simmetry 1.1.0

NumPy-first similarity scores for strings, vectors, points, and sets.

simmetry

Similarity scores for strings, vectors, points, and sets with a small, NumPy-first API.

PyPI · GitHub · Docs · Changelog

Install

pip install simmetry
pip install "simmetry[fast]"      # Numba acceleration for euclidean_sim / manhattan_sim
pip install "simmetry[ann-hnsw]"  # hnswlib ANN backend
pip install "simmetry[ann-faiss]" # FAISS ANN backend
pip install "simmetry[ann]"       # both ANN backends

Project Status

• Current version: 1.1.0
• Maturity: Beta (API stabilising; pin to minor versions in production)
• Versioning: semantic versioning; breaking changes bump the minor version until 2.0

Quickstart

One function

from simmetry import similarity

similarity("kitten", "sitting", metric="levenshtein")
similarity([1, 2, 3], [1, 2, 4], metric="cosine")
similarity((41.1, 29.0), (41.2, 29.1), metric="haversine_sim")  # returns [0, 1]
similarity({1, 2, 3}, {2, 3, 4}, metric="jaccard")

Pairwise matrices (vectors)

import numpy as np
from simmetry import pairwise

X = np.random.randn(1000, 128)
S = pairwise(X, metric="cosine")          # (1000, 1000)
D = pairwise(X, metric="cosine_distance") # 1 - cosine, sklearn-compatible

Top-k search (exact)

import numpy as np
from simmetry import topk

X = np.random.randn(5000, 64)
q = np.random.randn(64)
idx, scores = topk(q, X, k=10, metric="cosine")
# idx and scores are sorted descending (highest similarity first)

Available Metrics

from simmetry import available

available()           # all registered metrics
available("vector")
available("string")
available("point")
available("set")

Vectors

Metric	Returns
cosine	[-1, 1]
cosine_distance	[0, 2] · 1 - cosine
dot	unbounded inner product
euclidean_sim	(0, 1] · 1 / (1 + dist)
manhattan_sim	(0, 1] · 1 / (1 + dist)
pearson	[-1, 1]
hamming	[0, 1] · normalized for equal-length sequences

Strings

Metric	Notes
levenshtein	normalized edit distance
jaro_winkler	prefix-weighted character matching
ngram_jaccard	character trigram Jaccard (default n=3)
token_jaccard	whitespace-token Jaccard

Points / Geo

Metric	Returns
euclidean_2d	(0, 1] · 2D Cartesian similarity
haversine_sim	[0, 1] · geographic similarity (antipodal ≈ 0)

haversine_km is available as a utility function (not a registered metric) that returns raw kilometers:

from simmetry.points import haversine_km

km = haversine_km((40.7, -74.0), (51.5, -0.1))  # ~5 570 km

Sets

Metric	Formula
jaccard	|A∩B| / |A∪B|
dice	2|A∩B| / (|A| + |B|)
overlap	|A∩B| / min(|A|, |B|)
tversky	|A∩B| / (|A∩B| + α|A\B| + β|B\A|)

tversky with alpha=beta=1 equals Jaccard; alpha=beta=0.5 equals Dice. Call it directly with custom weights: tversky(A, B, alpha=0.3, beta=0.7).

Auto Metric Selection

Auto mode applies fixed type-based rules — no learning, no randomness.

from simmetry import infer_metric, similarity

infer_metric("samplecorp", "sample corp")   # "jaro_winkler"
infer_metric((41.0, 29.0), (41.1, 29.1))   # "haversine_sim"
infer_metric({1, 2, 3}, {2, 3, 4})         # "jaccard"

similarity("samplecorp", "sample corp")     # uses inferred metric

Selection order:

1. list[str] / tuple[str] (including empty) → jaro_winkler
2. str + str → jaro_winkler
3. tuple of 2 numbers with valid lat/lon range ([-90, 90] × [-180, 180]) → haversine_sim
4. set / frozenset → jaccard
5. numeric vectors → cosine
6. fallback → cosine

Note: Only tuple inputs (not list) trigger the geo heuristic to avoid ambiguity with 2D numeric vectors. [1.0, 2.0] routes to cosine; (1.0, 2.0) routes to haversine_sim. Pass metric="haversine_sim" explicitly when in doubt.

Batch String APIs

from simmetry.strings import pairwise_strings, topk_strings

S = pairwise_strings(
    ["item_one", "item_two"],
    ["item_one", "item_alt"],
    metric="jaro_winkler",
)
idx, scores = topk_strings(
    "samplecorp",
    ["samplecorp", "examplefinance", "testgroup"],
    k=2,
    metric="levenshtein",
)

Batch Point APIs (Geo / 2D)

from simmetry.points import pairwise_points, topk_points

pts = [(41.0, 29.0), (41.01, 29.01), (40.9, 28.9)]
S = pairwise_points(pts, metric="haversine_sim")              # similarity matrix
idx, scores = topk_points((41.0, 29.0), pts, k=2, metric="haversine_sim")

haversine_km is also accepted by pairwise_points and topk_points for raw-distance use cases. topk_points(..., metric="haversine_km") ranks by ascending distance (nearest first).

ANN Top-k (Optional)

For large vector corpora (100k+), use approximate nearest neighbour backends.

hnswlib

import numpy as np
from simmetry.ann import build_hnsw

X = np.random.randn(200_000, 128).astype("float32")
X /= np.linalg.norm(X, axis=1, keepdims=True)

index = build_hnsw(X, space="cosine")
labels, distances = index.query(X[0], k=10)

faiss

import numpy as np
from simmetry.ann import build_faiss

X = np.random.randn(200_000, 128).astype("float32")
X /= np.linalg.norm(X, axis=1, keepdims=True)

index = build_faiss(X, metric="ip")
labels, scores = index.query(X[0], k=10)

SimIndex (Exact or ANN)

SimIndex provides a unified interface across all backends. query() always returns (indices, similarities) — ANN distances are converted to similarities internally so results are directly comparable across backends.

import numpy as np
from simmetry import SimIndex

X = np.random.randn(50_000, 128).astype("float32")
index = SimIndex(metric="cosine", backend="exact").add(X)
idx, scores = index.query(X[0], k=10)
# scores are cosine similarities, sorted descending

Switch backends without changing the query code:

index_hnsw  = SimIndex(metric="cosine", backend="hnsw").add(X)
index_faiss = SimIndex(metric="cosine", backend="faiss").add(X)

Composite Records

Weight multiple fields with different metrics:

from simmetry import similarity

a = {"name": "Entity One", "city": "CityAlpha", "loc": (41.0, 29.0)}
b = {"name": "Entity One Extended", "city": "CityAlpha", "loc": (41.01, 28.99)}

score = similarity(
    a,
    b,
    metric={"name": "jaro_winkler", "loc": "haversine_sim"},
    weights={"name": 0.7, "loc": 0.3},
)

Fields missing from either record raise KeyError with a descriptive message.

Custom Metrics

from simmetry import register, similarity

def my_metric(a, b):
    return 1.0 if a == b else 0.0

register("exact_match", my_metric, kind="generic")
similarity("foo", "foo", metric="exact_match")  # 1.0

Benchmarks

The project includes a benchmark harness in bench/run.py. Comparative benchmarks against rapidfuzz, scikit-learn, and ANN libraries are not yet published.

python bench/run.py

Scope and Roadmap

Planned additions:

• String metrics: Hamming (string variant), BM25-style text ranking helpers
• Published comparative benchmarks (RapidFuzz / sklearn / faiss baselines)
• Hosted docs site

License

MIT