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docs: Update prompt optimizer code in readme to use methods from prompts module #6296

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copybara-service merged 1 commit into from
Jan 26, 2026
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docs: Update prompt optimizer code in readme to use methods from prompts module #6296

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14 changes: 7 additions & 7 deletions README.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -173,19 +173,19 @@ You can also run queries against the deployed agent:
Prompt optimization
^^^^^^^^^^^^^^^^^^^

To do a zero-shot prompt optimization, use the `optimize_prompt`
To do a zero-shot prompt optimization, use the `optimize`
method.

.. code-block:: Python

prompt = "Generate system instructions for a question-answering assistant"
response = client.prompt_optimizer.optimize_prompt(prompt=prompt)
response = client.prompts.optimize(prompt=prompt)
print(response.raw_text_response)
if response.parsed_response:
print(response.parsed_response.suggested_prompt)

To call the data-driven prompt optimization, call the `optimize` method.
In this case however, we need to provide `vapo_config`. This config needs to
To call the data-driven prompt optimization, call the `launch_optimization_job` method.
In this case however, we need to provide a VAPO (Vertex AI Prompt Optimizer) config. This config needs to
have either service account or project **number** and the config path.
Please refer to this [tutorial](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/data-driven-optimizer)
for more details on config parameter.
Expand All @@ -206,7 +206,7 @@ for more details on config parameter.
# Set up logging to see the progress of the optimization job
logging.basicConfig(encoding='utf-8', level=logging.INFO, force=True)

result = client.prompt_optimizer.optimize(method="vapo", config=vapo_config)
result = client.prompts.launch_optimization_job(method=types.PromptOptimizerMethod.VAPO, config=vapo_config)

If you want to use the project number instead of the service account, you can
instead use the following config:
Expand All @@ -219,11 +219,11 @@ instead use the following config:
wait_for_completion=False
)

We can also call optimize method async.
We can also call the `launch_optimization_job` method asynchronously.

.. code-block:: Python

await client.aio.prompt_optimizer.optimize(method="vapo", config=vapo_config)
await client.aio.prompts.launch_optimization_job(method=types.PromptOptimizerMethod.VAPO, config=vapo_config)

Prompt Management
^^^^^^^^^^^^^^^^^
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