Metadata-Version: 2.4
Name: aurora-vault
Version: 1.0.4
Summary: Universal RAG engine
Author-email: Tareq <tareqshah.027@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/Tareq905/aurora-vault
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: platformdirs
Requires-Dist: rich
Dynamic: license-file

# Aurora-Vault ◌

Developed by **Md Tareq Shah Alam**

A high-performance, lightweight **RAG package** designed to simplify building **Retrieval-Augmented Generation (RAG)** applications.

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## Overview

**Aurora-Vault** is a compact and efficient Python package that provides the core components required to build RAG-based applications quickly and effectively.

It is powered by approximately **650,000+ supervised data records**, enabling strong contextual retrieval while maintaining a minimal footprint.

This is **Version 1**, focused on delivering a stable and developer-friendly foundation. Future versions will expand capabilities, improve performance, and introduce advanced retrieval techniques.

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## Key Features

* **Fast & Efficient** — Optimized for quick retrieval and response
* **Lightweight Package** — Easy to install and integrate
* **RAG-Ready** — Designed to plug into any LLM pipeline
* **650K+ Data Backbone** — Strong contextual understanding
* **Developer-Friendly API** — Simple and clean usage
* **Extensible** — Built for scaling and customization

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## Installation

```bash
pip install aurora-vault
```

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## What This Package Does

Aurora-Vault provides:

* Retrieval utilities
* Structured data access
* Lightweight knowledge base handling
* Components to integrate with LLMs

 It is **not a full standalone system**, but a **building block** to create your own RAG pipelines.

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## Use Cases

* AI Chatbots
* Knowledge Retrieval Tools
* SaaS AI Products
* Document Search Systems
* Internal AI Assistants

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## Roadmap

* Improved retrieval modules
* Expanded dataset support
* Better embedding integration
* Configurable pipelines
* Domain-specific extensions

## Contributing

Contributions and ideas are welcome.
Let’s build a better RAG ecosystem together.

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## License

MIT License
