The search and embeddings API for RAG — long-context embeddings, rerankers, and a reader for any URL.
Jina AI builds the unglamorous but essential pieces of retrieval: best-in-class embedding models (including long-context and code), a Reader API that turns any URL into clean markdown, and rerankers that fix what naive similarity misses. It's the toolkit teams reach for when 'just stuff chunks in a vector DB' stops being good enough.
Who it's for: Developers building RAG, search, and knowledge apps who need better retrieval than default embeddings.
Long-context and code embeddings in one endpoint.
Convert any webpage to clean, LLM-ready markdown.
Re-order results for far better precision.
Chunk long docs intelligently.
If your RAG answers feel off, Jina's embeddings + reranker combo is the cheapest quality jump you can make. Pair with Qdrant or Pinecone and watch recall climb.