Large Language Models (LLMs) have made many tasks easier like making chatbots, language translation, text summarization, and many more. In the past, we used to write models for different tasks, and then there was always the issue of their performance. Now, we can do most of the tasks easily with the help of LLMs. However, LLMs do have some limitations when they are applied to real-world use cases. They lack specific or up-to-date information leading to a phenomenon called hallucination (opens new window)where the model generates incorrect or un-predictable results.

Vector databases (opens new window)proved to be very helpful in mitigating the hallucination issue in LLMs by providing a database of domain-specific data that the models can reference. This reduces the instances of inaccurate or nonsensical responses.

In this blog post, we are going to see how the integration of vector databases with SQL has made life easier for businesses. We will discuss some of the limitations of traditional databases and what led to this new integration, SQL vector database. At the end of the blog, we will see how these databases work and Why MyScale (opens new window)could be your first choice while selecting vector databases.

# What is SQL Vector Databases

A SQL vector database is a specialized type of database that combines the capabilities of traditional SQL databases with the abilities of a vector database. Providing you the ability to efficiently store and query high-dimensional vectors with the help of SQL.

In simple terms, it's like a regular database that you can use to store both structured data and unstructured data, but with an added capability to perform rapid queries across various data types, including images, video, audio, and text. The mechanism behind this efficiency lies in the creation of vectors for the data, facilitating the swift identification of similar entries.

Now, let's try to understand the core concepts of SQL vector databases that would help to get the reason why we need SQL vector database.

# Key Concepts in SQL Vector Databases

SQL vector databases introduced some innovative concepts that significantly enhance data retrieval and analysis, especially in the context of unstructured and high-dimensional data. Let’s explore a few of them:

• Handling the unstructured data: Vector representations of data give you the ability to perform ANN search on unstructured data. When you find the embeddings of unstructured data like text, images, or audio. You capture semantic meaning which allows you to perform similarity comparisons by measuring the distance between vectors to find nearest neighbors, regardless of the original data format.
• ANN search: SQL vector databases store data as vectors and perform a type of search known as similarity search, which doesn't operate against a single row but rather conducts an Approximate Nearest Neighbor (ANN) search. This process involves identifying the vectors that are closest to a given query vector, meaning those whose properties most closely align with the properties of the query vector.
• Vector indexing: Vector indexing refers to specialized data structures and algorithms used to efficiently organize and query large amount of vector data. Vector databases use various vector indexing strategies to optimize data retrieval and management. Some vector database uses hierarchical graph algorithm to accelerate search performance. Some vendors may develop their own indexing algorithm, for example, MyScale has developed a novel technique called Multi-Scale Tree Graph (MSTG) which significantly outperforms existing approaches (opens new window).

# Why We Need SQL Vector Databases

So, here comes a question into mind: Why do we need SQL vector databases? Traditional databases like MySQL, PostgreSQL, and Oracle have been working well for ages and have all the necessary features to keep the data organized. They have quick indexing methods, make sure you get the exact data you need without any trouble. Why do we need a SQL vector database?

No doubt, traditional databases are great but they do have some limitations when the data becomes huge and unstructured. Let's take a look:

• Lack of speed and semantic understanding: Traditional databases rely on exact keyword matching and indexing to retrieve data. But with the exponential growth of unstructured data from social media, sensors, etc. traditional databases don’t understand the semantics of the data. There is a need of databases that can not only fetch data rapidly but also understand the context and semantics of queries. For example, when dealing with natural language queries or complex data relationships, traditional methods struggle to provide quick and relevant results.
• Issues with high-dimensional data: Relational databases store data in the form of rows and columns. As the number of columns or dimensions increases, the query performance decreases and led to phenomena called “curse of dimensionality”. So, we need a database that can eradicate the issue of dimensionality without losing the query performance.
• Unstructured data: Relational databases require structured data to be transformed and flattened into rows and columns in tables. But an increasing amount of valuable data today is unstructured - images, video, audio, text documents, etc. which is very difficult to store in the relational databases.
• Scalability concerns: Scalability is challenging for traditional databases, especially when you're dealing with massive volumes of data. This becomes an issue for organizations dealing with large datasets, creating issues for them to process and analyze data effectively. So, we need a database that can handle large amounts of data while maintaining the same speed and efficiency.

To address these challenges, the development of SQL vector databases emerged, presenting a superior alternative to traditional databases.

# How SQL Vector Databases Outperform Traditional Databases

Combining SQL with Vectors brings a lot of benefits, among which several advantages stand out for their significant impact:

• Faster performance and semantic search: The vector representation allows the database to extract the semantic meanings from the stored data. Also, the process becomes even faster because we find the vector similarity here. This is helpful for many applications like recommendation systems where the semantic relationship between the data is more important.
• Efficient data retrieval: SQL vector databases use the Approximate Nearest Neighbor(ANN) technique to find the matching records. By calculating the cosine similarity between your query and the dataset, it efficiently gives the most relevant top 'K' results.
• Support for both structured and unstructured data: The introduction of SQL in the vector database gives the ability to represent the unstructured data in vectors and store the semantic meanings. This way, you can easily query any structure or unstructured data.
• Familiar SQL interface: One of the biggest advantages of SQL vector databases is that they provide a familiar SQL interface for querying data. It allows you to use your SQL skills and minimize the learning curve when adopting vector capabilities. Queries can be written using standard SQL syntax.

# How SQL Vector Databases Work

The integration of SQL and vector databases involves storing and indexing high-dimensional vectors in a way that can be efficiently queried using SQL. This process involves certain steps.

# Step 1: Setting Up the Database

Firstly, you need to set up a database that supports both SQL and vector operations. Some modern databases have built-in support for vectors, while others can be extended with custom data types and functions.

In this example, we create a products table with a 1536D vector column that stores the high-dimensional vectors.

# Step 2: Inserting Data

When inserting data, you would store both the structured attributes and the vector representations of the unstructured data.

In this SQL statement, we insert a new product record along with its vector.

# Step 3: Indexing Vectors

The next step is to create vector indexes. It's the technique that defines how fast the database applies the similarity search. Many vector databases use specialized indexing techniques such as KD-trees, R-trees, or inverted index structures to optimize these operations.

Here, we create a MSTG index, which is suitable for indexing multidimensional data.

# Step 4: Querying Data

To query the data, you just combine traditional SQL queries with vector operations. For instance, if you want to find products similar to a query vector, you can use the vector distance function.

This query finds the distance between the vector representations of the vector column and the query_vector . Then, it orders the results in ascending order with respect to distance.

# The Ideal Solution: MyScale - a SQL Vector Database

This is where MyScale comes into play as a solution that combines relational databases and vector databases. Built on the open-source SQL database ClickHouse, MyScale allows running advanced vector queries directly with standard SQL syntax. This eradicates the hassle of integrating separate relational and vector databases. Unlike other vector databases like Pinecone, Milvus and Qdrant, MyScale provides a single SQL interface for vector search. It allows the storage of scalar and vector data, all within a single database. Now you can get lightning-fast vector results directly with familiar SQL.

It is widely assumed that relational databases cannot provide performance that matches that of vector databases. MyScale breaks this myth. In head-to-head comparisons, MyScale not only significantly outperforms pgvector (opens new window) in terms of search accuracy and query processing speed, but also shows a significant edge over specialized vector databases like Pinecone (opens new window), especially in cost-effectiveness and index building times. This outstanding performance, combined with the simplicity of SQL, makes MyScale the first choice for the businesses.

If you have more questions or are interested in our offering, don't hesitate to contact us Discord (opens new window) or follow MyScale on Twitter (opens new window).