1 00:00:00,000 --> 00:00:00,000 Hello guys! 2 00:00:00,000 --> 00:00:05,000 In this video and in the upcoming series of video, we are going to develop this amazing end to end 3 00:00:05,000 --> 00:00:12,000 j'en AI project that is nothing but conversational Q&A chat bot where we are specifically going to chat 4 00:00:12,000 --> 00:00:14,000 with a PDF here. 5 00:00:14,000 --> 00:00:17,000 We are also going to use our conversation history, right? 6 00:00:17,000 --> 00:00:20,000 So there will be a lot many things that we really need to discuss over here. 7 00:00:20,000 --> 00:00:25,000 It is a complete end to end project, and already in our previous module we have discussed about conversational 8 00:00:25,000 --> 00:00:26,000 Q&A chatbot. 9 00:00:26,000 --> 00:00:30,000 Okay, so first of all, what I will do is that, uh, in this video I will show you the demo. 10 00:00:30,000 --> 00:00:34,000 So let me just go ahead and click and press my, uh, grok API key. 11 00:00:34,000 --> 00:00:40,000 Once I click, uh, uh, you know, enter my grok API key, there will be a default session that will 12 00:00:40,000 --> 00:00:40,000 be created. 13 00:00:40,000 --> 00:00:41,000 Okay. 14 00:00:41,000 --> 00:00:44,000 And this is just for me as a user, right? 15 00:00:45,000 --> 00:00:50,000 Uh, if I probably go ahead and implement this in any web application, I will make sure to track the 16 00:00:50,000 --> 00:00:53,000 session ID and based on the people using it, that session ID will be different. 17 00:00:53,000 --> 00:00:54,000 Okay. 18 00:00:54,000 --> 00:00:57,000 Now let me just go ahead and drag and drop one of the files. 19 00:00:57,000 --> 00:00:59,000 So here I have my attention dot pdf. 20 00:00:59,000 --> 00:01:05,000 Now once I do this, this entire PDF text will get converted into vectors and it will get stored in 21 00:01:05,000 --> 00:01:06,000 a vector stored database. 22 00:01:06,000 --> 00:01:06,000 Okay. 23 00:01:06,000 --> 00:01:09,000 Now here I will just go ahead and ask one question. 24 00:01:09,000 --> 00:01:10,000 Okay. 25 00:01:10,000 --> 00:01:11,000 Tell me about Transformers. 26 00:01:11,000 --> 00:01:15,000 Something like that okay I'll just search transformer. 27 00:01:15,000 --> 00:01:17,000 Let's see whether I'll be able to get the response or not. 28 00:01:17,000 --> 00:01:20,000 So here you can see that I'm getting the response. 29 00:01:20,000 --> 00:01:27,000 The transformer, um, generalizes well to English constituency parsing, achieves F1 score of 91.3 30 00:01:27,000 --> 00:01:29,000 which is trained on a set. 31 00:01:29,000 --> 00:01:31,000 And this is semi-supervised. 32 00:01:31,000 --> 00:01:31,000 Right. 33 00:01:31,000 --> 00:01:34,000 So these are some of the information that I am able to get from the PDF. 34 00:01:34,000 --> 00:01:40,000 Then I will say attention is all you need if I go ahead and search for this okay. 35 00:01:41,000 --> 00:01:44,000 Let's see what kind of information I'll be able to get from here. 36 00:01:44,000 --> 00:01:44,000 Right. 37 00:01:44,000 --> 00:01:49,000 So here it says the attention mechanism is used in transform architecture to weigh the importance of 38 00:01:49,000 --> 00:01:51,000 different input elements when computing the output. 39 00:01:51,000 --> 00:01:52,000 Okay. 40 00:01:52,000 --> 00:02:02,000 Uh, then I will just go and ask uh, uh, which topic were we discussing about? 41 00:02:02,000 --> 00:02:03,000 Okay. 42 00:02:03,000 --> 00:02:04,000 I'll just go and search like this. 43 00:02:05,000 --> 00:02:05,000 Okay. 44 00:02:05,000 --> 00:02:12,000 So here when we search this switch topic we'll be discussing about here you'll be able to see, um, 45 00:02:12,000 --> 00:02:15,000 we were discussing about a transformer model and its architecture. 46 00:02:15,000 --> 00:02:15,000 Okay. 47 00:02:15,000 --> 00:02:17,000 So this is the information. 48 00:02:17,000 --> 00:02:20,000 And this is able to get it from the previous context. 49 00:02:20,000 --> 00:02:20,000 Okay. 50 00:02:20,000 --> 00:02:26,000 Tell me about a detailed detail. 51 00:02:26,000 --> 00:02:31,000 Summary of attention is all you need okay I'll ask this particular question. 52 00:02:32,000 --> 00:02:34,000 Let's see whether I'll be able to get the answer or not. 53 00:02:34,000 --> 00:02:40,000 So once I press enter and here I'm able to get something. 54 00:02:40,000 --> 00:02:40,000 Okay. 55 00:02:40,000 --> 00:02:41,000 It says, uh, tell me. 56 00:02:41,000 --> 00:02:42,000 Okay, I don't know. 57 00:02:42,000 --> 00:02:43,000 Okay, fine. 58 00:02:43,000 --> 00:02:46,000 I think, uh, in the research paper I just have put two pages. 