AI-Powered YouTube Playlist & Video Summarization and Analysis

• AI-Powered Summarization: Utilizes Google Gemini models to generate concise and informative summaries of video content.
• Playlist and Video Support: Handles both YouTube playlist URLs and individual video URLs.
• Transcript Extraction: Extracts transcripts from YouTube videos for detailed analysis.
• Structured Output: Generates summaries in a structured markdown format, highlighting key points and technical details.
• Retrieval-Augmented Generation (RAG): Enables users to ask questions about the video content and receive AI-generated answers based on the retrieved information.
• Vector Store Integration: Stores embeddings of the analyzed content in Qdrant for efficient semantic search and retrieval.
• Context Management: Maintains conversation context using Redis to allow for follow-up questions and ongoing analysis.

1. Initiate Chat: Start a conversation with the n8n chatbot.
2. Provide URL: Paste a YouTube playlist URL or a YouTube video URL.
3. Receive Summary: The workflow will process the URL, fetch transcripts, summarize them, and provide an overview.
4. Ask Questions: Engage in a conversation to ask specific questions about the video content. The AI will use the analyzed summaries to answer your queries.

Workflow Nodes:

• Chat Trigger: Listens for incoming user messages.
• Message Intent: Identifies the user's intent (PLAYLIST, VIDEO, NONE) and extracts URL/ID.
• Get Previous Context Intent: Retrieves prior conversation context from Redis.
• Update Context Intent: Saves the current conversation context to Redis.
• Handle Queries: Processes user queries based on the identified intent and context.
• Split Out1: Separates individual videos from a playlist.
• YouTube Transcript: Fetches transcripts for each video.
• Split Out: Splits transcript data for further processing.
• Summarize & Analyze Transcript: Generates a concise summary for each video.
• Edit Fields: Organizes and formats the video data.
• Merge: Combines video titles with their transcripts.
• Recursive Character Text Splitter: Chunks text for embedding.
• Embeddings Google Gemini: Creates vector embeddings for the text.
• Qdrant Vector Store: Stores embeddings in the Qdrant database.
• AI Agent: Orchestrates the RAG process for answering user questions.
• Full Summary: Concatenates individual video summaries into a complete overview.
• Google Gemini Chat Model2/4: Used for summarization and analysis tasks.
• Default Data Loader: Prepares data for the vector store.
• Concatenate: Joins summarized transcripts.