qmd

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🤔 Why MinerU Document Explorer?

MinerU Document Explorer equips your agent with three tool suites — Retrieve, Deep Read, and Ingest — closing the full knowledge loop:

• 🔍 Retrieve — Cross-collection search: BM25, vector, and hybrid with LLM reranking and query expansion
• 📖 Deep Read — Navigate inside a single document without loading the whole file: table of contents, section reading, inline search, and element extraction
• 📝 Ingest — Build and maintain a LLM wiki from raw documents, following the Karpathy LLM Wiki pattern

Developed by the MinerU team, building on QMD and Karpathy's LLM Wiki.

💡 What can you do with it?

• Research assistant — Point it at a folder of PDFs and ask your AI agent to survey the literature, compare methods, or find specific results across papers
• Project knowledge base — Index your codebase, docs, and design specs; let the agent answer questions about architecture, find relevant code, or trace requirements
• Study companion — Upload textbooks or lecture notes; use doc_toc + doc_read to navigate, doc_grep to search, and build a wiki of key concepts
• Enterprise document search — Index contracts, reports, or manuals and search them with natural language queries enhanced by LLM reranking

See it in action: The demo/ folder contains a complete end-to-end example — an AI agent automatically reads ~10 arXiv papers on RAG, builds an interlinked wiki knowledge base, and writes a research survey. See the Demo Guide for step-by-step instructions.

🚀 Quick Start

Agent-Assisted Setup: If you're using an AI agent (Claude Code, Cursor, etc.), simply ask it to help you deploy MinerU Document Explorer and install skills — the agent can handle the entire setup process for you, including MCP configuration.

Follow the https://github.com/opendatalab/MinerU-Document-Explorer/blob/main/docs/quickstart.md to install MinerU Document Explorer and walk the user through configuration.

📖 Document Deep Reading

Navigate and search within a single document without reading the whole file:

# View document structure
qmd doc-toc papers/attention-is-all-you-need.pdf

# Read specific sections by address
qmd doc-read papers/attention-is-all-you-need.pdf "line:45-120"

# Search within one document
qmd doc-grep papers/attention-is-all-you-need.pdf "self-attention"

🔌 MCP Server — 15 Tools for AI Agents

Integrate with AI agents via Model Context Protocol.

MCP Server vs CLI: The MCP server runs as a persistent process — LLM models (embeddings, reranker, query expansion) are loaded once and stay in memory across requests. CLI commands like qmd query must reload all models on every invocation, adding ~5–15 s of startup overhead each time. For agent workflows, always prefer the MCP server.

Two transport modes:

Mode	Command	Best for
stdio	qmd mcp	Claude Desktop, Claude Code — client spawns and manages the process
HTTP daemon	qmd mcp --http --daemon	Cursor, Windsurf, VS Code, multi-client setups — one shared persistent server

# Start the HTTP daemon (recommended — models stay loaded across all requests)
qmd mcp --http --daemon             # default port 8181
qmd mcp --http --daemon --port 8080 # custom port

# Verify server is running
curl http://localhost:8181/health