LLM Project | End to end LLM project Using Langchain, Google Palm in Retail Industry

in today's video we are going to build end to end llm project where we are going to use all these Technologies at is a store that sells t-shirts their data is stored in a mySQL database we will build a tool similar to chat GPD where you can ask a question in natural human language it will convert that question into SQL query and execute it on our database you will get a feeling as if you're are talking to a database in a plain English language it's going to be a very interesting project let us discuss project requirements our atck te's t-shirt store sells four Brands mainly V Hussein leis Nike and Adidas and the mySQL database has first table which is called t-shirts where we maintain the inventory count so basically Levy's black color small size t-shirt have 15 stock quantity left okay so 15 64 these are the stock quantities and this price is price per unit so one Le black Smalls size t-shirt will cost you $19 the second table I have is discounts so for example t-shirt ID one which is leev black small T-shirt has 10% discount in real life the database will have so many different tables to make things simple for learning I'm just going to use two tables the t-shirt store manager is Tony Sharma whenever he has questions related to stock quantity discounts and so on he uses a software which is built on top of this mySQL database if you look at retail domain overall they will have these softwares where you can use various UI options on the software to get answers of your questions and Tony is fine using this software but many times what happens is he has custom questions little complex questions and the software can't figure it out so then he will have to download the data in Excel do certain things manually whenever he's busy he goes to Loi who is a data analyst working for this company and Loki knows SQL so let's say if Tony asked this question that how many white color Nike T-shirts do we have in stock and he will just simply run the SQL query on that database and get the answer back to Tony Sharma but Loki is busy as well he's busy building power bear dashboards and he doesn't have uh too much time for these ad hoc queries also L is the only data analyst working for this company and Tony sometimes have issues where you know Lok is out on leave and he's not available and then he has to do all this work manually because Tony himself doesn't know SQL so then he goes to a data scientist who is working for the same company and you might guess what is the name of that data scientist well Peter P who looks somewhat like me and he says hey Peter buddy we are living in Chad GPT era llm Lang chain all these cool Frameworks have come up why don't you build a tool similar to chat GPT where I can ask a question in a human language and it somehow converts that to a SQL query executes it on a database and gets me the answer that the answer is 3165 Peter likes this thought and he agrees to build this particular tool so let's look at the technical architecture of this tool whenever you have a question you need to convert that to a SQL query using some llm now we are going to use Google Palm here which will do this version and we will use Google pal from Lang chain framework within Lang chain framework you can use Google pal and other type of llms we will use a SQL database chain class within Lang chain framework this will work okay for simple queries but as the queries get little complex out of the box Google Palm model will fail sometimes it will give errors and we need to do some special handling we will use a concept of few short learning here few short learning means you need to prepare the training data set where you have a sample question and a corresponding SQL query here you will list down all those queries where out of the box Google model is failing and you can prepare these queries with the help of your data analyst Mr lokal and you prepar this data set it is called few short learning because you don't need to prepare like thousand samples you know you can have some few samples here and then you will convert this training data set into embedding vectors if you don't have any idea on what is word embedding sentence embedding go to YouTube search for code Basics embedding or code Basics word embedding you will find couple of videos where I have provided very simple intuitive explanation for these embeddings we will use hugging face Library once embeddings are created we will store them into a vector database when you think about Vector database there are a couple of options that you have pine cone mil chroma DB face Etc we are going to use chrom ADB it is open source and it will work perfectly okay for our project once the vector database is ready we will pair it up with uh Google Palm llm we'll use few short prom template to create the SQL database chain and the last piece will be building a UI in streamlet we will write just few lines of code five or six lines of code and our UI will be ready to continue further on this project obviously you need to have Lang chain Basics clear for which I have this particular video where I have covered all the basics in this one single video so make sure uh you have either watched it or you already know the Lang chain fundamental just the basics you also need to know what is Vector database in this six minute video I have given a very simple explanation of what is Vector database so if you have not seen it please uh see that now let's do a review of Google Palm there are three popular