Embedding models
Embedding turns text, images, or videos into numbers.
These models convert text, images, and videos into numbers. They then store them in a vector database.
Later, you can use these numbers to find similar items. You compare the numbers and pick the closest match. This is called vector search.
This is how RAG works. It pulls extra context from the vector database. It then passes that context to the LLM to make the final output.
Note that the LLM doesn't call the vector database itself. The agent calls it. The agent gets the data and passes it to the LLM as context.
In AI, "embedding" means placing something complex into a simpler space. This space often has fewer dimensions. It still keeps the key traits and links.
In short, you place text, images, and videos into another space.