Vector Database

When implementing Retrieval-Augmented Generation (RAG), a vector database is used to store and efficiently retrieve embeddings, which are vector representations of data like documents, images, or other knowledge sources. During the RAG process, when a query is made, the system converts it into an embedding and searches the vector database for the most relevant, similar embeddings (e.g., related documents or snippets). These retrieved pieces of information are then fed to a generative model, which uses them to produce a more accurate, context-aware response.

Visit the following resources to learn more:

• @article@How to Implement Graph RAG Using Knowledge Graphs and Vector Databases
• @article@Retrieval Augmented Generation (RAG) with Vector Databases: Expanding AI Capabilities