Rebuild BM25 full-text and vector indexes for Mnemonic semantic search.
npx skills add https://github.com/zircote/mnemonic --skill qmd-reindex
Please help me install this Agent Skill. Skill: qmd-reindex Source: https://github.com/zircote/mnemonic/tree/main/skills/qmd-reindex Command: npx skills add https://github.com/zircote/mnemonic --skill qmd-reindex
This Skill ensures that your Mnemonic memories are accurately searchable by rebuilding the search index after changes.
Run qmd update and qmd embed to re-index mnemonic memories for qmd semantic search.
High-intent search queries and answers about installing and using this skill.
To rebuild the semantic search index for Mnemonic memories, run the qmd update and qmd embed commands. This regenerates BM25 full-text and vector embeddings to synchronize your search capabilities with newly captured or bulk-imported data.
Semantic search fails to find recently added Mnemonic memories because the search index is outdated. You must re-index the memory store by running qmd update and qmd embed to regenerate the full-text and vector embeddings for accurate discoverability.
You need to re-index Mnemonic memories for semantic search after adding new information, such as client meeting notes, or completing bulk imports of research papers. Re-indexing ensures your full-text and vector indexes reflect the latest stored data for accurate retrieval.
Yes, qmd re-indexing updates both BM25 full-text and vector embeddings. By running the update and embed scripts, you ensure your Mnemonic memory store maintains comprehensive search accuracy and data synchronization for semantic queries.
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ourmemSearch the ourmem persistent memory store with natural language queries.
zircoteConfigure the qmd CLI for semantic search indexing and embedding generation.
Greenmamba29Generate and store vector embeddings for semantic search pipelines.
