1 00:00:00,000 --> 00:00:04,480 Welcome to the deep dive. Today we're really getting into something fascinating, a 2 00:00:04,480 --> 00:00:05,040 technology 3 00:00:05,040 --> 00:00:09,280 that's kind of fundamental for the next wave of AI. But hang on, before we jump in, 4 00:00:09,280 --> 00:00:09,840 a quick shout 5 00:00:09,840 --> 00:00:14,640 out to our supporter for this deep dive, safeserver.de. They're the ones handling 6 00:00:14,640 --> 00:00:15,840 the hosting for exactly 7 00:00:15,840 --> 00:00:19,170 this kind of cutting-edge software, and they can definitely support you with your 8 00:00:19,170 --> 00:00:19,520 digital 9 00:00:19,520 --> 00:00:27,120 transformation. You can find more info at www.safeserver.de. Okay, so today's focus, 10 00:00:27,120 --> 00:00:31,240 QDrant. It's called a vector database, sometimes a vector search engine. And our 11 00:00:31,240 --> 00:00:31,920 mission here is 12 00:00:31,920 --> 00:00:37,350 simple. Break down what QDrant actually is, why it's becoming so important for 13 00:00:37,350 --> 00:00:38,080 modern AI, 14 00:00:38,080 --> 00:00:42,450 and basically how it works, even if this whole area is new to you. Think of it as 15 00:00:42,450 --> 00:00:43,280 your easy guide. 16 00:00:43,280 --> 00:00:46,950 Yeah, and what's really exciting, I think, is how QDrant helps AI go way beyond 17 00:00:46,950 --> 00:00:47,520 just like 18 00:00:47,520 --> 00:00:51,320 simple keyword searching. It helps it really understand the meaning behind the 19 00:00:51,320 --> 00:00:52,160 information. 20 00:00:52,160 --> 00:00:55,920 Okay, so let's start right there. The basics. When we say vector database, like QDrant, 21 00:00:56,880 --> 00:01:01,280 what are these vectors exactly? Right, so you can think of vectors as these 22 00:01:01,280 --> 00:01:02,560 numerical representations, 23 00:01:02,560 --> 00:01:08,370 like a digital fingerprint maybe. For any bit of data could be text, an image, even 24 00:01:08,370 --> 00:01:09,120 audio. 25 00:01:09,120 --> 00:01:13,650 And these numbers, these vectors, they're designed to capture the core meaning, the 26 00:01:13,650 --> 00:01:14,560 essence of that 27 00:01:14,560 --> 00:01:19,120 data. QDrant itself, well, it's a really high-performance system. Its main job is 28 00:01:19,120 --> 00:01:23,810 storing, searching, and managing these points, the vectors, but also, crucially, 29 00:01:23,810 --> 00:01:24,480 any extra 30 00:01:24,480 --> 00:01:27,890 information, what we call a payload, that's attached to them. Okay, that makes 31 00:01:27,890 --> 00:01:28,400 sense. A kind 32 00:01:28,400 --> 00:01:33,950 of meaningful numerical ID for data. But why is that suddenly so critical for this 33 00:01:33,950 --> 00:01:34,720 next generation 34 00:01:34,720 --> 00:01:38,900 of AI? Why do we need this vector search stuff? Isn't keyword search good enough? 35 00:01:38,900 --> 00:01:39,440 Well, traditional 36 00:01:39,440 --> 00:01:43,190 search is pretty limited, right? It finds exact words or maybe slight variations. 37 00:01:43,190 --> 00:01:44,000 It's like asking 38 00:01:44,000 --> 00:01:48,020 for books with cat in the title. But what if you want books about cats or stories 39 00:01:48,020 --> 00:01:48,800 that just feel 40 00:01:48,800 --> 00:01:52,520 like they involve cats? That's where conceptual similarity comes in. QDrant is 41 00:01:52,520 --> 00:01:53,440 built for that. 42 00:01:53,440 --> 00:01:57,940 It's tailored for semantic matching, finding things that are conceptually close, 43 00:01:57,940 --> 00:01:58,160 even if the 44 00:01:58,160 --> 00:02:03,480 words are completely different. That's vital for AI to really grasp nuance. And I 45 00:02:03,480 --> 00:02:04,080 guess doing that 46 00:02:04,080 --> 00:02:08,400 kind of complex matching, especially with lots of data, needs speed, reliability 47 00:02:08,400 --> 00:02:09,280 too. What makes 48 00:02:09,280 --> 00:02:13,630 QDrant handle that? Absolutely. Performance is key. QDrant is actually written in 49 00:02:13,630 --> 00:02:15,040 Rust. Ah, Rust, 50 00:02:15,040 --> 00:02:19,300 okay. Yeah, and Rust is known for being incredibly fast and very reliable, 51 00:02:19,300 --> 00:02:20,320 especially when you're 52 00:02:20,320 --> 00:02:26,400 throwing a lot at it. High load conditions, so it keeps up. Good to know. And how 53 00:02:26,400 --> 00:02:27,280 easy is it for 54 00:02:27,280 --> 00:02:30,950 someone to actually get their hands on it? Is it accessible? Oh yeah, definitely. 