This n8n workflow automates the process of transforming YouTube transcripts into blog content, enhancing content creation efficiency. Starting with a Webhook trigger to receive data, it splits the transcript into manageable chunks, generates embeddings via OpenAI’s language models, and stores these vectors in Pinecone for semantic search. The workflow includes a retrieval step to find relevant transcript segments, which are then processed through an AI language model (using Anthropic’s chat model) to synthesize blog-ready content. The generated content is logged into Google Sheets for record-keeping, and any errors trigger a Slack alert. This setup is ideal for content creators, marketers, or teams wanting to automate blog generation from YouTube videos.
Automated YouTube Transcript to Blog Workflow
Node Count | 11 – 20 Nodes |
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Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStorePinecone, googleSheets, slack, stickyNote, webhook |
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