A local RAG server that enables document indexing and sentence window retrieval across multiple file formats like PDF, MD, and DOCX. It supports both local Hugging Face models and OpenAI embeddings for efficient context-aware querying through the Model Context Protocol.
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process.env. You'll be asked to provide them before it can run.OPENAI_API_KEY— With OpenAI embedding endpoint (put your in env)EMBED_ENDPOINT— (Optional) Path to an OpenAI compatible embedding endpoint (ends with /v1). If not set, a local Hugging Face model is used by default.EMBED_MODEL— (Optional) Name of the embedding model to use. Default value of BAAI/bge-large-en-v1.5.INDEX_ROOT— The root directory for the index, used by the retriever. This is mandatory for MCP (Multi-Cloud Platform) querying.MCP_DESCRIPTION— The exposed name and description for the MCP server, used for MCP querying only. This is mandatory for MCP querying. For example: "RAG to my local personal documents"[](https://m8ven.ai/mcp/bixentemal-mcp-ragnar-m6hq0g)