Starting April 29, 2025, Gemini 1.5 Pro and Gemini 1.5 Flash models are not available in projects that have no prior usage of these models, including new projects. For details, see
Model versions and lifecycle.
Generate responses using the RAG file
Stay organized with collections
Save and categorize content based on your preferences.
This sample demonstrates how to generate the content using a RAG file.
Explore further
For detailed documentation that includes this code sample, see the following:
Code sample
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","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)."]]