n8n Slack Bot + AI Agent: Answer Team Questions from Your Docs
Your team asks the same question in Slack every week. Wire a Slack Trigger to an n8n AI agent with a vector store and the bot answers from your own documents, in the thread, in seconds.
Tag
#RAG
Contextual Retrieval: Fix RAG Chunks That Lose Context
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.
RAG Chunking Strategies: Fixed vs Recursive vs Semantic (and What to Pick)
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
Long-Context vs RAG: When a 200K-1M Token Window Beats Chunking
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.
Hybrid Search for RAG in n8n: BM25 + Vector, and When to Use Each
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.
Fully Local RAG in n8n: Private Embeddings, No Cloud APIs (2026)
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.
Why Your n8n RAG Chatbot Gives Wrong Answers (Fix Retrieval) (2026)
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.
Best Embedding Model for n8n RAG: OpenAI vs Gemini vs Local (2026)
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.
Build a Question and Answer Chain in n8n (Answer From Your Docs)
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.
n8n AI Automation Ideas: 8 Agent Workflows Worth Building (2026)
Finished the n8n AI tutorial and wondering what to actually build? These 8 n8n AI automation ideas come with the exact nodes, the honest Chain-vs-Agent call, and the right Claude model for each job.
Build a Vector Store in n8n (Embeddings for RAG)
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.
Build an AI Customer-Support Bot in n8n (RAG, With Real Monthly Cost)
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.
pgvector vs Chroma vs Qdrant: Choosing a Vector Database for RAG
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.
Local RAG With Ollama: Chat With Your Business Docs Privately
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.