벡터 데이터베이스: AI의 혁신적인 데이터 스토리지 솔루션

벡터 데이터베이스는 유연하고 확장 가능한 고성능 비교를 위해 데이터를 수치 형식으로 저장하여 AI 애플리케이션을 혁신합니다.

### 벡터 데이터베이스: 특성, 사용 사례 및 이점
* 📊  벡터 데이터베이스는 컴퓨팅을 혁신하는 획기적인 기술이며 AI 애플리케이션에 필수적인 요소입니다.
* 🔍  벡터와 임베딩 형태로 데이터를 저장하여 복잡한 객체를 숫자 형식으로 표현하고 대규모 언어 모델을 관리할 수 있습니다.
* 🗂️  벡터 데이터베이스의 사용 사례에는 자연어 처리, 비디오 및 이미지 인식, 유사도 검색이 포함되어 있어 챗봇 및 추천 엔진과 같은 AI 애플리케이션을 지원합니다.
* 💡  벡터 데이터베이스의 장점은 유연성, 확장성, 속도 등이며, 이를 통해 비정형 데이터를 쉽게 수집하고 수백만 개의 데이터 포인트를 관리하며 지연 시간이 짧은 쿼리를 수행할 수 있습니다.
* 🌐  벡터 데이터베이스용 오픈 소스 기술을 탐색하고 이를 AI 프로젝트에 통합하여 성능을 향상시키는 것이 좋습니다.

over the past year, we can all agree that AI applications have really captured the imagination of everyone. This groundbreaking technology has really revolutionized how we will be computing now and also in the future. Now, as I took a deep dive to really understand how what works in the background, it led me to find our topic for today: what is a vector database? Now let's kind of take a stroll down memory lane and let's look at some other groundbreaking moments in technology when it comes to the database area. Here first, we all know about SQL, which stores structured data in tables, been around for a couple of decades. I think we're all aware of that, um, and where it's been. Then came non-Nosql, which takes unstructured data in the form of documents, and this has been great for a lot of, uh, real-time web applications as well as Big Data. You know that that really came about, and then the hint of mobile when we needed to collapse a lot of these better rate APIs. The graph, which stores data in

nodes, and that's how it formulates a lot of its relationships, which really takes us to where we are now with the vector database, which is naturally all our AI applications, very, very useful and supplemental to that. So, now let's get into the characteristics of a vector database. When I started my research, I realized there are two major concepts that I had to really get down. The first is what is a vector, and the second is what is an embedding. I'm really going to simplify things now. Think about a vector as an array of data that gets put into the database. Now, any type of complex object you put in, whether it's images, text documents, they all get represented in some type of numerical value. So, I'm going to say this as an array, all right, and then at some point as data scales up, in order to keep the relationships and naturally keep in mind you're not only going to have user data that you put in, but this is really going to be a database for a lot of your large

language models to be able to store its safe points. Um, it's it, its actual data sets for comparison as we get to the use cases here. So the embedding is lots of vectors that are staved in a multi-dimensional, I abbreviate that their format where they can then be used as groupings of vectors for data sets that can really start to grow and go from there. Now, with this understanding of vector and embeddings, now we have the proper context to really discuss the use cases that really bring this to technology to life. Now, we have our large language models and we've all interacted with a chatbot in the past. I think if everybody that's the number one way you've interacted, especially if you've actually used chat GPT and the major thing that that uses is a concept called natural language processing. So let's take this from our chat box. It's the number one, I would say feature that you'll see, uh, this being used, um, and and it's going to work a lot by taking the context of understanding the

semantics of conversation. Well, that model will be able to leverage a vector database to keep its ever-growing database to understand a car is similar to an engine or the relationships between the terms that you have here. Now, I also have video and image recognition. We've all used these types of applications to build AI art, as they call it. But let's say with the voice recognition, the ability to take sound waves or audio file and be able to represent it as a numerical set of data, and then be able to make comparisons about this equals this particular semantics of speech. All right. And then also, last but not least, let's talk about search. Also very important. We may have similarity searches, being able to identify certain images. We've all interacted with recommendation engines. All right. So search is another bit, a one here, and we'll just say the similarities. All right. Let's just summarize that. There, very important thing of understanding when I'm searching for what is related to that. Those relationships can definitely be.

