Building RAG chatbot for movie recommendations with Qdrant and Open AI

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB top1000 dataset. You can provide your wi...

Best for

Engineering automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.github, n8n-nodes-base.extractfromfile, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplittertokensplitter, @n8n/n8n-nodes-langchain.vectorstoreqdrant, @n8n/n8n-nodes-langchain.chattrigger

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Jenny .

Original n8n.io source

1.1 Workflow description

Title: Building RAG chatbot for movie recommendations with Qdrant and Open AI
Workflow name: Building RAG chatbot for movie recommendations with Qdrant and Open AI

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations.

How it works

a video with the full design process
Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI;
Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat;
Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples.

Set Up Steps

You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials
You'll OpenAI credentials
You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 2 - GitHub

Type / Role: n8n-nodes-base.github - github
Config choices: Version 1

Block 3 - Extract from File

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1

Block 4 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1

Block 5 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 6 - Token Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterTokenSplitter - textSplitterTokenSplitter
Config choices: Version 1

Block 7 - Qdrant Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices: Version 1

Block 8 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 9 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1

Block 10 - Call n8n Workflow Tool

Type / Role: @n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices: Version 1.2

Block 11 - Window Buffer Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.2

Block 12 - Execute Workflow Trigger

Type / Role: n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices: Version 1

Block 13 - Merge

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3

Block 14 - Split Out

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Block 15 - Split Out1

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Block 16 - Merge1

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3

Block 17 - Aggregate

Type / Role: n8n-nodes-base.aggregate - aggregate
Config choices: Version 1

Block 18 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.6

Block 19 - Embedding Recommendation Request with Open AI

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 20 - Embedding Anti-Recommendation Request with Open AI

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 21 - Extracting Embedding

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 22 - Extracting Embedding1

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 23 - Calling Qdrant Recommendation API

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 24 - Retrieving Recommended Movies Meta Data

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Showing the first 24 of 27 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Building RAG chatbot for movie recommendations with Qdrant and Open AI
Complexity	advanced
Nodes	27
Categories	Engineering, AI RAG
Author	Jenny
Published	28 Sept 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2440/2440.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI RAG workflows

Frequently asked questions

What does Building RAG chatbot for movie recommendations with Qdrant and Open AI do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.

Jenny

7 workflows

Nodes

n8n-nodes-base.manualtrigger n8n-nodes-base.github n8n-nodes-base.extractfromfile @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplittertokensplitter @n8n/n8n-nodes-langchain.vectorstoreqdrant @n8n/n8n-nodes-langchain.chattrigger

Complexity

advanced

Published 28 Sept 2024

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/2440/2440.json

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Building RAG chatbot for movie recommendations with Qdrant and Open AI

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How it works

Set Up Steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Block 2 - GitHub

Block 3 - Extract from File

Block 4 - Embeddings OpenAI

Block 5 - Default Data Loader

Block 6 - Token Splitter

Block 7 - Qdrant Vector Store

Block 8 - When chat message received

Block 9 - OpenAI Chat Model

Block 10 - Call n8n Workflow Tool

Block 11 - Window Buffer Memory

Block 12 - Execute Workflow Trigger

Block 13 - Merge

Block 14 - Split Out

Block 15 - Split Out1

Block 16 - Merge1

Block 17 - Aggregate

Block 18 - AI Agent

Block 19 - Embedding Recommendation Request with Open AI

Block 20 - Embedding Anti-Recommendation Request with Open AI

Block 21 - Extracting Embedding

Block 22 - Extracting Embedding1

Block 23 - Calling Qdrant Recommendation API

Block 24 - Retrieving Recommended Movies Meta Data

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions