Hey everyone! I’m trying to set up a RAG (Retrieval-Augmented Generation) Agent in n8n, but I’m not sure where to start. Has anyone done this before? I’m looking for a basic template or workflow that I can use as a starting point. Any tips on how to integrate it with my existing n8n setup would be super helpful too. I’ve heard RAG can really improve the quality of AI-generated responses, so I’m excited to give it a try. Thanks in advance for any advice or resources you can share!
hey mia, i’ve dabbled with RAG in n8n too. start simple - use HTTP Request to grab docs, Text Manipulation to clean em up. for retrieval, try Pinecone (easy to setup). OpenAI node works great for embeddings & generation. Function node helps combine context+prompt. it’s a bit tricky at first but you’ll get there! lmk if u need more help
I’ve worked on implementing RAG in n8n, and my experience was that a methodical approach really pays off. I began by building a robust document processing workflow. Initially, I imported my knowledge base using the CSV node, and then used the Text Manipulation node to properly refine the data.
For the retrieval stage, I chose Milvus as the vector database, integrating it with n8n through the HTTP Request node. I generated embeddings with the Hugging Face node, which fit well into my setup.
In the generation phase, a Function node was useful to dynamically construct prompts that incorporated the retrieval output. Although fine-tuning the setup was necessary to optimize accuracy, the overall workflow significantly improved the quality and relevance of the generated responses.
I’ve actually implemented a RAG agent in n8n recently, and I can share some insights. The key is to break it down into manageable steps. First, set up a document ingestion workflow to process and store your knowledge base. I used the HTTP Request node to fetch documents and the Text Manipulation node to clean and chunk the text.
For the retrieval part, I integrated Elasticsearch as my vector database. The Elasticsearch node in n8n made this relatively straightforward. You’ll need to create embeddings for your documents - I used the OpenAI node for this, but there are other options.
The generation step involves using the retrieved context to augment your prompts. I set up a custom function node to format the prompt with the retrieved information before passing it to the language model (again, I used OpenAI, but you could use others).
It took some trial and error to get the workflow right, especially fine-tuning the retrieval relevance. But once set up, it significantly improved the quality and accuracy of the generated responses. Happy to provide more specifics if you need them!