represented. Now, let's get into the benefits of doing this, 'cause naturally, if you did a quick search on the internet, you'll be able to see the ability for you to represent Vector data into some of these other databases that I discussed earlier: SQL databases, NoSQL databases. All right, but you truly get certain great benefits when you use the direct Vector databases to do that. Number one, I would say, is flexibility. Now, flexibility in the terms to take any type of data, whether it's docs, images, any type of text data, speech. You kind of heard a lot of the things I was discussing, that all these. It doesn't matter when you use different type of database, you may have to prepare your data to go in that, but with a vector database, it's very easy just to throw in or insert a bunch of unstructured data for comparison to see. Now, once you have data in, be able to ingest any type of data. The second is the scalability. All right, being able to scale out to millions and billions of data points.

vectors that you'll be able to have for comparison. And if you think about this, this is really where the power of large language models really comes to shine with this extensive database that it has for comparison. And if you wanted to start from scratch with your model, you often have to throw give it a bunch of training data for it to start to grow and maintain some of his expertise. Uh, to go so flexibility, the ability to put data in, the ability to scale to millions or billions of data points. And once that data is in, let's not forget the speed and performance of everything here being able to index a lot of these vectors, these embeddings, being able to query in a low latency mode since it's all in a numerical format. It's very easy to run queries, the large language models to um, if you are in chat B is trying to take the conversation and compare it out and do some comparison. It's going to leverage this vector database to put save points if I want to call them that.

or certain like uh you could probably just say like a cache of data that it can use to make that operation go that algorithm work and whatever the workflow you have uh to really perform like it should. So this has been Vector databases. I'm always an advocate of having your polyglot, meaning that your architecture can have many different types of Technologies, multiple databases. As a matter of fact, you really don't have to always depend on one type of database. But one thing we can agree, we're all, you're all technologists like myself, you all are starting to think about how you can infuse AI into your architecture. Well, I recommend that you take that next step, look at some of the open source Technologies for Vector databases and get off to the races. Thanks for watching. In the comments below, let us know how you've used Vector databases with your AI projects. And as always, please remember to like and subscribe.

What is a Vector Database?

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벡터 데이터베이스의 영향과 기능

AI 애플리케이션은 컴퓨팅에 큰 영향을 미쳤으며 벡터 데이터베이스가 부상하게 된 이유이기도 합니다.

SQL은 정형 데이터를 저장하고, NoSQL은 비정형 데이터를 처리하며, 그래프 데이터베이스는 노드를 통해 관계를 관리합니다.

벡터 데이터베이스는 배열을 사용해 이미지와 텍스트 문서와 같은 복잡한 데이터를 숫자 형태로 표현하고 저장하여 데이터가 확장됨에 따라 관계를 유지하는 AI 애플리케이션에 필수적입니다.

벡터 임베딩은 자연어 처리를 위한 언어 모델과 대화의 의미를 이해하기 위한 챗봇에 사용됩니다.

또한 AI 예술을 위한 비디오 및 이미지 인식과 음파를 수치 데이터로 표현하기 위한 음성 인식에도 활용됩니다.

또한 벡터 임베딩은 검색 목적의 유사도 검색과 추천 엔진에서 중요한 역할을 합니다.

벡터 데이터베이스 사용의 이점

벡터 데이터베이스는 다양한 유형의 데이터를 유연하게 처리할 수 있어 비교를 위해 비정형 데이터를 쉽게 수집할 수 있습니다.

또한 수백만 또는 수십억 개의 데이터 포인트를 처리할 수 있는 확장성을 제공하므로 대규모 언어 모델에 유용합니다.

또한 벡터 데이터베이스는 데이터 색인 및 쿼리에서 빠른 속도와 높은 성능을 제공하므로 챗봇의 대화 비교와 같은 작업에 효율적입니다.

AI 아키텍처에서 벡터 데이터베이스의 중요성

벡터 데이터베이스는 여러 기술과 데이터베이스를 사용할 수 있는 다국어 아키텍처의 중요한 부분입니다.

아키텍처에 AI를 도입해야 할 필요성이 점점 더 커지고 있으며, 벡터 데이터베이스용 오픈 소스 기술이 이러한 전환에 도움이 될 수 있습니다.

AI 프로젝트에서 벡터 데이터베이스를 사용하는 것은 기술 전문가에게 권장되는 다음 단계로, 탐색과 구현의 필요성을 불러일으키고 있습니다.