Vector Databases in RAG Applications

The Crucial Role of Storage in RAG Applications

Without a way to store data persistently, it gets lost after a while. Applications typically only hold data as long as they’re running. Once they stop, the data’s lost. Sometimes, data in the application’s memory can even be lost due to the underlying framework or operating system actions.

In RAG applications, if you don’t persistently store your retrieved data, you’ll constantly be starting from scratch. Without a vector database, you’d need to re-index your data every time the application starts, slowing it and making it less effective. Vector databases are essential for RAGs because LLMs work with data represented in vector embeddings.

Why Databases Matter for RAGs

Databases provide effective memory storage for your app. They remember not only the retrieved data but also context from previous prompts.

Vector databases enable asynchronous data retrieval, enhancing efficiency and user experience. This allows your application to continue other tasks while waiting for data, especially when combined with asynchronous libraries.

Vector databases are scalable. You can store vast amounts of data from one or many sources to feed your RAG application. Other retrieval methods can significantly slow your application as the source data grows.

Working with data from a database gives you control over retrieval speed. By optimizing embedding parameters, database choices, and server infrastructure, you can achieve fast retrieval even with large datasets. In contrast, working directly with data from websites, PDFs, or other sources is typically much slower.

Storing data in a database facilitates cleaning and storing only high-quality data. When you retrieve it, there’s less clutter and no need for further cleanup. Achieving similar performance without a vector-based data store requires more effort.

Data sources might not always be accessible or available in the desired format. With a data store, you have your own copy. You can enhance it with curated data and apply necessary data protection mechanisms.

Vector Databases and RAG

For LLMs, vector databases are the ideal storage solution. Vector embeddings are numerical representations designed to capture the semantic meanings of data — most commonly text, but also other data types like emojis and media.