This n8n workflow automates the process of generating and storing highlights from Twitch clips. It is designed to listen for incoming clip data via a webhook, process and analyze the content, and store relevant highlights in a structured database for easy retrieval.
The workflow starts with a webhook node that receives data from Twitch. The data then flows to a text splitter node, which divides the content into manageable chunks for analysis. These chunks are processed through an embeddings node using Cohere’s API to generate semantic vector representations.
To enhance searchability, the embeddings are inserted into a Weaviate vector database, which allows efficient similarity searches later on. When a query is made, the workflow fetches relevant embeddings from the database, which are then processed by a language model that utilizes context buffering via a memory node.
A chatting node powered by a Hugging Face language model refines and generates summaries or highlights based on the analyzed clips. These insights are finally logged into a Google Sheet for record-keeping and further review.
This automation is ideal for content creators or community managers aiming to generate content summaries, highlight reels, or archival data from Twitch streams seamlessly and efficiently, saving time and enhancing content engagement.
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