Vector Search

Vector search—also called semantic or similarity search—works by converting text into high-dimensional vectors (embeddings) and then finding items whose vectors are closest to your query vector. Unlike keyword search, it understands meaning: a query like "pricing plans" can match content that says "subscription costs" or "payment options."

Leafra uses vector search (via Pinecone) to quickly retrieve the most relevant sections of your PDFs when you ask a question, so the AI gets the right context every time.