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Retrieval-augmented generation is how you get an AI to answer from your data instead of guessing. These guides walk the full path: building a vector store, choosing a vector database, wiring a retrieval app, and keeping the whole thing private and local when the documents are sensitive.
Build a vector store in n8n and choose between pgvector, Chroma, and Qdrant.
Build an n8n vector store that retrieves your own documents by meaning, not keywords. Embedding 1,000 docs costs ~1.3 cents; Supabase free-tier storage costs $0. Full node wiring and step-by-step setup inside.
Compare pgvector, Chroma, and Qdrant for local RAG pipelines. Get the three-line decision: already on Postgres, use pgvector; prototyping locally, use Chroma; need scale or heavy filtering, use Qdrant.
Choosing between n8n RAG embeddings comes down to four verifiable axes: dimensions, price per 1M tokens, max input context, and cloud vs local. This guide compares OpenAI, Gemini, and Ollama options so you pick the one you can live with.
End-to-end retrieval apps — a customer-support bot, a question-and-answer chain, and fixing retrieval when answers come back wrong.
Build an n8n AI customer support bot that answers repeat questions from your own docs using RAG. Two workflows, about $4.50 per 1,000 answers on Claude Haiku 4.5, and $0 platform cost if you self-host.
The n8n Question and Answer Chain retrieves passages from your own vector store and answers from them, not from the model's memory. Index once for about $0.01, then answer 1,000 questions for about $2.50 on Claude Haiku 4.5.
When your n8n RAG chatbot returns wrong answers, the fix is retrieval, not the model. This guide walks 6 fixable causes: chunking, overlap, top-k, embeddings, metadata filtering, and reranking.
Chat with your business docs privately using local models — no data leaves your machine.
Local RAG with Ollama lets you ask questions across hundreds of private documents without uploading a single file to the cloud. This guide covers three paths from 20-minute desktop setup to n8n pipelines, all at $0 running cost.
Most "local RAG" setups still send your documents to a cloud embeddings API. This guide closes every leak: local embeddings via Ollama, a self-hosted Qdrant or PGVector store, and a local answering model, all in n8n.
Plain RAG embeds chunks that have lost their document context, so 'revenue grew 3%' matches nothing useful. Contextual retrieval writes a short per-chunk context and prepends it before embedding and BM25. Here is the method, the build, and the honest ingest trade-off.
You assumed more embedding dimensions mean better retrieval, and you are about to over-pay on vector storage for a margin you will never notice. OpenAI's own numbers show text-embedding-3-large at 256 dims beating ada-002 at 1536. Here is what dimensions cost and how to pick a
Your n8n RAG bot keeps answering with the wrong client's file. That is not a weak model, it is a missing filter. Here is the two-step metadata plumbing (Default Data Loader on insert, Metadata Filter on retrieval) for PGVector, Qdrant, and Supabase.
The n8n Pinecone Vector Store and Supabase Vector Store nodes wire into an agent the same way, but one is a managed service and one is Postgres you run. Here is how their free tiers, cost at scale, latency, and node wiring compare, and when to pick each for RAG.
Your RAG pipeline feels okay, but no number says if retrieval is good. This is the reproducible scorecard: context precision and recall for retrieval, faithfulness and answer relevancy for generation, scored 0 to 1 with Ragas, plus which metric to read when an answer is wrong.
RAG reranking re-sorts the chunks you already retrieved so the best one rises to the top, but it cannot recover a chunk you never retrieved. Here is what a cross-encoder reranker actually does, when it is worth the latency and cost, the real 2026 options (Cohere, Voyage, BGE),
RAG chunking strategies decide whether the right passage is whole, findable, and small enough to rank before your model sees it. Here is how fixed, recursive, and semantic splitting differ, what chunk size and overlap to start with, why the public benchmarks disagree, and a
Now that 1M-token windows ship at flat pricing, should you stuff the whole corpus in one prompt or build a retrieval pipeline? This breaks long context vs RAG into reproducible per-query cost math, the recall limits of big windows, and four variables that decide it.
Vector search nails meaning but misses exact tokens like SKUs, error codes, and function names. Hybrid search adds a BM25 keyword leg and fuses both with Reciprocal Rank Fusion. Here is when each wins and how to wire it in n8n with Qdrant or pgvector.
