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Return the response from the LLM
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This sample demonstrates how to run a retrieval query to get a response from the LLM.
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For detailed documentation that includes this code sample, see the following:
Code sample
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[[["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,["# Return the response from the LLM\n\nThis sample demonstrates how to run a retrieval query to get a response from the LLM.\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 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 response = rag.retrieval_query(\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 text=\"Hello World!\",\n rag_retrieval_config=rag.RagRetrievalConfig(\n top_k=10,\n filter=rag.utils.resources.Filter(vector_distance_threshold=0.5),\n ),\n )\n print(response)\n # Example response:\n # contexts {\n # contexts {\n # source_uri: \"gs://your-bucket-name/file.txt\"\n # text: \"....\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)."]]