options when you talk about building llm application open AI gp4 model which is best in the market but it is paid the other two unpaid are meta Lama and Google Palm I could have used meta Lama for this project but you have to download that llama model locally or less in your Google collab and it is very heavy like sometimes it's the the size is in gigabytes and it's kind of little bit hard to set up whereas Google's p is very easy to set up it works similar to open a API where you just make a query to their Google server and the Beautiful Thing here is it is all free okay so we are going to use that as a next step we are going to set up API key for Google pal I have opened makers suit. goole.com website where you can login using your Google account you need to go to gate API key and you can create API key in your existing Google Cloud project and if you don't have that just click on create API key in a new project so here I will use just any project and create an API key now this API key short of like a password so make sure you don't share it with others I'm showing you this API key right now but I'm going to delete it after I use it in my project so I'll copy it save it at a safe place so that I can use it later on in my code talking about maker suit it gives you a teste pad where you can try different prompts for example taex prompt let's go here and here you can write different prompts and it will use this text bison model Google p is the architecture but the specific model that it is using is text bison the creativity parameter means if it is more closer to one then it will be more creative if if it is more closer to zero it will be less creative you can try some sample prompts for example summarize a paragraph and when you run it it will summarize the paragraph you can try poem writing or write your own custom prompt behind the scene it is using the same API that we will be using in our project therefore if you want to quickly taste your API this taste pad allows you to do that very easily you can play with different prompts but as far as API key is concerned we are all set now we will set up our mySQL database now I have launched MySQL workbench by going here and typing MySQL workbench if you're not aware about MySQL don't worry you can go to YouTube type code basic SQL tutorial I have this 1 and a half hour tutorial where I have given a complete idea for any beginner uh so you can follow that and learn my SQL easily this tool is by the way free you can download it easily by going to Google searching for my SQL workbench I will open this local instance and if you check video description below I have given you all the code files this will have a database directory you can go here and drag Dr and drop this SQL file here this file is taking care of creating database and tables within it you can click on this execute icon and it will create the tables and data within it when you click on this refresh icon that's when you will see atck t-shirt database you can right click on it and set it as a default data set if you have not set it like that before so just say set as a default schema and you will see that this font will convert into bold now table wise we have first table which is T-shirts if you click on this third icon you will see some sample record for example t-shirt ID one is Van hends red color T-shirt in small size price of one t-shirt is $15 we have total 70 t-shirts available in our store that's a stock quantity if you talk about discounts t-shirt ID one which is the same van who and red color T-shirt has 10% discount what it means is the $15 is the original price 10% of 15 is 1.5 so when I sell this one t-shirt I'm going to give $1.5 discount to a customer so they'll get it for $13.5 these records by the way will be different when you execute this SQL script because we are using some random numbers here so don't worry if if you don't see the same exit numbers in your case they are likely going to be different all right our database is set up now let's start coding in our jupyter notebook I ran python hyphen M notebook to launch my jupyter notebook and here I have created a new python notebook I'm going to import Google pal model from Lang chain. llm okay now you can use uh openi my all kind of models Google Palm is free so let's create a an object for this llm and here I'm going to pass Google API key which will be stored in a variable called API key and I will initialize that variable here and add my specific key here folks as I said before please use your key I'm going to delete my key later on so code will not work if you use my key once llm object is created you can ask some sample prompt for example write a poem on my love for do DOA is a South Indian food I love that and you can you know print a poem on that and you see it is working good now folks before you run this code make sure all your libraries are installed so you can run this command pip install hyph r with requirement. txt file and if you look at the requirement. txt file it has all these requirements Lang chain chrom ADB all of that so I'm assuming you have installed all of that all right now let's create a an SQL database object and for that you can import this particular class and when you create SQL database object you will say from URI okay and here you are going to pass a URI a URI is like URL it specifies what is your database what is a host username password and so on so we'll store all this information in different variables my database is running locally therefore Local Host username password is root and atore