🛠️ Vault Project: Offline Knowledge Vault

In a world of uncertainty, knowledge must endure.
Vault Project is a tool for creating an offline-optimized database of Wikipedia and other open knowledge repositories, ensuring accessibility even without an internet connection.

🌐 What is it?

Vault Project takes publicly available datasets (e.g., Wikipedia, OpenStreetMap, WikiHow) and structures them into an SQLite database with:

• Full-Text Search (FTS) for efficient offline queries.
• Embeddings for semantic search.
• Language-Specific Dumps for multiple Wikipedia editions.

🧠 Why Vault?

• Resilience: Access knowledge offline, even if online resources become unavailable.
• Efficiency: Indexed and optimized with embeddings and FTS for fast lookups.
• Customizable: Plug in custom datasets or personal documents.

📦 Dataset Structure

The dataset is organized by language:

• /en/wikipedia_indexed.db - English Wikipedia
• /it/wikipedia_indexed.db - Italian Wikipedia
• /zh/wikipedia_indexed.db - Chinese Wikipedia
• ...

Each database file includes:

1. Articles with embeddings for semantic search.
2. Full-text search (FTS5) on titles and content.
3. Metadata tables for cross-referencing sources.

🛠️ Usage

Install dependencies:

pip install sqlite3 transformers huggingface_hub

Query the Database

import sqlite3

conn = sqlite3.connect("wikipedia_indexed.db")
cursor = conn.cursor()

query = "SELECT title, snippet FROM articles WHERE articles MATCH 'quantum physics';"
for title, snippet in cursor.execute(query):
    print(f"{title}: {snippet}")

conn.close()

⚙️ Technical Insights

• SQLite for portability.
• FTS5 for full-text indexing.
• Embeddings generated with open-source NLP models.

📜 License

Distributed under the MIT License.

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license: mit