polars-matmul

High-performance similarity search for Polars.

Why?

Computing similarity between embedding vectors in Polars is slow:

Approach	Time (1000×10000, 256d)	Memory
Polars cross-join + list ops	> 10 min	OOM (>32GB)
NumPy matmul + argpartition	~75ms	~800MB
polars-matmul	~45ms	~160MB

polars-matmul provides efficient similarity search by:

Using faer, a pure Rust high-performance linear algebra library
Avoiding cross-join memory explosion
Operating directly on contiguous arrays via zero-copy extraction
Compiling on all platforms without complex dependencies

Installation

pip install polars-matmul

Pre-built wheels available for macOS, Linux, and Windows (x86_64 and arm64).

Quick Start

import polars as pl
import polars_matmul  # registers the .pmm namespace

# Sample data
queries = pl.DataFrame({
    "id": [0, 1, 2],
    "embedding": [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
})

corpus = pl.DataFrame({
    "embedding": [[0.9, 0.1, 0.0], [0.1, 0.9, 0.0], [0.0, 0.1, 0.9]],
    "label": ["a", "b", "c"],
})

# Find top-2 similar items per query
result = queries.with_columns(
    pl.col("embedding").pmm.topk(corpus["embedding"], k=2).alias("matches")
)

Output:

┌─────┬─────────────────┬───────────────────────────────┐
│ id  │ embedding       │ matches                       │
│ i64 │ list[f64]       │ list[struct[2]]               │
╞═════╪═════════════════╪═══════════════════════════════╡
│ 0   │ [1.0, 0.0, 0.0] │ [{0, 0.994}, {1, 0.110}]      │
│ 1   │ [0.0, 1.0, 0.0] │ [{1, 0.994}, {0, 0.110}]      │
│ 2   │ [0.0, 0.0, 1.0] │ [{2, 0.994}, {1, 0.110}]      │
└─────┴─────────────────┴───────────────────────────────┘

API Reference

`topk(corpus, k, metric="cosine")`

Find top-k similar items for each embedding.

pl.col("embedding").pmm.topk(corpus["embedding"], k=10)

Parameters:

corpus: Series of embeddings
k: Number of results per query
metric: Similarity metric (see table below)

Metric	Description	Use Case
`"cosine"`	Cosine similarity (default)	Text embeddings
`"dot"`	Raw dot product	Pre-normalized vectors
`"euclidean"`	L2 distance (lower = more similar)	Clustering

Returns: List[Struct{index: u32, score: f64}]

`matmul(corpus, flatten=False)`

Compute all pairwise dot products.

# Default: Array per row
pl.col("embedding").pmm.matmul(corpus["embedding"])

# Flatten: 1D array (for NumPy interop)
pl.col("embedding").pmm.matmul(corpus["embedding"], flatten=True)

Parameters:

corpus: Series of embeddings
flatten: If True, returns flat 1D series instead of per-row arrays

Returns: Array[f64, N] or Array[f32, N] (where N = len(corpus))

Common Patterns

Explode and Join

Flatten results and join with corpus metadata:

flat_results = (
    queries
    .with_columns(pl.col("embedding").pmm.topk(corpus["embedding"], k=2).alias("match"))
    .explode("match")
    .unnest("match")
    .join(corpus.with_row_index("index"), on="index")
)

Performance Tips

1. Use Array Type (2.4x faster)

Fixed-size Array enables zero-copy extraction:

# Convert List to Array for best performance
df = df.with_columns(
    pl.col("embedding").cast(pl.Array(pl.Float32, 256))
)

Input Type	vs NumPy
`pl.Array`	2.1x slower
`pl.List`	5.0x slower

2. Use Float32 (2x memory savings)

df = df.with_columns(
    pl.col("embedding").cast(pl.Array(pl.Float32, dim))
)

3. Float16 Not Recommended

Float16 requires conversion to Float32 for computation (no CPU support), so there's no performance benefit. Use Float16 for storage only.

Performance Benchmarks

Benchmarked on 1000 queries × 10000 corpus, 256 dimensions:

Operation	NumPy	polars-matmul	Ratio
Top-K Search (k=10)	73ms	45ms	0.64x ✅
Raw Matmul (f32, Array)	5ms	11ms	2.1x
Raw Matmul (f64, Array)	17ms	22ms	1.3x

Why Top-K is faster: polars-matmul fuses normalization, multiplication, and selection into a single Rust pass, avoiding Python data materialization.

Run benchmarks yourself:

python examples/benchmark_topk.py    # End-to-end search
python examples/benchmark_matmul.py  # Raw matmul

Development

git clone https://github.com/NivekNey/polars-matmul
cd polars-matmul

python -m venv .venv && source .venv/bin/activate
pip install maturin pytest numpy pyarrow

