Ai Youtube Playlist & Video Analyst Chatbot 3408 – n8n Workflows – Free Template

What This Workflow Does

This AI-powered workflow automates the analysis and summarization of YouTube playlists and individual videos. It fetches video transcripts, processes them through advanced language models, and provides intelligent summaries and answers to user queries using retrieval-augmented generation (RAG) technology.

How It Works

The workflow follows a multi-stage process to deliver comprehensive video analysis:

• Extracts transcripts from YouTube videos using the dedicated YouTube transcription node
• Splits lengthy transcripts into manageable chunks using recursive character text splitting
• Generates embeddings for each text segment using Google Gemini embeddings
• Stores embeddings in a Qdrant vector database for efficient retrieval
• Processes user queries through an AI agent that retrieves relevant transcript sections
• Generates summarized responses and detailed analysis using Google Gemini LLM
• Maintains conversation context with buffer memory for coherent multi-turn interactions
• Caches results in Redis for improved performance on repeated queries

Use Cases

• Educational Content Analysis: Automatically summarize lecture series and create study guides from educational YouTube playlists
• Podcast Research: Extract key insights and answers from podcast episode collections without manual listening
• Content Marketing: Generate blog posts and social media content from video transcript data
• Training Documentation: Create searchable knowledge bases from instructional video playlists
• Market Research: Analyze industry expert discussions and extract trends from video content collections

Nodes Used

• YouTube Transcription: Extracts full transcripts from YouTube videos
• Google Gemini Chat: Processes natural language and generates responses
• AI Agent: Orchestrates workflow logic and decision-making
• Recursive Character Text Splitter: Breaks large transcripts into optimized chunks
• Google Gemini Embeddings: Converts text into vector representations
• Qdrant Vector Store: Stores and retrieves similar text segments
• Vector Store Tool: Enables semantic search across stored embeddings
• Memory Buffer Window: Maintains conversation history for context
• HTTP Request: Fetches data from external APIs
• Redis: Caches frequently accessed results
• Chat Trigger: Initiates workflow from user messages
• Merge: Combines data from multiple branches
• Split Out: Distributes data across parallel processing paths
• Structured Output Parser: Formats AI responses in defined structures
• Code: Executes custom JavaScript logic
• Switch: Routes data based on conditions

Prerequisites

• n8n instance with access to Google Gemini API credentials
• Qdrant vector database instance deployed and configured
• Redis instance for caching (optional but recommended)
• Valid YouTube API access or transcript extraction capability
• n8n nodes installed: YouTube Transcription (dmr), all Google Gemini nodes, and Qdrant vector store node
• Basic understanding of RAG (Retrieval-Augmented Generation) concepts
• API keys for Google Gemini service configured in n8n credentials

Difficulty Level

Advanced. This workflow combines multiple complex components including vector databases, embeddings, RAG pipelines, and agent-based orchestration. It requires familiarity with AI concepts, database configuration, and n8n’s advanced node types. Recommended for users with experience in workflow automation and machine learning pipelines.

This workflow template is shared under the n8n fair-code license. Free to use and modify.