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The Retrieval-Augmented Generation (RAG) model integrates two robust methodologies: information retrieval and language generation. The model initially gathers pertinent information from an extensive dataset in response to a query, subsequently formulating a reply utilizing the context obtained. This design improves the precision of produced responses by anchoring them in real data, rendering it especially beneficial for intricate information requests across extensive datasets, like lengthy PDF files.

This tutorial will walk you through the process of utilizing Python to extract and process text from a PDF document, create embeddings, conduct cosine similarity calculations, and respond to queries derived from the extracted content.

Ensure you have the following libraries installed in your Python environment:

Import the libraries and open the PDF using this code:

The text needs to be broken down into smaller, manageable chunks. We use to split each page's text into overlapping chunks.

Using the to break text into smaller, manageable chunks with overlapping sections is important for several reasons, especially when dealing with natural language processing (NLP) tasks, large documents, or continuous text analysis. Here's why it's beneficial:

To extract meaningful phrases from the text, we use , a Python implementation of the Rapid Automatic Keyword Extraction (RAKE) algorithm.

RAKE is an algorithm for extracting keywords from text, designed to be fast and efficient. It works by identifying words or phrases that are statistically significant within a document. Here's an overview of how it works:

Generate embeddings for each phrase using OpenAI's model and save in the Excel format. This model generates numerical representations (embeddings) of text. These embeddings capture the semantic meaning of the text, allowing you to compare and analyze pieces of text based on their content.

Generate embeddings for query phrases and find the most similar chunks using cosine similarity. Cosine similarity is a measure used to determine how similar two vectors are based on the angle between them in a multi-dimensional space. It's commonly used in text analysis and information retrieval to compare text embeddings or document vectors, as it quantifies similarity irrespective of the vectors' magnitude. In the context of text embeddings, cosine similarity helps identify which documents or sentences are closely related based on their meaning, rather than just their content or word count.

Compose the context for the query from the most similar chunks and retrieve the answer using OpenAI's GPT model.

Finally, this is the answer that I received after asking that question:

Answer: The 2DRA model was utilized to perform data recovery on the Virtual Machine (VM) affected by ransomware. It was successful in retrieving all the 14,957 encrypted files. Additionally, an analysis of the encrypted files and their associated hash values on the VM was conducted using the 2DRA model after the execution of WannaCry ransomware. The analysis revealed that the hexadecimal values of the files were distinct prior to encryption, but were altered after the encryption.

The solution is based on the PDF I used in step 1. You'll see the solution in the PDF you submitted.

This concludes implementing a basic RAG pipeline that reads PDF content, extracts meaningful phrases, generates embeddings, calculates similarities, and answers queries based on the most relevant content.