55 00:02:30,950 --> 00:02:32,000 It's available as 56 00:02:32,000 --> 00:02:37,020 like a ready-to-go service with a nice API, plus there's a fully managed cloud 57 00:02:37,020 --> 00:02:38,160 version, QDrant 58 00:02:38,160 --> 00:02:42,300 cloud, and they even have a free tier, which is great for just trying things out, 59 00:02:42,300 --> 00:02:43,280 experimenting. 60 00:02:43,280 --> 00:02:47,380 Right. This is where it gets really practical. How does QDrant actually tower these 61 00:02:47,380 --> 00:02:48,080 smart AI 62 00:02:48,080 --> 00:02:51,920 applications we keep hearing about? Got any real world examples? Yeah, absolutely. 63 00:02:51,920 --> 00:02:52,320 Let's look at 64 00:02:52,320 --> 00:02:57,070 some demos. They really show it off. Take semantic text search. Instead of just 65 00:02:57,070 --> 00:02:58,240 matching keywords, 66 00:02:58,240 --> 00:03:03,440 like we said, QDrant finds meaningful links in text. So you could ask it for, I don't 67 00:03:03,440 --> 00:03:03,600 know, 68 00:03:03,600 --> 00:03:08,230 a movie that feels inspiring and it gets the feeling, not just the word inspiring. 69 00:03:08,230 --> 00:03:08,560 You can 70 00:03:08,560 --> 00:03:12,590 actually set up a neural search pretty quickly using pre-trained models. It really 71 00:03:12,590 --> 00:03:13,200 changes how 72 00:03:13,200 --> 00:03:18,000 you interact with text. Okay, that's text. What about other things? Images. Exactly. 73 00:03:18,000 --> 00:03:18,800 Similar image 74 00:03:18,800 --> 00:03:23,230 search. Think about food discovery. We often pick food based on how it looks, right? 75 00:03:23,230 --> 00:03:24,080 So if you see 76 00:03:24,080 --> 00:03:27,690 a picture of some amazing dish but you have no idea what it's called, with QDrant 77 00:03:27,690 --> 00:03:28,480 you could use 78 00:03:28,480 --> 00:03:32,550 that image to find visually similar meals. It's pretty neat. That is neat. Visual 79 00:03:32,550 --> 00:03:33,360 search for food. 80 00:03:33,360 --> 00:03:38,240 Okay, what else? Then there's something maybe a bit more technical but really 81 00:03:38,240 --> 00:03:39,600 powerful. Extreme 82 00:03:39,600 --> 00:03:45,160 classification, particularly for e-commerce. Imagine online stores with millions, 83 00:03:45,160 --> 00:03:45,760 literally 84 00:03:45,760 --> 00:03:50,260 millions of products. Assigning categories, maybe multiple labels, to each one. 85 00:03:50,260 --> 00:03:51,040 That's a huge 86 00:03:51,040 --> 00:03:55,600 challenge. QDrant, combined with the right AI models, can handle these massive 87 00:03:55,600 --> 00:03:56,320 multi-label 88 00:03:56,320 --> 00:04:00,590 problems. It can seriously streamline how products get categorized, making stuff 89 00:04:00,590 --> 00:04:01,360 much easier for 90 00:04:01,360 --> 00:04:06,950 shoppers to find. Wow. Okay, so QDrant basically takes these vector fingerprints 91 00:04:06,950 --> 00:04:07,360 and makes them 92 00:04:07,360 --> 00:04:12,220 usable. Turns them into the engine for apps that can match, search, recommend, all 93 00:04:12,220 --> 00:04:12,880 that good stuff. 94 00:04:12,880 --> 00:04:17,120 Precisely. And that capability branches out into loads of other key areas, like 95 00:04:17,120 --> 00:04:17,760 recommendation 96 00:04:17,760 --> 00:04:21,900 systems. QDrant helps build really responsive, personalized recommendations because 97 00:04:21,900 --> 00:04:22,240 it can 98 00:04:22,240 --> 00:04:25,770 understand preferences from different angles using multiple vectors at once. So you 99 00:04:25,770 --> 00:04:26,240 get much 100 00:04:26,240 --> 00:04:30,170 better suggestions. You mentioned ARAG earlier. Retrieval augmented generation. 101 00:04:30,170 --> 00:04:30,800 That's everywhere 102 00:04:30,800 --> 00:04:36,110 now. Yes, RE. It's crucial there. QDrant helps improve the quality of what AI 103 00:04:36,110 --> 00:04:37,120 generates. 