genpark-tfidf-vectorizer-cosine-similarity-skill

TF-IDF sublinear vectorizer with smooth inverse document frequency and pairwise cosine similarity.

Part of the GenPark AI Agent Skills Matrix. Production-ready, zero external dependencies, native Python 3.9+ standard library.

Architecture

flowchart TD
    A[Text Input Corpus] --> B[Tokenization & Subword Extraction]
    B --> C[Vector & Similarity Kernels]
    C --> D[Ranked / Segmented Output]
    D --> E[MCP Protocol Endpoint]

Features

• Zero Third-Party Dependencies: Pure Python standard library (collections, re, math).
• High-Performance NLP: Subword BPE merges, Okapi BM25 ranking, and Damerau-Levenshtein metrics.
• Native MCP Protocol Support: Integrated JSON-RPC 2.0 stdio server ready for Claude Desktop, Cursor, and Windsurf.

Installation

pip install genpark-tfidf-vectorizer-cosine-similarity-skill

Or clone directly:

git clone https://github.com/alphaparkinc/genpark-tfidf-vectorizer-cosine-similarity-skill.git
cd genpark-tfidf-vectorizer-cosine-similarity-skill
python example_usage.py

Quick Start

from client import *
# Refer to example_usage.py for end-to-end execution

Model Context Protocol (MCP) Setup

Add to your claude_desktop_config.json or cursor.json:

{
  "mcpServers": {
    "genpark-tfidf-vectorizer-cosine-similarity-skill": {
      "command": "python",
      "args": ["-m", "genpark-tfidf-vectorizer-cosine-similarity-skill.mcp_server"]
    }
  }
}

License

MIT License. Copyright (c) 2026 AlphaPark Inc. & Alpha-Park.