RAG 파일을 사용하여 응답 생성
컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
이 샘플에서는 RAG 파일을 사용하여 콘텐츠를 생성하는 방법을 보여줍니다.
더 살펴보기
이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.
코드 샘플
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
[[["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"]],[],[],[],null,["# Generate responses using the RAG file\n\nThis sample demonstrates how to generate the content using a RAG file.\n\nExplore further\n---------------\n\n\nFor detailed documentation that includes this code sample, see the following:\n\n- [RAG Engine API](/vertex-ai/generative-ai/docs/model-reference/rag-api-v1)\n- [Use a Weaviate database with Vertex AI RAG Engine](/vertex-ai/generative-ai/docs/rag-engine/use-weaviate-db)\n- [Use Vertex AI Feature Store in Vertex AI RAG Engine](/vertex-ai/generative-ai/docs/rag-engine/use-feature-store-with-rag)\n- [Use Vertex AI Search as a retrieval backend using Vertex AI RAG Engine](/vertex-ai/generative-ai/docs/rag-engine/use-vertexai-search)\n- [Use Vertex AI Vector Search with Vertex AI RAG Engine](/vertex-ai/generative-ai/docs/rag-engine/use-vertexai-vector-search)\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n\n from vertexai import rag\n from vertexai.generative_models import https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.GenerativeModel.html, https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.Tool.html\n import https://cloud.google.com/python/docs/reference/vertexai/latest/\n\n # TODO(developer): Update and un-comment below lines\n # PROJECT_ID = \"your-project-id\"\n # corpus_name = \"projects/{PROJECT_ID}/locations/us-central1/ragCorpora/{rag_corpus_id}\"\n\n # Initialize Vertex AI API once per session\n https://cloud.google.com/python/docs/reference/vertexai/latest/.init(project=PROJECT_ID, location=\"us-central1\")\n\n rag_retrieval_tool = https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.Tool.html.from_retrieval(\n retrieval=rag.Retrieval(\n source=rag.VertexRagStore(\n rag_resources=[\n rag.RagResource(\n rag_corpus=corpus_name,\n # Optional: supply IDs from `rag.list_files()`.\n # rag_file_ids=[\"rag-file-1\", \"rag-file-2\", ...],\n )\n ],\n rag_retrieval_config=rag.RagRetrievalConfig(\n top_k=10,\n filter=rag.utils.resources.Filter(vector_distance_threshold=0.5),\n ),\n ),\n )\n )\n\n rag_model = GenerativeModel(\n model_name=\"gemini-2.0-flash-001\", tools=[rag_retrieval_tool]\n )\n response = rag_model.https://cloud.google.com/python/docs/reference/vertexai/latest/vertexai.preview.generative_models.GenerativeModel.html#vertexai_preview_generative_models_GenerativeModel_generate_content(\"Why is the sky blue?\")\n print(response.text)\n # Example response:\n # The sky appears blue due to a phenomenon called Rayleigh scattering.\n # Sunlight, which contains all colors of the rainbow, is scattered\n # by the tiny particles in the Earth's atmosphere....\n # ...\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=generativeaionvertexai)."]]