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Python Quick Start

Radixor's Python implementation is a native Rust extension with a Python API. It uses the same learned patch-command model and version 7 compiled-trie format as the Java implementation, without requiring a JVM.

1. Install the runtime and standard models

Create an isolated environment and install Radixor:

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

python -m pip install --only-binary=:all: radixor

python -m pip install --only-binary=:all: \
  --index-url https://leogalambos.github.io/Radixor/python/simple/ radixor

PyPI publication is pending. The GitHub option becomes live when the first Python GitHub Releases populate the Pages-backed package index. See Installation and Builds for availability and source builds.

The radixor wheel contains code. Its required radixor-models-standard dependency contains 20 precompiled models. The standard package excludes textual source dictionaries and optional PoliMorf data, which keeps startup on the direct compiled-model path.

2. Select and reuse a stemmer

Construct a stemmer once and retain it for the lifetime of the application:

from radixor import Stemmer

english = Stemmer("en")
polish = Stemmer("pl")

print(english.stem("running"))  # 'run'
print(polish.stem("koty"))

Short aliases such as en, de, and pl resolve to the documented default model IDs. A full ID such as us-uk-default selects the same model explicitly. The complete mapping is listed under Built-in Languages.

3. Prefer batch calls for collections

Crossing the Python/native boundary once per collection is substantially more efficient than a Python loop of scalar calls:

words = ["running", "studies", "better", "cars"]
stems = english.stem_batch(words)

stem_batch() preserves input order and returns one item per word. Entries can be None when the trie has no applicable patch command.

Repeated natural-language tokens use a bounded result cache shared by the scalar and batch APIs. Its default capacity is 10,000 entries, matching PyStemmer; choose another bound or pass 0 to disable it:

english = Stemmer("en", cache_size=10_000)
uncached = Stemmer("en", cache_size=0)

The cache covers stem(), stemWord(), stem_batch(), and stemWords(); the stem_all*() methods are not cached.

4. Migrate from PyStemmer

Radixor exposes PyStemmer's familiar scalar and batch method names:

stemmer = Stemmer("en")

stemmer.stemWord("running")
stemmer.stemWords(["running", "unknown_word"])

stemWord() and stemWords() return unmatched input unchanged. This removes the None fallback checks required by Radixor's original stem() and stem_batch() methods, so most migration work is limited to the package import and dependency change.

5. Load a custom compiled model

The standard installation covers the maintained default catalog. A custom version 7 model can be loaded directly:

custom = Stemmer(compiled="models/domain-english.rxc")

To compile a maintained textual dictionary during a preparation step:

from radixor import compile

compile("dictionaries/domain.tsv.gz", "models/domain-english.rxc", language="en")

Deploy the resulting .rxc file and load it at application startup. See Dictionary Compilation for format interoperability and the production compilation profile.

6. Production checklist

  • Pin compatible radixor and radixor-models-standard releases in the application's dependency lock.
  • Construct and reuse stemmers instead of rebuilding them per request.
  • Use batch calls for token collections.
  • Choose stem* or stemWord* semantics deliberately for unmatched words.
  • Treat custom dictionaries and compiled models as trusted application input.
  • Regression-test representative vocabulary before changing model versions.

Continue with Installation and Builds for wheel/platform details, Usage and API for the complete call surface, or Performance for benchmark methodology and results.