Vector Databases

Chapter 5. Building an ArXiv Paper Search System with PostgreSQL pgvector

This chapter will teach you how to build an ArXiv paper search system with PostgreSQL pgvector, so let’s dive in.

The Challenge of Searching Scientific Literature

In the rapidly evolving landscape of scientific research, staying current with the latest developments has become increasingly challenging. ArXiv alone publishes thousands of new papers each month across physics, mathematics, computer science, and other fields. Traditional keyword-based search often fails to capture the semantic relationships between papers, missing relevant work that uses different terminology or approaches the same problem from a different angle.

Consider a researcher investigating “neural network optimization techniques.” A keyword search might miss papers discussing “gradient descent improvements” or “backpropagation efficiency,” even though these are directly relevant. This semantic gap between search queries and relevant content is where vector databases excel: understanding the meaning behind text rather than just matching keywords.

Why ArXiv Makes an Ideal Data Source

ArXiv presents unique advantages for building a vector search system. First, it provides free, open access to scientific papers with consistent metadata through its API. Second, the papers follow relatively standard academic formatting, making text extraction more predictable than general web content. Third, the technical nature of the content benefits significantly ...