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YDRAG is a versatile Retrieval-Augmented Generation (RAG) system built in Go, designed to enhance AI assistant capabilities with custom knowledge bases. It leverages YZMA for efficient local embedding generation using GGUF models, employs DuckDB as a fast, in-process vector database for similarity search, and integrates seamlessly with AI assistants via the Model Context Protocol (MCP). This system empowers users to ingest, query, and manage their documents, providing relevant context to large language models for more accurate and informed responses, all while supporting flexible deployment options through various server transports.