t-shirt is a database name see you see it here okay now the way URI is formed is using this syntax I'll just copy paste to save time on recording you don't need to remember all these things anyway so this is the syntax of it all right and then the second parameter is sample rows in table and and I'll show you what this uh number three means so here the result that I got I will store it in this variable called DB and this will have a property called DB info which we can print when I do this uh I will get a confirmation that I'm able to connect to my SQL database from my Jupiter notebook and see it is able to pull all this information which means my jupyter notebook is now connected to my database now we are ready to create our SQL database chain okay so in L chain there are all kind of chains like SQL database chain um and for different type of use cases you will have these different chains so this SQL database chain if you notice is imported from Lang chain experimental module now if you are seeing this video in the future it is possible you can import it directly from a linkchain module but as of right now it is part of the experimental module in the future if they make it available just just remove this thing you know use your common sense and you should be able to run it now let's create the chain object okay and this chain object will take first parameter will be llm that we created second one is the DB object see this particular DB object and then you can store this into DB chain and now you can run a simple query before I do that I will pass one more parameter veros true so that I can see the scho query that it is generating and I can see some internal details the first question I'm asking is this okay let me just copy paste here how many Nike white color extra small size t-shirts do I have and let's store it in this UNS variable control enter okay this is happening because here I need to use from llm see it pulled a right answer it is saying 59 and if you look at this query the query that it generated if you run it it it is actually the right query that it generated okay so here uh let me see here I can run that query see 59 if you just look at uh Nike T-shirts overall uh or let's say let me just do star here only Nike T-shirts see Nike t-shirts are this if you look at Nike white t-shirts this much and in that extra small size t-shirt quantity is 59 and the answer that it is giving is 59 if you do qns1 see it is giving 59 by the way it is giving a dictionary as an output if you want to get directly 59 here you can use run so when you do that q&s will have the direct answer now there are a couple of observations I have llm is actually doing pretty good job because I said extra small size and it is smart enough to figure out that extra small means excess and it is able to map that to size column when I say white color W is small but it is able to map it to capital W because it looked into our database and figured that our color starts with a capital letter so you see this is the power of Alm now this was relatively simple query let me try a different query and the query is what is the price of the inventory for all my small size t-shirts now while it executes this let me run that code here so we want to get all small size t-shirt okay so here I will say where small size okay let me get all small size t-shirt these are all small size t-shirt and what is the total price total price will be price into stock quantity so here I need to run sum I will say sum price into quantity okay and when I run that it is actually stock quantity so when I run that this is the price I get now let's see what we got in our notebook 215 wrong answer folks so why did that happened let's just think about it so here the problem is it said some of price it did not say sum of price into stock quantity it forgot to multiply by quantity if you think a little bit you will actually find an obvious reason llm is thinking that whatever t-shirts I have so let me show you these t-shirts it is thinking that the price column is for all the t-shirts so for leevy white color small size t-shirts I have total 51 t-shirts available and the price of total total price of all 51 t-shirt is 13 that is what llm is thinking because the column name is not price per unit it says price so price could be total price or it could be price per unit it is assuming it is the total price and if this was a total price then the answer that llm gave would be correct but in real life database column names are not going to be perfect so this is representing a real life scenario okay so the conclusion that we get is llms will make mistake and we need to tell it somehow that the price column is price per unit it is not the total price we can do this using few short learning we will do that after some time let me run some few more queries okay and meanwhile I will store the right q&s answer okay and the way you can do that is you can actually run the explicit query so the explicit query that we have is this okay so I'm going to run that query here so in DB chain. run you can actually run the explicit query and we got this right answer which is stored in my qs2 all right right so far it is looking good now I will run the third query which will be little bit complex so I'm saying if I sell all my leis t-shirt today with discounts how much revenue my store will generate now when you want to apply discounts you need to do some kind of join okay so you have all the Ley t-shirts okay so these are all the Ley t-shirts that you have you need to multiply price by