prollytree 0.3.0

Python bindings for ProllyTree - a probabilistic tree for efficient storage and retrieval

ProllyTree Python Bindings

Python bindings for ProllyTree - a probabilistic tree data structure that combines B-trees and Merkle trees for efficient, verifiable data storage.

🚀 Quick Start

Installation

pip install prollytree

Basic Usage

from prollytree import ProllyTree

# Create a tree and insert data
tree = ProllyTree()
tree.insert(b"hello", b"world")
value = tree.find(b"hello")  # Returns b"world"

📚 Documentation

👉 Complete Documentation

The full documentation includes:

• Quickstart Guide
• API Reference
• Examples
• Advanced Usage

✨ Features

• 🌳 Probabilistic Trees - High-performance data storage with automatic balancing
• 🤖 AI Agent Memory - Multi-layered memory systems for intelligent agents
• 📚 Versioned Storage - Git-like version control for key-value data
• 🔐 Cryptographic Verification - Merkle proofs for data integrity
• ⚡ SQL Queries - Query your data using SQL syntax

🔥 Key Use Cases

Probabilistic Trees

from prollytree import ProllyTree

tree = ProllyTree()
tree.insert(b"user:123", b"Alice")
tree.insert(b"user:456", b"Bob")

# Cryptographic verification
proof = tree.generate_proof(b"user:123")
is_valid = tree.verify_proof(proof, b"user:123", b"Alice")

AI Agent Memory

from prollytree import AgentMemorySystem

memory = AgentMemorySystem("./agent_memory", "agent_001")

# Store conversation
memory.store_conversation_turn("chat_123", "user", "Hello!")
memory.store_conversation_turn("chat_123", "assistant", "Hi there!")

# Store facts
memory.store_fact("person", "john", '{"name": "John", "age": 30}',
                  confidence=0.95, source="profile")

Versioned Storage

from prollytree import VersionedKvStore

store = VersionedKvStore("./data")
store.insert(b"config", b"production_settings")
commit_id = store.commit("Add production config")

# Branch and experiment
store.create_branch("experiment")
store.insert(b"feature", b"experimental_data")
store.commit("Add experimental feature")

SQL Queries

from prollytree import ProllySQLStore

sql_store = ProllySQLStore("./database")
sql_store.execute("CREATE TABLE users (id INT, name TEXT)")
sql_store.execute("INSERT INTO users VALUES (1, 'Alice')")
results = sql_store.execute("SELECT * FROM users WHERE name = 'Alice'")

🛠️ Development

Building from Source

git clone https://github.com/zhangfengcdt/prollytree
cd prollytree
./python/build_python.sh --all-features --install

Running Tests

cd python/tests
python test_prollytree.py

📄 License

Licensed under the Apache License, Version 2.0