PostgreSQL, enhanced by the pgvector extension, offers a powerful yet flexible way to use traditional relational databases for vector storage. Each vector is saved as a row, allowing developers to store both vector data and additional metadata within the same table. This hybrid approach provides a unique advantage over pure vector databases, as it combines the strengths of relational data management with the capabilities of vector search. In enterprise applications, this flexibility allows teams to handle both structured and unstructured data in a seamless manner, making PostgreSQL with pgvector an attractive choice for many developers.
While pure vector databases are designed for high performance, pgvector may not reach the same level of optimization. However, for medium-sized retrieval-augmented generation (RAG) applications, such as those involving around 100,000 documents, PostgreSQL’s performance is typically more than sufficient. This makes PostgreSQL an excellent starting point for knowledge management systems or departmental applications, where the cost and complexity of a dedicated vector database may not be necessary. For smaller, single-user applications, alternatives like SQLite with the sqlite-vss extension could also be considered, offering a lightweight solution for basic needs.
The beauty of using PostgreSQL for RAG applications lies in its simplicity and scalability. Many developers find that using PostgreSQL as their backend database, combined with the pgvector extension, is a reliable and straightforward approach to building AI-driven applications. Should the application’s needs grow over time, migrating to a more specialized vector database is always an option. Until then, PostgreSQL provides all the necessary functionality to build an effective and scalable system without the upfront complexity of more advanced databases.
For those new to building RAG applications, it might be helpful to review some foundational concepts. My previous articles, “Retrieval-augmented generation, step by step” and “Fully local retrieval-augmented generation, step by step,” cover the essential techniques for building these applications. In these articles, I guide you through creating a basic RAG system using Python, LangChain, and OpenAI models. We cover the process of generating embeddings, storing them in a local vector store like FAISS, and then using those embeddings to retrieve and generate meaningful responses from a specific document. These resources provide a solid starting point for anyone looking to dive deeper into RAG and vector databases.