stock quantity and then you need to sum all of this you will get total revenue then you need to go to Discount table and figure out on leis t-shirts how much discount you have for example one of the T-shirt ID for Ley is three Ley white extra small is three and for three we have 20% discount so on this price so 44 into 94 you need to apply 20% discount in that so let's see if llm can handle this kind of complex case no so it failed it failed because in the query now you see all these columns discount do start date discount. ended usually whenever you have discount you will have start date and ended column because discounts can't run forever right there will be start dat and end it but in our database if you look at discount table we don't have start and end date so llm is using its general knowledge and it is assuming that there will be start date and end date in our table we need to tell it that hey buddy don't use your brain okay look at the table and if you find a column then only use it here start doesn't exist how come you just use it you know so we need to tell that and again we will do that using few short learning after some time for now let me run that query uh explicitly okay so here this is the query uh to get the answer and we'll run it and by the way this is not a MySQL tutorial so I'm not going into detail uh but let me just just very quickly explain how this thing works so here if you look at this particular query it has this part is a subquery and if you execute this query it will pull all leis t-shirt it will multiply price by quantity and it will give you that so for t-shirt ID 17 if you sell all the T-shirt you will get this much revenue for 64 you'll get this much you can sum them up to get a total revenue and then you need to um make a join of this table with discounts table see this query by the way the result is stored in table called a and you are doing a left join with discount table and then you are applying the discount here so if you execute this this is actually the answer okay 24367 and if you look at this 24367 is the answer we got which we have stored in this qns 3 variable similarly let me run a few more queries so this is if I sell all Le t-shirts you know how much revenue will I generate I will generate this much and another question I have is how many white color leis t-shirts I have now let's go and figure that out so you want to know how many white color Le leis t-shirts So when you say total leis t-shirts it is this much and white color right so you will say and color is equal to White okay this much so total white color lais t-shirts are 94 + 51 + 29 15 and 95 but in our code what's happening is see 94 151 it it pulled all that numbers but it did not sum it up so if you look at the answer the answer is 94 why it did that because it is not able to figure out that it it needs to do sum here okay so the right query here is sum of stock quanti so that will be 284 that's the right answer again it failed so what what what do we do now well we run the query explicitly so that later on I can use it in my few short learning okay so qns 5 query I will just copy paste whatever I wrote in my C workbench and qns 5 now is 284 so now we have all these answers and we have all these queries uh let's try few short learning uh so that our llm can improve uh on the errors that it is making in few short learning the first thing we need to do is provide the question and query pairs where llm was getting confused once we have those training example the second step would be to convert it into embeddings and we're going to use hugging pH for that so let's go to our notebook and start putting together those few short examples in a simple python list and each of these examples would be a dictionary and dictionary will have one element which will be uh your question okay so let's say my question is this and then the SQL query corresponding to that question would be this so we previously ran all this queries so I'm just copy pasting just to save time other than these two we need to have SQL result and answer as a parameter now why do we need this well just hold on we will uh see this later this is the syntax that that the default Lang chain SQL prompt is using therefore we are using the same syntax I'll show you a little later and this first answer if you remember we stored that into qns1 okay so qns1 is nothing but it is this 59 okay so that's what we are having so we put we take all these samples and put them into this single aray and once we have this thing ready the second thing is we use hugging phase for generating embedding and for that we'll use uh we'll import the hugging face embedding class and we are going to use this particular embedding now folks there are so many different ways to generate these embeddings I tried this particular embedding it was working fine so that's what why I'm using this you can even try opening embedding if you're ready to pay the price and there are instructor embedding uh in the other project that we did for at Tech domain we used instructor embedding so you can use whatever embedding can serve your need and this will be stored in this particular variable and let me just you know we can say embed query what embedding will do is you can type any query and it will generate an embedding which is which is just an array okay so let me save it here and the example you can use is okay let's say we generate embedding for this particular sentence so this e will be a list of size 384 and when you look at these numbers they don't actually make sense but they