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alphaparkinc/genpark-cosine-bm25-reciprocal-rank-fusion-skill

Zero-dependency hybrid retrieval engine fusing sparse lexical BM25 Okapi and dense Cosine vector similarities via Reciprocal Rank Fusion.

⭐ 7 stars
⑂ 0 forks
Python
momentum ▲ 35.0
created 2026-09-28
on radar since 2026-09-28
ai-agentbm25knowledge-graphmcpmcp-servermemory-augmentedmodel-context-protocolragsemantic-cachevector-searchzero-dependency
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About genpark-cosine-bm25-reciprocal-rank-fusion-skill

genpark-cosine-bm25-reciprocal-rank-fusion-skill

Production-Grade Agentic Memory, Vector Search & Graph RAG Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

genpark-cosine-bm25-reciprocal-rank-fusion-skill delivers zero-dependency, low-latency, deterministic agentic memory and retrieval primitives engineered strictly using Python 3.9+ standard library.

🌟 Key Architectural Capabilities - Zero External Dependencies: Operates exclusively via pure Python (math, re, collections, heapq, hashlib, json). Zero pip install overhead, zero C-extension compile errors. - Enterprise RAG & Memory Invariants: Implements formal algorithms for cognitive decay, BM25 Okapi lexical scoring, Reciprocal Rank Fusion, knowledge graph traversal, semantic query caching, and lost-in-the-middle context reordering. - Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.

🏗️ Architectural Topology & State Machine

🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claudedesktopconfig.json or cursor.json:

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Autonomous Cognitive Agents 🌍

From the project README.

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