Mulai 29 April 2025, model Gemini 1.5 Pro dan Gemini 1.5 Flash tidak tersedia di project yang belum pernah menggunakan model ini, termasuk project baru. Untuk mengetahui detailnya, lihat
Versi dan siklus proses model.
Membuat respons menggunakan file RAG
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh ini menunjukkan cara membuat konten menggunakan file RAG.
Mempelajari lebih lanjut
Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:
Contoh kode
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","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)."]]