rustfuzz 0.1.19

rapid fuzzy string matching

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[!WARNING] 🚧 Under Heavy Construction

This library is actively being developed and APIs may change between releases. We're shipping fast — expect frequent updates, new features, and occasional breaking changes. Pin your version if stability matters to you: pip install rustfuzz==0.1.12

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🤖 This project was built entirely by AI.

The idea was simple: could an AI agent beat RapidFuzz — one of the fastest fuzzy matching libraries in the world — by writing a Rust-backed Python library from scratch, guided only by benchmarks?

The development loop was: Research → Build → Benchmark → Repeat.

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rustfuzz is a blazing-fast fuzzy string matching library for Python — implemented entirely in Rust. 🚀

Zero Python overhead. Memory safe. Pre-compiled wheels for every major platform.

Features

⚡ Blazing Fast	Core algorithms written in Rust — no Python overhead, no GIL bottlenecks
🧠 Smart Matching	Ratio, partial ratio, token sort/set, Levenshtein, Jaro-Winkler, and more
🔒 Memory Safe	Rust's borrow checker guarantees — no segfaults, no buffer overflows
🐍 Pythonic API	Clean, typed Python interface. Import and go
📦 Zero Build Step	Pre-compiled wheels on PyPI for Python 3.10–3.14 on all major platforms
🏔️ Big Data Ready	Excels in 1 Billion Row Challenge benchmarks, crushing high-throughput tasks
🔍 3-Way Hybrid Search	BM25 + Fuzzy + Dense embeddings via RRF — 25ms at 1M docs, all in Rust
🔎 Filter & Sort	Meilisearch-style filtering and sorting with Rust-level performance
📄 Document Objects	First-class Document(content, metadata) + LangChain compatibility
🧩 Ecosystem Integrations	BM25, Hybrid Search, and LangChain Retrievers for Vector DBs (Qdrant, LanceDB, FAISS, etc.)

Installation

pip install rustfuzz
# or, with uv (recommended — much faster):
uv pip install rustfuzz

Quick Start

import rustfuzz.fuzz as fuzz
from rustfuzz.distance import Levenshtein

# Fuzzy ratio
print(fuzz.ratio("hello world", "hello wrold"))          # ~96.0

# Partial ratio (substring match)
print(fuzz.partial_ratio("hello", "say hello world"))    # 100.0

# Token-order-insensitive match
print(fuzz.token_sort_ratio("fuzzy wuzzy", "wuzzy fuzzy")) # 100.0

# Levenshtein distance
print(Levenshtein.distance("kitten", "sitting"))         # 3

# Normalised similarity [0.0 – 1.0]
print(Levenshtein.normalized_similarity("kitten", "kitten")) # 1.0

Batch extraction

from rustfuzz import process

choices = ["New York", "New Orleans", "Newark", "Los Angeles"]
print(process.extractOne("new york", choices))
# ('New York', 100.0, 0)

print(process.extract("new", choices, limit=3))
# [('Newark', ...), ('New York', ...), ('New Orleans', ...)]

3-Way Hybrid Search (BM25 + Fuzzy + Dense)

from rustfuzz.search import Document, HybridSearch

# Create documents with metadata
docs = [
    Document("Apple iPhone 15 Pro Max 256GB", {"brand": "Apple", "price": 1199}),
    Document("Samsung Galaxy S24 Ultra", {"brand": "Samsung", "price": 1299}),
    Document("Google Pixel 8 Pro", {"brand": "Google", "price": 699}),
]

# Optional: add dense embeddings for semantic search
embeddings = [[1.0, 0.0, 0.0], [0.9, 0.1, 0.0], [0.1, 0.9, 0.0]]

hs = HybridSearch(docs, embeddings=embeddings)

# Handles typos via fuzzy, keywords via BM25, meaning via dense — all in Rust
results = hs.search("appel iphon", query_embedding=[1.0, 0.0, 0.0], n=1)
text, score, meta = results[0]
print(f"{text} — ${meta['price']}")
# Apple iPhone 15 Pro Max 256GB — $1199

Also works with LangChain Document objects — no dependency required, auto-detected via duck-typing!

With Real Embeddings (FastEmbed)

Use FastEmbed for lightweight, local, ONNX-based embeddings — no GPU needed:

from fastembed import TextEmbedding
from rustfuzz.search import Document, HybridSearch

model = TextEmbedding("BAAI/bge-small-en-v1.5")  # ~33 MB, CPU-only

docs = [
    Document("Apple iPhone 15 Pro Max 256GB", {"brand": "Apple"}),
    Document("Samsung Galaxy S24 Ultra",      {"brand": "Samsung"}),
    Document("Sony WH-1000XM5 Headphones",    {"brand": "Sony"}),
]

embeddings = [e.tolist() for e in model.embed([d.content for d in docs])]
hs = HybridSearch(docs, embeddings=embeddings)

query = "wireless noise cancelling headset"
query_emb = list(model.embed([query]))[0].tolist()

results = hs.search(query, query_embedding=query_emb, n=1)
text, score, meta = results[0]
print(f"{text} — {meta['brand']}")
# Sony WH-1000XM5 Headphones — Sony

With Rust-Native Embeddings (EmbedAnything)

