Parameterized Runs

In production, it is common for the same DAG code to be run on different days, or to call an external service (for example, an LLM for data enrichment). Bauplan supports these scenarios with parameters and secrets.

You can define a parameter using the CLI either as a simple value or by loading from a file.

Value

bauplan parameter set prompt_summary "Write a concise, incisive summary of the news article." --description "Prompt passed to GPT for article summarization"

File

For longer values like LLM prompts, SQL templates, or configuration blobs, you can load a parameter from a file:

bauplan parameter set prompt_template --type str --file prompts/summary.txt

note

For other commands related to parameters, see the reference.

Each parameter is defined in bauplan_project.yml with a type and a default value.

parameters:
  interest_rate:
    default: 3.5
    type: float
  loan_amount:
    default: 200000
    type: int

Parameters can be:

• Strings (str)
• Numbers (int, float)
• Booleans (bool)
• Secrets (a special, encrypted type of parameter used for API keys or credentials)

Parameters

Parameters let you pass runtime values to models without changing code. They can be accessed in a variety of ways.

As a variable

Parameters can be passed as variables to your model code:

@bauplan.model()
@bauplan.python('3.11')
def loan_model(
    interest_rate=bauplan.Parameter('interest_rate'), # passing parameters as variables
    loan_amount=bauplan.Parameter('loan_amount'),
    loan_term_years=bauplan.Parameter('loan_term_years'),
):
    ...

note

To learn more, see reference.

As a filter

Parameters can be passed in bauplan.Model as a filter, using the $param_name syntax:

@bauplan.model()
@bauplan.python('3.11')
def qualifying_loans(
    data=bauplan.Model(
        'loans',
        filter='interest_rate <= $interest_rate AND amount >= $loan_amount',
    ),
):
    ...

SQL query

In SQL, use $param_name directly in your query:

SELECT * FROM loans
WHERE interest_rate <= $interest_rate
AND amount >= $loan_amount

Defaults can be overridden at runtime:

CLI

bauplan run --param interest_rate=4.5 --param loan_amount=500000

SDK

import bauplan

client = bauplan.Client()
client.run(
    project_dir="./pipeline",
    parameters={"interest_rate": 4.5, "loan_amount": 500000},
)

---

Secrets

Secrets are encrypted parameters for sensitive values like API keys, database credentials, or tokens. They are encrypted, versioned, and injected into your pipeline code at runtime, avoiding hardcoding.

Use the CLI with --type secret to create one:

bauplan parameter set openai_api_key sk-abc123... --type secret

Bauplan encrypts the value via AWS KMS and stores only the ciphertext in your bauplan_project.yml. Secrets are never stored or transmitted in plaintext.

In your code, reference secrets with bauplan.Parameter just like regular parameters.

For a complete walkthrough using secrets with an LLM pipeline, see the Using LLMs with Secrets example.