RAG System: Paul Graham Essays with Milvus & OpenAI

This workflow creates a Retrieval-Augmented Generation (RAG) system using Paul Graham's essays as the knowledge base. It handles two core phases: data ingestion and query response. In the first phase, it scrapes essay lists from paulgraham.com, extracts titles and content, splits text into chunks, generates OpenAI embeddings, and indexes them into a Milvus vector database for efficient similarity search.

The second phase triggers on incoming chat queries (potentially via Microsoft Teams integration), retrieves top relevant chunks from Milvus using vector similarity, prepares context for OpenAI, generates accurate answers grounded in the essays, and appends citations for transparency and trust.

Benefits include rapid setup of a custom AI Q&A system without manual coding, high accuracy via citations reducing hallucinations, and scalability for larger document sets. Use cases: research assistants, educational tools, knowledge bases for essays/blogs, or internal company document search with Teams chatbots.