RAG, from the ground up.
Somebody asks your app a question about a document it has never seen, and it answers confidently and wrongly. Fixing that is what RAG is, and the fix turns out to be a search engine with a very unusual definition of similar. This guide assumes you have never built one. It starts at what the problem actually is, adds one piece at a time in the order each piece becomes necessary, then shows the real calls and the four things that trip everyone up on their first afternoon.
Want code first? The 15-minute quickstart gets you to a cited answer and skips the reasoning. Come back when a search returns something strange and you want to know why, because that is the day these pages pay for themselves.
- 01What RAG isWhy a model needs your documents handed to it, and the shape of the whole flow.6 min
- 02Embeddings & vector searchChunks, embeddings, and how "find the relevant bit" actually works.7 min
- 03Graphs & GraphRAGWhen plain search misses the answer, and how a graph of your entities catches it.7 min
- 04Ingesting & queryingThe real calls: create, upload, the async ingest, file_search, reading citations.8 min
- 05Encrypted (sealed) RAGPutting regulated data through RAG: what each mode protects, and what it does not.9 min
The whole API is OpenAI-compatible, so most of what you already know aboutvector_storesand file_search carries over. Where Ringside differs (pricing, file types, GraphRAG, encryption) the guide calls it out.