roaringrange (Python)

Build static, range-fetchable search datasets from Python, then search millions of records in the browser with no backend. These bindings wrap the core Rust build module, so the files they emit are byte-identical to the Go and Rust builders and are read by the same WASM reader. Two index types: a trigram text index (Builder) and a similarity / vector index (VectorBuilder).

What it produces

Builder.build(out_dir) writes the four files the text reader serves over HTTP Range; VectorBuilder.build(path) writes one .rrvi similarity index:

file format contents
index.rrs RRSI trigram text index (popularity-split postings)
index.rrf RRSF facet sidecar (field → category → doc-ID bitmap, with counts)
records.idx / records.bin RRSR per-doc record bytes (your encoding)
*.rrvi RRVI IVFPQ similarity index (range-fetched coarse clusters + PQ codes)

Upload them to S3/CloudFront and point the WASM reader at the URLs.

Install

Prebuilt abi3 wheels (one wheel for CPython 3.8+) are published to PyPI:

pip install roaringrange

CI builds and tests the extension on CPython 3.12, 3.13, and 3.14.

From source (dev)

cd python
maturin develop --release      # builds + installs into the active venv
# or: maturin build --release   # produces a wheel in target/wheels/

Requires a Rust toolchain and pip install maturin.

Usage

import roaringrange as rr, json

b = rr.Builder(gram_size=3)
for row in rows:                              # rows from a DataFrame, DB, JSONL, …
    b.add(
        rank=row["citations"],                # higher rank = listed first (doc-ID order)
        text=f'{row["title"]} {row["abstract"]}',   # tokenized into trigram keys
        record=json.dumps({"t": row["title"], "y": row["year"]}).encode(),
        facets={"year": [str(row["year"])], "type": [row["type"]]},  # field → categories
    )

stats = b.build("out/")        # writes out/index.rrs, index.rrf, records.idx, records.bin
print(stats)                   # BuildStats(docs=..., ngrams=..., fields=...)

rr.tokenize(text, gram_size=3) returns the n-gram keys a string maps to — useful for understanding why a query does or doesn't match.

Vector / similarity search

VectorBuilder trains an IVFPQ index over your embeddings and writes a single .rrvi file that the WASM reader range-fetches like the text index. Use the same doc_id as the text index so a vector hit maps to the same record (and can hybridize with trigram search). Vectors are L2-normalized for the default "ip" (cosine) metric.

import roaringrange as rr

vb = rr.VectorBuilder(dim=256, nlist=4096, m=32, metric="ip")  # m must divide dim
for doc_id, embedding in enumerate(embeddings):     # embeddings: any float sequences
    vb.add(doc_id, embedding.tolist())              # numpy row → list of floats
# or in one call: vb.add_many([(i, e.tolist()) for i, e in enumerate(embeddings)])

stats = vb.build("out/vectors.rrvi")
print(stats)   # VectorBuildStats(vectors=..., dim=256, nlist=..., m=32, nbits=8)

Parameters: nlist coarse clusters (≈ 4·√N, clamped to the vector count), m PQ subquantizers (must divide dim), nbits (1–8) → 2^nbits codes per subspace, metric "ip"/"cosine" or "l2". Training is deterministic (seed, kmeans_iters). One .rrvi per embedding model — each model is a different vector space. See ../VECTORS.md for the byte layout.

This pure-Rust trainer suits small/medium corpora and tests; at very large scale train with FAISS and export the same RRVI layout (the reader is identical).

Scale: train with FAISS, export to RRVI

For large corpora, train OPQ,IVF,PQ with FAISS and export the trained parts — no retraining in Rust. python/scripts/faiss_to_rrvi.py does this end to end (install the extra: pip install 'roaringrange[train]' for numpy + faiss-cpu):

from faiss_to_rrvi import export_to_rrvi
stats = export_to_rrvi(vectors, doc_ids, "vectors.rrvi", nlist=4096, m=32, metric="ip")

Under the hood it calls the low-level roaringrange.write_rrvi_from_faiss(...), which takes the FAISS arrays (OPQ rotation, coarse centroids, PQ codebooks, per-vector cluster + 8-bit codes) as little-endian byte buffers — so the wheel needs no numpy dependency. The export is verified against the Rust reader (recall@10 ≈ 0.9995 vs FAISS's own search on the same index).

Embedding text (mode 2: model2vec, no backend)

python/scripts/model2vec_embed.py embeds text with a model2vec static model (minishlab/potion-retrieval-32M, 512-d, mean-pooled token vectors — no transformer, fast on CPU) and builds a .rrvi. Install the extra: pip install 'roaringrange[embed]'.

from model2vec_embed import build_rrvi_from_texts
stats, _ = build_rrvi_from_texts(titles, doc_ids, "vectors.rrvi", nlist=256, m=32)

It's "mode 2" because the same model2vec recipe can run in the browser at query time, so similarity search needs no backend at all. The query embedding must use the identical model + pooling as the corpus, or the spaces won't match.

