YouTube Comment Summarizer Automates Contextual Analysis

This workflow automatically processes YouTube comments to generate concise summaries and store them efficiently. It starts with a webhook triggered by new comments, which are then split into manageable chunks for natural language processing. Using OpenAI’s API, comments are embedded into vectors stored in Pinecone, a vector database, enabling quick similarity searches. The workflow employs a Language Model to synthesize context-aware summaries, utilizing a Retrieval-Augmented Generation (RAG) approach. The resulting summaries are logged into Google Sheets, and any errors during the process trigger Slack alerts for prompt troubleshooting. This automation streamlines comment analysis, making it ideal for content creators, marketing teams, or community managers seeking to gauge viewer sentiment and feedback insights efficiently.