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