WEBVTT 00:00.440 --> 00:07.280 Before we move on, let me show you one more time this fabulous slide that describes the simple three 00:07.310 --> 00:13.130 abstractions and one method call to build a rag pipeline the abstractions. 00:13.160 --> 00:16.190 Again, you create the LM, you create your memory. 00:16.220 --> 00:20.690 You create your retriever just by calling your chroma vector store or whatever vector store you're working 00:20.690 --> 00:22.520 with dot as retriever. 00:22.520 --> 00:28.190 And then you can simply create your conversation retrieval chain with this one line passing in your 00:28.190 --> 00:30.770 LM, your retriever, your memory, and you're done. 00:30.770 --> 00:36.080 And of course, with this conversation chain you call invoke and you pass in a dictionary with your 00:36.080 --> 00:43.400 question and with what you get back, you can simply call you look up the answer key and you will get 00:43.400 --> 00:46.010 back the response from the model. 00:46.340 --> 00:51.770 And so with that I would say that you have leveled up your skills. 00:51.770 --> 00:53.540 You are upskilled. 00:53.540 --> 00:59.030 You are now accomplished in the art of building rag pipelines. 00:59.030 --> 01:00.380 Congratulations! 01:00.740 --> 01:06.170 You can build your own rag knowledge worker not just for a fictitious company like Endure Elm, but 01:06.170 --> 01:11.060 hopefully for real companies too, including your own using lamp chain. 01:11.060 --> 01:12.200 And you can use chroma. 01:12.230 --> 01:19.370 You could also see how easy it is to switch that up for other vector data stores, and using models 01:19.370 --> 01:23.480 other than OpenAI, Lang chain makes that very simple indeed. 01:23.570 --> 01:29.810 Next time we're going to talk about lang chains declarative language briefly, it's not something we're 01:29.810 --> 01:31.100 going to use, but I'll show it to you. 01:31.130 --> 01:32.900 So you could use it if you wish to. 01:33.200 --> 01:38.390 Uh, I'm going to talk a bit about how Lang Chain works under the covers, just so you have more insight. 01:38.390 --> 01:44.750 And in particular, we're going to look at a common problem with Rag, diagnose it and then talk about 01:44.750 --> 01:45.770 how to fix it. 01:45.770 --> 01:53.300 So that will all, uh, equip you to be able to use rag in anger in production projects as you'll really 01:53.300 --> 01:54.950 understand the inner workings. 01:55.040 --> 01:57.200 And with that, I will see you next time.