This n8n workflow automates the process of fetching the latest videos from a specific YouTube channel, extracting and chunking video transcripts into smaller segments, and indexing them into a Qdrant vector store for efficient semantic search. It then provides a web-based UI to search for relevant video content based on user queries, leveraging AI to analyze transcripts and return precise video timestamps. The workflow also includes rate limiting to prevent API abuse, and it offers a user-friendly interface for exploring video content with embedded players. Practical use cases include research, content analysis, and building custom YouTube search engines with advanced filtering capabilities.
Automated YouTube Video Search & Transcript Analysis Workflow
| Node Count | >20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.informationExtractor, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter, @n8n/n8n-nodes-langchain.vectorStoreQdrant, executeWorkflow, executeWorkflowTrigger, html, httpRequest, if, manualTrigger, markdown, noOp, redis, removeDuplicates, respondToWebhook, scheduleTrigger, set, sort, splitInBatches, splitOut, stickyNote, wait, webhook |
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