pgvector is the popular PostgreSQL extension that adds a vector column type, distance operators, and approximate nearest-neighbour indexes for similarity search over embeddings. It turns any PostgreSQL database into a vector database, removing the operational overhead of running a separate ANN store for many RAG and recommendation workloads.
What it provides
A reranker is a model that takes an initial set of retrieved candidates and re-orders them to improve precision. In a typical retrieval pipeline, a fast first-stage retriever returns 50 to 200 candidates, and a slower but more accurate reranker scores each (query, candidate) pair and returns the top 5 to 10.
How it works
Most rerankers are cross-encoders: a transformer model that takes the…
Chunking is the process of splitting documents into smaller passages before embedding them for retrieval. Chunk size and boundaries directly determine what a retrieval system can find: a chunk that is too large blurs the meaning of its embedding, and a chunk that is too small lacks the context to answer most questions.
Common strategies
Embeddings are dense vector representations of text, images, audio, or other data, learned so that semantically similar inputs end up near each other in vector space. They are the foundation of similarity search, recommendation systems, semantic clustering, and retrieval-augmented generation.
How they work
An embedding model maps an input (a sentence, an image, a chunk of code) to a…