2858e0a344
Features: - Vector search with Pinecone + Vertex AI embeddings - Document relationships (link, unlink, related, graph) - Auto-link with LLM analysis - Intelligent merge with Gemini Modular structure: - clients/: Pinecone, Vertex AI - tools/: core, relations, stats - utils/: validation, logging Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
44 lines
907 B
Python
44 lines
907 B
Python
"""
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RAG System MCP Server Entry Point
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Refactored modular structure with:
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- config.py: Environment variables and constants
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- clients/: Pinecone and Vertex AI clients
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- tools/: MCP tool implementations
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- utils/: Validation and logging
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"""
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from fastmcp import FastMCP
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from config import FASTMCP_HOST, FASTMCP_PORT
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from utils import setup_logging
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from tools import (
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rag_save,
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rag_retrieve,
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rag_update,
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rag_delete,
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rag_link,
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rag_unlink,
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rag_related,
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rag_graph,
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rag_stats
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)
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# Setup logging
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setup_logging()
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# Initialize FastMCP server
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mcp = FastMCP("RAG")
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# Register tools
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mcp.tool()(rag_save)
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mcp.tool()(rag_retrieve)
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mcp.tool()(rag_update)
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mcp.tool()(rag_delete)
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mcp.tool()(rag_link)
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mcp.tool()(rag_unlink)
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mcp.tool()(rag_related)
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mcp.tool()(rag_graph)
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mcp.tool()(rag_stats)
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if __name__ == "__main__":
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mcp.run(transport="http", host=FASTMCP_HOST, port=FASTMCP_PORT)
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