永久记忆：SQLite FTS5 + LanceDB 混合检索
解决事实检索不准与 embedding 成本问题：SQLite 负责精确事实，LanceDB 负责语义召回，并用 TTL 分层控制记忆衰减。
REDDITDiscovered 2026-02-14Author u/adamb0mbNZ
Prerequisites
- Can run SQLite + LanceDB locally and configure background cleanup jobs.
- Able to edit retrieval pipeline and memory schema.
Steps
- Create structured facts table and FTS5 index for entity/key/value queries.
- Keep LanceDB for fuzzy recall, then merge + dedup results after FTS pass.
- Add memory tiers (permanent/stable/active/session/checkpoint) with periodic TTL cleanup.
- Extract ‘decision facts’ (chose X because Y) into permanent memory.
Commands
sqlite3 memory.db 'CREATE VIRTUAL TABLE IF NOT EXISTS facts_fts USING fts5(entity,key,value,source);'npm i better-sqlite3 @lancedb/lancedb openaiopenclaw gateway statusVerify
Factual questions return exact rows from SQLite, while semantic questions still retrieve relevant context chunks.
Caveats
- Community pattern; exact chunking/TTL values need local tuning (需验证).
- Embedding path still depends on provider latency/quotas.
Source attribution
This tip is aggregated from community/public sources and preserved with attribution.
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