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 "my_collection"
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.