prefix_tree
📦 prefix_tree
A lightweight, pure-Python prefix tree (trie) implementation for fast in-memory prefix search, autocomplete, and filtering based on metadata. Useful for building autocomplete engines, suggestion systems, and efficient word lookups.
✨ Features
- In-memory key-value storage using a prefix tree (trie)
- Fast search by prefix
- Sortable results by dictionary key
- Query filtering by field values (exact match)
- Pure Python, no dependencies
- Compatible with Python 3.10+
📅 Installation
pip install prefix-tree
🚀 Usage Example
from prefix_tree import Trie
# Create a new trie
trie = Trie()
# Insert words with associated metadata
trie.insert("hello", {"name": "hello", "amount": 10, "gender": "t", "type": "t"})
trie.insert("help", {"name": "help", "amount": 5, "gender": "f", "type": "f"})
trie.insert("hell", {"name": "hell", "amount": 7, "gender": "t", "type": "f"})
# Search by prefix and sort by amount (descending)
results = trie.get_by_prefix_sort_desc_by("hel", "amount")
print(results)
# Output:
# [{'name': 'hello', 'amount': 10, ...}, {'name': 'hell', 'amount': 7, ...}, {'name': 'help', 'amount': 5, ...}]
# Search by prefix and filter by query
filtered = trie.get_by_prefix_and_query("hel", {"gender": "t"})
print(filtered)
# Output:
# [{'name': 'hello', 'amount': 10, ...}, {'name': 'hell', 'amount': 7, ...}]
🔧 Build & Upload to PyPI
python3 -m build
twine upload dist/*
🧠 Use Cases
- Autocomplete and typeahead suggestions
- Named entity lookup with filters
- Efficient in-memory keyword searches
- Building simple text-based databases
🔗 Related Projects
📚 Keywords
autocomplete, trie, prefix search, in-memory database, suggestions, python trie, word search, autocompletion, fast lookup, filtering
📝 License
MIT License (see LICENSE for details)
✨ Author
Created by ilia iakhin