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Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

27 nodes · manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter

Engine
n8n
Nodes
27
Trigger
scheduled/triggered
Source file
1798_Splitout_GitHub_Create_Webhook.json

Workflow flow

IntegrationsManualGitHubExtractFromFileEmbeddingsOpenAiDocumentDefaultDataLoaderTextSplitterTokenSplitterVectorStoreQdrantChatLmChatOpenAiToolWorkflowMemoryBufferWindowExecuteWorkflow+15 more
100%
TRIGGER

When clicking ‘Test workflow’

Manual

Embeddings OpenAI

@n8n/n8n-nodes-langchain.embeddingsOpenAi

Default Data Loader

@n8n/n8n-nodes-langchain.documentDefaultDataLoader

Token Splitter

@n8n/n8n-nodes-langchain.textSplitterTokenSplitter

TRIGGER

When chat message received

@n8n/n8n-nodes-langchain.chat

OpenAI Chat Model

@n8n/n8n-nodes-langchain.lmChatOpenAi

Call n8n Workflow Tool

@n8n/n8n-nodes-langchain.toolWorkflow

Window Buffer Memory

@n8n/n8n-nodes-langchain.memoryBufferWindow

TRIGGER

Execute Workflow Trigger

ExecuteWorkflow

GitHub

GitHub

AI Agent

@n8n/n8n-nodes-langchain.agent

Embedding Recommendation Request with Open AI

HTTP

Embedding Anti-Recommendation Request with Open AI

HTTP

Extract from File

ExtractFromFile

Extracting Embedding

Set

Extracting Embedding1

Set

Qdrant Vector Store

@n8n/n8n-nodes-langchain.vectorStoreQdrant

Merge

Merge

Calling Qdrant Recommendation API

HTTP

Split Out1

Split Out

Retrieving Recommended Movies Meta Data

HTTP

Split Out

Split Out

Merge1

Merge

Selecting Fields Relevant for Agent

Set

Aggregate

Aggregate

25 steps · flows top to bottom, branches left to right

Browse by integration

manualgithubextractFromFileembeddingsOpenAidocumentDefaultDataLoadertextSplitterTokenSplittervectorStoreQdrantchatlmChatOpenAitoolWorkflowmemoryBufferWindowexecuteWorkflowsplitOutaggregateagenthttpRequest
Browse all integrations →
Download n8n JSONDownload sanitized JSONDownload flow graph

Import into n8n

  1. Download the n8n JSON file (full or sanitized).
  2. In n8n: Workflows → Import from file.
  3. Open each node and reconnect credentials to your accounts.
  4. Test with manual execution before enabling triggers.

Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.

View source corpus ↗

Overview

Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI is a free n8n workflow on OpenRuna — 27 nodes connecting manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. Download the JSON, view the flow diagram, and import it into your n8n instance.

What this workflow does

Built in n8n, "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" chains 27 nodes to move data between manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. 27 nodes · manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter Execution begins at the scheduled/triggered trigger and flows through the 27 configured nodes. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

  • Fork it to add error handling, retries, or a notification step without designing the 27-node flow from scratch.
  • Benchmark it against your current manual integration to spot missing steps or redundant nodes.
  • Study how data moves from manual to github before building your own variant.
  • Share it with your team as a documented reference for scheduled/triggered automations in the manual stack.

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.

Tips by platform

Claude

Ask Claude to turn the "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" JSON into a plain-English runbook, node by node. It can flag where manual rate limits or missing retries might bite before you go live.

ChatGPT

Drop the workflow JSON or this OpenRuna page URL into ChatGPT and ask for a step-by-step runbook: triggers, branches, and which fields to customise for manual, github, extractFromFile, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, vectorStoreQdrant, chat. Handy for onboarding teammates new to n8n.

Cursor

In Cursor, ask Agent mode to parameterise "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI" — extract manual IDs and secrets into environment variables without changing the 27-node structure.

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.

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