Organize and query local structured data with embedded SQL and vector embeddings.
npx skills add https://github.com/peteknowsai/swain-agents --skill stoolap
Please help me install this Agent Skill. Skill: stoolap Source: https://github.com/peteknowsai/swain-agents/tree/main/sprite/skills/stoolap Command: npx skills add https://github.com/peteknowsai/swain-agents --skill stoolap
Local teams often need to store structured data locally with fast SQL-backed querying while also enabling semantic search over embeddings. Stoolap provides an embedded, file-based database that supports vector columns for similarity lookup, all on the local filesystem.
Start Stoolap by pointing it at a local database path and begin creating tables as you ingest data.
High-intent search queries and answers about installing and using this skill.
You can run semantic search over a local SQL database by using an embedded, file-based system that supports vector columns for nearest-neighbor similarity lookup directly on the local filesystem.
The best way to combine vector embeddings with structured data logs is storing them together in an embedded SQL database, enabling fast relational queries alongside semantic search for maintenance logs or operational records.
No, you do not need a separate server to query local knowledge bases with SQL and vector search; an embedded database runs directly on the local filesystem using a specified file path for predictable performance.
To set up a local database for vector similarity search, point the embedded SQL engine at a local filesystem path, then begin creating tables that include vector columns as you ingest your data.
Use an embedded SQL database for local data management when you need predictable performance, file-based storage without server overhead, and native support for querying structured records alongside vector embeddings.
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