Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

Overview

Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI (27-node n8n automation, manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat) is free on OpenRuna — view the graph and export the JSON to import into n8n.

What this workflow does

"Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" is an n8n automation built from 27 nodes spanning manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. 27 nodes · manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter It starts from a scheduled/triggered trigger and routes data through every node in order. OpenRuna shows the full flow diagram, an integration breakdown, and a one-click JSON export for n8n's "Import from file". Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

• Benchmark it against your current manual integration to spot missing steps or redundant nodes.
• Fork it to add error handling, retries, or a notification step without designing the 27-node flow from scratch.
• Use it as a starting template when wiring manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat — adjust node parameters to your accounts and record IDs.
• Import "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" into n8n, reconnect credentials for manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat, and run the same automation in your own workspace.

Example output

Imported into n8n, "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" exposes 27 configured nodes. Trigger type: scheduled/triggered. Integrations: manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. Set your credentials on the manual node, then verify each downstream node picks them up. 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 "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" 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 "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" use?

It uses: manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. The flow diagram above shows how each service connects across the 27 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 manual and github credentials in n8n after import.

What trigger type does "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" 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 "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" 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 manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat.