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genpark-agentic-rag-hyde-hypothetical-document-generator-skill

mcp
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SUMMARY

Agentic RAG HyDE (Hypothetical Document Embeddings) Query Expander. Bridges dense lexical and semantic gaps by generating diverse hypothetical answer passages from sparse user queries to dramatically boost vector search recall. | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-rag-hyde-hypothetical-document-generator-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)

Agentic RAG HyDE (Hypothetical Document Embeddings) Query Expander. Bridges dense lexical and semantic gaps by generating diverse hypothetical answer passages from sparse user queries to dramatically boost vector search recall.

🌐 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 AgenticRAGHyDEQueryExpander

def main():
    expander = AgenticRAGHyDEQueryExpander()
    query = "how to prevent postgresql deadlocks on concurrent bulk updates"
    res = expander.generate_hypothetical_passages(query, domain="database", perspectives_count=3)
    print("--- HyDE Hypothetical Expansion ---")
    print(f"Original Query: {res['original_query']}")
    for p in res['passages']:
        print(f"[{p['perspective_id']}] (Weight: {p['dense_retrieval_weight']}): {p['hypothetical_passage'][:100]}...")

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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