Book Recommendation System in Python with LLMs

Key Takeaways

what is going on guys welcome back in this video today we're going to learn how to code a book recommendation system by utilizing large language models in Python so let us get right into [Music] it all right so we're going to build a book recommendation system in Python today by utilizing large language models and I want to briefly sketch the process nothing too complicated and nothing too detailed I just want to show you basically here uh visually what we're going to do very basic I'm going to use my mouse so this is not going to be the most beautiful drawing but our goal is to build up a vector store so a vector database I'm going to say VSS here Vector store uh and this is going to contain Vector representations of a bunch of different books so the idea is we have a data set full of different books and these books have certain um attributes like a title a description an author a publishing date and so on so we have a bunch of attributes here and what we want to do is who want to get those uh buil a textural representation so basically just raw text containing this information in some way and then we want to somehow intelligently take that uh and turn it into a vector so all representation are going to be turned into vectors and these vectors have some values I don't know four 0.5 something and this is an uh a very high dimensional Vector so I think let me just double check here in my prepared code this is going to be a 4,096 dimensional Vector so it's going to have 496 values numerical values uh which are not random which are intelligently Chosen and then these vectors are going to be stored in the vector store and when I get a new book with new information what I do is I take that turn it into the same kind of textual representation turn it into the same kind of vector using the same model so into the same kind of uh Vector representation here and then I ask what's the closest Vector to this one this is a simp similarity search so um mathematically if I have two points in a 496 dimensional Vector space there is a distance between all the different points and what I'm asking is using this new Vector what's the closest Vector what's the closest point in this high-dimensional Vector space to this one and I assume this is going to be the most similar book so I'm going to use the five uh closest vectors to determine the five most similar books and recommend them uh as a result now the interesting part of this whole system is uh basically this Arrow here because that is the embedding process taking text and turning it into a vector intelligently and for this we're going to use llms so large language models this is where the intelligence is needed because we need to take a text and we need to somehow take the content of this text and turn it into something that is meaningfully represented in a 496 dimensional Vector space and then we need to be able to do a similarity search there so that's what we're going to do now for the embedding model um if you want to change the code to use something else you can do whatever you want you can use chat GPT so the open AI API you can use GPT you can use uh any kind of self-hosted model I'm going to use olama just for convenience ol Lama I have a video on his Channel showing how to install and use olama basically it allows you to easily run models locally I'm going to use uh I think let me just double check here I'm going to use llama 2 um just because it fits into my hardware and I'm going to use llama 2 to get text feed it into it and get an embedding out of a 496 dimensional embedding uh and as a vector store we're going to use face so the Facebook uh Vector store um all right and as a data set we're going to use a kaggle data set which is the 7K books data set the reason I chose this one is because it also has descriptions so we have actually text describing the content of the book not just the title because the title can be very misleading or very uh simple so we want to have these descriptions here as well so this is the data set I'm going to use you will find a link to it in the description down below um and actually I think I have to download it because I don't have it in my directory so I'm going to just download um going to go to python current here I'm going to download the archive zip and in here we have the book CSV file I'm going to extract it and uh I'm going to just close this all right so now I should have the book CSV file here I'm going to open up a new Jupiter notebook instance and we should install a couple of packages first the basic data assign stuff as always so pandas numpy should always be part of the equation here uh and I think for this we're going to use um face as well as I said so we're going to use also the request package because we need to send a request to the API of AMA and we're going to use face and then you can use uh I think phase GPU and phase CPU I think I'm using phas GPU here so this utilizes of course the graphical Processing Unit uh and not the CPU but if you don't have a GPU a strong GPU you can also go with face CPU but this is what we need for this video today all right so we're going to start by loading the data set and taking a look at it this should not be too difficult so the data frame is equal to pandas read uh the CSV file which is called book CSV and then I can look at it basically what kind of textual representation you want to use is up to you you can be creative with this one because at the end of the day you want to pick a representation that contains only the necessary information only the useful information uh and you also want to structure it in a way that is the most useful for the llm now what this way is I don't really know you have to play around with it you have to see if different representations give you better results my Approach here is to just craft a string saying um listing the specific information that we need so for example saying the title is this the author is this the publishing year is this the categories are these and so on and so forth um so yeah this is the data that we have I'm going to use the title I'm going to use the authors I'm going to use the category I'm going to use the description uh the publishing year and maybe maybe let's go also with the rating so the rating could also be interesting because yeah I mean if a book has an average rating of one it's probably not that good um all right so what we're going to do is we're going to create a function which is going to take a row and turn it into a textual representation so we're going to say textual representation by the way I have a very very similar video where I do the same thing with movies if you're more interested in movie recommendation you can check this out uh today we're going to do books so textual representation uh we get a row ENT input here uh and then basically we just take the content of this row so title the authors and so on and turn it into a string so I'm going to use an F string here A multi-line F string so three quotation marks uh and I'm going to say that this is the text ual [Applause] representation uh and we're going to start by saying first of all the title of the book is