AI Agents

Intelligent movie recommendation engine with Qdrant and AI

27 nodes 293 151 Automatic trigger
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Workflow Description

An augmented retrieval system that extracts film data from GitHub repositories, chunks content, generates vector embeddings via OpenAI, stores them in Qdrant, and delivers personalized recommendations through a smart agent that interacts with users in real time.

How it works

  1. 1.Extract movie metadata from GitHub repositories and load document files
  2. 2.Split content into chunks and generate vector embeddings using OpenAI
  3. 3.Store embeddings in Qdrant and enable semantic search queries
  4. 4.Deploy an AI agent that receives user inquiries and produces recommendations
  5. 5.Share results through chat interface and distribute updates to Twitter/X

Use cases

  • Build a movie platform that understands user preferences and suggests relevant titles
  • Enhance internal search engines by capturing semantic meaning and context
  • Create an intelligent assistant that answers film questions based on knowledge base

Requirements

  • GitHub repository with organized film data and metadata
  • OpenAI API key for embeddings generation and conversational AI
  • Active Qdrant instance configured for vector storage and retrieval

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

GitHub OpenAI Document Default Data Loader Text Splitter Vector Store Chat Trigger Tool Memory Twitter/X AI Agent

Details

Trigger Automatic trigger
Nodes 27
Apps 10
Views 293
Downloads 151

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (27)

/

When clicking ‘Test workflow’

Manual Trigger

#1

GitHub

Github

#2

Extract from File

Extract From File

#3

Embeddings OpenAI

OpenAI

#4

Default Data Loader

Document Default Data Loader

#5

Token Splitter

Text Splitter Token Splitter

#6

Qdrant Vector Store

Vector Store Qdrant

#7

When chat message received

Chat Trigger

#8

OpenAI Chat Model

OpenAI

#9

Call n8n Workflow Tool

Tool Workflow

#10

Window Buffer Memory

Memory Buffer Window

#11

Execute Workflow Trigger

Execute Workflow Trigger

#12

Merge

Merge

#13

Split Out

Split Out

#14

Split Out1

Split Out

#15

Merge1

Merge

#16

Aggregate

Aggregate

#17

AI Agent

Agent

#18

Embedding Recommendation Request with Open AI

HTTP Request

#19

Embedding Anti-Recommendation Request with Open AI

HTTP Request

#20

Extracting Embedding

Set

#21

Extracting Embedding1

Set

#22

Calling Qdrant Recommendation API

HTTP Request

#23

Retrieving Recommended Movies Meta Data

HTTP Request

#24

Selecting Fields Relevant for Agent

Set

#25

Sticky Note

Sticky Note

#26

Sticky Note1

Sticky Note

#27

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