---
# 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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"description": "\r\n---\r\n\r\n# RAG-X Library\r\n\r\n## Overview\r\n\r\nRAG-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.\r\n\r\n### Key Features:\r\n- **Adaptive Chunking:** Incorporates four advanced text chunking methodologies to enhance the handling of diverse document structures.\r\n - Specific Text Splitting\r\n - Recursive Text Splitting\r\n - Sentence Window Splitting\r\n - Semantic Window Splitting\r\n- **Expandability:** Future versions will introduce additional chunking strategies and enhancements based on user feedback and ongoing research.\r\n- **Compatibility:** Designed to seamlessly integrate with a wide range of embedding models and vector databases.\r\n\r\n## Getting Started\r\n\r\n### Prerequisites\r\n\r\nDue 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.\r\n\r\n```bash\r\npip install tiktoken chromadb trulens-eval 'unstructured[pdf]' openai -q\r\n```\r\n\r\n### Installation\r\n\r\nAfter resolving the dependencies, install the RAG-X library using the following command:\r\n\r\n```bash\r\npip install -i https://test.pypi.org/simple/ RAG-X -q\r\n```\r\n\r\nTo verify the installation and view library details, execute:\r\n\r\n```bash\r\npip show RAG-X\r\n```\r\n\r\n### Setting Up Your Environment\r\n\r\nBefore 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:\r\n\r\n```python\r\nimport os\r\n\r\nos.environ['OPENAI_API_KEY'] = \"YOUR_OPENAI_API_KEY\"\r\nos.environ['HF_TOKEN'] = \"YOUR_HUGGINGFACE_TOKEN\"\r\n```\r\n\r\n## Usage\r\n\r\nThe following steps guide you through the process of utilizing the RAG-X library to optimize your RAG parameters:\r\n\r\n```python\r\nfrom RAG_X.prag import parent_class\r\n\r\n# Specify the path to your PDF document\r\nfile_path = \"PATH_TO_YOUR_PDF_FILE\"\r\n\r\n# Initialize the RAG-X instance\r\nmy_instance = parent_class(file_path)\r\n\r\n# Generate the optimal RAG parameters for your document\r\nscore_card = my_instance.get_best_param()\r\n\r\n# Output the results\r\nprint(score_card)\r\n```\r\n\r\n",
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