File size: 3,967 Bytes
6b7123c 8df4562 01c0652 c82b9c8 6b7123c 413eb4e cf393d0 cd81479 c82b9c8 413eb4e cd81479 413eb4e 01c9291 413eb4e 01c9291 413eb4e 01c9291 413eb4e 01c9291 413eb4e 9e5eace 413eb4e 01c9291 413eb4e 01c9291 413eb4e 01c9291 413eb4e 01c9291 413eb4e 0e1cf95 413eb4e 0e1cf95 413eb4e 0e1cf95 413eb4e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
license: gfdl
language:
- it
- es
- sc
- en
- de
- zh
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)**
More languages may be added in the future.
## Dataset Structure
Each language is stored as a separate compressed file (`.db.gz`) within the dataset. For example:
* `it.db.gz` - Italian Wikipedia database (lexical only)
* `sc.all-mini.db.gz` - Sardinian Wikipedia database (lexical and semantic)
## How to Use This Dataset
1. **Install Wikilite**
Download a precompiled binary for your operating system from the [Wikilite Releases](https://github.com/eja/wikilite/releases/latest) page. Once downloaded, uncompress the binary.
2. **Run the `--setup` Command**
Execute the binary with the `--setup` option to install a pre-built database. This command will display a list of available databases, allowing you to select and download the one you need, along with the corresponding GGUF model. For example:
```bash
./wikilite --setup
```
Note: Databases in the "lexical" folder are for full-text search only and do not support semantic search.
3. **Run Wikilite with the Installed Database**
After completing the setup, navigate to the directory containing the `wikilite` binary. Use the following commands to run Wikilite:
- **To start the web interface** for browsing and searching:
```bash
./wikilite --web
```
- **To search via the command line**:
```bash
./wikilite --cli
```
4. **Access the Web Interface**
If you started the web server using the `--web` option, open your browser and go to `http://localhost:35248` to access the web interface for browsing and searching the Wikipedia content.
## About Wikilite
[Wikilite](https://github.com/eja/wikilite) is a tool that provides offline access to Wikipedia content, featuring:
* **Fast and Flexible Lexical Searching:** Uses FTS5 (Full-Text Search 5) for efficient keyword-based searching.
* **Enhanced Semantic Search:** Integrates semantic search capabilities, allowing you to find information based on meaning rather than just keywords.
* **Offline Access:** Enables access to Wikipedia articles without an internet connection.
* **Command-Line Interface (CLI):** Allows direct searching from the terminal.
* **Web Interface (Optional):** Provides a user-friendly way to browse and search content.
### Semantic Search Details
Wikilite leverages text embeddings for its optional semantic search. This allows you to find results even if your query does not match keywords directly, handling cases like:
* Typos in your search query.
* Different wordings to express the same concept.
* The article uses synonyms or related terms.
**Note:** To enable semantic search, you'll need a local GGUF model or an OpenAI-compatible remote server and configure Wikilite accordingly. See the Wikilite GitHub repository for more details.
## 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.
* [Ollama](https://ollama.ai) For enabling the internal generation of embeddings.
* [Wikilite](https://github.com/eja/wikilite): For making this project possible. |