I’ve been thinking about this more and more. With our knowledge base being a mess of different formats—some docs, some CSVs with data, some code snippets we need searchable—I realized routing different data types through different specialized models would probably get better results. But logistically, I wasn’t sure if it was worth the complexity.
Latenode’s catalog has 400+ models, so there are definitely specialized ones for different tasks. But I’m wondering if actually implementing a router that detects data type and sends it to the right model is more trouble than it’s worth.
Like, would you set up separate retrieval paths for text documents versus tabular data? And then do you need different embedding models for each, or do you route during generation? I’m trying to figure out if this is an optimization that real teams actually do, or if I’m overcomplicating things by chasing perfect accuracy when good enough is faster to deploy.
It’s absolutely practical, and honestly easier than you’d think in Latenode. The routing itself is just conditional logic in the builder—if data type is table, send to one model, if it’s code, send to another.
The real payoff comes when you pick specialized models. A model trained for code understanding will outperform a general one on code questions. Same with tabular reasoning. You’re not just making the workflow more complex. You’re actually improving output quality.
What makes it doable is that you can build this in the visual builder without writing complex logic. Drag retrieval nodes for text, retrieval nodes for tables, route based on detected type, then merge results into generation. It sounds elaborate but it flows pretty naturally.
I did something similar last year, and my honest take is it depends on your data mix. If you’re like 80% text with a few tables, the extra complexity might not be worth it. But if you’ve got a real multimodal knowledge base—which it sounds like you do—then routing makes a difference.
Here’s what I found: you don’t necessarily need different embedding models. What matters more is having retrieval strategies that make sense per data type. For code, you might want semantic search. For tables, you might prefer keyword matching because structure matters. Then at generation, sure, route to a model that’s decent with that type.
The setup in Latenode was straightforward enough. Conditional nodes, multiple retrieval paths, merge. Took an afternoon to build.
Routing is practical, especially for quality-sensitive applications. The complexity argument is real, but it’s not as bad as it sounds. You’re essentially creating a decision tree: detect data type, route appropriately, generate response. Modern platforms like Latenode make this visual and manageable. The upside is meaningful—a model trained on code will genuinely handle code retrieval better than a generalist model. Whether to pursue it depends on your accuracy requirements and how much your knowledge base is fragmented across formats.
From a systems perspective, multimodal routing is justified when your knowledge base has substantively different data types that benefit from specialized handling. Code retrieval via semantic search with a code-aware model will outperform keyword search through a general LLM. Tables require structural understanding that’s different from narrative text. Implementation complexity in Latenode is low because the visual builder abstracts conditional logic. The decision point is whether your query distribution actually exercises these specialized paths frequently enough to justify the maintenance overhead.
Yes it’s practical. Routing in latenode is just conditional logic. Specialized models do better on their domain. Worth it if ur data is actually mixed.