2.Chunking: This involves dividing the loaded text into
smaller chunks. This is necessary because language mod-
els typically have a limit on the amount of context they can
handle, so it is necessary to create as small text chunks as
possible.

3. Embedding and Creating Index: This is the process of
encoding text into vectors through a language model. The re-
sulting vectors will be used in the subsequent retrieval process
to calculate the similarity between the vector and the problem
vector.The embedding models require a high inference speed.
Since it is necessary to encode a large amount of corpus and
encode the problem in real time when the user asks a question,
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey