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How To Do Offline Recommendations With Sqlite And Gemini

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rodydavis·vlatest·database

Build offline recommendation systems using SQLite for data storage and Gemini for model inference – a practical guide.

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Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add rodydavis/skills --skill how-to-do-offline-recommendations-with-sqlite-and-gemini -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

rodydavis

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Live repository

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

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    Makes no network calls.

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    Scanned rodydavis/skills at 804913dcbe.

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Commit
804913dcbe
Manifest
skills/offline-vector-recommendations/SKILL.md
Scanned
1 file, 104 lines
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About this skill

What it does

This skill enables AI agents to perform personalized recommendations entirely offline by combining a local SQLite database with the Gemini API. It allows agents to query user-specific data without needing an active internet connection or cloud-based vector search infrastructure.

When to use it

  • Building privacy-focused applications where user data must remain on-device.
  • Creating mobile apps that need to function seamlessly in areas with poor or no connectivity.
  • Implementing recommendation engines for small datasets that don't require heavy cloud compute resources.
  • Prototyping offline-first AI features before scaling to complex distributed systems.

Key capabilities

  • Stores user interaction history and preferences in a local SQLite file.
  • Embeds data locally using the Gemini API to generate vector representations.
  • Performs similarity searches against the local vector store to find relevant items.
  • Returns ranked recommendations based on calculated cosine similarity scores.

Example prompts

  • "Find me movies similar to 'Inception' based on my viewing history stored in this SQLite database."
  • "Suggest books I might enjoy given that I recently read 'Dune', using only my local data."
  • "What products are recommended for users with a purchase pattern matching mine, without sending my data online?"

Tips & gotchas

Ensure the Gemini API key is configured correctly to generate embeddings locally; this step requires internet access but subsequent queries do not. This approach is best suited for smaller datasets, as embedding large volumes of data locally can be resource-intensive on mobile devices.