Metadata-Version: 2.4
Name: aurora-vault
Version: 2.1.0
Summary: Universal RAG engine
Author-email: Md Tareq Shah Alam <tareqshah.027@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://www.linkedin.com/in/md-tareq-shah-alam/
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: rich
Requires-Dist: requests
Requires-Dist: sentence-transformers
Dynamic: license-file

# Aurora-Vault

Developed by **Md Tareq Shah Alam**

Aurora-Vault is a lightweight, production-ready **RAG (Retrieval-Augmented Generation) engine** that enables instant semantic search without any setup or preprocessing.

---

## Overview

Aurora-Vault is designed to remove the complexity of building RAG systems.

Unlike traditional pipelines that require:

* Data preprocessing
* Embedding generation
* Index building

Aurora-Vault provides a **prebuilt knowledge index**, allowing developers to use RAG instantly.

---

## Key Idea

> Install → Load → Use

No setup. No build. No waiting.

---

## Features

* Instant setup (no index building)
* Lightweight package (no large files inside)
* Automatic index download on first run
* Fast semantic search
* Precomputed embeddings for efficiency
* Plug-and-play API
* Production-ready architecture

---

## Installation

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

---

## Quick Start

### 1. Import

```python
import aurora_vault
```

---

### 2. Initialize

```python
vault = aurora_vault.load()
```

---

## First-Time Setup (Automatic)

On the first run, Aurora-Vault will:

* Create a `rag/` directory in your project
* Download a prebuilt index
* Initialize the retrieval system

You will see:

```
Aurora Vault Setup
Downloading core index (~1GB)...
```

---

## Subsequent Runs

* No download
* No setup
* Instant loading

---

## Data Storage

Aurora-Vault creates:

```
your_project/
 └── rag/
      └── index.pkl
```

* `index.pkl` → prebuilt vector index
* Stored locally for reuse
* No repeated downloads

---

## Custom Storage Path

You can control where data is stored:

```python
vault = aurora_vault.load(path="D:/my_data")
```

---

## How It Works

Aurora-Vault follows a simplified RAG pipeline:

```
User Query
   ↓
Query Embedding (on demand)
   ↓
Vector Similarity Search
   ↓
Retrieve Relevant Context
   ↓
(Optional) LLM Processing
   ↓
Final Output
```

---

## Why Aurora-Vault

Traditional RAG systems are:

* Slow to initialize
* Complex to manage
* Resource heavy

Aurora-Vault solves this by:

* Removing build-time overhead
* Using precomputed embeddings
* Providing instant usability

---

## Use Cases

* AI Chatbots
* Knowledge Retrieval Systems
* SaaS AI Platforms
* Document Search
* Internal Assistants
* Automation Systems

---

## Important Notes

* First run requires internet (for index download)
* Download happens only once
* Do not delete `rag/index.pkl` unless reset is needed

---

## Roadmap

* FAISS-based ultra-fast search
* Advanced embedding strategies
* Incremental updates
* Multi-dataset support
* Monitoring tools

---

## Contributing

Contributions are welcome.

Aurora-Vault aims to make RAG systems simple, fast, and accessible.

---

## License

MIT License
