#320: Store Embeddings in ChromaDB
We paused our AI journey after we figured out a way to work with large PDF files. It is now time to continue and find a solution to create and store vectors in a way that we can incrementally add new documents. The vectors, or embeddings, represent the semantic meaning and are a key part of the "chat with our docs" feature.
There are a handful solutions we can use. I start with Chroma (or ChromaDB) because it is an open-source vector database designed specifically to make building AI applications easy. It is built on top of SQLite, what helps us to work with the persisted data should we want to know more about how the magic works behind the scenes. Let us explore how we can use Chroma to calculate and store embeddings.