Embedding models are specifically designed to take text and create a numerical representation of it (I.e. a vector)
Then when you query something, the query text is embedded, and using cosine similarity the most similar nodes can be retrieved and sent to the LLM as context to answer that query
In the link I sent, it downloads and runs a local embedding model from huggingface