WEBVTT 00:00.380 --> 00:02.360 So it's business time right now. 00:02.360 --> 00:09.020 We are going to build a Rag pipeline to estimate the price of products, drawing on context for similar 00:09.020 --> 00:11.780 products that we have in our training dataset. 00:12.110 --> 00:14.510 Uh, and so we are right here. 00:14.540 --> 00:19.580 Day 2.3 building a Rag pipeline with GPT four mini. 00:19.850 --> 00:22.640 Uh, we're going to go pretty quickly today through a lot of code. 00:22.640 --> 00:24.860 So bear with me then. 00:24.860 --> 00:30.440 Remember the trick is to come back, run the code yourself and get a good sense of what's going on. 00:30.440 --> 00:37.310 So we do some imports, we log in as usual, and then we, uh, connect with OpenAI. 00:37.490 --> 00:41.360 Um, and it's using the fact that we've set the OpenAI API key. 00:41.390 --> 00:44.450 We're going to load in the test data set. 00:44.480 --> 00:49.430 Now, we don't need to load in the training data set because we'll be using the chroma data store for 00:49.460 --> 00:50.120 that. 00:50.570 --> 00:51.350 All right. 00:51.350 --> 00:55.250 So here's a function make context. 00:55.250 --> 01:01.790 We're going to use this function to produce the context that we're going to send GPT four mini that 01:01.790 --> 01:04.910 tells it about similar products that it could use. 01:04.910 --> 01:10.680 So we're going to say to provide some context, here are some other items that might be similar to the 01:10.680 --> 01:12.120 one you need to estimate. 01:12.120 --> 01:18.000 And then do you remember this zip construct that allows us to iterate through two different lists together. 01:18.300 --> 01:24.840 So we're going to collect similar and price from iterating through the similars and prices that are 01:24.840 --> 01:25.680 passed in. 01:26.070 --> 01:32.670 And then we're going to add in a message there's a potentially related product similar with this price. 01:33.600 --> 01:35.670 I'll execute this in just a second. 01:35.670 --> 01:37.410 So you see what it actually looks like. 01:37.800 --> 01:44.640 But then just as we've done in the past, I'm making this function messages for and it takes an item, 01:44.640 --> 01:51.360 it takes things similar to it and it takes prices and it's going to build that standard list of dicts 01:51.360 --> 01:53.160 that we know so, so well. 01:53.520 --> 01:58.170 There's going to be a system message which is going to be just the same one we've used before. 01:58.200 --> 02:04.410 You estimate price of items, reply only with the price we're going to add in the context. 02:04.410 --> 02:07.980 We're going to add in the stuff that we just built right here. 02:08.040 --> 02:09.300 And then we're going to say. 02:09.330 --> 02:10.980 And now the question for you. 02:10.980 --> 02:18.420 And now this is basically exactly the same as we used before when we called GPT four back in week six 02:18.750 --> 02:20.550 when we built that pipeline. 02:20.640 --> 02:27.870 So the thing to notice here is that we're doing this Rag pipeline ourself without Lang chain. 02:28.350 --> 02:30.510 And it's actually not that hard. 02:30.600 --> 02:35.040 Lang chain gave us a nice little abstraction on top with with a few simple objects. 02:35.040 --> 02:39.060 And I remember going on about how it was just like one line of code or something to do it, but it's 02:39.060 --> 02:40.890 not that much more just to do it ourselves. 02:40.890 --> 02:45.900 Particularly now that you understand what's going on under the hood and you know how to call llms and 02:45.900 --> 02:49.230 you know how to look up similar objects and so on. 02:49.350 --> 02:52.830 Um, it becomes relatively straightforward. 02:53.010 --> 03:01.260 Uh, so, um, hopefully this is not going to be too, uh, um, difficult, but let's keep going. 03:01.290 --> 03:04.020 Um, so we're going to, um. 03:06.060 --> 03:07.830 Collect our chroma. 03:08.610 --> 03:10.740 Um, and once more, I've done this again, haven't I? 03:10.770 --> 03:13.920 I deleted the cell where I defined the DB variable. 03:13.920 --> 03:17.050 It should be products Underscore. 03:18.460 --> 03:19.780 Vector store. 03:22.690 --> 03:23.860 Vector store. 03:23.890 --> 03:25.930 Some possibly hard word to spell. 03:25.930 --> 03:26.560 There we go. 03:26.560 --> 03:27.700 And now run that again. 03:27.700 --> 03:28.450 Fine. 03:28.450 --> 03:33.700 So we've now we're looking in the products collection in our vector data store. 03:34.510 --> 03:35.950 Um okay. 03:35.950 --> 03:38.980 And so we're now getting to the meat of the whole thing. 03:38.980 --> 03:46.150 So we're going to have a method, a function description that's going to take an item. 03:46.150 --> 03:49.990 And it's going to strip out from that item the stuff that we don't care about. 