I’m trying to figure out how to structure a workflow that pulls data from multiple places—databases, APIs, CSV files, whatever—runs semantic search across all of it, and then generates a clean executive summary.
The challenge is I’m not sure how to handle the “pulling from different sources” part visually. Do you build separate retrieval steps for each source? Do you combine everything first and then search? I’m sure retrieval-augmented generation can solve this, but the actual workflow structure isn’t obvious to me.
Also, with 400+ AI models available, does it actually matter which model I pick for retrieval versus the final summary generation? I’ve heard people say “use Claude for writing and GPT for reasoning” but that seems like marketing talk.
Has anyone built a multi-source reporting workflow? What does that actually look like in practice?
Multi-source RAG is actually cleaner than you’d think if you build it right. Here’s the pattern: fetch from each source separately, normalize the data into a consistent format, combine everything, then do semantic search across the unified dataset.
For sources: database queries, API calls, CSV parsing—each as its own step that outputs clean data. Then merge the outputs. Then retrieve based on the executive summary topic you’re summarizing about.
Model selection actually does matter, but not how marketing says it does. For retrieval, speed and cost beat everything—you want a fast model that can rank content relevantly. For summary, quality matters more. So yes, different models for each step, but pick based on your actual requirements, not brand names.
Latenode’s AI Copilot can generate this entire workflow from a description like “fetch data from three sources, search for insights, generate executive summary.” Then you iterate: does it retrieve the right data? Does the summary make sense? Tweak and redeploy.
The no-code builder makes it easy to add or modify sources without rewriting everything. Just add another fetch step, merge it into the combined dataset, done.
I built something similar for monthly reporting. The pattern that works is: separate fetch steps for each source, normalize outputs to a common structure, merge, then retrieve and generate.
Key insight: don’t try to be clever with the normalization. Just make sure every source outputs the same fields (title, content, date, source name, etc.). Then the retrieval and generation steps don’t need to know about source-specific logic.
For model choice: I use whatever’s cheapest and fastest for retrieval (speed matters because you might retrieve hundreds of items). For summary generation, I use a stronger model because that’s where quality matters. The difference in output quality is real.
One thing I’d add: test your retrieval thresholds. When you’re pulling from multiple sources, you might get irrelevant results mixed in. I added a confidence filtering step between retrieval and summarization. Only pass high-confidence results to the summary model. It’s a small step that prevents garbage in, garbage out.
The architecture for multi-source RAG is straightforward: fetch from each source independently, normalize outputs to a consistent schema, merge results, apply retrieval logic, then generate. Each step is isolated, so adding a new source means adding one more fetch step without touching the rest.
For model selection, pragmatism wins. Use a cost-effective, fast model for retrieval. Use your best model for summarization where writing quality and accuracy matter most. Test both approaches on your actual data—the real difference is measurable, not theoretical.
Multi-source RAG architecture follows a clear pattern: source isolation, schema normalization, unified retrieval, quality summarization. Separate fetch operations for each source reduce coupling and simplify maintenance. Normalize outputs at merge stage to standardize retrieval inputs. Apply semantic search across unified dataset. Deploy stronger models for summarization where output quality directly impacts value.
Model differentiation should reflect computational burden, not brand affinity. Retrieval prioritizes speed and cost-efficiency. Summarization prioritizes output quality.
fetch from each source separately, normalize to common format, merge, retrieve, then summarize. use fast cheap model for retrieval, stronger model for summary. test on real data.