The explosion of large models in 2023 has also brought a new trend to the database field, and vector databases have become a popular fried chicken in the database field. According to IDC survey data, global spending on AI technology and services will reach $154 billion in 2023 and exceed $300 billion by 2026. Among them, the vector database provides important technical support for the development of AI and the enhancement of the accuracy of content generation.
How hot is the vector database as a popular fried chicken? At the capital level, in April 2023 alone, two U.S. vector database companies received investments worth more than 1 billion yuan. At the same time, QDRANT, Chroma, and We**IATE have successively received financing, and Pinecone, which has only been established for a few years, announced a $100 million Series B financing with a valuation of 7$500 million. In addition, by 2030, the global vector database market is expected to reach 50 billion US dollars, and the domestic vector database market is expected to exceed 60 billion yuan.
Favored vector database.
What is a vector database? It is a storage system specifically designed to store and efficiently retrieve vector representations, such as word embeddings or digital representations of text data. A vector database is also a repository that stores vectors associated with words or phrases, allowing you to quickly find and compare them based on similarity metrics.
The role of vector databases is to make the processing of large vector spaces more efficient, while optimizing operations such as storage, retrieval, and comparison. In the author's opinion, this new type of database technology can process and analyze big data more effectively, so it has received extensive attention and application in the era of big data.
While vector databases are attracting attention, we have also noticed the explosion of artificial intelligence in 2023, and the integration of AI and databases has become one of the important trends in the database field. AI can help databases better process and analyze data, improve the efficiency and accuracy of data processing, and AI can also help databases better support business decisions and improve the competitiveness of enterprises.
Why? Knowing that context plays an extremely important role in everyday human conversations, helping people communicate smoothly and understand the words of others, large language models capture semantic and semantic relationships by encoding conversations into digital representations called "vectors." These vectors allow the model to understand the context in which the conversation occurs, whether it is a specific cultural context, the context of the topic being discussed, or other contextual cues.
To be sure, almost all types of databases are actively moving closer to AI, such as adding vector indexes to databases, databases and AI are already inextricably linked, and AI also urgently needs to create value from unstructured data.
The role of vector databases.
Traditional databases do not perform well in AI applications that focus on natural language processing due to the delay in information retrieval. In contrast, vector databases provide a more efficient solution for the storage and retrieval of unstructured data. Vector databases focus on processing large-scale vector data with the following core features:
Efficient retrieval: Vector databases can quickly and accurately retrieve vector representations based on queries or similarity metrics, ensuring that language models can quickly access the vector embeddings they need.
Indexing and searching: By providing indexing and search capabilities, vector databases can efficiently find and search vector data based on various criteria, such as similarity search, nearest neighbor search, or range query.
Scalability: Designed with large-scale data processing in mind, it can efficiently store and retrieve millions or even billions of vectors.
Similarity measurement: Vector databases measure similarity or distance between vectors, which can help with tasks such as semantic similarity comparison, clustering, and recommender systems.
Support for high-dimensional vectors: It is suitable for processing high-dimensional vectors that are common in language models, and can store and retrieve complex vector representations.
Multi-type data storage: In addition to the core vector data, vector databases can store hashes of geospatial data, text, features, user profiles, and vector-related metadata. Note, however, that while it can store hashes, the design focus is not on the management of cryptographic hashes.
Overall, vector databases play a key role in AI applications, especially in scenarios that require efficient processing of unstructured data.
Database 2024 Trend Outlook.
It is foreseeable that 2024 will still be a hot year for the development of vector databases. In the field of vector databases, cross-domain knowledge and skills are indeed required to achieve the optimal application of deep learning techniques. This includes an in-depth understanding of AI, expertise in database management, and hands-on experience in data security. The security of sensitive data stored in databases is paramount, especially as deep learning technology is increasingly integrated into vector databases.
With the rapid development and popularization of large models, the market demand for vector databases is also growing. This demand provides a powerful impetus for the advancement of vector database technology. This impetus not only promotes the continuous improvement of technology, but also accelerates the elimination of unsuitable technologies, providing space for the development and innovation of new technologies.
In the long run, we can expect vector databases to become more mature and stable over time. At the same time, they will be able to provide more accurate and efficient vector search results for various application scenarios to meet different business needs. It is a process of continuous technological advancement, selection, and optimization, which heralds a bright future in the field of vector databases.
In addition to the development of vector databases, we have also noticed the continuous rise of domestic databases. In 2023, the global database industry will show rapid growth in many aspects. Significant progress has been made in terms of industry scale, software and hardware innovation, and talent ecology. However, with the rapid growth of the market, the competition is also becoming increasingly fierce.
Although there is still a certain gap between the domestic database and the top international brands in terms of technology and products, this gap is rapidly narrowing. More and more domestic database manufacturers have begun to achieve remarkable results in the international market. For example, Renmin Jincang has established cooperative relations with a number of overseas enterprises and successfully deployed and applied them in Southeast Asia and Europe.
In addition, Alibaba Cloud's analyticdb, Huawei's OpenGauss database, and Kuke Data's hashdata cloud data warehouse have also made important progress in the international market.
These successful cases fully show that domestic database products have the ability to compete with international leading brands in technology and market. The gradual replacement of overseas databases by domestic databases is not only because of domestic demand and promotion, but also because of the continuous improvement and progress of its own technical strength.
Write at the end. With the widespread application of large models, the demand for vector databases continues to grow. The general view is that all product applications are worth redesigning and optimizing with the help of AI technology. In this context, enterprises are paying more and more attention to how to combine advanced technologies such as AI and large models with actual business.
This requires that the vector database is designed to take into account the challenges and pain points faced by enterprises in practical applications. Through the vector database, enterprises can build a strong and adaptable technical foundation, and provide solid support for enterprises to smoothly enter the era of large models, helping enterprises to maintain a leading position in the wave of AI and large models.