Youtube RAG search with frontend using Apify, Qdrant and AI

About This Workflow

What This Workflow Does

This n8n template enables you to build a robust YouTube search functionality using RAG (Relevance-Accuracy-Goodness) search with the help of Apify, Qdrant, and AI integration. It automates the process of finding relevant YouTube videos based on user queries, providing a powerful tool for market research and analysis. The workflow utilizes machine learning models to improve the accuracy and relevance of search results.

Who Should Use This

This workflow is ideal for developers, marketers, and business owners who want to conduct in-depth market research on YouTube videos, analyze consumer behavior, or create targeted advertising campaigns. If you need to gather insights from YouTube videos, this automation can help streamline your process.

Key Features

• RAG Search Integration: The workflow leverages Apify and Qdrant to implement a robust RAG search algorithm, ensuring accurate and relevant results.
• AI-Powered Search: The integration of AI models enhances the search results, improving relevance and accuracy.
• Customizable Search Parameters: Users can adjust search filters to suit their specific requirements, making the workflow highly adaptable.
• YouTube Video Retrieval: The automation retrieves and processes YouTube videos, providing a comprehensive dataset for analysis.

How to Get Started

To start using this workflow, simply import it into your n8n environment and customize the settings to fit your needs. Configure the workflow to connect with your YouTube API, Apify, and Qdrant accounts to begin leveraging the power of RAG search and AI-driven insights.