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genpark-bloomreach-ai-semantic-merchandising-search-reranker-skill

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SUMMARY

Bloomreach-inspired AI Semantic E-Commerce Search & Merchandising Reranker. Parses customer shopping intent, expands synonyms, applies visual/category boost & bury rules, and balances semantic relevance against gross margin & stock availabi | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-bloomreach-ai-semantic-merchandising-search-reranker-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Bloomreach-inspired AI Semantic E-Commerce Search & Merchandising Reranker. Parses customer shopping intent, expands synonyms, applies visual/category boost & bury rules, and balances semantic relevance against gross margin & stock availability.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import BloomreachAISemanticMerchandisingReranker

def main():
    reranker = BloomreachAISemanticMerchandisingReranker()
    catalog = [
        {"id": "p1", "title": "White Runner Trainers", "category": "Footwear", "price": 120.0, "margin_rate": 0.45, "stock": 15, "tags": ["sneakers", "running"]},
        {"id": "p2", "title": "Vintage Leather Boots", "category": "Footwear", "price": 250.0, "margin_rate": 0.30, "stock": 0, "tags": ["boots", "leather"]},
        {"id": "p3", "title": "Merino Wool Pullover", "category": "Apparel", "price": 85.0, "margin_rate": 0.60, "stock": 25, "tags": ["sweater", "hoodie"]}
    ]
    rules = {"boost_categories": ["Footwear"], "bury_categories": ["Apparel"]}
    result = reranker.rerank_catalog("athletic sneakers", catalog, rules)
    print("--- Bloomreach AI Discovery Rerank Output ---")
    print(f"Query: {result['query']} | Intent Tokens: {result['intent_analysis']['expanded_tokens']}")
    for idx, item in enumerate(result['top_products'], 1):
        print(f"{idx}. {item['title']} - Score: {item['discovery_score']} (Stock: {item['stock']}, Margin: {item['margin_rate']})")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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