Learn how to build a Retrieval-Augmented Generation (RAG) Wiki application using MongoDB and Spring AI. The tutorial details setting up MongoDB Atlas Vector Search for storing documents, adding necessary dependencies, and configuring the application to save and retrieve documents based on context. The application leverages a vector store for similarity search and utilizes LLM for generating responses, making it suitable for developing chatbots, automated wikis, and search engines.
Table of contents
1. Overview2. RAG Applications3. MongoDB Atlas Vector Search4. Dependencies and Configuration5. Save Documents to the Vector Store6. Similarity Search7. Prompt Endpoint8. ConclusionSort: