Build Your Own AI Movie Recommendation Chatbot with RAG, Qdrant, and OpenAI
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Empower your users with personalized movie recommendations by building a Retrieval-Augmented Generation (RAG) chatbot. This workflow integrates Qdrant for efficient vector storage and OpenAI for advanced language understanding to deliver intelligent suggestions.
About This Workflow
This n8n workflow empowers you to construct a sophisticated RAG chatbot capable of providing movie recommendations. By leveraging the power of Qdrant for vector storage and retrieval, coupled with OpenAI's advanced language models, the workflow ingests movie data, generates embeddings, and enables a conversational interface for users to discover their next favorite film. It automates the process of building a smart recommendation system, making it accessible for developers and enthusiasts alike. The workflow is triggered by chat messages, processes user queries through OpenAI's models, and retrieves relevant movie information from the Qdrant vector store to generate tailored suggestions.
Key Features
- Intelligent Recommendation Engine: Leverages RAG architecture for context-aware movie suggestions.
- Vector Database Integration: Utilizes Qdrant for efficient storage and retrieval of movie embeddings.
- OpenAI Powered: Employs OpenAI models for natural language understanding and response generation.
- Automated Data Ingestion: Reads movie data from a GitHub repository for easy setup.
- Conversational Interface: Enables interactive movie discovery through chat.
How To Use
- Trigger Setup: Configure the
When chat message receivednode to activate the workflow upon receiving chat input. - Data Ingestion: The
GitHubnode fetches movie data (e.g.,Top_1000_IMDB_movies.csv) from a specified repository. - Data Extraction: Use the
Extract from Filenode to parse the relevant movie information. - Embedding Generation: The
Embeddings OpenAInode creates vector embeddings for movie descriptions. - Data Loading and Splitting:
Default Data Loaderattaches metadata (movie name, release date, description), andToken Splitterprepares text for Qdrant. - Vector Storage: The
Qdrant Vector Storenode inserts the processed movie data and their embeddings into a Qdrant collection (e.g.,imdb). - Chat Interaction: The
OpenAI Chat Modelprocesses user queries, and theCall n8n Workflow Toolacts as an intermediary to query the vector store. - Memory Management: The
Window Buffer Memorynode helps maintain conversation context. - Response Aggregation: Nodes like
MergeandSplit Outare used to combine and structure the retrieved information before the finalAggregatenode formats the response.
Apps Used
Workflow JSON
{
"id": "7d88a161-c88d-4e47-96a1-ff3d6cfea4d2",
"name": "Build Your Own AI Movie Recommendation Chatbot with RAG, Qdrant, and OpenAI",
"nodes": 14,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 7d88a161-c88d...
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