benmaster82-Kwipu

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#1	"""
#2	Kwipu MCP Server - Expose your knowledge graph to AI agents.
#3
#4	This server implements the Model Context Protocol (MCP) to allow
#5	Claude Code, Cursor, Windsurf, VS Code Copilot, and other MCP-compatible
#6	clients to query your Obsidian vault / knowledge base.
#7
#8	All queries are processed locally by Ollama, minimizing token usage
#9	on the client side.
#10
#11	Usage:
#12	# As MCP server in Claude Desktop (claude_desktop_config.json):
#13	{
#14	"mcpServers": {
#15	"kwipu": {
#16	"command": "C:/path/to/python.exe",
#17	"args": ["C:/path/to/kwipu_mcp_server.py"]
#18	}
#19	}
#20	}
#21	"""
#22
#23	import os
#24	import sys
#25
#26	# Ensure UTF-8 on Windows
#27	os.environ["PYTHONUTF8"] = "1"
#28
#29	if sys.platform == "win32":
#30	import asyncio
#31	asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
#32
#33	import nest_asyncio
#34	nest_asyncio.apply()
#35
#36	from mcp.server.fastmcp import FastMCP
#37
#38	# Set working directory to script location (resolves relative paths)
#39	_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
#40	sys.path.insert(0, _SCRIPT_DIR)
#41	os.chdir(_SCRIPT_DIR)
#42
#43	from geode_graph import (
#44	WritHerGraphRAG,
#45	_init_llm,
#46	)
#47
#48	# ==========================================
#49	# MCP SERVER
#50	# ==========================================
#51	mcp = FastMCP("Kwipu")
#52
#53	# Redirect engine logs to stderr (stdout is reserved for MCP protocol)
#54	import geode_graph
#55
#56	def _stderr_print(args, *kwargs):
#57	kwargs["file"] = sys.stderr
#58	try:
#59	print(args, *kwargs)
#60	except Exception:
#61	pass
#62
#63	geode_graph.safe_print = _stderr_print
#64
#65	# Lazy RAG instance
#66	_rag_instance: WritHerGraphRAG \| None = None
#67
#68
#69	def _get_rag() -> WritHerGraphRAG:
#70	"""Get or create the RAG engine (lazy, on first query)."""
#71	global _rag_instance
#72	if _rag_instance is None:
#73	_init_llm()
#74	_rag_instance = WritHerGraphRAG(fast_mode=True)
#75	return _rag_instance
#76
#77
#78	# ==========================================
#79	# TOOL
#80	# ==========================================
#81	@mcp.tool()
#82	def query_graph(question: str) -> str:
#83	"""Ask a question about your knowledge base. Searches across all notes
#84	using a knowledge graph with vector similarity, BM25, and temporal matching.
#85	Returns an answer with cited source files. Supports multiple languages.
#86
#87	Args:
#88	question: Your question in natural language
#89	"""
#90	rag = _get_rag()
#91	response = rag.ask(question)
#92	return str(response)
#93
#94
#95	# ==========================================
#96	# ENTRY POINT
#97	# ==========================================
#98	if __name__ == "__main__":
#99	mcp.run()
#100

z6MkqRzA/benmaster82-Kwipu