row title like this um actually shouldn't this oh I'm using double why okay we're not in flask here we use just single uh curly brackets so this is the title now and then I can do a line break and I can say the next thing is uh the authors the authors is going to be equal equal to row and then authors and then I can do the same thing for all the other rows uh for all the other columns so again we have title we have authors categories description so let's go with uh description first description is going to be equal to row description obviously then the categories or maybe we should say I mean c atories is fine maybe we should say say genre but I'm not sure about that categories um and then what did I say we want to go with the publishing year and the average rating that should be it so we're going to go with publishing year is going to be row published year is the column name and then finally we have uh average rating row uh average rating and I think one more feature that is useful is uh the number of pages because maybe I'm only interested in very small books so I usually only read books like 70 pages long uh that might also be a factor even though maybe not the most important one so let's say number of pages going to be equal to row num Pages all right so that function takes a row and turns it into such a string and then all I can do is or all I have to do is I have to just return this textual representation like this all right so let's see what happens when I apply it so let's go and say DF and then iog up until five and then apply the function textual representation um do I have to provide an access I think so there you go so this function now applied gives us that maybe we can go values zero and actually I can go and print that to get the result here and you can see we get the title the author's description categories publishing your average rating and number of pages so that is what our function does now now we need to apply that function to all the individual um rows so we're going to say DF textual representation is going to be equal to DF apply textual representation AIS equals 1 that is uh turning our data set into one where we have these textual representations here all right so we have the this now and the next thing we want to do is we want to take all of this and make an embedding and put everything into a vector store so we're going to say here import Face import requests now we don't need to really interact with ol Lama uh the only thing that you need with AMA is of course you need to say uh AMA surf I think or AMA run um and then you need to say AMA pull llama 2 that's important because you need to have the model on your system um and again I have a video on ama if you have struggles with AMA check out that video so import phase import request and then import numpy as NP then we say the dimensionality is 4,096 as I said and then the index so the vector store is going to be face index flat L2 with the dimensionality here we choose this dimensionality because that is the dimensionality of the response we get from llama 2 when it comes to the embedding uh and then we want to say x is equal to np0 and here we pass length data frame textual representation and dimensionality so we just initialize uh these uh actually we need to also pass the data type D type is float 32 uh we just initialize uh input full of zeros here now um and what we want to do next is we want to actually get the embeddings from llama 2 so we say 4 I representation in enumerate so we have an index enumerate DF textual representation uh for that we say take the representation and make a request so say response equal to requests. poost and now we just need to use the Local Host URL of llama 2 uh o Lama sorry sorry uh which is by default HTTP Local Host and then port 11 434 if you didn't change that that should be the default Port of olama again here if you don't want to use ol Lama you can also use the open AI API there is an API for embeddings if you want to replace this code with code that gets you the embeddings from open AI where you have to pay money you can do that uh you don't have to do it with ama if you don't want to you just have to get somehow the embeddings so / API embeddings and here now we need to pass some data the data is going to be adjacent object and the Json object will say I want to use the model llama 2 and I want to use the prompt for the embedding which is the representation um all right so that is our request and then the result is going to be or the embedding is going to be equal to the response do get the Json and then get a specific field called embedding and then in order to store that go to index I this is why we do the enumeration here uh go to index I and say that that is now our new uh input here so NP array embedding actually I think we can also use npm uh that would save us some time I guess but it doesn't really matter so yeah uh and in the end when we're done with that we want to do index. add X so we can do it like that and it will take quite some time so I can run this and you will see it will start working and it takes some time so I can actually go ahead and add a line here saying if I modulo 100 is equal to Zer print I and remember we have uh how many we have 6,810 rows so I can run this you can see I get zero then at some point I'm going to get 100 200 and so on but you see the progress is quite slow so when you see 100 and when you see 200 you're going to see how slow this actually is so I'm not going to do all of this here now on camera and I think actually okay it seems like I cannot run this while recording because it crashes my recording or at least it makes it very laggy but you can see it doesn't uh work very quickly at least on my Hardware maybe you have some power GPU and it works instantly so you have to run this for a while I'm not going to do this on camera I already did this this is why I have this index file here I'm going to show you how to create that here in a second but you can run this for example on the first couple of instances if you want to you can run it on the whole data set and just wait but the idea is that once this process is done so once this Loop here is finished and you add everything to the index what you can do easily is let me just close this here um what you can do easily is you can export the index by saying phase right index and then uh you take the index and you save it to index now in my case I already did that so uh I'm not going to do this but this is the line of code you would run to do that I'm just adding a two here in case I accidentally run this and delete my index uh and what you can do then is you can load the index from the file Again by saying ph. read index and then just a file name so index here and then you can store that in an index so in this case what I'm doing now is I'm loading the index from a file instead of creating it here by training because I already did this exact code here I ran it I weighed it I produced the index I wrote it to dis and now I can just say index face read index and I have the full index so everything that is the result of running this just that I now uh terminated that but I now have the index and this is now the same thing that you get when you just run this and let it run until it's finished or you can also go ahead and just truncate it you can say okay give me a random sample of the data I don't need all of it uh it's up to you so it takes some time um now what do we do with this index what we can do with this index now is we can provide a new instance and find the most similar instance of the database so for example uh let's use from our uh data frame here so let's go and say data frame DF where the title contains uh let's look for a book uh classic self-improvement book would be something like uh how to in friends and influence people so let's look for friends uh uh what's the problem here DF title contains oh title. string contains so we have little house friends friends friends how do I friends and influence people there you go or actually this is the book so it's 4533 let's say I want to find the most similar book to this one now this is not a new book you can also do that with a book that is not part of the data frame uh but then you would have to craft your string yourself but let's go and say that my favorite book now is equal to DF iog and it's 4533 so if I look at my favorite book you can see it's this one I can also look at the textual representation here and I will get the data for the book now let's say I like this book and I want to find similar books because I want to learn more about this or similar topics here what I can do is I can use the vector store to embed this again and this again this could be something completely different I could go ahead now and craft this myself I don't need to use a string that is already part of the um of the data frame I can go and say title uh python Bible 7 and one which is a book from me I can say authors my name and I can I can put my book here if I want to and I can feed that in as well so it doesn't have to be a book that's already part of the data frame you just craft your string and you feed it into it it doesn't even have to have the structure so you can also go ahead and feed in hello world and embed it it also works uh but it's not very useful so we have this book here and what I want to do now is I want to embed this again assuming that this is not part of the data frame or you can again embed your own string and then take that embedding and perform a similarity search so we do that again by saying basically the exact same thing that we did here so we're going to copy that code the response is equal to requests post and then that but here now instead of representation we pass favorite book uh textual representation or you could also as I said pass your own string um yeah so that is that we get a response from this now we need to get an embedding so what's the embedding of this particular book it's equal to NP array of uh response response. [Music] Json uh yeah we need to actually use this uh thing here for the shape so response Json embedding and then the data type is equal to float 32 this is now the closing bracket actually we need no we need it like this there you go so that's the betting and now we have to feed this into our index and search for similarities so we say di is equal to index sech so we performed the search based on the embedding and we're interested in a top five results so I pass Five here and then I can get the matches by saying best matches is equal to NP array um DF textual represent ation so I get only the column with the representations from the data frame and I say that I'm interested in particular in a couple of indices and these indices are what I get as a result here from I so I flatten that because what you need to understand is that I'm doing this and as a result I don't get a textual representation I get positions I get indices of the individual entries and I then need to translate them back to actual representations from the data frame so I can say for match in best matches print the match print an empty line and basically run this and you can see now not surprising actually this is surprising because this is not what I was expecting let me see this is I think the issue is that okay so I actually figured out that the problem was a different one and it was that I was not using the exact same structure that I was using uh when training the previous index because of course I trained the index with my prepared code and there I had a slightly different structure now I changed this you can see now title is no longer the first thing we have categories title authors average raing number of pages publishing year then a blank line in description not because that's necessarily the best way to do it just because that's the way I did it when I trained my index which I loaded so this is just the reason you want to keep this the same you can train or you cannot build your vector store with examples like these and then use a completely different structure so you have to keep it the same in your case it shouldn't make a difference you should uh get good results immediately because you have only been using one structure in my case it made a difference so just as a side note here it's good that we can learn from mistakes uh you need to keep this the same so you cannot just swap things around here because it's going to mess up the database so I changed the structure to be the exact same as the one I used so now I can run uh these these things here again I'm not going to run this one uh I can read index I can find this book again I can post I can get the best matches and then I can get my results which are in this case now way better now of course this one here is going to be number one because it's the exact same thing but besides that we have here conduct of life from Steven cvy also a self-improvement book we have psychology the of intimacy from this author here we have uh Marketing in the bottom line oh actually this was not the type conduct of life is not the type first things first is the type of the book uh and the dance of intimacy is the type the title of this book so this is just a category here um but yeah so you can see that what we get here how to talk so teens will listen and listen so team will talk yeah whatever but these are all like self-improvement SL productivity SL communication books maybe we can go and look at a couple of more here and we see that for the most part art of Happiness these are all self-improvement books so it seems to work to some degree you can play around with that you can play around with different representations you can also try first of all smaller samples and then do it on the whole Vector store or on the whole data uh data set but this is how you can build a uh recommendation system because all you have to do now is you have to come up with new books like uh in this structure here and then you can just feed them in and get recommendations for uh similar books so that's it for today's video I hope you enjoyed it and hope you learned something if so let me know by hitting a like button and leing a comment in the comment section down below and of course don't forget to subscribe to this Channel and hit the notification Bell to not miss a single future video for free other than that thank you much for watching see you on the next video and bye