Try Gemini 1.5 models , the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models , the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Get information about an index

This sample demonstrates how to get information about an index.

Explore further

For detailed documentation that includes this code sample, see the following:

LlamaIndex on Vertex AI for RAG API

Code sample

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

 from vertexai.preview import rag
import vertexai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# corpus_name = "projects/{project_id}/locations/us-central1/ragCorpora/{rag_corpus_id}"

# Initialize Vertex AI API once per session
vertexai.init(project=project_id, location="us-central1")

corpus = rag.get_corpus(name=corpus_name)
print(corpus)

What's next

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