RAG on living data

Overview

Free n8n workflow: RAG on living data. 34 nodes across embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule, with a downloadable JSON and a visual flow diagram on OpenRuna.

What this workflow does

This n8n workflow, "RAG on living data", wires together embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule in 34 nodes. 34 nodes · embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi Execution begins at the scheduled/triggered trigger and flows through the 34 configured nodes. On OpenRuna you get the visual graph, the integration list, and a downloadable JSON ready to import into n8n. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

• Import "RAG on living data" into n8n, reconnect credentials for embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule, and run the same automation in your own workspace.
• Use it as a starting template when wiring embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule — adjust node parameters to your accounts and record IDs.
• Study how data moves from embeddingsOpenAi to textSplitterTokenSplitter before building your own variant.
• Share it with your team as a documented reference for scheduled/triggered automations in the embeddingsOpenAi stack.

Example output

Imported into n8n, "RAG on living data" exposes 34 configured nodes. Trigger type: scheduled/triggered. Integrations: embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule. Open each node after import to map credentials to your own embeddingsOpenAi and textSplitterTokenSplitter accounts. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

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Frequently asked questions

How do I import "RAG on living data" into n8n?

Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.

Which integrations does "RAG on living data" use?

It uses: embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule. The flow diagram above shows how each service connects across the 34 nodes. Filter OpenRuna by integration to find related workflows and tools.

Can I import a sanitized export without credentials?

Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own embeddingsOpenAi and textSplitterTokenSplitter credentials in n8n after import.

What trigger type does "RAG on living data" use?

Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.

Is "RAG on living data" free to use?

OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa, retrieverVectorStore, lmChatOpenAi, chat, schedule.