query-normalizer 0.2.1

Modern Python library for search-query normalization

Query Normalizer

Modern Python library for search-query normalization with bilingual support (Russian/English).

Features

• Text Cleaning: Removes BBCode, HTML/XML tags, HTML entities
• Keyboard Layout Fixing: Automatically fixes mixed latin/cyrillic layouts
• Mixed Script Detection: Handles confusable characters and mixed alphabets
• Lemmatization: Converts words to base forms (classic mode)
• Stopword Removal: Filters out common words (classic mode)
• Punctuation Preservation: Keeps punctuation for embedding models
• Dual Modes: Optimized for classic search and embedding models

Installation

pip install query-normalizer

Or with server support:

pip install query-normalizer[server]

Library Usage

from query_normalizer import QueryNormalizer

normalizer = QueryNormalizer()

# Classic mode: lemmatized, stopwords removed
result = normalizer.normalize_for_classic("Это ghbdtn алфaвиты и машины")
print(result.normalized_query)  # "привет алфавит машина"
print(result.tokens)  # ["привет", "алфавит", "машина"]

# Embedding mode: natural language preserved
result = normalizer.normalize_for_embedding("Это ghbdtn алфaвиты и машины")
print(result.normalized_query)  # "это привет алфавиты и машины"

Configuration

You can customize normalization behavior via NormalizationConfig:

from query_normalizer import QueryNormalizer, NormalizationConfig

config = NormalizationConfig(
    keyboard_layout_fix_threshold=0.9,  # Higher threshold layout
    known_word_bonus=1.5,                # Increase trust in known words
    stopword_bonus=0.5,                  # Increase trust in stopwords
    stop_words={"custom", "stop", "words"},  # Custom stopword list
)

normalizer = QueryNormalizer(config=config)
result = normalizer.normalize_for_classic("test query")

Available config options:

• keyboard_layout_fix_threshold: Threshold for keyboard layout fixing (default: 0.75)
• known_word_bonus: Bonus for dictionary words in language detection (default: 1.0)
• stopword_bonus: Bonus for stopwords in language detection (default: 0.25)
• english_stop_words: Custom English stopwords set
• russian_stop_words: Custom Russian stopwords set
• stop_words: Custom combined stopwords set
• keyboard_latin_to_cyrillic: Custom latin-to-cyrillic keyboard mapping
• keyboard_cyrillic_to_latin: Custom cyrillic-to-latin keyboard mapping
• script_aliases: Supported script aliases
• punctuation_tokens: Punctuation tokens to handle

CLI Usage

# Basic normalization
query-normalizer "Это ghbdtn алфaвиты и машины"

# Classic mode only
query-normalizer "test query" --mode classic

# Embedding mode only  
query-normalizer "test query" --mode embedding

# Show debug info
query-normalizer "test query" --debug

Server Usage

# Install with server dependencies
pip install query-normalizer[server]

# Run FastAPI server
uvicorn query_normalizer.server:app --reload

API will be available at http://127.0.0.1:8000, Swagger UI at http://127.0.0.1:8000/docs

API Endpoints

• POST /normalize/classic - Optimized for classic search (lemmatized, stopwords removed)
• POST /normalize/embedding - Optimized for embedding models (natural language preserved)
• POST /normalize - Both normalizations in one response
• GET /health - Health check

Example Request

curl -X POST http://127.0.0.1:8000/normalize \
  -H 'Content-Type: application/json' \
  -d '{"query":"Это ghbdtn алфaвиты и машины", "debug": true}'

Example Response:

{
  "classic": {
    "normalized_query": "привет алфавит машина",
    "tokens": ["привет", "алфавит", "машина"],
    "corrections_applied": [
      "stopword:это",
      "keyboard-layout:ghbdtn->привет",
      "mixed-alphabet:алфaвиты->алфавиты",
      "lemma:алфавиты->алфавит",
      "lemma:машины->машина",
      "stopword:и"
    ]
  },
  "embedding": {
    "normalized_query": "это привет алфавиты и машины",
    "tokens": [],
    "corrections_applied": [
      "keyboard-layout:ghbdtn->привет",
      "mixed-alphabet:алфaвиты->алфавиты"
    ]
  }
}

Development

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=query_normalizer --cov-report=term-missing

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy query_normalizer/

Dependencies

• pymorphy3 - Russian lemmatization
• simplemma - English lemmatization
• nltk - English stopwords
• stop-words - Russian stopwords
• confusable-homoglyphs - Mixed alphabet detection
• beautifulsoup4 - HTML/XML parsing

License

MIT License - see LICENSE for details.