Sumit189

Sumit189 / x-search

Public

Gemini Embeddings Powered CLI app for image search

10
3
100% credibility
Found Apr 03, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

A simple notebook that builds a searchable collection of your personal images, letting you find them using natural language descriptions.

How It Works

1
🔍 Discover x-search

You find a handy tool that lets you search all your photos just by describing what you're looking for in simple words.

2
📥 Get the notebook

Download the easy-to-use guide file from the project page and open it in your notebook app.

3
🔗 Connect AI helper

Link up a smart AI service so it can understand and match your pictures to your descriptions.

4
🛠️ Ready the helpers

Add a few supporting tools to make sure everything runs smoothly on your computer.

5
📂 Build your photo library

Tell it which folders hold your pictures, and it gathers them into a personal searchable collection.

6
💭 Ask away

Type everyday phrases like 'sunglasses on a beach' and see the best matching images pop up with their locations.

🎉 Photos found easily

Now you can instantly rediscover any image across your entire computer without endless folder hunting.

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Star Growth

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AI-Generated Review

What is x-search?

x-search is a Jupyter Notebook that lets you build a global semantic search engine for images on your machine using Gemini embeddings and ChromaDB. Point it at directories full of PNGs, JPGs, or other formats, and it indexes them into a persistent database at ~/.xsearch, automatically resizing large files to fit Gemini embeddings quota and dimensions like 001 size or 002. Query with natural language via search_images("beach sunglasses") to get ranked results with file paths—no more digging through folders.

Why is it gaining traction?

It skips heavy setups for a lightweight notebook that handles Gemini embeddings performance in Python, respecting API limits while delivering fast multimodal searches across your entire photo library. Developers dig the simplicity over bloated tools, especially with ChromaDB's persistence and Gemini's edge on embeddings Reddit threads praise for x advanced search github alternatives. The global indexing hook means one database rules them all, no per-project hassle.

Who should use this?

Photo hoarders or designers sifting design assets daily. ML tinkerers prototyping image retrieval without cloud costs. Content creators querying "vintage car sketches" across scattered drives, or devs integrating Gemini embeddings 1 or 2 into apps via this x ray search github blueprint.

Verdict

Try it for quick personal hacks—10 stars and 1.0% credibility score scream early experiment, with solid docs but zero tests or polish. Fine prototype, but wait for maturity before production. (187 words)

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