Rank documents

Rank documents

Explore further

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

• Rank and rerank documents with RAG

Code sample

Python

For more information, see the Vertex AI Agent Builder Python API reference documentation.

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

from google.cloud import discoveryengine_v1 as discoveryengine

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"

client = discoveryengine.RankServiceClient()

# The full resource name of the ranking config.
# Format: projects/{project_id}/locations/{location}/rankingConfigs/default_ranking_config
ranking_config = client.ranking_config_path(
    project=project_id,
    location="global",
    ranking_config="default_ranking_config",
)
request = discoveryengine.RankRequest(
    ranking_config=ranking_config,
    model="semantic-ranker-512@latest",
    top_n=10,
    query="What is Google Gemini?",
    records=[
        discoveryengine.RankingRecord(
            id="1",
            title="Gemini",
            content="The Gemini zodiac symbol often depicts two figures standing side-by-side.",
        ),
        discoveryengine.RankingRecord(
            id="2",
            title="Gemini",
            content="Gemini is a cutting edge large language model created by Google.",
        ),
        discoveryengine.RankingRecord(
            id="3",
            title="Gemini Constellation",
            content="Gemini is a constellation that can be seen in the night sky.",
        ),
    ],
)

response = client.rank(request=request)

# Handle the response
print(response)

What's next

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