Mark Smith
Developer Advocate
Edinburgh, United Kingdom
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Mark has been a developer since the mid-90s, these days specialising in Python, Rust and Go. He's built everything from online games to medical imaging software & travel sites. When he's not sitting at his computer keyboard, you'll probably find him soldering a new one from scratch.
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Build a Powerful Autonomous Assistant with AI
Many of the latest LLMs now have the ability to use tools - functions you can write that the LLM can call if it needs more data, say from web search, or to execute a task, like sending an email. When combined with the ability to look up data from a vector database using unstructured inputs, that gives you inputs, outputs, memory, and a brain - everything I need to build a smart butler to make my life easier!
I'll go through each of these systems to explain how they work, and then I'll put them together using the POLM stack (that's Python, OpenAI, LlamaIndex, & MongoDB).
If I have time, I'll also talk about some of the other things LLMs can do (like writing and executing code) that could be useful for an AI agent.
If everything goes to plan, I'll show you a system that can schedule tasks, tell me interesting things that are happening,
and generally make me look like someone who know's what they're doing on a daily basis. Let's hope I don't accidentally unleash an unrestrained digital robot upon the Internet.
Vector Search and RAG with Python & MongoDB
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.
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