Vector Databases: How Does it Work? Advantages and Use Cases

Vector Databases: How Does it Work? Advantages and Use Cases

The unprecedented growth of Artificial Intelligence (AI) in the past few years has revolutionized almost every industry by automating and optimizing a wide spectrum of tasks. Be it healthcare, medicine, agriculture, or education, AI systems have significantly enhanced efficiency and innovation. With this capability, however, comes the challenge of efficient data processing for applications leveraging Large Language Models (LLMs) or Generative AI. This is where vector databases have proved crucial in developing such systems by ensuring better data management, scalability, and security.

As the name suggests, vector databases store data as high-dimensional vectors or mathematical representations of features. Depending on the complexity of the dataset, the dimensions of each vector can range from tens to even thousands, where more dimensions better capture the intricacies between the attributes. Many of today's advanced AI applications use vector embeddings to maintain long-term memory and execute complex tasks, thereby necessitating vector databases for optimized storage and querying capabilities.

How does a vector database work?

In vector databases, a similarity metric is applied to find the vector that is the most similar to the query. Various algorithms are used to provide fast and accurate retrieval of vectors. A common pipeline for a vector database consists of three components:

  • The database indexes vectors and maps the same to a data structure to enable faster search.
  • The database then applies a similarity metric to find the nearest neighbors to the indexed query vector.
  • In the last step, the vector database finds the final nearest vector from the database and processes it to return the final result.

Vector Indexing algorithms

Some of the algorithms for creating vector indexes are mentioned below.

  • Flat Indexing: In this method, the vector is stored without any transformation and is known for its speed. However, it is a slow process and is ideal when speed is not a necessity.
  • Locality Sensitive Hashing (LSH): LSH is an indexing strategy that generates indexes using a hashing function where similar vectors are hashed to the same bucket. This method results in a much smaller search, thereby increasing query speed.
  • Hierarchical Navigable Small World (HNSW): This method uses a multi-layered graph approach for indexing vectors, where at the lowest level, each vector is captured, and as we move up the layers, the data points are grouped (based on similarity) to reduce the number of points in each layer.

The above list is not an exhaustive one, and many other algorithms also exist, like locality-sensitive hashing, product quantization, and others.

Similarity measures

The vector database identifies the most similar and relevant results for a query using similarity measures. Cosine similarity is one of the most popular measures, measuring the angle's cosine between the two vectors, where 1 represents identical vectors and -1 diametrically opposite vectors. Other measures include the Euclidean distance, which calculates the straight-line distance between the vectors, and the dot product, which computes the product of the magnitude of the two vectors to the cosine of the angle between them.

Advantages of a vector database

  • Speed and performance: Compared to traditional databases, vector databases are much faster and can even give optimized results by searching across billions of data points. 
  • Scalability: Vector databases are designed to handle growing volumes of data by scaling horizontally and maintaining the same performance.
  • Flexibility: Vector databases can handle a wide array of data types, such as images, videos, or other multi-dimensional data.
  • Metadata storage and filtering: Not only vectors but also their metadata can be stored and queried in a vector database for finer-grained queries.
  • Integration: Vector databases can be very easily integrated with ETL pipelines like Spark, analytics tools like Tableau, and even AI related tools such as LangChain and LlamaIndex.

Some use cases 

Some of the key use cases include the following:

  • Semantic search: This is the process of performing a search operation based on the context of the query. A vector embedding captures the essence of the data and stores it in the form of numerical representation; hence, understanding the user intent becomes easier.
  • Retrieval Augmented Generation (RAG): Vector databases play an important role in RAG applications and significantly increase their accuracy and efficiency.
  • Conversational AI: Vector databases can enhance the information parsing capabilities of virtual agents through relevant knowledge bases (such as a corpus of source documents).
  • Similarity search: As mentioned earlier, a vector database allows for searching items similar to the user query. Using this method, we can find similar text, image, or audio for better image and speech recognition, natural language processing, etc.
  • Recommendation engines: Vector databases can be used to store the customer preferences and product attributes of an e-commerce store and enable better recommendations based on vector similarity.

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