---
# RAG-X Library
## Overview
RAG-X is a comprehensive library designed to optimize Retrieval-Augmented Generation (RAG) processes. It provides a suite of tools to automatically determine the best parameters for processing specific documents. This includes selecting appropriate chunking techniques, embedding models, vector databases, and Language Model (LLM) configurations.
### Key Features:
- **Adaptive Chunking:** Incorporates four advanced text chunking methodologies to enhance the handling of diverse document structures.
- Specific Text Splitting
- Recursive Text Splitting
- Sentence Window Splitting
- Semantic Window Splitting
- **Expandability:** Future versions will introduce additional chunking strategies and enhancements based on user feedback and ongoing research.
- **Compatibility:** Designed to seamlessly integrate with a wide range of embedding models and vector databases.
## Getting Started
### Prerequisites
Due to existing dependency conflicts, it is crucial to install the required dependencies before using the RAG-X library. We are actively working on a resolution and appreciate your understanding.
```bash
pip install tiktoken chromadb trulens-eval 'unstructured[pdf]' openai -q
```
### Installation
After resolving the dependencies, install the RAG-X library using the following command:
```bash
pip install -i https://test.pypi.org/simple/ RAG-X -q
```
To verify the installation and view library details, execute:
```bash
pip show RAG-X
```
### Setting Up Your Environment
Before diving into the functionality of RAG-X, ensure that your environment variables are properly configured with your OpenAI API key and your Hugging Face token:
```python
import os
os.environ['OPENAI_API_KEY'] = "YOUR_OPENAI_API_KEY"
os.environ['HF_TOKEN'] = "YOUR_HUGGINGFACE_TOKEN"
```
## Usage
The following steps guide you through the process of utilizing the RAG-X library to optimize your RAG parameters:
```python
from RAG_X.prag import parent_class
# Specify the path to your PDF document
file_path = "PATH_TO_YOUR_PDF_FILE"
# Initialize the RAG-X instance
my_instance = parent_class(file_path)
# Generate the optimal RAG parameters for your document
score_card = my_instance.get_best_param()
# Output the results
print(score_card)
```
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