Built a RAG Pipeline to Answer Questions from Resumes

Introduction

Recruiters often sift through hundreds of resumes to find the best-fit candidates. An overwhelming and inefficient process when relying on keyword-based search alone. This is where AI-powered PDF search transforms the game by enabling semantic understanding of both resumes and job descriptions. 

In this guide, we will take a deep dive into the world of intelligent document search, showing you how to create a highly efficient, salable, and context-aware PDF search engine using FastAPI, Azure AI Search, and OpenAI‘s AI-driven embedding to completely transform how we retrieve and interact with digital documents. 

Traditional Resume Search 

The traditional way of searching for content within PDFs is not only inefficient but also lacks context-awareness. AI-driven search technology is revolutionizing document management by enabling faster, more accurate retrieval based on meaning rather than mere keywords. 

Why Use AI for Resume Search? 

Traditional PDF search systems rely heavily on exact keyword matching, which means they fail to deliver relevant results when users phrase queries differently. AI-powered search engines solve this issue by introducing semantic understanding and natural language processing (NLP) to retrieve the most meaningful information—even if it is phrased differently. 

Key Advantages: 

• Instant access to resumes based on meaning, not just keywords 
• Context-aware retrieval powered by OpenAI’s deep learning embeddings 
• Smart document chunking and indexing for efficient database performance 
• Scalability to handle large resume datasets quickly and accurately 

Tools & Technologies Used