104 00:04:37,120 --> 00:04:41,760 It lets the AI quickly pull in relevant factual snippets from a huge knowledge base 105 00:04:41,760 --> 00:04:42,320 represented 106 00:04:42,320 --> 00:04:47,280 as vectors. So the AI's answers are more accurate, more grounded in facts, not just, 107 00:04:47,280 --> 00:04:52,010 you know, made up stuff that sounds okay. That's a big deal. Huge. And it's also 108 00:04:52,010 --> 00:04:52,640 great for data 109 00:04:52,640 --> 00:04:57,820 analysis and anomaly detection. Finding weird patterns or outliers in really 110 00:04:57,820 --> 00:04:58,960 complex data. 111 00:04:58,960 --> 00:05:03,750 QDrant helps spot those anomalies in real time. Think fraud detection, things like 112 00:05:03,750 --> 00:05:04,560 that. And one 113 00:05:04,560 --> 00:05:09,510 more AI agents. Giving these agents a kind of memory. QDrant lets them draw on past 114 00:05:09,510 --> 00:05:10,080 interactions 115 00:05:10,080 --> 00:05:14,560 or relevant data to handle complex tasks, adapt better, and make smarter decisions. 116 00:05:14,560 --> 00:05:18,240 It's a really broad set of applications. How does QDrant actually manage all that 117 00:05:18,240 --> 00:05:18,640 under the hood? 118 00:05:18,640 --> 00:05:22,020 What are the key features making it so flexible? Well, a big one is what's called 119 00:05:22,020 --> 00:05:22,560 filtering and 120 00:05:22,560 --> 00:05:26,880 payload. Remember we mentioned payload? That extra info attached to the vector. You 121 00:05:26,880 --> 00:05:27,440 can attach 122 00:05:27,440 --> 00:05:32,200 basically any JSON data you want. And then you can filter your search results based 123 00:05:32,200 --> 00:05:33,280 on that payload. 124 00:05:33,280 --> 00:05:38,760 Not just similarity but specific criteria. You can filter by keywords, numbers, 125 00:05:38,760 --> 00:05:40,240 geographic locations, 126 00:05:40,240 --> 00:05:43,950 and you can combine these filters too. Like find things that are similar and match 127 00:05:43,950 --> 00:05:45,120 this keyword or 128 00:05:45,120 --> 00:05:49,390 are within this price range but not in this location. Lots of control. Okay, so you 129 00:05:49,390 --> 00:05:49,520 get 130 00:05:49,520 --> 00:05:54,070 semantic search plus precise filtering. What about combining semantic search with 131 00:05:54,070 --> 00:05:54,960 good old-fashioned 132 00:05:54,960 --> 00:05:58,460 keyword relevance? Sometimes you still need that exact word match, right? You 133 00:05:58,460 --> 00:05:59,280 mentioned hybrid 134 00:05:59,280 --> 00:06:03,360 search, sparse vectors. Yeah, exactly. That's where sparse vectors come in. Dense 135 00:06:03,360 --> 00:06:04,000 vectors are 136 00:06:04,000 --> 00:06:07,760 great for meaning, for the semantic stuff, but sometimes keyword relevance is still 137 00:06:07,760 --> 00:06:08,480 important. 138 00:06:08,480 --> 00:06:14,160 Sparse vectors are kind of like a modern take on order methods like BM-25 or TF-IDF 139 00:06:14,160 --> 00:06:14,480 that ranked 140 00:06:14,480 --> 00:06:19,250 documents based on word counts. But sparse vectors use modern AI, often transformer 141 00:06:19,250 --> 00:06:20,320 networks, to weigh 142 00:06:20,320 --> 00:06:24,240 those individual words or tokens much more effectively. So you get the best of both 143 00:06:24,240 --> 00:06:24,560 worlds, 144 00:06:24,560 --> 00:06:28,460 semantic understanding and strong keyword matching when needed. And handling all 145 00:06:28,460 --> 00:06:30,480 this data, potentially 146 00:06:30,480 --> 00:06:35,820 billions of vectors, how does it stay efficient, especially at scale? That sounds 147 00:06:35,820 --> 00:06:36,960 computationally 148 00:06:36,960 --> 00:06:42,140 expensive. It uses some clever tricks. One is called vector quantization and on-disk 149 00:06:42,140 --> 00:06:42,960 storage. 150 00:06:42,960 --> 00:06:46,480 Think of it like compressing the vector fingerprints intelligently and storing them 151 00:06:46,480 --> 00:06:51,570 efficiently on disk, not just in expensive RAM. This can slash RAM usage by like up 152 00:06:51,570 --> 00:06:52,960 to 97 percent. 