RAG in n8n

Take the drag out of building with RAG

Retrieval-Augmented Generation (RAG) combines language models with external data sources instead of relying solely on the model's internal training data. This gives you:

• Access to fresh and domain-specific data: RAG augments LLMs with up-to-date and proprietary information beyond pre-training limitations.
• Improved trust and relevance: By grounding responses in real sources, RAG mitigates hallucination and enhances factual accuracy and user confidence.
• Cost-effectiveness: RAG is more efficient than retraining LLMs for new content; just inject retrieval logic.

Standard RAG is accurate but inflexible. Agentic RAG is the upgrade, using smart AI agents in dynamic workflows. These agents choose the right tool for any query, like a database or web search, and verify their own answers.

A vector store is a special database designed to store and search high-dimensional vectors: numerical representations of text, images, or other data. When you upload a document, the vector store splits it into chunks and converts each chunk into a vector using an embedding model.

Yes! n8n’s hundreds of native integrations include all the most popular LLMs, vector stores, and databases. You can build an entire RAG agent in n8n’s drag-and-drop visual builder — from uploading to retrieving your data. Without writing a single script. Building a more complex agentic RAG system? Extending with code is available, too.

RAG sharpens model outputs and prevents reliance on vague or outdated training data by retrieving contextually relevant information from reliable sources.

RAG anchors responses in fact-based documents rather than the model's internal memories. This reduces hallucinations by improving your data quality. But it may not eliminate all errors. Using n8n’s Evaluations feature, you can analyze and optimize outputs to reduce hallucinations in your RAG system even further.

n8n has native integrations with Pinecone, Qdrant, Supabase, Weaviate, Milvus, Zep, MongoDB Atlas, and Postgres PGVector. You can access additional vector stores via API or custom setups.

n8n allows you to evaluate RAG workflows using techniques like string similarity, exact match, and LLM-as-a-Judge. You can also define custom metrics (like factual correctness and document relevance) to measure the relevance of the retrieved documents. For Agentic RAG systems, n8n also supports an evaluation tool to measure how well the RAG systems call their tools.

You can maintain fresh, well-organized documents in n8n by regularly indexing new data, updating stale embeddings, and dynamically maintaining metadata.