Design your RAG pipeline visually
Retrieval-augmented generation has a standard backbone but many tunable stages. This designer lets you toggle each stage — loader, splitter, embedder, vector store, retriever, optional reranker, and generator — set its key parameters, and export both a Mermaid diagram for your documentation and a pseudocode skeleton to start building.
How a RAG pipeline fits together
A RAG system has two phases. At ingest time you load documents, split them into chunks, embed each chunk, and store the vectors. At query time you embed the user’s question, retrieve the most similar chunks, optionally rerank them for precision, and pass them as context to the generator. Optional stages — query rewriting before retrieval and reranking after — trade extra latency and cost for better answer quality.
Tips
- Ingest once, query many. Keep expensive embedding work in the ingest phase; the query path should be fast.
- Add the reranker only if you need it. It noticeably improves precision but adds a model call per query.
- Log retrieved chunks. Most RAG quality problems are retrieval problems — inspect what was fetched before blaming the generator.