Use EmbedAnything for Rust-native embeddings via Candle — no PyTorch, no ONNX:

import embed_anything
from embed_anything import EmbeddingModel
from rustfuzz.search import Document, HybridSearch

model = EmbeddingModel.from_pretrained_hf(
    model_id="sentence-transformers/all-MiniLM-L6-v2",
)

docs = [
    Document("Apple iPhone 15 Pro Max 256GB", {"brand": "Apple"}),
    Document("Samsung Galaxy S24 Ultra",      {"brand": "Samsung"}),
    Document("Sony WH-1000XM5 Headphones",    {"brand": "Sony"}),
]

# Embed corpus with EmbedAnything
embed_data = embed_anything.embed_query([d.content for d in docs], embedder=model)
embeddings = [item.embedding for item in embed_data]

hs = HybridSearch(docs, embeddings=embeddings)

query = "wireless noise cancelling headset"
query_emb = embed_anything.embed_query([query], embedder=model)[0].embedding

text, score, meta = hs.search(query, query_embedding=query_emb, n=1)[0]
print(f"{text} — {meta['brand']}")
# Sony WH-1000XM5 Headphones — Sony

Or use the callback pattern for fully automatic query embedding:

def embed_fn(texts: list[str]) -> list[list[float]]:
    return [r.embedding for r in embed_anything.embed_query(texts, embedder=model)]

hs = HybridSearch(docs, embeddings=embed_fn)
results = hs.search("wireless headset", n=1)  # query auto-embedded!

Filtering & Sorting (Meilisearch-style)

from rustfuzz import Document
from rustfuzz.search import BM25

docs = [
    Document("Apple iPhone 15 Pro Max",  {"brand": "Apple",   "category": "phone",  "price": 1199, "in_stock": True}),
    Document("Samsung Galaxy S24 Ultra", {"brand": "Samsung", "category": "phone",  "price": 1299, "in_stock": True}),
    Document("Google Pixel 8 Pro",       {"brand": "Google",  "category": "phone",  "price": 699,  "in_stock": False}),
    Document("Apple MacBook Pro M3",     {"brand": "Apple",   "category": "laptop", "price": 2499, "in_stock": True}),
]

bm25 = BM25(docs)

# Fluent builder: filter → sort → match (executes immediately)
results = (
    bm25
    .filter('brand = "Apple" AND price > 500')
    .sort("price:asc")
    .match("pro", n=10)
)

for text, score, meta in results:
    print(f"  {text} — ${meta['price']}")

# Supports: =, !=, >, <, >=, <=, TO (range), IN, EXISTS, IS NULL, AND, OR, NOT
# Works with BM25, BM25L, BM25Plus, BM25T, and HybridSearch

Filter and sort also work with HybridSearch (BM25 + Fuzzy + Dense):

from rustfuzz import Document
from rustfuzz.search import HybridSearch

docs = [
    Document("Apple iPhone 15 Pro Max", {"brand": "Apple", "price": 1199}),
    Document("Samsung Galaxy S24 Ultra", {"brand": "Samsung", "price": 1299}),
    Document("Google Pixel 8 Pro",       {"brand": "Google", "price": 699}),
]

hs = HybridSearch(docs, embeddings=embeddings)

# Filter + sort + semantic search
results = (
    hs
    .filter('brand = "Apple"')
    .sort("price:asc")
    .match("iphone pro", n=5, query_embedding=query_emb)
)

Supported Algorithms

Module	Algorithms
rustfuzz.fuzz	ratio, partial_ratio, token_sort_ratio, token_set_ratio, token_ratio, WRatio, QRatio, partial_token_*
rustfuzz.distance	Levenshtein, Hamming, Indel, Jaro, JaroWinkler, LCSseq, OSA, DamerauLevenshtein, Prefix, Postfix
rustfuzz.process	extract, extractOne, extract_iter, cdist
rustfuzz.search	BM25, BM25L, BM25Plus, BM25T, HybridSearch, Document
rustfuzz.filter	Meilisearch-style filter parser & evaluator
rustfuzz.sort	Multi-key sort with dot notation
rustfuzz.query	Fluent SearchQuery builder (.filter().sort().search().collect())
rustfuzz.utils	default_process

The BM25 Search Engines

rustfuzz.search implements lightning-fast Text Retrieval mathematical variants. The core differences:

• BM25 (Okapi): The industry standard. Employs term frequency saturation (logarithmic decay) and document length normalization.
• BM25L: Focuses on length penalization corrections. Introduces a static term shift delta, guaranteeing that matching terms yield a minimum baseline score even in massive documents where normalisation would normally suppress them.
• BM25Plus: Also creates a lower-bound for any given matching term, but applies the shift after term saturation. Widely considered the best default for highly mixed-length corpuses.
• BM25T: Introduces Information Gain adjustments to dynamically calculate the saturation limit $k_1$ per term, restricting dominant variance. rustfuzz hyper-optimises this by pre-computing term limits natively within the inverted index.

You can see an end-to-end benchmark comparison of these algorithms resolving the BEIR SciFact dataset in examples/bench_retrieval.py.

Documentation

Full cookbook with interactive examples and benchmark results: 👉 bmsuisse.github.io/rustfuzz

License

MIT © BM Suisse