Index local documents for hybrid BM25 and vector search with reranking.
npx skills add https://github.com/fufuonline/openclaw --skill qmd-fufuonline
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/fufuonline/openclaw/tree/main/skills/qmd Command: npx skills add https://github.com/fufuonline/openclaw --skill qmd-fufuonline
Local searches across documents are slow and fragmented. qmd accelerates this by indexing content and enabling fast BM25 and vector-based ranking, delivering relevant results quickly.
Index a folder of documents and search with a query using BM25 and vector reranking.
High-intent search queries and answers about installing and using this skill.
To perform local document search, you index files into multiple collections and query them using a single command. The system applies BM25, vector embeddings, and reranking to retrieve relevant results across diverse file types like codebases and PDFs.
Yes, you can index diverse file types like codebases, PDFs, and text files entirely offline. The tool operates in local environments without requiring external network connectivity for indexing or retrieval operations.
The best way to manage multiple collections is using the tool's configurable multi-collection indexing features. This allows you to organize different document sets separately and query across them with mixed scoring and deterministic retrieval.
BM25 mixed scoring works by combining keyword-based frequency matching with vector-based semantic similarity. The system then applies a reranking step to refine these combined scores, ensuring fast and accurate document retrieval.
Yes, the local search CLI supports deterministic retrieval and includes an MCP mode. This ensures consistent search results across queries while allowing integration into broader automated workflows.
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