---
license: gfdl
language:
- it
- es
- sc
- en
- de
- zh
- ar
tags:
- sqlite
- wikipedia
- wikilite
- eja
pretty_name: wikilite
---
# Processed Wikipedia SQLite Databases for Wikilite

This dataset provides pre-processed SQLite databases of Wikipedia articles for use with the [Wikilite](https://github.com/eja/wikilite) tool. These databases allow you to quickly and efficiently search and access Wikipedia content offline using Wikilite's lexical and semantic search capabilities.

## Supported Languages

Currently, the dataset includes databases for the following languages:

*   **Sardinian (sc)**
*   **Italian (it)**
*   **Spanish (es)**
*   **English (en)**
*   **German (de)**
*   **Chinese (zh)**
*   **Arabic (ar)**

More languages may be added in the future.

## Dataset Structure

Each language is stored as a separate compressed file (`.db.gz`). There are two types of databases available:

1.  **Lexical Only**: Located in the `lexical/` directory. These support standard FTS5 full-text search (exact word matching) and are lighter in size.
2.  **Semantic & Lexical**: Located in the root or specific model directories. These databases contain **embedded GGUF models** directly within the file structure, enabling both keyword search and vector-based conceptual search without external dependencies.

## How to Use This Dataset

### 1. Command Line & Web Interface (Linux, macOS, Windows, Termux)

Wikilite runs primarily as a command-line tool that can also serve a local web interface.

1.  **Install Wikilite**  
    Download the precompiled binary for your operating system from the [Wikilite Releases](https://github.com/eja/wikilite/releases/latest) page and extract it.

2.  **Download a Database**  
    You can download the `.db.gz` files manually from this repository and extract them, or use Wikilite's built-in tools to handle it for you:
    
    *   **Interactive Wizard:** Run `./wikilite` without arguments to launch a wizard that guides you through selecting and downloading a database.
    *   **Setup Command:** Run `./wikilite --setup` to view and download available databases automatically.

3.  **Run Wikilite**  
    Once you have a database file (e.g., `wikilite.db`), you can use it in three ways:

    *   **Interactive Mode:**  
        ```bash
        ./wikilite
        ```
        (Follow the on-screen prompts)

    *   **Command Line Search:**  
        ```bash
        ./wikilite --cli --db <file.db>
        ```

    *   **Web Interface:**  
        ```bash
        ./wikilite --web --db <file.db>
        ```
        Access the interface at `http://localhost:35248` in your browser.

### 2. Android Application

A native Android application is available that uses these exact databases.

1.  Download the [Android App APK](https://github.com/eja/wikilite/releases/latest/download/wikilite-android.apk).
2.  **External Storage:** If you manually download a `wikilite.db` file from this repository and place it on your external SD card, the app will detect and use it automatically.
3.  **In-App Download:** If no database is found on launch, the app provides an option to download these pre-built databases directly.

## About Wikilite

[Wikilite](https://github.com/eja/wikilite) is a self-contained tool for creating and browsing local SQLite databases of Wikipedia articles.

### Features
*   **Lexical Search:** Uses FTS5 for efficient, exact keyword matching.
*   **Semantic Search:** Uses embedded GGUF models (via llama.cpp integration) contained within the database files to find content based on meaning, synonyms, and conceptual similarity.
*   **Offline Operation:** Complete functionality without internet connectivity.
*   **Cross-Platform:** Available for Linux, macOS, Windows, Termux, and Android.

### Semantic Search Capabilities
The semantic search functionality employs text embeddings to handle:
*   Query misspellings and typographical errors.
*   Conceptual similarity despite different terminology.
*   Synonym and related term matching.
*   Morphological variations (plurals, verb tenses).

*Note: The semantic databases in this repository are fully self-contained. No external model files or API keys are required.*

## Contributing

If you would like to contribute databases for additional languages, please feel free to submit a pull request.

## Acknowledgments

*   [Wikipedia](https://www.wikipedia.org/): For providing the valuable data.
*   [SQLite](https://www.sqlite.org/): For the robust database engine.
*   [LLaMA.cpp](https://github.com/ggml-org/llama.cpp): For enabling the internal generation of embeddings.
*   [Wikilite](https://github.com/eja/wikilite): For the software powering these datasets.