Vector Database: A New Type of Database in the AI Era

In the field of modern artificial intelligence, vector databases have become a key technology, supporting advanced features such as semantic search and intelligent applications. This article will explore what vector databases are, how they work , and their growing importance in AI/ML.

What is a vector database?

A vector database is a database system specifically designed for storing, indexing, and retrieving high-dimensional vector data. These vectors are usually generated by machine learning models, capturing the semantic features of unstructured data such as text, images, and audio by mapping them into mathematical vectors.

Traditional databases are mainly used to store and query structured data (such as numbers, text, dates), and retrieve data through exact matches or simple range queries. Vector databases are designed for unstructured data, converting data into high-dimensional vectors (embedding) and implementing "semantic search" through similarity calculations.

It not only matches keywords, but also understands the deep meaning and searches for the same intention. It can quickly find the most similar results in billions of data.

Why do vector databases play an important role in building AI models?

Data Embedding

AI models, especially deep learning models, generate a large number of high-dimensional vector embeddings. Language models such as BERT and GPT convert text into high-dimensional vector representations. Vector databases can efficiently store and index these high-dimensional embedded data. Traditional databases are often inefficient in processing such high-dimensional data .

Semantic Understanding

By calculating the similarity between vectors, the vector database can quickly find the data most similar to the query vector, thereby achieving semantic search. This allows the AI model to better understand the semantic information of the data and provide users with more accurate and relevant results .

Understanding Memory

In generative AI applications, such as chatbots, vector databases can store a large amount of contextual information and knowledge bases. When users ask questions, the model can quickly retrieve the context and knowledge related to the question through the vector database, thereby generating more coherent and accurate responses, enhancing the model's contextual understanding and memory capabilities .

Optimizing the model

In the model training phase, the vector database can efficiently store and retrieve embedded representations of training data, accelerating the loading and preprocessing of data. In the inference phase, it can quickly provide data related to the input, helping the model make decisions faster and improving inference efficiency .

Large-scale data processing

As the amount of data continues to grow, AI applications need to process large-scale data sets. Vector databases have good scalability and can process massive amounts of vector data. While ensuring that the data scale is expanded, they still maintain efficient query performance and meet the real-time requirements of AI applications .

Vector databases are of great value in various AI application scenarios, including semantic search, recommendation systems, anomaly detection, computer vision, and natural language processing .

How does the vector database work?

Vector databases store and index high-dimensional vector data and use the similarities between vectors to achieve efficient query and retrieval.

1. Data Embedding

The core of vector databases is to process vector data, which is usually embedded by machine learning models. Embedding is a technique that converts raw data (such as text, images, or audio) into high-dimensional vectors. For example:

• Convert text snippets into vectors using Natural Language Processing (NLP) models.

• Image Embedding: Convert images into vectors using Convolutional Neural Networks (CNN).

• Audio Embedding: Convert audio signals into vectors using audio processing models.

These embedded vectors can capture the semantic or feature information of the original data, making similar data close to each other in the vector space.

2. Indexing

In order to efficiently retrieve vector data, vector databases need to build indexes. Use the approximate nearest neighbor (ANN) algorithm (such as HNSW, PQ) to build efficient indexes for vectors, cluster similar vectors, and significantly reduce the search scope.

The indexing method enables the vector database to quickly locate vectors similar to the query vector in massive data without having to compare each vector one by one.

3. Similarity Measurement

Vector databases retrieve data by calculating the similarity between vectors. Input queries (such as text or images) are also vectorized, and the set of vectors closest to the target is quickly found by calculating cosine similarity or Euclidean distance .

According to the specific application scenario and data type, choosing an appropriate similarity measurement method can improve the accuracy and efficiency of retrieval.

4. Results

The retrieved vectors and their related metadata are returned to the user. The search results are sorted by similarity and the top-K most relevant results (such as recommended products and similar images) are returned.

Application Scenario

Vector databases are widely used in many fields, including but not limited to:

Semantic Search : Enable semantic search for text, images, or audio through vector embedding and similarity retrieval.

Recommendation system : Generate vector embedding based on user behavior data, find similar users or items through the vector database, and achieve personalized recommendations.

Anomaly Detection : In time series data, we use vector embedding and similarity retrieval to quickly identify abnormal vectors that differ greatly from normal data patterns.

Natural Language Processing : In applications such as question-answering systems and chatbots, vector databases are used to quickly retrieve contextual information related to user questions.

Computer Vision : In tasks such as image classification and object detection, fast image retrieval and matching are achieved through vector embedding and similarity retrieval.

Vector databases provide strong technical support for these applications by efficiently storing and retrieving high-dimensional vector data, promoting the development and application of AI technology.

LunaProxy Enhances Vector Database Processes

The effectiveness of a vector database depends on two key factors:

• The quality of the machine learning model used to generate the embeddings.

• The richness and accuracy of the input data these models handle.

This highlights a fundamental fact: even the most advanced vector databases and AI models will perform poorly if they are fed with low-quality, fragmented, or noisy data.

1. Superior data quality for richer embedding

Precise targeting: Extract structured data from complex web sources (e.g., dynamic JavaScript pages, multi-format APIs) while filtering out irrelevant content (ads, duplicate entries).

Multimodal support: Capture text, images, video metadata, and real-time updates (e.g., prices, social media trends) to generate cross-modal embeddings.

Noise Reduction: Automatically validate and cleanse data before generating embeds (e.g. remove broken HTML, correct encoding errors).

2. Scalable infrastructure to enable continuous data flow

Global proxy network: with more than 200 million IP resources from 195+ countries , it can bypass geo-blocking, provide dedicated unlimited traffic , and ensure uninterrupted data collection.

Concurrency: unlimited concurrency , very suitable for building massive vector libraries (such as e-commerce product catalogs).

Dynamic Adaptation: Automatic retry mechanism and CAPTCHA  solver to handle site structure changes or temporary blocking.

Conclusion

Vector databases are a core component of modern AI data infrastructure, enabling semantic search and intelligent applications. By understanding their capabilities, exploring common options, and following real-world examples, you can leverage vector databases to enhance your AI applications. Use LunaProxy to obtain high-quality data to support your vector database initiatives.