Efficient Troubleshooting Section Extraction Using Azure AI Document Intelligence, Pinecone, and GPT-4 with RAG

In today's fast-paced digital world, extracting relevant information from large documents can be a time-consuming task. This is especially true for technical documents, where finding specific sections like troubleshooting guides can be crucial. To address this challenge, we have developed an application that automatically extracts troubleshooting sections from PDF documents using a combination of Azure AI, Pinecone database services, and OpenAI's GPT-4 Turbo model. This article will walk you through the functionality and setup of this innovative tool.

How It Works

Our application leverages several advanced technologies to ensure accurate and efficient extraction of troubleshooting sections:

• Text Extraction: The process begins with Azure AI Document Intelligence, which scrapes text from the uploaded PDF files. This powerful tool from Azure helps us convert the document's content into machine-readable text.
• Embedding Generation: Once the text is extracted, we use OpenAI's embedding model to convert this text into meaningful vector representations. These embeddings capture the semantic meaning of the text, making it easier to work with.
• Storage: The generated embeddings are stored in the Pinecone vector database. Pinecone is known for its efficient vector similarity search capabilities, which are crucial for our application.
• Retrieval: Using Retrieval-Augmented Generation (RAG), our application efficiently finds and extracts the troubleshooting sections from the stored embeddings. RAG combines the strengths of retrieval-based systems with generation-based models, allowing us to fetch relevant information and generate coherent, contextually appropriate responses.

By integrating these technologies, our application ensures that troubleshooting information within documents is identified accurately and quickly.

Getting Started

Setting up the project on your local machine is straightforward. Here are the steps you need to follow:

Prerequisites

Before you begin, make sure you have the following installed on your system:

• Python 3.8 or higher (our tests used version 3.11)
• Flask
• Azure AI Form Recognizer (now known as Azure AI Document Intelligence)
• Pinecone (Note: The free plan only allows one index per account, so you might need to delete and create a new index and re-upload PDFs if necessary)
• OpenAI GPT

Source code is here https://github.com/enesbasbug/PDF-TroubleshootExtractor-GPT4-RAG-Azure-Pinecone