[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-07-16(UTC)"],[],[],null,["# Fine-tune RAG transformations\n\n| The [VPC-SC security controls](/vertex-ai/generative-ai/docs/security-controls) and\n| CMEK are supported by Vertex AI RAG Engine. Data residency and AXT security controls aren't\n| supported.\n\nAfter a document is ingested, Vertex AI RAG Engine runs a set of transformations to\nprepare the data for indexing. You can control your use cases using the\nfollowing parameters:\n\nA smaller chunk size means the embeddings are more precise. A larger chunk size\nmeans that the embeddings might be more general but might miss specific details.\n\nFor example, if you convert 1,000 words into an embedding array that was meant\nfor 200 words, you might lose details. The embedding capacity is fixed for each\nchunk. A large chunk of text may not fit into a small-window model.\n\nWhat's next\n-----------\n\n- Use [Document AI layout parser with Vertex AI RAG Engine](/vertex-ai/generative-ai/docs/layout-parser-integration)."]]
