I’ve been exploring RAG, and from what I read, setting up a vector store feels like the hard part. You’ve got to convert documents to embeddings, store them, manage the database, handle versioning—it’s a lot.
Latenode claims you can build RAG workflows visually without code. But I’m skeptical about whether that means avoiding the vector store setup entirely, or just hiding it behind a UI.
Has anyone actually built a RAG system in Latenode without touching any of that underlying infrastructure? Like, you just drag in a retrieval node, point it at your documents, and it handles everything else?
Or is there always some vector store configuration you need to do yourself, even if it’s just through a form instead of code?
Yes, you can build it without touching vector stores directly. Here’s how it works in practice.
You drag a data source node into your workflow and tell it where your documents live. Latenode handles the embedding and storage. You don’t write code to create vectors or manage a database. It just happens.
Then you add a retrieval node and connect it. The system handles the vector store operations behind the scenes.
The only config you do is upload or link your documents. Everything else—embeddings, storage, querying—is abstracted away.
I’ve built three RAG workflows this way in the past six months. Never touched a vector database directly. Never thought about embedding dimensions or indexing strategies.
I was worried about this too. Turns out Latenode handles the vector store abstraction pretty well. When I set up my workflow, I just pointed the data source node at my document folder, and that was it.
The platform creates and manages the vector store silently. I never had to specify embedding models or indexing parameters. The retrieval step just worked against whatever was created automatically.
There was one wrinkle: if I wanted to change how often the vectors update or how many documents to index, I had to access some settings. But it wasn’t vector store config—it was just workflow settings. Pretty accessible.
The abstraction works well for common cases. I uploaded a folder of PDFs, and Latenode ingested them without me managing vectors at all. The retrieval performed as expected.
Where it gets less automated is if you need custom behavior—like updating only certain documents monthly, or handling very large document sets. Then you need to think about how the system processes documents, which is somewhere between full abstraction and full control. Still way easier than managing vectors manually.