capture the meaning of this particular sentence in a right way so that if someone types A Different query I mean the query is like similar to this but the words are different even then the embedding of that em and embedding of this query will be similar in terms of cosine similarity uh of course so I'm going to uh remove this and I'm going to now uh create a vector database and for that we need to create a blob of all these sentences okay so let's say I have this sentence what I need to do is I need to remove all these keys because they are really not needed okay so I remove all these keys and then I kind of merge these strings together so see I will merge all these strings together and I will generate a single big string with some space in between like this and this qns whatever that answer I think it was 51 59 whatever right so I want to generate this kind of blob and this text block I will vectorize and store it in my database now to do that I can use list comprehension I can say uh for example in few shorts so this is the array right like few shorts this is the array I can say um example do values so I'm interested only in the values these values not the keys so when I do that uh I will get this kind of uh list and each of these elements are dict values I want to generate a string out of it and how do you generate a string from a list well if you know python you can do join of this and I will say to vectorizes this see it generated this uh list and if you look at the first element it is simply taking all these four values and and making one one big string out of it okay now let's create a vector database for which we are going to import chroma chroma is the vector database that we are using in this project and then from chroma we can say from text where you supply the text okay the the array of text and the second parameter is embedding okay so embedding is equal to embeddings and the last parameter is the metadata which is our few short so the entire few short array that we have we are giving it to as a metadata you can go ahead and read the documentation but the essence of this statement is that this is how you generate a vector store so this Vector store is this Vector store it's already created and the job of vector store is to take an input question so let's say if I have an input question like this it will convert that into embedding and it will pull you the similar looking few short example so let's try that and uh to see how that thing works so for that similarity matching you need to import another class called semantic similarity example selector and in that you will pass two parameters so first first thing is obviously you need Vector store so you will say Vector store is equal to Vector store and then K is equal to 2 which means pull me two similar example K can be 1 2 3 I mean if you want three example just say three this I have stored in example selector variable and you can say select examples okay so you can give a a a sentence okay so you can give a sentence like this here and you can say can you pull me similar looking things from this and see the similar looking question is how many t-shirt do we have left so just read these two statement okay this and this they look similar and the second bestas match is this this is not exactly matching but this is like a second based match that you can get okay so this mechanic M that you give input sentence to Vector database and you can pull similar looking queries see if you can pull similar looking queries then my llm can look into those and from those quaries it can learn and it can produce a good result all right now if you remember we already discuss giving a custom prompt to our llm because our llm is making mistakes such as discount table doesn't have a start date it is still using start date in my my SQL quy so I want to have a custom my SQL prompt saying that only use database table columns right do not just make things up so I want to give some instructions though so that it doesn't make a mistake now I have to write that SQL prompt on my own but the good news is that Lang chain already provides this prompt to you you can import that prompt by doing this and if you print that prompt let's see how it looks see you are my SQL expert given the question create my SQL query first never query for all columns I don't want to say select star I want to say select X Y specific columns you must query Only The Columns that are needed to answer pay attention to use only the columns that you see in the table tables below see this is important we are saying we are going to give you the table info and only use see table info is this folks this table info that you printed use the columns only from these tables okay that's what we are seeing also if you're talking about any date uh use current date for the today so we giving lot of useful instructions uh and then we are forming a query okay okay if you look at the prefix so let me print that one also I think suffix so suffix is like this okay So eventually what we'll do is see we'll take our prefix so prefix is this we'll T our suffix suffix is this and our actual query will come in between so here if you look at our prefix see prefix has this this format question SQL qu qu SQL result answer and that is the format we have used here see look at these four elements that's exactly the format that we are using so now let's think about the query the the query in the middle okay what will go in the middle for this we need to import a prompt template okay and then that prompt template folks just to save