--- title: MiniSearch emoji: 👌🔍 colorFrom: yellow colorTo: yellow sdk: docker short_description: Minimalist web-searching app with browser-based AI assistant pinned: true custom_headers: cross-origin-embedder-policy: require-corp cross-origin-opener-policy: same-origin cross-origin-resource-policy: cross-origin --- # MiniSearch A minimalist web-searching app with an AI assistant that runs directly from your browser. Live demo: https://felladrin-minisearch.hf.space ## Screenshot ![MiniSearch Screenshot](https://github.com/user-attachments/assets/f8d72a8e-a725-42e9-9358-e6ebade2acb2) ## Features - **Privacy-focused**: [No tracking, no ads, no data collection](https://docs.searxng.org/own-instance.html#how-does-searxng-protect-privacy) - **Easy to use**: Minimalist yet intuitive interface for all users - **Cross-platform**: Models run inside the browser, both on desktop and mobile - **Integrated**: Search from the browser address bar by setting it as the default search engine - **Efficient**: Models are loaded and cached only when needed - **Customizable**: Tweakable settings for search results and text generation - **Open-source**: [The code is available for inspection and contribution at GitHub](https://github.com/felladrin/MiniSearch) ## Prerequisites - [Docker](https://docs.docker.com/get-docker/) ## Getting started Here are the easiest ways to get started with MiniSearch. Pick the one that suits you best. **Option 1** - Use [MiniSearch's Docker Image](https://github.com/felladrin/MiniSearch/pkgs/container/minisearch) by running in your terminal: ```bash docker run -p 7860:7860 ghcr.io/felladrin/minisearch:main ``` **Option 2** - Add MiniSearch's Docker Image to your existing Docker Compose file: ```yaml services: minisearch: image: ghcr.io/felladrin/minisearch:main ports: - "7860:7860" ``` **Option 3** - Build from source by [downloading the repository files](https://github.com/felladrin/MiniSearch/archive/refs/heads/main.zip) and running: ```bash docker compose -f docker-compose.production.yml up --build ``` Once the container is running, open http://localhost:7860 in your browser and start searching! ## Frequently asked questions
How do I search via the browser's address bar?

You can set MiniSearch as your browser's address-bar search engine using the pattern http://localhost:7860/?q=%s, in which your search term replaces %s.

Can I use custom models via OpenAI-Compatible API?

Yes! For this, open the Menu and change the "AI Processing Location" to Remote server (API). Then configure the Base URL, and optionally set an API Key and a Model to use.

How do I restrict the access to my MiniSearch instance via password?

Create a .env file and set a value for ACCESS_KEYS. Then reset the MiniSearch docker container.

For example, if you to set the password to PepperoniPizza, then this is what you should add to your .env:
ACCESS_KEYS="PepperoniPizza"

You can find more examples in the .env.example file.

I want to serve MiniSearch to other users, allowing them to use my own OpenAI-Compatible API key, but without revealing it to them. Is it possible?

Yes! In MiniSearch, we call this text-generation feature "Internal OpenAI-Compatible API". To use this it:

  1. Set up your OpenAI-Compatible API endpoint by configuring the following environment variables in your .env file:
    • INTERNAL_OPENAI_COMPATIBLE_API_BASE_URL: The base URL for your API
    • INTERNAL_OPENAI_COMPATIBLE_API_KEY: Your API access key
    • INTERNAL_OPENAI_COMPATIBLE_API_MODEL: The model to use
    • INTERNAL_OPENAI_COMPATIBLE_API_NAME: The name to display in the UI
  2. Restart MiniSearch server.
  3. In the MiniSearch menu, select the new option (named as per your INTERNAL_OPENAI_COMPATIBLE_API_NAME setting) from the "AI Processing Location" dropdown.
How can I contribute to the development of this tool?

Fork this repository and clone it. Then, start the development server by running the following command:

docker compose up

Make your changes, push them to your fork, and open a pull request! All contributions are welcome!

Why is MiniSearch built upon SearXNG's Docker Image and using a single image instead of composing it from multiple services?

There are a few reasons for this: