Here's a dirty secret of search: "the closest match" and "the most useful result" are not the same thing. Return the mathematically nearest document and you'll often hand someone something technically related and practically useless. Relevance is a harder problem than similarity — and it's where good search is won or lost.
Getting that right was the core challenge in the GovernAI Research Atlas, a semantic discovery platform I built to unify research across papers, repositories, and policy.
Semantic search gives you a superpower: embed everything into vectors and find items close in meaning, not just wording. But raw nearest-neighbor retrieval has a blind spot. The vector-closest result might be a tangential paper that happens to share vocabulary, while the genuinely useful one sits slightly further out. Distance in embedding space is a proxy for relevance — a good one, but not the whole story.
If you stop at "closest vector," your search is clever and still frustrating. The Atlas runs ChromaDB vector search with Sentence-Transformer embeddings across sources like OpenAlex and GitHub — that's the retrieval layer, the "what's semantically near this query." On top of it sits a custom relevance score that decides what actually surfaces first.
That two-stage shape is the pattern behind every search system worth using:
Retrieval gets the attention; ranking gets the results. Anyone can wire up a vector database and get "semantically similar" documents. Turning that into something that reliably surfaces the most relevant item first — that's the engineering that separates a search demo from a search product.
Building the Atlas taught me that the last mile of search — deciding what deserves the top spot — is where most of the real value hides. The full retrieval-and-ranking architecture is on the project page.
👉 Explore it: www.divyakush.com/projects/governai-research-atlas Divyakush Punjabi — Full-Stack & AI Systems Engineer
🌐 https://www.divyakush.com · 💼 LinkedIn · 💻 GitHub