Automate Twitter Direct Messages to New Followers with RAG Agent

Overview

This n8n workflow is designed to automate direct messages to new Twitter followers. It leverages a Retrieval Augmented Generation (RAG) Agent to create personalized and contextually relevant messages. The process starts with a Webhook Trigger, which receives new follower data. This data is then split and embedded using the Text Splitter and Cohere Embeddings nodes, respectively. These embeddings are inserted into a Pinecone vector store for efficient retrieval. When a new follower is detected, the workflow queries the Pinecone vector store to retrieve relevant context. This context, along with memory from the Window Memory node, is fed into the RAG Agent (powered by an OpenAI Chat Model) to generate a personalized DM. The result of this process is logged to a Google Sheet via the Append Sheet node, and any errors encountered are sent as alerts to Slack.