pgvector
pgvector at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Curriculum-approved question banks built from your own past papers”, “Migrate Between Embedding Models Without Rebuilding Your Entire Corpus”, and “Explore how hybrid search transforms discovery on Papers with Code”. A school's own past papers, split into tagged sub-questions and regenerated by AI, sound like a practical answer to the endless hunt for on-syllabus practice material. Backfilling a million vectors just to switch embedding models is the kind of cost that quietly stalls progress. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every pgvector story on Beyond Market Intelligence, newest first.
Curriculum-approved question banks built from your own past papers
A school's own past papers, split into tagged sub-questions and regenerated by AI, sound like a practical answer to the endless hunt for on-syllabus practice material. The 80% pass rate on multiple choice is promising, but the real test is structured questions, where a small model inventing wrong chemistry is a genuine safety issue. Human approval remains the right guard rail. For those building similar systems, the related article on production data shifts is worth a look.
Migrate Between Embedding Models Without Rebuilding Your Entire Corpus
Backfilling a million vectors just to switch embedding models is the kind of cost that quietly stalls progress. One developer found a smarter path: instead of re-embedding everything, pull a small set of documents from the old index and rerank them with the new model. With enough samples, retrieval quality matches native performance. In one test, just 50 documents closed the gap. That is practical, accessible innovation. The tooling supports Qdrant, pgvector, and FAISS, and it is ready to try.
Explore how hybrid search transforms discovery on Papers with Code
Building a better search engine for research papers rarely comes down to a single clever trick. On Papers with Code, Niels Rogge shows how combining PostgreSQL's pgvector with Qwen3 embeddings creates a hybrid system that outperforms keyword or semantic search alone. What stands out is the pragmatic stack: batch embeddings via Hugging Face Jobs, storage in Buckets, and a live model on Inference Endpoints. That same infrastructure drives related-paper recommendations. It's a thoughtful, reproducible approach.