A. It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.

B. It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.

C. It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.

D. It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.

E. It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.