AI Agent with Milvus RAG Knowledge Base

Dive into the world of Retrieval-Augmented Generation (RAG) with this comprehensive n8n workflow. It begins by programmatically scraping valuable textual content from a specified website, in this case, Paul Graham's essays. The extracted data is meticulously cleaned, chunked, and transformed into high-quality embeddings using OpenAI's powerful models. These embeddings are then efficiently stored in a Milvus vector database, forming a robust and searchable knowledge base. The second part of the workflow orchestrates an intelligent AI Agent capable of interacting via chat. This agent leverages the Milvus database as a custom tool, enabling it to retrieve highly relevant context from the ingested essays to formulate accurate and informed responses to user queries. This setup is ideal for building domain-specific chatbots or enhanced search applications, ensuring your AI has access to up-to-date and specific information.