Markov Chain Engine

~ System Architecture

~ Engine Trailer

~ About the Project

This is a robust, dependency-free Python implementation of an Order-1 Markov chain and graph traversal engine. It tokenizes text using intelligent NLP look-ahead boundaries, builds a statistical model of token transitions, and provides tools for probabilistic generation and multi-hop graph pathfinding.

Engineered exclusively using the Python standard library, the project focuses heavily on memory optimization (using itertools.pairwise), lazy evaluation (via yield pipelines), and algorithmic efficiency (BFS, DFS, and memoization via @cache).

~ Tech Stack & Architecture

~ How to Run

1. Ensure you have Python 3.10+ installed.

2. Run the demo suite to see tokenization, generation, and graph pathfinding in action:

python main.py

3. Run the test suite:

pytest tests/

~ Want to try it?

Download the source code, review the algorithms, and let me know if you have any optimizations or new features in mind!