How we built a SOTA search engine using PostgreSQL, pgvector, and Qwen3 embeddings [P]

I wrote a technical breakdown of how search works on Papers with Code. The system combines keyword and semantic search, which produced better results than either approach alone. The stack includes: PostgreSQL with pgvector Qwen3-Embedding-0.6B for text embeddings Hugging Face Jobs with an NVIDIA L4 for batch embedding generation Hugging Face Buckets for storing artifacts A live embedding model served through Hugging Face Inference Endpoints The same infrastructure also powers the “related papers” recommendations shown on individual paper pages. Full write-up: How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code I’d be interested to hear how others are implementing hybrid search for research papers or similarly technical content. Disclosure: I work at Hugging Face and on Papers with Code. submitted by /u/NielsRogge [link] [留言]