PostgreSQL is one of the most well-known and proven relational databases on the market. For over three decades, this database technology has been successfully used in various software development projects. Especially in mid-sized companies, PostgreSQL is often part of the IT infrastructure foundation. This widespread adoption and trust in PostgreSQL result from its stability, flexibility, and extensibility. One of these extensions that is increasingly gaining traction in the field of Machine Learning and AI applications is pgvector. But why is this extension so well-suited for embeddings, and why should you prefer pgvector over other specialized vector databases when you’re not using several billion data points?
What is PostgreSQL?
Before we dive deeper into the world of embeddings and pgvector, it’s important to understand what PostgreSQL actually is. PostgreSQL is a relational database known for its reliability, flexibility, and scalability. Originally developed in the 1980s, it has become one of the most widely used open-source databases in the world. The advantages of PostgreSQL include its ACID compliance (Atomicity, Consistency, Isolation, and Durability), which ensures high data security and integrity.
In many classic software development projects, PostgreSQL is used. Whether in e-commerce, content management systems, or internal company databases – PostgreSQL is often the first choice due to its many features and extension capabilities. A very well-known extension is PostGIS, for example, as it is used for geographic data at OpenStreetMap. For data types related to vectors, there is the aforementioned pgvector extension. Perfect for embeddings.
What are Embeddings?
To understand why pgvector is so well-suited for embeddings, you first need to explain the term embeddings. Embeddings are numerical representations of data – usually text, images, or other unstructured data – that are embedded in a vector space. These vectors summarize the semantic meaning of the data in a way that allows machines to recognize complex relationships between them.
For example, Large Language Models (LLMs) like GPT, Gemini, or Mistral convert text into vectors. These vectors contain information about the relationships between words, sentences, and concepts. When two words are close together in the vector space, it means they have a similar meaning.
Embeddings are of central importance in many AI applications, especially in areas such as:
– Text classification – Recommendation systems – Similarity search – Speech recognition – Image recognition
This is where pgvector comes into play to enable the storage and querying of these vectors in PostgreSQL.
What is pgvector?
pgvector is an extension for PostgreSQL specifically developed for storing and comparing vectors. Vectors can, as described above, be numerical representations of data created through embeddings. pgvector offers the ability to store these vectors in a PostgreSQL database and execute queries on their similarity.
This opens up a variety of possibilities, especially for companies that already rely on PostgreSQL and want to develop Machine Learning or AI-based applications at the same time. The central strength of pgvector lies in the fact that it uses the existing PostgreSQL infrastructure and thus enables seamless integration into existing software landscapes.
Advantages of pgvector Compared to Other Vector Databases
While there are specialized vector databases like Pinecone or Weaviate that are also designed for processing and querying embeddings, pgvector offers some significant advantages that make it particularly attractive:
1. Seamless Integration into Existing PostgreSQL Infrastructures
Many companies already use PostgreSQL as their central database technology. With pgvector, it’s possible to integrate vector operations directly into the existing database without having to build a separate infrastructure for vector databases. This saves not only time and resources but also reduces complexity.
2. ACID Compliance
Since pgvector is based on PostgreSQL, companies benefit from the ACID compliance of the database. This means that transactions run safely and reliably even when storing and querying vectors. Other vector databases don’t always offer this robust transaction security to the same extent.
3. Flexible and Mature Query Language
PostgreSQL is known for its powerful SQL query language and the ability to execute complex queries. With pgvector, companies can use the familiar SQL syntax to perform vector operations like similarity queries. This makes the work of developers who are already familiar with PostgreSQL easier.
4. Extensibility and Community Support
Another great advantage of PostgreSQL in general and pgvector in particular is the large open-source community. Extensions and improvements are continuously developed and implemented. This keeps PostgreSQL always at the cutting edge of technology, which also applies to pgvector.
Use Case for pgvector: Retrieval-Augmented Generation (RAG)
A particularly relevant use case for pgvector is Retrieval-Augmented Generation (RAG), a technique that combines Large Language Models (LLMs) with external knowledge. Embeddings are used to retrieve relevant information from a database and include it in the generation process of the LLMs.
With pgvector, embeddings can be efficiently stored and searched in PostgreSQL. When a query is made, the extension compares the semantic similarity between the input text and stored vectors to find the most relevant data points and pass them to the LLM. This makes text generation not only more precise but also more substantive.
Conclusion: Old but Gold
The PostgreSQL extension pgvector offers a powerful and flexible way to process embeddings directly in a relational database. For companies that already rely on PostgreSQL, pgvector is an extremely attractive solution for performing vector operations without having to implement a separate vector database.
Compared to specialized vector databases, pgvector offers seamless integration, the proven reliability of PostgreSQL, and the ability to execute complex queries with familiar SQL syntax. For mid-sized companies dealing with topics such as Machine Learning, Artificial Intelligence, and processing embeddings e.g., for RAG, pgvector represents a practical and efficient solution.