A vector database is a database that shops and retrieves vectors. Converting text data into numerical vectors is called "vectorization", and vectorization is one of the text preprocessing methods mainly used in natural language processing.
In recent years, large language models (LLMs) have grown rapidly and are having a major impact on all industries and jobs. Utilizing large-scale language models requires massive data processing, and vector databases play an important role as storage destinations for that data.
In this article, we will explain what a vector database is, its basics, advantages, and examples of its actual use.
What is a vector database?
A vector database is a database that shops and manages information in vector format. A vector is a set of data that has numerical values and directions, and can express positional relationships and features in space.
Vector databases are often used in the fields of machine learning and data analysis, and play an important role in calculating similarities and distances. For example, if you upload a product image, you can automatically search and display products similar to that image.
Vector databases can handle high-dimensional data, and can efficiently process and search large amounts of data. Therefore, it is used in a wide range of applications such as image recognition and natural language processing.How is it distinct from conventional databases?
Traditional databases (relational databases) store data in tabular form. On the other hand, in vector databases, data is represented by numerical arrays called "vectors".
Because it has a flexible data structure compared to conventional databases, it is characterized by being able to handle various data types. Therefore, it can efficiently handle data used in fields such as machine learning and natural language processing.
Vector databases also allow faster and more accurate searches than traditional databases. For example, in traditional databases, when searching for an image, the search is based on image attributes (color, shape, size, etc.).
However, since the vector database expresses the image itself as a vector, it goes beyond attribute similarity to achieve more advanced image retrieval.
Vector databases can handle high-dimensional data, and can efficiently process and search large amounts of data. Therefore, it is used in a wide range of applications such as image recognition and natural language processing.
How is it different from traditional databases?
Traditional databases (relational databases) store data in tabular form. On the other hand, in vector databases, data is represented by numerical arrays called "vectors".
Because it has a flexible data structure compared to conventional databases, it is characterized by being able to handle various data types. Therefore, it can efficiently handle data used in fields such as machine learning and natural language processing.
Vector databases also allow faster and more accurate searches than traditional databases. For example, in traditional databases, when searching for an image, the search is based on image attributes (color, shape, size, etc.).
However, since the vector database expresses the image itself as a vector, it goes beyond attribute similarity to achieve more advanced image retrieval.
Advantages and scope of use of vector databases
The greatest advantage of the vector database is that it enables fast and accurate searches. It is also very efficient with respect to the dimensionality and quantity of data, enabling fast retrieval and analysis.
Therefore, vector databases are used in a wide range of fields. This section describes the specific scope of use.
image recognition
Vector databases can be used for image recognition tasks by converting image data into "feature vectors" and computing similarities between the vectors. It is suitable for applications such as face recognition, image classification, and object detection because it can quickly search and extract images with high similarity.
For example, an online store allows customers to provide photos of their products. By analyzing this photo, it is possible to grasp the type and color of the product provided by the customer and automatically suggest the appropriate product.
Thus, the use of vector databases enables efficient processing of large amounts of image data and real-time response.
Natural Language Processing (NLP)
Vector databases can vectorize words and documents in natural language processing (NLP) to capture similarities and contexts between words. It also supports tasks such as improving the accuracy of search engines, document classification, and machine translation.
For example, online services can analyze customer reviews to understand product features and problems, and propose improvements.
In this way, using a vector database can improve the accuracy of natural language processing, making it possible to handle large-scale corpora.
voice recognition
Speech data can also be converted into feature vectors, which can be applied to speech recognition tasks using vector databases. By extracting voice features and speaker attributes and calculating similarities between voices, it can be used in applications such as voice search, voice classification, and speaker recognition.
For example, a speech recognition system can analyze speech data to understand speaker characteristics and utterance content, and generate appropriate responses.
This high-speed search performance also supports real-time speech analysis and interactive systems.
data analysis
Vector databases are also suitable for database analysis because they can handle high-dimensional vectors of various data types. By vectorizing and analyzing multidimensional data such as customer purchase histories and user behavior data, it is possible to handle tasks such as extracting customer segments, detecting anomalies, and suggesting recommended items.
For example, in the marketing department of a company, it is possible to implement appropriate marketing measures by analyzing data such as customer purchase histories and behavior histories, as well as understanding purchasing trends.
In this way, similarities and relationships within the database can be calculated quickly, which is useful for quick decision-making and efficient strategy planning.
0 Comments