Wouldn't it be great if you could dump your logs into a database and then ask questions like "Were there any errors around 9pm yesterday? What caused them?"

Wouldn't it also be awesome to be able to store chat-logs in a database and then ask "Was Dave happy after his support call yesterday?"

Wouldn't it be kind of amazing if you could upload the manuals for all of your bike components, and then be able to ask "how do I fix my brakes?"

Retrieval-Augmented Generation (or RAG) offers the potential to do all of the above, and this hands-on tutorial will show you how, with Python, LangChain (an AI framework) and MongoDB.

This hands-on tutorial will assume very little knowledge of MongoDB, LLMs, Vector Search, or RAG. By the end, you'll have a good idea of how to work with each of these technologies, and you'll have a database you can put data into and then ask questions about the data it contains.