maturin develop --release
pytest tests/

License

MIT

Release files for polars-matmul 0.1.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for polars-matmul 0.1.4
File	Size	Uploaded
polars_matmul-0.1.4.tar.gz	83.8 kB	Feb 9, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for polars-matmul 0.1.4
File	Interpreter	ABI	Platform	Reset
polars_matmul-0.1.4-cp313-cp313-win_amd64.whl 4.5 MB Feb 9, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
polars_matmul-0.1.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4.5 MB Feb 9, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.17+ x86-64	Details
polars_matmul-0.1.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4.4 MB Feb 9, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.17+ ARM64	Details
polars_matmul-0.1.4-cp313-cp313-macosx_11_0_arm64.whl 4.1 MB Feb 9, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
polars_matmul-0.1.4-cp313-cp313-macosx_10_12_x86_64.whl 4.3 MB Feb 9, 2026	CPython 3.13	CPython 3.13	macOS 10.12+ x86-64	Details
polars_matmul-0.1.4-cp312-cp312-win_amd64.whl 4.5 MB Feb 9, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
polars_matmul-0.1.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4.5 MB Feb 9, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.17+ x86-64	Details
polars_matmul-0.1.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4.4 MB Feb 9, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.17+ ARM64	Details
polars_matmul-0.1.4-cp312-cp312-macosx_11_0_arm64.whl 4.1 MB Feb 9, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
polars_matmul-0.1.4-cp312-cp312-macosx_10_12_x86_64.whl 4.3 MB Feb 9, 2026	CPython 3.12	CPython 3.12	macOS 10.12+ x86-64	Details
polars_matmul-0.1.4-cp311-cp311-win_amd64.whl 4.5 MB Feb 9, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
polars_matmul-0.1.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4.5 MB Feb 9, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.17+ x86-64	Details
polars_matmul-0.1.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4.4 MB Feb 9, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.17+ ARM64	Details
polars_matmul-0.1.4-cp311-cp311-macosx_11_0_arm64.whl 4.1 MB Feb 9, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
polars_matmul-0.1.4-cp311-cp311-macosx_10_12_x86_64.whl 4.3 MB Feb 9, 2026	CPython 3.11	CPython 3.11	macOS 10.12+ x86-64	Details
polars_matmul-0.1.4-cp310-cp310-win_amd64.whl 4.5 MB Feb 9, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
polars_matmul-0.1.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4.5 MB Feb 9, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.17+ x86-64	Details
polars_matmul-0.1.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4.4 MB Feb 9, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.17+ ARM64	Details
polars_matmul-0.1.4-cp310-cp310-macosx_11_0_arm64.whl 4.1 MB Feb 9, 2026	CPython 3.10	CPython 3.10	macOS 11.0+ ARM64	Details
polars_matmul-0.1.4-cp310-cp310-macosx_10_12_x86_64.whl 4.3 MB Feb 9, 2026	CPython 3.10	CPython 3.10	macOS 10.12+ x86-64	Details
polars_matmul-0.1.4-cp39-cp39-win_amd64.whl 4.5 MB Feb 9, 2026	CPython 3.9	CPython 3.9	Windows x86-64	Details
polars_matmul-0.1.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4.5 MB Feb 9, 2026	CPython 3.9	CPython 3.9	Linux glibc 2.17+ x86-64	Details
polars_matmul-0.1.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4.4 MB Feb 9, 2026	CPython 3.9	CPython 3.9	Linux glibc 2.17+ ARM64	Details
polars_matmul-0.1.4-cp39-cp39-macosx_11_0_arm64.whl 4.1 MB Feb 9, 2026	CPython 3.9	CPython 3.9	macOS 11.0+ ARM64	Details
polars_matmul-0.1.4-cp39-cp39-macosx_10_12_x86_64.whl 4.3 MB Feb 9, 2026	CPython 3.9	CPython 3.9	macOS 10.12+ x86-64	Details

Total release size:108.8 MB

Release files / polars_matmul-0.1.4.tar.gz

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Workflow

Publishing configuration: github.com/HolosStack/polars-matmul/blob/cd9155c0e235d25a98557f034a334dd04c5f680c/.github/workflows/CI.yml
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Release files / polars_matmul-0.1.4-cp312-cp312-macosx_11_0_arm64.whl

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Identity

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Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp312-cp312-macosx_10_12_x86_64.whl

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Identity

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Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp311-cp311-win_amd64.whl

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Provenance

Identity

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Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Provenance

Identity

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Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp311-cp311-macosx_11_0_arm64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Artifact

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Release files / polars_matmul-0.1.4-cp311-cp311-macosx_10_12_x86_64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Artifact

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Release files / polars_matmul-0.1.4-cp310-cp310-win_amd64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp310-cp310-macosx_11_0_arm64.whl

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Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp310-cp310-macosx_10_12_x86_64.whl

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Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Release files / polars_matmul-0.1.4-cp39-cp39-win_amd64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Artifact

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Release files / polars_matmul-0.1.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

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Artifact

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Release files / polars_matmul-0.1.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Upload date	Feb 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HolosStack/polars-matmul
Publishing commit: github.com/HolosStack/polars-matmul/tree/cd9155c0e235d25a98557f034a334dd04c5f680c

Workflow

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Artifact

Subject: polars_matmul-0.1.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / polars_matmul-0.1.4-cp39-cp39-macosx_11_0_arm64.whl

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Release files / polars_matmul-0.1.4-cp39-cp39-macosx_10_12_x86_64.whl

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This release

0.1.4 This release

Feb 9, 2026 26 release files

0.1.0

Feb 8, 2026 20 release files

polars-matmul 0.1.4

polars-matmul

Why?

Installation

Quick Start

API Reference

topk(corpus, k, metric="cosine")

matmul(corpus, flatten=False)

Common Patterns

Explode and Join

Performance Tips

1. Use Array Type (2.4x faster)

2. Use Float32 (2x memory savings)

3. Float16 Not Recommended

Performance Benchmarks

Development

License

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

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Release files for polars-matmul 0.1.4

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Release files / polars_matmul-0.1.4.tar.gz

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`topk(corpus, k, metric="cosine")`

`matmul(corpus, flatten=False)`