Before we move on, let me show you one more time this fabulous slide that describes the simple three
abstractions and one method call to build a rag pipeline the abstractions.
Again, you create the LM, you create your memory.
You create your retriever just by calling your chroma vector store or whatever vector store you're working
with dot as retriever.
And then you can simply create your conversation retrieval chain with this one line passing in your
LM, your retriever, your memory, and you're done.
And of course, with this conversation chain you call invoke and you pass in a dictionary with your
question and with what you get back, you can simply call you look up the answer key and you will get
back the response from the model.
And so with that I would say that you have leveled up your skills.
You are upskilled.
You are now accomplished in the art of building rag pipelines.
Congratulations!
You can build your own rag knowledge worker not just for a fictitious company like Endure Elm, but
hopefully for real companies too, including your own using lamp chain.
And you can use chroma.
You could also see how easy it is to switch that up for other vector data stores, and using models
other than OpenAI, Lang chain makes that very simple indeed.
Next time we're going to talk about lang chains declarative language briefly, it's not something we're
going to use, but I'll show it to you.
So you could use it if you wish to.
Uh, I'm going to talk a bit about how Lang Chain works under the covers, just so you have more insight.
And in particular, we're going to look at a common problem with Rag, diagnose it and then talk about
how to fix it.
So that will all, uh, equip you to be able to use rag in anger in production projects as you'll really
understand the inner workings.
And with that, I will see you next time.