Understanding how Physics Education Research (PER) has changed and matured can help researchers and materially improve physics teaching and learning. The advent of large language models (LLMs) and embedding-based NLP techniques opens new avenues for this work by enabling the analysis of large corpora with minimal manual coding. This preliminary study builds off the work of [T. O. B. Odden et al., Physical Review Physics Education Research 20, 020151 (2024)]. We explore the use of text embeddings and retrieval-augmented generation (RAG)-like methods without generative AI to analyze themes across a random sample of 94 articles from major PER journals. Specifically, we examine how two methodological choices affect topic modeling performance: (1) representing articles using a single embedding versus multiple sentence-level embeddings, and (2) deriving topic centroids from representative texts versus researcher-defined queries. All results are evaluated against a human-coded dataset focused on four overarching thematic categories: teacher-centered, student-centered, physics content, and journal business. Our findings inform best practices for researchers seeking scalable, interpretable, and low-barrier approaches to literature analysis in PER.