153 00:06:52,960 --> 00:06:57,920 Huge savings. Wow, 97 percent. And for really big scale distributed deployment, it 154 00:06:57,920 --> 00:06:58,960 basically breaks 155 00:06:58,960 --> 00:07:02,060 the data up that's sharding across multiple machines and it makes copies 156 00:07:02,060 --> 00:07:03,200 replication. So if 157 00:07:03,200 --> 00:07:07,580 one machine fails, it's okay. This also lets you do updates without any downtime, 158 00:07:07,580 --> 00:07:08,240 zero downtime 159 00:07:08,240 --> 00:07:12,680 rolling updates. The system just keeps running. That all sounds incredibly powerful, 160 00:07:12,680 --> 00:07:13,200 but maybe a 161 00:07:13,200 --> 00:07:16,630 bit intimidating. So if someone listening is thinking, okay, I want to try this, 162 00:07:16,630 --> 00:07:16,880 what's the 163 00:07:16,880 --> 00:07:21,320 actual barrier to entry? How easy is it to just start? It's actually surprisingly 164 00:07:21,320 --> 00:07:21,920 easy to get 165 00:07:21,920 --> 00:07:26,640 started, really. If you use Python, it's literally just pip install quadrant client. 166 00:07:26,640 --> 00:07:26,960 You're up and 167 00:07:26,960 --> 00:07:30,320 running in minutes. Okay, that is simple. Yeah. And if you want the full setup 168 00:07:30,320 --> 00:07:30,880 locally, like the 169 00:07:30,880 --> 00:07:34,560 server and everything, you can run it in a Docker container that bundles everything 170 00:07:34,560 --> 00:07:35,120 up. There's a 171 00:07:35,120 --> 00:07:44,630 simple command docker run nowsp 6333.63333. Done. And it's not just Python, right? 172 00:07:44,630 --> 00:07:45,680 No, not at all. 173 00:07:45,680 --> 00:07:51,230 There are official client libraries for Go, Rust, JavaScript, TypeScript, .NET, C 174 00:07:51,230 --> 00:07:52,160 Sharp, Java, 175 00:07:52,160 --> 00:07:57,600 plus community ones for Elixir, PHP, Ruby, pretty much covered. And it clearly 176 00:07:57,600 --> 00:07:57,840 plays 177 00:07:57,840 --> 00:08:01,840 well with others in the AI world. You mentioned Langchain, Coheer, Lama, Index. 178 00:08:01,840 --> 00:08:02,800 Yeah. Even using 179 00:08:02,800 --> 00:08:07,840 it as memory for ChatGPT with OpenAI's retrieval plugin. That integration seems key. 180 00:08:07,840 --> 00:08:08,240 Definitely. 181 00:08:08,240 --> 00:08:11,840 It slots right into the existing AI ecosystem, which makes it super versatile for 182 00:08:11,840 --> 00:08:12,560 developers. 183 00:08:12,560 --> 00:08:16,240 So to wrap up our deep dive here, you've basically heard how Qdrent is becoming 184 00:08:16,240 --> 00:08:16,480 this 185 00:08:16,480 --> 00:08:19,740 essential building block for making AI smarter. Better search, better 186 00:08:19,740 --> 00:08:20,640 recommendations, more 187 00:08:20,640 --> 00:08:25,290 capable AI agents. It's really about enabling AI to not just process information, 188 00:08:25,290 --> 00:08:25,840 but to understand 189 00:08:25,840 --> 00:08:29,440 and organize it in a meaningful way. And that does lead to a bigger thought, doesn't 190 00:08:29,440 --> 00:08:30,560 it? As AI keeps 191 00:08:30,560 --> 00:08:35,110 advancing so rapidly, how are tools like Qdrent, these vector databases, going to 192 00:08:35,110 --> 00:08:36,000 fundamentally 193 00:08:36,000 --> 00:08:41,230 change how we interact with information, how we interact with technology every day? 194 00:08:41,230 --> 00:08:41,760 The potential 195 00:08:41,760 --> 00:08:45,540 there is just enormous and we're really only scratching the surface. Something to 196 00:08:45,540 --> 00:08:46,400 think about. 197 00:08:46,400 --> 00:08:51,360 Absolutely. And that brings us to the end of our deep dive on Qdrent. A huge thank 198 00:08:51,360 --> 00:08:52,000 you once again 199 00:08:52,000 --> 00:08:56,660 to our supporter, safeserver.de. They help make this show possible by handling 200 00:08:56,660 --> 00:08:57,440 hosting for this 201 00:08:57,440 --> 00:09:01,600 kind of advanced software and supporting digital transformation efforts. Check them 202 00:09:01,600 --> 00:09:01,840 out at 203 00:09:01,840 --> 00:09:06,560 www.safeserver.de. We really hope you pick up some valuable insights today.