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.

pip install tiktoken chromadb trulens-eval 'unstructured[pdf]' openai -q

Installation

After resolving the dependencies, install the RAG-X library using the following command:

pip install -i https://test.pypi.org/simple/ RAG-X -q

To verify the installation and view library details, execute:

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:

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:

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)