RAG & Reply to User Query

• Retrieves and provides answers to user queries combining retrieval-augmented generation.
• Processes messages without specific routing rules.

Fetch and prepare Playlist video transcripts data for processing

• Collects and organizes playlist video transcripts.
• Prepares data for analysis and summarization.

Summarize & Analyze Transcript

• Creates summarized data from transcripts.

Store Embeddings

• Saves embedded data for future use.
• Updates current context to maintain the flow of the conversation.

First Summary Analysis

• Conducts initial analysis of summarized data.
• Return to the user insights from processed transcripts.

Message intent routing

• Retrieves the previous context for continuity.
• Ensures data integrity before processing.
• Routes incoming messages based on intent.

Update Context

• Updates any issues detected in the context.
• Prepares data for workflow progression.

Fetch and prepare single video transcripts data for processing

• Retrieves and pre-processes single video transcripts.
• Prepares data for analysis and summarization.

Process or ask for more details

• Decides next steps based on workflow conditions e.g. missing number of playlist videos to process.
• Route to Playlist or Video processing.

Already Processed?

• Check if we already have embeddings in the vector store.

AI-Powered YouTube Playlist & Video Summarization and Analysis Chat Bot

This N8N workflow transforms YouTube playlists or individual videos into interactive knowledge bases. Instead of watching videos, users can chat with the workflow to get summaries and answers based on the video transcripts.

How it Works:

1. Chat Interaction & Intent Detection:

   • The workflow starts via a chat interface (Chat Trigger), prompting the user for a YouTube playlist or video URL.
   • It retrieves any previous conversation context stored in Redis.
   • A Google Gemini AI agent (Message Intent) analyzes the user's input to determine if it contains a valid YouTube playlist URL, a video URL, or neither (NONE). It extracts the URL, the corresponding ID (Playlist ID or Video ID), and checks if the user specified a limit for the number of playlist videos to process.
   • The workflow maintains context (like the current URL, ID, and processing status) using Redis (Update Context Intent, Get Previous Context Intent).

2. Routing & Pre-processing Checks:

   • Based on the detected intent and whether the content needs processing, a Switch node (Route Message Intent) directs the workflow.
   • If a valid URL is provided and hasn't been processed yet (status != 'DONE'), it proceeds to the processing pipeline.
   • It checks if embeddings for the given ID already exist in the Qdrant vector store (Qdrant Vector Store2, If). If they do, it skips processing and moves to the query handling stage.
   • If the input is a playlist URL but no video limit is specified (limit=0), it prompts the user to provide one (Numb of Videos agent).

3. Video/Playlist Processing Pipeline:

   • Data Fetching:
     • For Playlists: Fetches the playlist page (Playlist HTTP Request), extracts video details using custom code (Get Playlist Videos Data), limits the videos if specified (Limit), and then fetches the transcript for each video (YouTube Transcript).
     • For Videos: Fetches the video page (Video HTTP Request), extracts title/description (Get Title and Desc), and fetches the transcript (YouTube Transcript1).
   • Transcript Processing & Summarization:
     • Transcripts are retrieved using the youtubeTranscripter node.
     • Transcripts are concatenated (Concatenate, Concatenate1).
     • Data (like video ID, title, transcript text) is merged and structured (Merge, Edit Fields).
     • Each video's transcript is summarized by a Google Gemini AI chain (Summarize & Analyze Transcript).
   • Embedding & Storage:
     • Any existing data for the playlist/video ID is cleared from the Qdrant vector store (Delete Collection).
     • The processed transcripts (potentially alongside summaries/metadata) are loaded (Default Data Loader).
     • Text is split into manageable chunks (Recursive Character Text Splitter).
     • Google Gemini generates vector embeddings for the text chunks (Embeddings Google Gemini).
     • These embeddings are stored in a Qdrant collection, indexed by the playlist or video ID (Qdrant Vector Store).
   • Status Update & Final Summary:
     • The context status in Redis is updated to 'DONE' (Update Context Process Done1).
     • All individual video summaries are combined (Full Summary).
     • An AI agent (AI Agent) may generate a final, comprehensive summary or response based on the processed data, using a tool to query the vector store (Answer questions with a vector store1).

4. Query Handling (Chatting with Content):

   • If the user's input is identified as a query rather than a new URL to process (or if processing for a URL is complete), the workflow activates the main query handler.
   • A conversational AI agent (Handle Queries), powered by Google Gemini and equipped with chat memory (Chat Buffer Memory), answers user questions.
   • This agent uses a specialized tool (Answer questions with a vector store) that retrieves relevant information directly from the Qdrant vector store based on the user's query and the stored embeddings.

⚡AI-Powered YouTube Playlist & Video Summarization and Analysis v2

Purpose and structure

This folder contains the ⚡AI-Powered YouTube Playlist & Video Summarization and Analysis v2 workflow. Its executable nodes use agent, chainLlm, chatTrigger, code, documentDefaultDataLoader, embeddingsGoogleGemini, httpRequest, if, limit, lmChatGoogleGemini, memoryBufferWindow, merge, outputParserStructured, redis, set, splitOut, summarize, switch, textSplitterRecursiveCharacterTextSplitter, toolVectorStore, vectorStoreQdrant, youtubeTranscripter. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

Chat

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Limit/0971_Limit_Splitout_Automation_Webhook.json MIT; preserve source notices.