Create a RAG system with Paul Essays, Milvus, and OpenAI for cited answers

Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: 1. Data collection/processing 2. Retrieval/response generation The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations. Step 1: Data Collection and Processing 1. Set up a Milvus server using the official guide 2. Create a collection named "mycollection" 3. Execute the workflow to scrape Paul Graham essays: - Fetch essay lists - Extract names - Split content into manageable items - Limit results (if needed) - Fetch texts - Extract content - Load everything into Milvus Vector Store This step uses OpenAI embeddings for vectorization. Step 2: Retrieval and Response Generation When a chat message is received, the system: Sets chunks to send to the model Retrieves relevant information from the Milvus Vector Store Prepares chunks Answers the query based on those chunks Composes citations Generates a comprehensive response This process uses OpenAI embeddings and models to ensure accurate and relevant answers with proper citations. For more information on vector databases and similarity search, visit Milvus documentation.