The RAG (Retrieval-Augmented Generation) stack
RAG (Retrieval-Augmented Generation) combines a retriever—often a vector database over chunked, embedded documents—with a large language model (LLM). The flow: user query → embed query → semantic search in the VDB → top-k chunks returned as context → LLM generates an answer conditioned on that context, reducing hallucination and grounding output in your data.
Summary
- RAG (Retrieval-Augmented Generation) combines a retriever—often a vector database over chunked, embedded documents—with an LLM. Flow: query → embed → semantic search in VDB → top-k chunks as context → LLM generates answer; reduces hallucination, grounds output in your data.
- Components: Ingestion (chunking, embed, store with payloads); Retrieval (embed query, VDB returns nearest chunks; hybrid or re-ranking); Generation (context to LLM). Optional: LLM response caching via VDB. VDB must be fast and accurate; chunking, embedding choice, filtering affect RAG quality.
- Pipeline: chunk docs → embed → store in VDB; at query time embed query → VDB top-k → (optional re-rank) → LLM with context. Trade-off: more chunks improve coverage but increase cost and latency. Practical tip: tune chunk size and top-k for your domain; use metadata filters by source/date.
RAG components
Components: (1) Ingestion—documents are split (chunking), embedded with the same model used at query time, and stored in the VDB with optional payloads (text, metadata). (2) Retrieval—query is embedded; VDB returns nearest chunks; you may use hybrid search or re-ranking. (3) Generation—retrieved text is passed to the LLM as context; the model answers based on it. (4) Optional—LLM response caching (e.g. GPTCache) can use a VDB to cache past Q&A for similar queries.
Trade-off: larger chunks give more context per slot but may mix topics; smaller chunks improve precision but require more top-k and increase latency. Filtering by source, date, or metadata narrows the search space and improves relevance.
Role of the VDB in RAG
The VDB is central: it must be fast and accurate enough that the right chunks are in the top-k; chunking, embedding model choice, and filtering (e.g. by source or date) all affect RAG quality. Many production RAG systems use a dedicated vector database rather than a generic DB with vector extensions for scale and latency.
Pipeline: chunk docs → embed → store in VDB; at query time embed query → VDB top-k → (optional re-rank) → LLM with context. Trade-off: more chunks improve coverage but increase cost and latency. Practical tip: tune chunk size and top-k for your domain; use metadata filters by source or date.
Frequently Asked Questions
What is RAG?
Retrieval-Augmented Generation: combines a retriever (often a vector database over chunked, embedded documents) with a large language model (LLM). User query → embed → semantic search in VDB → top-k chunks as context → LLM generates answer. Reduces hallucination and grounds output in your data. See use cases.
What are the main RAG components?
Ingestion: chunk documents, embed, store in VDB with payloads. Retrieval: embed query, VDB returns nearest chunks; optionally hybrid search or re-ranking. Generation: pass context to LLM. Optional: LLM caching with VDB.
Why is the VDB central to RAG?
It must be fast and accurate so the right chunks are in the top-k. Chunking, embedding model choice, and filtering (e.g. by source or date) all affect RAG quality. Many production RAG systems use a dedicated vector database for scale and latency.
Can I use hybrid search in RAG?
Yes. Hybrid search (vector + keyword) can improve retrieval; re-ranking can refine the top-k before passing to the LLM. Use metadata filters by source or date to narrow the search space.