WEBVTT

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So it's business time right now.

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We are going to build a Rag pipeline to estimate the price of products, drawing on context for similar

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products that we have in our training dataset.

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Uh, and so we are right here.

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Day 2.3 building a Rag pipeline with GPT four mini.

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Uh, we're going to go pretty quickly today through a lot of code.

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So bear with me then.

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Remember the trick is to come back, run the code yourself and get a good sense of what's going on.

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So we do some imports, we log in as usual, and then we, uh, connect with OpenAI.

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Um, and it's using the fact that we've set the OpenAI API key.

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We're going to load in the test data set.

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Now, we don't need to load in the training data set because we'll be using the chroma data store for

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that.

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All right.

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So here's a function make context.

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We're going to use this function to produce the context that we're going to send GPT four mini that

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tells it about similar products that it could use.

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So we're going to say to provide some context, here are some other items that might be similar to the

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one you need to estimate.

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And then do you remember this zip construct that allows us to iterate through two different lists together.

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So we're going to collect similar and price from iterating through the similars and prices that are

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passed in.

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And then we're going to add in a message there's a potentially related product similar with this price.

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I'll execute this in just a second.

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So you see what it actually looks like.

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But then just as we've done in the past, I'm making this function messages for and it takes an item,

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it takes things similar to it and it takes prices and it's going to build that standard list of dicts

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that we know so, so well.

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There's going to be a system message which is going to be just the same one we've used before.

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You estimate price of items, reply only with the price we're going to add in the context.

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We're going to add in the stuff that we just built right here.

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And then we're going to say.

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And now the question for you.

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And now this is basically exactly the same as we used before when we called GPT four back in week six

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when we built that pipeline.

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So the thing to notice here is that we're doing this Rag pipeline ourself without Lang chain.

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And it's actually not that hard.

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Lang chain gave us a nice little abstraction on top with with a few simple objects.

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And I remember going on about how it was just like one line of code or something to do it, but it's

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not that much more just to do it ourselves.

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Particularly now that you understand what's going on under the hood and you know how to call llms and

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you know how to look up similar objects and so on.

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Um, it becomes relatively straightforward.

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Uh, so, um, hopefully this is not going to be too, uh, um, difficult, but let's keep going.

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Um, so we're going to, um.

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Collect our chroma.

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Um, and once more, I've done this again, haven't I?

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I deleted the cell where I defined the DB variable.

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It should be products Underscore.

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Vector store.

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Vector store.

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Some possibly hard word to spell.

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There we go.

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And now run that again.

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Fine.

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So we've now we're looking in the products collection in our vector data store.

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Um okay.

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And so we're now getting to the meat of the whole thing.

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So we're going to have a method, a function description that's going to take an item.

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And it's going to strip out from that item the stuff that we don't care about.

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So basically we'll take the prompt and we're going to take out this.

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How much does it cost to the nearest dollar.

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And we're going to then uh, ignore everything that comes after price is dollars.

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Let me show you exactly what's going on here.

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Uh, so if I have a quick look at my first training data point.

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Oops.

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Sorry.

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What have I done?

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Test the first test data point.

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We're not looking at training data anymore.

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The first test data point is this the repair kit for Ford, blah blah, blah, blah, blah with a price

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on it.

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Uh, if we look at its prompt, you'll see that that that's got all of this gubbins in there with the

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price and the question at the top.

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But what I can do is I can just say describe description of test zero.

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And what we should now get is this just the blurb without the price.

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So that should be clear.

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All right.

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Now we're going to load the model that is the sentence transformer from hugging face.

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That gives us our simple vector encodings.

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It is our vectorizer.

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And so this method here, this is in fact our Vectorizer function.

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It takes an item.

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It it calls description on that item to turn it into text.

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And it then puts it in a list and calls model dot encode.

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Simple as that.

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And now and now we get to a function find similars.

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It will be given an item and it will return similar items.

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And this, this is the some of the hardest part that Lang was doing for us before, but it's not that

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hard.

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You can see what we do is we say to our collection, our chroma DB collection, I want to query this.

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These are the query embeddings.

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This is it's going to be based on this, uh, the vector that we will pass in.

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We do have to turn that into a floating point number from being a numpy array.

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That's uh, from being a numpy float 32.

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You have to turn them into floats, and then we have to turn it into a list instead of being a numpy

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array.

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And then you just simply say, uh, number of results and that's how many you want back.

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So you can pass in a vector as your query embedding and get back five results.

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That's all there is to it.

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And what we'll get back is uh, is some similars, uh, so let's just run this.

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So now what we can do is we can look at test number one and let's look at test number one's prompt.

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So this is its prompt.

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Um how much does it cost to the nearest dollar.

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It's a fan clutch package.

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Uh, from, um motorcraft.

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So anyway, you get a sense.

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So we can now say documents and prices are fine.

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Find Similares from test one.

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We're going to call this function right here to find five similar results.

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Let's do that.

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And now it's thinking.

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And now let's print that.

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And here you will find five related results are potentially related products.

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And they are all potentially related product.

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They are all sort of fan clutchy kind of things that have various prices.

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Uh, and they do appear to be similar products at first blush.

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Uh, so you should convince yourself, do some more testing and make sure you're comfortable that indeed

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we can call this find similar function, which is simply calling query on our Cromer collection.

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And we will be able to collect similar products from our Cromer database.

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It's as simple as that.

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Uh, and once we've done that, we will then be able to put the final touches on our Rag data flow on

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our Rag pipeline and then use that to call GPT four zero.

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And we will do that in the next video.