Semantic search gets interesting when the product stops feeling like a database and starts feeling like a collaborator. The research paper discovery platform was built around that idea: query by meaning, surface the strongest matches, and make the path from question to paper feel immediate.

System Shape

The core loop is intentionally small:

• Normalize metadata from arXiv exports.
• Generate embeddings with Sentence Transformers.
• Store vectors in FAISS for low-latency nearest-neighbor retrieval.
• Serve results through FastAPI and render them in a focused Next.js interface.

That shape kept the product understandable while leaving room for better ranking, filters, and citation-aware exploration later.

Interface Notes

The frontend needed to communicate confidence without becoming noisy. The result cards prioritize title, abstract signal, and immediate external actions. Search feels fast because the interface avoids visual churn between request and result.

query -> embedding -> FAISS top-k -> metadata hydrate -> ranked cards

The lesson: search products live or die by the gap between a user's language and the system's representation of intent.