Building RAG for movie recommendations using Qdrant and AI

How it works

1. 1.Load movie data from GitHub and split content into tokens using Token Splitter
2. 2.Convert text segments to vector embeddings with OpenAI and store them in Qdrant
3. 3.Receive user queries via Chat Trigger and route them to the AI agent
4. 4.Retrieve relevant movies from Qdrant based on vector similarity scoring
5. 5.Generate personalized recommendations using OpenAI language model with contextual memory
6. 6.Publish results to Twitter/X and respond to user inquiries

Use cases

• Streaming platforms that require intelligent, user-personalized movie recommendations
• Chatbot systems answering movie questions and suggesting films based on user preferences
• Automated analysis of user feedback on social media to continuously improve recommendation quality

Requirements

• OpenAI API key for language models and embedding generation
• Configured Qdrant instance ready for vector storage and retrieval operations

Service Value

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