance)

For maximum performance optimized to your CPU, build from source.

Shared prerequisites — both approaches below require Rust nightly:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup install nightly
rustup default nightly

Approach 1 — compile from PyPI source:

RUSTFLAGS="-C target-cpu=native" pip install --no-binary :all: tachiom

Approach 2 — build from GitHub (development/editable mode):

git clone https://github.com/TusKANNy/tachiom.git
cd tachiom

Create a virtual environment (recommended):

python3 -m venv ./venv
source ./venv/bin/activate  # On Windows: venv\Scripts\activate

Or with conda:

conda create -n tachiom python=3.11
conda activate tachiom

Install maturin and build:

pip install maturin
RUSTFLAGS="-C target-cpu=native" maturin develop --release

Changes to Python code take effect immediately without reinstalling — ideal for development.

Rust

To compile all the Rust binaries in src/bin/:

RUSTFLAGS="-C target-cpu=native" cargo build --release

Details on how to use Tachiom's Rust CLI can be found in docs/RustUsage.md.

Quick start

import tachiom

# ── Build ─────────────────────────────────────────────────────────────────────
# Inputs (all .npy files):
#   vectors.npy    — [N, dim]   f16  one row per token
#   token_ids.npy  — [N]        i64  vocabulary id of each token
#   doclens.npy    — [n_docs]   i32  number of tokens per document

index = tachiom.Tachiom.build(
    "vectors.npy",
    "token_ids.npy",
    "doclens.npy",
    total_centroids=2_097_152,
)
index.save("my_index.bin")

# ── Load & search ─────────────────────────────────────────────────────────────
index = tachiom.Tachiom.load("my_index.bin")

# queries: [n_queries, n_tokens, dim] f32 array
scores, doc_ids = index.batch_search(queries, k=10, num_threads=0)
# scores, doc_ids: [n_queries, k]

See docs/PythonUsage.md for the full API, all build and search parameters, and the two-step TAC workflow.

Datasets

Pre-processed datasets and pre-built indexes are available on HuggingFace, ready to use with the experiment configs in experiments/sigir2026/.

Dataset	HuggingFace	Index
MS MARCO-v1 (ColBERT v2)	tuskanny/ms_marco_colbertv2	tachiom_msmarco_4M_normalized
LoTTE Pooled (ColBERT v2)	tuskanny/lotte_pooled_colbertv2	tachiom_lotte_2M_normalized

Each dataset contains documents.npy, token_ids.npy, doclens.npy, queries.npy, doc_ids.npy, queries_ids.npy, a qrels .tsv file, and a pre-built Tachiom index. Download with:

pip install huggingface_hub
huggingface-cli download tuskanny/ms_marco_colbertv2 --repo-type dataset --local-dir ./ms_marco
huggingface-cli download tuskanny/lotte_pooled_colbertv2 --repo-type dataset --local-dir ./lotte

Resources

Document	Description
Python API	Tachiom and Tac classes, all parameters, search guide
Rust CLI	bench_tac, tachiom_build, tachiom_search binaries, experiment runner, SIGIR 2026 reproduction
Jupyter notebooks	End-to-end demo on TAC and TACHIOM
Experiments	TOML configs used for the SIGIR 2026 benchmarks

License

This software is released under the MIT License (see LICENSE).

Citation license

By downloading and using this software, you agree to cite the following paper in any material you produce where it was used to conduct a search or experimentation, whether it be a research paper, dissertation, article, poster, presentation, or documentation. By using this software, you have agreed to the citation license.

Bibliography

This paper has been accepted at SIGIR 2026. The full proceedings entry will be available after the conference.

@misc{martinico2026efficientmultivectorretrievaltokenaware,
      title={Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing}, 
      author={Silvio Martinico and Franco Maria Nardini and Cosimo Rulli and Rossano Venturini},
      year={2026},
      eprint={2604.28142},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2604.28142}, 
}