Notes

  • Ranking is baked in. Doc IDs are assigned in descending rank, so the top-K of any query is free at read time (no query-time scoring). Pick a good rank signal (citations, holdings, popularity, …).
  • Records are opaque. record= is raw bytes; the format never dictates your schema. Decode them however you like on the client.
  • In-memory build. This builds the whole index in RAM — ideal for up to many millions of records. For corpora whose index exceeds memory, the core crate's chunked path (build::chunk) is the route; exposing it here is a follow-up.

MIT — see ../LICENSE.

Project links

Data verified by PyPI on Jul 16, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.42.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for freeeve from gravatar.com freeeve

Credits

Author: freeeve

GitHub Statistics

Data verified by PyPI on Jul 16, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.8

Provides Extra

embed gemma train

Tags

search full-text roaring static-site serverless

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

roaringrange-0.41.0-cp38-abi3-win_amd64.whl (483.6 kB view details)

Uploaded CPython 3.8+Windows x86-64

roaringrange-0.41.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (595.0 kB view details)

Uploaded CPython 3.8+manylinux: glibc 2.17+ x86-64

roaringrange-0.41.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (563.6 kB view details)

Uploaded CPython 3.8+manylinux: glibc 2.17+ ARM64

roaringrange-0.41.0-cp38-abi3-macosx_11_0_arm64.whl (528.1 kB view details)

Uploaded CPython 3.8+macOS 11.0+ ARM64

roaringrange-0.41.0-cp38-abi3-macosx_10_12_x86_64.whl (544.5 kB view details)

Uploaded CPython 3.8+macOS 10.12+ x86-64

File details

Details for the file roaringrange-0.41.0-cp38-abi3-win_amd64.whl.

File metadata

  • Download URL: roaringrange-0.41.0-cp38-abi3-win_amd64.whl
  • Upload date:
  • Size: 483.6 kB
  • Tags: CPython 3.8+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for roaringrange-0.41.0-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 6fedcadafcdad5a861fe3ed6c443f2ed720423f03299681f6d758c2932721636
MD5 a28f67cb7fd62b34611af997e725e496
BLAKE2b-256 ec32898a9144b6ca0833661aff81a01a1fb85bf043ac0cdfd188bf5e45bd4387

See more details on using hashes here.

Provenance

The following attestation bundles were made for roaringrange-0.41.0-cp38-abi3-win_amd64.whl:

Publisher: release.yml on freeeve/roaringrange

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file roaringrange-0.41.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for roaringrange-0.41.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3853a490bd76dad16160bc300385ff35083ee360666dfe0aa4a885a7778f2d88
MD5 b063b1517d60343191619ad39f4ad905
BLAKE2b-256 018b76d806e49fcc5a1820f290cf727df03e402a6afaced9a90f7b6c439bb389

See more details on using hashes here.

Provenance

The following attestation bundles were made for roaringrange-0.41.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: release.yml on freeeve/roaringrange

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file roaringrange-0.41.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for roaringrange-0.41.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 61e5d0f2d92d05c5b2d711f5da85972124be2c057e2d9668763bc523edb7a936
MD5 ff422e0f7db1f3e0799f69d82ec600e7
BLAKE2b-256 c95e5e97059152bb6a8c345ea8c219f3b6cbd5cc99c4f036518e96776c87231e

See more details on using hashes here.

Provenance

The following attestation bundles were made for roaringrange-0.41.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: release.yml on freeeve/roaringrange

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file roaringrange-0.41.0-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for roaringrange-0.41.0-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 71b2e1e17341e3f96b7d5abcc9f97624b3f0f376d669d26ead3690e9a6895ad2
MD5 45bc0834362def72b2c4167750fc843e
BLAKE2b-256 7aa99a55006d20278e7788c89a2c557b76a2d5a1c3e001d156518a6530121f16

See more details on using hashes here.

Provenance

The following attestation bundles were made for roaringrange-0.41.0-cp38-abi3-macosx_11_0_arm64.whl:

Publisher: release.yml on freeeve/roaringrange

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file roaringrange-0.41.0-cp38-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for roaringrange-0.41.0-cp38-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 941d253813a0379efacde1410fb8d9d691e2dcccd4d1351325a3f2d89ca48275
MD5 176ad830ac2627381fc410298eb79a1d
BLAKE2b-256 88d940aba99e2a779e803644a7c8a33ee4954861250dc33ad6f6f797a471dfe2

See more details on using hashes here.

Provenance

The following attestation bundles were made for roaringrange-0.41.0-cp38-abi3-macosx_10_12_x86_64.whl:

Publisher: release.yml on freeeve/roaringrange

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.42.0

5 files

This release

0.41.0 This release

5 files

0.40.0

5 files

0.39.1

5 files

0.39.0

5 files

0.38.1

5 files

0.38.0

5 files

0.37.1

5 files

0.37.0

5 files

0.36.0

5 files

0.35.0

5 files

0.34.0

5 files

0.33.0

5 files

0.32.0

5 files

0.31.0

5 files

0.30.0

5 files

0.29.0

5 files

0.27.0

5 files

0.26.0

5 files

0.25.0

5 files

0.24.3

5 files

0.24.2

5 files

0.1.1

5 files

0.1.0

5 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page