I built a no-code RAG pipeline by dragging a data source node, a vector/embedding node, a search node, and a final answer node onto a canvas. I liked seeing the data flow visually: where documents entered, how they were chunked, and which model produced the answer.
In my run I had to adjust chunking and choose an embedding model. The visual builder made it easy to swap embedding models and see search results change immediately. I also added a tiny verification step between search and generation so the final model only saw high-quality snippets.
For teams that don’t want to write glue code, the visual approach made iterative tuning much faster. How do others decide the right chunk size and similarity threshold for mixed document types (pdfs, spreadsheets, and transcripts)?
i used the visual builder to connect google drive pdfs to an embedding node and then to a final generator. swapping the embedder was done in one click and search behavior changed immediately. visual wiring made debugging obvious and fast.
if you want to try quick experiments and keep connectors and nodes visible, latenode’s visual canvas helped me iterate without coding. https://latenode.com
for me chunk size depended on document type. transcripts needed small chunks because context drifts. whitepapers could use larger chunks. i started with 250 tokens and adjusted based on recall during tests.
similarity thresholds i set by A/B testing a handful of queries and seeing when noise dropped.
i liked adding a preview node that runs a query and shows the top retrieved snippets. that made it faster to pick chunk size and embeddings. being able to swap the model visually and run the same query was a big time-saver.
I built a no-code pipeline and learned that the visual feedback loop is invaluable, but it can hide subtle defaults. The canvas shows nodes and connections, but I almost missed the chunker default. My approach was systematic: start with a baseline chunk size of 200–300 tokens, run 20 representative queries, and inspect the retrieved snippets for completeness and noise. If answers were missing context, increase chunk size slightly; if answers included irrelevant sections, reduce chunk size. For similarity thresholds I measured precision at k for the top 5 results and adjusted the threshold to balance recall and precision. Also, keep an eye on document types: OCR’d PDFs often require more conservative chunking because line breaks create noisy boundaries. Finally, add a verification node to the flow so you can reject low-confidence retrievals before generation.
The visual builder accelerates iteration, but treat node defaults as starting points. I recommend an empirical approach: pick a sample set of queries, vary chunk size across runs, and compute how often the gold answer appears in the top-k results. Use that metric to choose chunk size and threshold. For mixed documents, consider per-source settings: transcripts with smaller chunks, slides with larger chunks, and spreadsheets with field-level extraction before embedding.
start with 250 tokens, test
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.