59 00:02:46,000 --> 00:02:49,000 So that is the reason it is not able to find out. 60 00:02:49,000 --> 00:02:49,000 Okay. 61 00:02:49,000 --> 00:02:58,000 Tell me more about transformer and s architecture. 62 00:02:58,000 --> 00:02:58,000 Okay. 63 00:02:59,000 --> 00:03:00,000 I'll just go ahead and ask this question. 64 00:03:00,000 --> 00:03:06,000 Let's see whether it should be able to retrieve the information and give me some kind of response. 65 00:03:06,000 --> 00:03:08,000 So here is my detailed. 66 00:03:08,000 --> 00:03:12,000 The transformer is the type of neural network architecture designed by so and so right. 67 00:03:12,000 --> 00:03:14,000 Self-attention mechanism and all. 68 00:03:14,000 --> 00:03:17,000 So I'll go ahead and ask what is self-attention mechanism now. 69 00:03:17,000 --> 00:03:23,000 So now let me see what is self attention mechanism okay. 70 00:03:24,000 --> 00:03:25,000 I'll go ahead and search for it. 71 00:03:27,000 --> 00:03:31,000 Um so here I should be able to get some kind of response. 72 00:03:31,000 --> 00:03:32,000 And it is very fast. 73 00:03:32,000 --> 00:03:33,000 Okay. 74 00:03:33,000 --> 00:03:35,000 Uh, it says I don't know. 75 00:03:35,000 --> 00:03:37,000 Let me just go ahead and enter. 76 00:03:37,000 --> 00:03:40,000 Once again, I should be very much precise while I'm writing the output. 77 00:03:40,000 --> 00:03:41,000 So. 78 00:03:41,000 --> 00:03:47,000 So here you can see the self-attention mechanism because there I did not write this particular word 79 00:03:47,000 --> 00:03:48,000 properly okay. 80 00:03:48,000 --> 00:03:52,000 A self-attention mechanism is a key component of transformer architecture and is used to so and so all 81 00:03:52,000 --> 00:03:53,000 the information. 82 00:03:53,000 --> 00:03:59,000 And here you can see the entire chat history is also basically getting considered right now if I go 83 00:03:59,000 --> 00:04:01,000 ahead and ask like what is the previous message? 84 00:04:01,000 --> 00:04:09,000 I asked previous topic that I asked that I ask I should be able to get the answer. 85 00:04:10,000 --> 00:04:13,000 So this is my history and this is all history. 86 00:04:13,000 --> 00:04:14,000 You'll also be able to see it. 87 00:04:14,000 --> 00:04:15,000 Okay. 88 00:04:15,000 --> 00:04:19,000 Uh, you asked about transformer, then followed up with question about the self-attention mechanism 89 00:04:19,000 --> 00:04:20,000 and transformer architecture. 90 00:04:20,000 --> 00:04:22,000 This is amazing, right? 91 00:04:23,000 --> 00:04:29,000 Uh, provide me a detailed summary of the conversation we had. 92 00:04:31,000 --> 00:04:32,000 I'm just going to ask this. 93 00:04:33,000 --> 00:04:34,000 Okay. 94 00:04:34,000 --> 00:04:35,000 Isn't it amazing? 95 00:04:35,000 --> 00:04:36,000 Use case. 96 00:04:36,000 --> 00:04:37,000 Think over it. 97 00:04:37,000 --> 00:04:37,000 Okay. 98 00:04:38,000 --> 00:04:39,000 So here I'm just saying. 99 00:04:39,000 --> 00:04:41,000 Provide me a detailed summary of the conversation. 100 00:04:41,000 --> 00:04:42,000 We had it. 101 00:04:42,000 --> 00:04:43,000 And here you can. 102 00:04:43,000 --> 00:04:45,000 You initially asked about transformer model architecture. 103 00:04:45,000 --> 00:04:48,000 See, based on the chat history everything is available over here. 104 00:04:48,000 --> 00:04:53,000 I provided a brief summary of the transformer model and its architecture, highlighting its use of self-attention 105 00:04:53,000 --> 00:04:56,000 mechanism and parallelization to process the input sequence. 106 00:04:56,000 --> 00:05:01,000 You asked me to provide more information about this, this, this, all this things has been visible, 107 00:05:01,000 --> 00:05:02,000 right? 108 00:05:02,000 --> 00:05:03,000 And this is my chat history. 109 00:05:03,000 --> 00:05:09,000 And this is based on my session ID, the default session ID, what all conversation we had. 110 00:05:10,000 --> 00:05:11,000 So yes, this was it. 111 00:05:11,000 --> 00:05:15,000 Now in the next video we will go ahead and implement this entire end to end solution. 112 00:05:15,000 --> 00:05:20,000 And I hope uh, please make sure that you follow this because already I have discussed about conversational 113 00:05:21,000 --> 00:05:22,000 bot in the previous video. 114 00:05:22,000 --> 00:05:24,000 So as this was it for my side, I will see you in the next video. 115 00:05:24,000 --> 00:05:24,000 Thank you.