again time I'm just copy pasting things it will have question SQL query SQL result answer and the template will be something like this okay so what happens is actually when you actually type in a query um this this this query will have that format okay question will go here the actual question that you're generating the the SQL query and so on okay I think intuitive if you have seen my previous videos you will you will get some idea now comes the time to create our few short prompt template okay and in this fot prompt template we will pass bunch of parameters the first one is obviously the example selector that we have created see this is the example selector so if you do this you will establish the association between your LM and Vector database you'll say hey llm if you're confused look into this Vector database okay so that is what we are doing here the second one is the example prompt that we have created and then the next two are the prefix and suffix so now using these three it will generate this kind of single prompt that you can pass to your Google pal llm and the last parameter is the input variable okay so in the input variable you'll see things like table info so table info is this this is the table info okay and if you look at our query see that is a table in info so here actually wherever you see this this bracket here you will actually put that table info okay so you will put all of this so your actual prompt will be a little bigger you will say this see now you're saying that um use the table info so here you I think you said somewhere right info info okay see pay attention to use only the column names you can see in the tables below so which tables see only use following tables this so this type of big prompt will be formed when you write this particular this particular few short prompt template we are going to save it in a variable here okay and then we are creating the same chain see we created this this this chain before okay if you if you look at this code remember we created this object so it's exactly same okay we are doing that here but now we need to add one more parameter which is prompt this is the only additional thing we are passing so that whenever it is confused it uses that new information and now let's give those queries which were failing so how many white color lais t-shirts we have okay if you remember it was not using sum before now it is using sum so see it it worked second question is how much is the price of the inventory for all small size t-shirts and previously it was not multiplying it with uh this quantity so let me just show you this one so if you look at the previous query see it was saying 215 because it was not multiplying it with the stock quantity now it is doing that see it is producing the right answer and it's not like see you can give little similar query so I will say how much is the price of all the extra small size t-shirts this is a little different and it will work uh it will see it will say size is excess that way so let's try the most difficult ones one that we had which was that Lees so instead of Lees I'm saying n and I'm saying after discount how much revenue will it generate see it worked it said brand is Nike this is this you can you can change this a little bit you know when you change the language it is still doing that semantic search you know when we when we did this semantic uh similarity example selector it is doing semantic search therefore you're not passing the exact query which you passed in your few shot you can pass ask little different queries as well so folks try different queries and uh it is possible that for some queries it may not work in that case you will take that query and the right SQL query and then you will add it to your few short example right now I have five but if you want to make all kind of queries work you might have 40 or 50 different type of few short examples okay so wherever it is failing take a question take a write SQL query and add it to few short example and after that it will not make a mistake all right we are all set to put all these things together and write a streamlit UI which you will see is only few line of code so we are almost there folks please stay on you have come so far I know it requires a lot of patience but learning llm is amazing for your career now we will write the code for our project I have created at project folder here and here you will find two files requirement txt and t-shirt sales jupyter notebook from here I'm going to launch pyam community Edition which is a free editor for Python and there you can open that particular folder so I will go here and in the C code directory I will open at Le project folder like that and we will not create any virtual environment so I'll just say cancel so there is no virtual environment and let me pull uh this window right here the first file we are going to create is main.py okay and in this file we are going to create our llm object so now I will copy paste the code from our jupter jupyter notebook to here we use jupyter notebook for all our experimentation and this is what data scientists do in uh when they're working for companies they will do some experimentation in the notebook and when they feel the code is ready they will try to productionize it and they will move it to a proper python file structure or a project structure so let's import all those uh libraries so I'm just going to copy paste all the libraries that we imported in our jupyter notebook you can configure your python here and the first thing if you remember we were doing was creating a Google Palm or object okay so here I'm creating Google Palm object and we need to give Google API key now in production code you don't hardcode your key here the standard practice is to create environmental file so do EnV and you will keep your key here okay so this is the key I have kept here and how do I get this key from here to m.p well we use this special python module called environment from there we will import this method load this one and when you execute this method it will specifically look for EnV file and it will read the content and it will set this as an environment variable so this key will be environment variable and this will be the value Okay so after this line it has set the environment variable now how do I get the value of that environment well you need OS module so you will say OS do environment and in that the variable that we want is this particular thing okay and temperature is 0.1 I I will not keep creativity very high otherwise it will start bluffing okay and once llm object is created the next one one is obviously the DB object and the third one is uh our embedding okay and what I'm thinking is I will create uh a function which will encapsulate all this code so let me put all this thing in a function here all right and here I'm going to copy paste all those things so if you remember we had embeddings we had um our Vector database and we have few short so few short was an array and I would like to put that array in a separate file so I will call it few short and this file will contain all of this now answer I have hardcoded folks because here we are giving this as an example to our Ln that's see this is how this is the format of your answer the exit answer it will get by executing this query so so that's something you need to keep in mind and you can import that thing here you can say from few shots import few shots okay so it will not give an error now after example selector I'll just copy paste all the code so you're creating a same exact same SQL database chain and returning here and we will create a main F python function we'll say if it is main this is how you create a main function in Python by the way and you will get this chain and then you will run this chain okay you will say whatever is your query and you will print the result okay so what is my query let's just give some sample query to test when you're doing this type of coding it makes sense that you write some code and then test it you write some code and test it so here I will just run this and see it g the answer and if you look at the query the query seems to be right you can try different queries here uh but let's say this is working now we are ready to write our streamlit code for stream lit I would like to keep the UI code in main.py and I would move all the Lang chain code in a separate file I'll call it Lang chain helper maybe and let me just crl a xrl v you know contrl c contrl v is the most powerful weapon for all programmers and then here I will import that method now let's do streamlit coding you will say import streamlet as St St do tile what is my title well my title is this and you'll have input box right so text input box you will type a question here and that question you will get here and then if question meaning if someone types in question and they hit enter the code flow will come here first let's uh teste this be skeleton so here in the terminal you can run streamlet run main.py and it will launch the UI in a browser so see my UI looks good when I type a question hit enter it will take that question in this question variable and the flow will come here so here what we need to do here we need to obviously first get a chain and then will say chain. run this is my question and you get the answer and then s. header so you will put another element on the UI and you will say st. write your answer the good thing about streamlet is that you don't have to rerun it from here you can go here and just click on rerun now let's ask those questions so I will hit enter here see hooray 3083 let's ask a different question and by the way if you want to see a corresponding query since we have set waros parameter to be true let's say you get 59 answer you're not sure if it's right or wrong you can either go to my SQL run the query or you can look at the query here see how many t-shirt do we have left for Nike and access and white see Nike access white sum of stock quantity this is perfect and then um yeah so folks you can ask uh different questions and get your answers now this tool will be very useful to our store manager Tony Sharma because he will be able to ask questions directly and get answers on most of the questions once again folks check the video description below for all the code learning coding is like learning swimming if you don't practice this there is no point so don't watch my video as if you watching Netflix series sitting on your sofa you have to code okay you might have realized we have put lot of affort in building uh this project so if you like it please give it a thumbs up subscribe to the channel and share it with your friends who wants to learn llm thank you for watching any questions there is a comment box below oh

Channel: codebasics

Views: 25,157

Keywords: yt:cc=on, llm, llm project, end to end projects, langchain tutorial, end to end project, lang chain, langchain, streamlit, llm project ideas, google palm, chatgpt, PALM, streamlit project, tshirt business, retail business

Id: 4wtrl4hnPT8

Length: 51min 0sec (3060 seconds)

Published: Thu Nov 02 2023