03:49.990 --> 03:53.950 So basically we'll take the prompt and we're going to take out this. 03:53.950 --> 03:55.810 How much does it cost to the nearest dollar. 03:55.810 --> 04:00.910 And we're going to then uh, ignore everything that comes after price is dollars. 04:00.940 --> 04:03.430 Let me show you exactly what's going on here. 04:03.610 --> 04:08.380 Uh, so if I have a quick look at my first training data point. 04:09.430 --> 04:09.910 Oops. 04:09.940 --> 04:10.390 Sorry. 04:10.420 --> 04:10.870 What have I done? 04:10.900 --> 04:12.250 Test the first test data point. 04:12.250 --> 04:13.780 We're not looking at training data anymore. 04:13.810 --> 04:19.600 The first test data point is this the repair kit for Ford, blah blah, blah, blah, blah with a price 04:19.600 --> 04:20.230 on it. 04:20.560 --> 04:27.180 Uh, if we look at its prompt, you'll see that that that's got all of this gubbins in there with the 04:27.180 --> 04:29.040 price and the question at the top. 04:29.040 --> 04:36.390 But what I can do is I can just say describe description of test zero. 04:36.390 --> 04:41.670 And what we should now get is this just the blurb without the price. 04:41.670 --> 04:44.130 So that should be clear. 04:44.400 --> 04:44.910 All right. 04:44.940 --> 04:50.340 Now we're going to load the model that is the sentence transformer from hugging face. 04:50.340 --> 04:53.070 That gives us our simple vector encodings. 04:53.070 --> 04:54.720 It is our vectorizer. 04:54.840 --> 04:59.430 And so this method here, this is in fact our Vectorizer function. 04:59.430 --> 05:00.840 It takes an item. 05:00.840 --> 05:04.890 It it calls description on that item to turn it into text. 05:04.890 --> 05:09.420 And it then puts it in a list and calls model dot encode. 05:09.840 --> 05:11.850 Simple as that. 05:12.690 --> 05:17.790 And now and now we get to a function find similars. 05:17.790 --> 05:22.950 It will be given an item and it will return similar items. 05:22.980 --> 05:28.840 And this, this is the some of the hardest part that Lang was doing for us before, but it's not that 05:28.840 --> 05:29.470 hard. 05:29.590 --> 05:36.490 You can see what we do is we say to our collection, our chroma DB collection, I want to query this. 05:36.520 --> 05:38.140 These are the query embeddings. 05:38.140 --> 05:42.880 This is it's going to be based on this, uh, the vector that we will pass in. 05:42.880 --> 05:46.120 We do have to turn that into a floating point number from being a numpy array. 05:46.150 --> 05:50.140 That's uh, from being a numpy float 32. 05:50.200 --> 05:53.440 You have to turn them into floats, and then we have to turn it into a list instead of being a numpy 05:53.470 --> 05:54.010 array. 05:54.010 --> 05:59.380 And then you just simply say, uh, number of results and that's how many you want back. 05:59.380 --> 06:04.210 So you can pass in a vector as your query embedding and get back five results. 06:04.210 --> 06:05.740 That's all there is to it. 06:05.740 --> 06:11.560 And what we'll get back is uh, is some similars, uh, so let's just run this. 06:11.680 --> 06:18.040 So now what we can do is we can look at test number one and let's look at test number one's prompt. 06:18.040 --> 06:19.840 So this is its prompt. 06:19.930 --> 06:21.700 Um how much does it cost to the nearest dollar. 06:21.700 --> 06:24.160 It's a fan clutch package. 06:24.490 --> 06:26.860 Uh, from, um motorcraft. 06:26.860 --> 06:28.210 So anyway, you get a sense. 06:28.210 --> 06:32.020 So we can now say documents and prices are fine. 06:32.020 --> 06:34.020 Find Similares from test one. 06:34.020 --> 06:39.030 We're going to call this function right here to find five similar results. 06:39.060 --> 06:40.440 Let's do that. 06:41.100 --> 06:42.630 And now it's thinking. 06:42.630 --> 06:44.190 And now let's print that. 06:44.550 --> 06:51.390 And here you will find five related results are potentially related products. 06:51.390 --> 06:54.600 And they are all potentially related product. 06:54.630 --> 06:59.640 They are all sort of fan clutchy kind of things that have various prices. 06:59.730 --> 07:04.080 Uh, and they do appear to be similar products at first blush. 07:04.260 --> 07:09.540 Uh, so you should convince yourself, do some more testing and make sure you're comfortable that indeed 07:09.540 --> 07:15.840 we can call this find similar function, which is simply calling query on our Cromer collection. 07:15.840 --> 07:21.720 And we will be able to collect similar products from our Cromer database. 07:21.720 --> 07:23.490 It's as simple as that. 07:23.790 --> 07:31.350 Uh, and once we've done that, we will then be able to put the final touches on our Rag data flow on 07:31.350 --> 07:36.030 our Rag pipeline and then use that to call GPT four zero. 07:36.030 --> 07:38.610 And we will do that in the next video.