This n8n workflow automates the process of engaging with new Twitter followers through automatic direct messages, while also maintaining a detailed log and managing contextual data for more personalized interactions.
The workflow is triggered via a webhook when a new follower is detected. It processes the follower data by splitting the content into manageable chunks using a Text Splitter node, which supports handling large texts or messages efficiently. These chunks are then embedded using the Cohere API to create vector representations.
The embedded data is stored in a Pinecone vector database, facilitating high-dimensional similarity searches for relevant content or past interactions. An initial query to Pinecone retrieves relevant contextual vectors, which are then fed into a Vector Tool node for further processing.
A RAG (Retrieve-and-Generate) agent, powered by OpenAI’s GPT model, is employed to generate personalized automatic direct messages based on the retrieved context. The dialog management is handled using a Memory Buffer node, ensuring coherent and context-aware interactions.
Once a message is crafted, the workflow logs the status into a Google Sheet for record-keeping. If any errors occur during the process, a Slack alert is triggered to notify the team for troubleshooting.
This workflow is ideal for social media managers or marketing teams aiming to engage new followers instantly with personalized messages, while maintaining a record of interactions for analysis and optimization.
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