Leo Galambos
5e3d3c7c7d
- add the Rust-backed Python API with PyStemmer compatibility - distribute standard compiled models as a separate Python package - generate model artifacts during builds instead of storing them in Git - add GitHub release and Pages-backed package index workflows - add Python tests, benchmarks, documentation, and Gradle integration - refresh the documentation site, branding, and language benchmarks
67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
# Python Fast Track
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This is the shortest path from an empty Python environment to a working
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Radixor stemmer. The installation includes the native runtime and the separate
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standard-model package with 20 precompiled language models.
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## 1. Install
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=== "PyPI"
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```bash
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python -m pip install --only-binary=:all: radixor
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```
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=== "GitHub Releases"
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```bash
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python -m pip install --only-binary=:all: \
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--index-url https://leogalambos.github.io/Radixor/python/simple/ radixor
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```
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PyPI publication is pending, and the GitHub index becomes live with the first
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Python releases. Until then, follow the source-checkout procedure on
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[Installation and Builds](installation.md).
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Radixor supports CPython 3.9 and newer. A JVM, Java dependency, and source
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dictionary are not required.
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## 2. Stem words
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```python
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from radixor import Stemmer
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stemmer = Stemmer("en")
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print(stemmer.stem("running"))
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print(stemmer.stem_batch(["running", "studies", "cars"]))
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```
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Expected first output:
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```text
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run
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```
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`stem()` and `stem_batch()` preserve Radixor's original API: a word for which
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the trie finds no patch command produces `None`.
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## 3. Use PyStemmer-compatible fallback semantics
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For a low-friction migration from PyStemmer, use the compatible method names:
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```python
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stemmer.stemWord("running")
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stemmer.stemWords(["running", "unknown_word"])
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```
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These methods return the original input whenever no patch command is found, so
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their results are always strings rather than `None`.
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## Next
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- Continue with the [Python Quick Start](quick-start.md) for model selection,
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batch processing, custom compiled models, and deployment guidance.
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- Use [Python Usage and API](usage.md) as the method reference.
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- Review the reproducible [Python performance results](performance.md).
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