The Choice of the Future: Why Vector Databases Are Your Data Management Tool

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Preface

Vector databases are good at handling complex, high-dimensional data and are revolutionizing data retrieval and analysis in the business world. Their efficiency in performing similarity searches makes them critical for applications such as recommendation systems, semantic search, personalized marketing, and more, opening up new avenues for data-driven decision-making.

On August 1, 2023, Amazon Cloud Technology launched a preview version of the Amazon OpenSearch Serverless vector engine, providing users with a simple, scalable, and high-performance similarity search function, allowing users to easily create modern machine learning (ML) ) enhanced search experiences and generative AI applications without the need to manage the underlying vector database infrastructure.

What is a vector database?

First, we first understand the concept of vector database. They represent a database management system (DBMS) designed to efficiently store, manage, and retrieve vectorized data. Unlike traditional databases that deal with scalar values, vector databases deal with multidimensional data, or vectors. Vector databases have found their place in large-scale machine learning applications, especially in areas such as recommender systems, semantic search, and anomaly detection that deal with high-dimensional vectors.

Mechanism of vector database

The power of vector database lies in its unique data indexing and query technology. To reduce the time required to retrieve similar vectors, vector databases do not iterate over every vector in the database. Instead, they use specific indexing techniques, such as KD trees, hierarchical navigable small world graphs (HNSW), or inverted multiple indexes (IMI), to organize vectors in a way that significantly reduces the search space during queries.

During a query, these databases identify regions in the vector space where similar vectors are likely to exist and search only within that region. This approach greatly reduces the computational time required to retrieve similar vectors, making vector databases very efficient for similarity search tasks.
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Advantages of vector databases

Vector databases are designed to perform high-speed similarity searches in massive data sets. They excel at vectorizing data because they leverage unique data indexing and querying techniques that significantly reduce the search space and speed up the retrieval process. Vector databases can handle complex data structures efficiently, making them ideal for advanced machine learning applications.

‍Query vector database

Now let's delve into the query vector database. Although it may seem daunting at first, it becomes very simple once you get the hang of it. The primary method of querying a vector database is through similarity search, using Euclidean distance or cosine similarity.
Here is a simple example of how to add vectors and perform a similarity search using pseudocode:

# 导入向量数据库库
import vector_database_library as vdb 

# 初始化向量数据库
db = vdb.VectorDatabase(dimensions= 128 ) 

# 添加向量
for i in  range ( 1000 ): 
    vector =generate_random_vector( 128 )   #generate_random_vector 是一个生成随机数的函数随机128维向量
    db.add_vector(vector, label= f"vector_ {
      
      i} " ) 

# 进行相似度搜索
query_vector =generate_random_vector( 128 )
相似向量 = db.search(query_vector, top_k= 10 )

In the above code, the db.add_vector(vector, label=f”vector_{i}”) method is used to add vectors to the database, and the db.search (query_vector, top_k=10) method is used to perform similarity searches.

What is vector Embedding?

Vector Embedding, also known as vector representation or word embedding, is a numerical representation of a word, phrase, or document in a high-dimensional vector space. They capture semantic and syntactic relationships between words, allowing machines to understand and process natural language more efficiently.

Vector embeddings are typically generated using machine learning techniques such as neural networks, which learn to map word or text input to dense vectors. The basic idea is to represent words with similar meaning or context as close vectors in vector space.

A popular method for generating vector embedding is Word2vec, which learns representations based on the distributional properties of words in large text corpora. It can be trained in two ways: the continuous bag-of-words (CBOW) model or the skip-gram model. CBOW predicts the target word based on the context words, while skip-gram predicts the context word given the target word. Both models learn to map words to vector representations that encode their semantic relationships.

Another widely used technique is GloVe (Global Vectors of Word Representations), which utilizes co-occurrence statistics to generate word embeddings. GloVe builds a word co-occurrence matrix based on the frequency of words appearing together in the corpus, and then applies matrix decomposition to obtain Embedding.

Vector Embedding has a variety of applications in natural language processing (NLP) tasks, such as language modeling, machine translation, sentiment analysis, and document classification.

By representing words as dense vectors, models can perform mathematical operations on these vectors to capture semantic relationships, such as word analogies (e.g., "king" - "man" + "woman" ≈ "queen"). Vector Embedding enables machines to capture the contextual meaning of words and enhance their ability to process and understand human language.

Amazon OpenSearch Service

OpenSearch is an extensible, flexible, and extensible open source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0 License. It includes a search engine OpenSearch (providing low-latency search and aggregation), OpenSearch Dashboard (visualization and dashboarding tools), and a set of plug-ins that provide advanced features such as alerting, fine-grained access control, observability, security monitoring, and more. Vector storage and processing. Amazon Open Search Service is a fully managed service that allows you to easily deploy, scale, and operate OpenSearch in the AWS Cloud.

With the OpenSearch Service's vector database capabilities, you can implement semantic search, Retrieval Augmentation Generation (RAG) using LLM, recommendation engines, and search rich media.

The Amazon OpenSearch Serverless vector engine has the following advantages:

1. The vector engine built on Amazon OpenSearch Serverless is naturally robust.

2. The Amazon OpenSearch Serverless vector engine is powered by the k-nearest neighbor search function in the open source OpenSearch project, which can provide reliable and accurate results.

3. The vector engine supports a wide range of use cases in different fields, including image search, document search, music retrieval, product recommendation, video search, location-based search, fraud detection, and anomaly detection.

Summarize

The future of data-driven decision-making depends on our ability to navigate and extract insights from high-dimensional data spaces. In this regard, vector databases are paving the way for a new era of data retrieval and analysis. With a deep understanding of vector databases, data engineers are equipped to address the challenges and opportunities that come with managing high-dimensional data, driving innovation across industries and applications.
Overall, Amazon Cloud Technology’s vector engine has strong performance and scalability to meet the needs of various applications. If you want to learn about or use vector databases, Amazon Cloud Technology recently also provided a free trial service of vector databases, which is worth a look.