Model#

relationalai

#class Model(
    name: str,
    *,
    format: str = "default",
    profile: str | None = None,
    connection: SnowflakeConnection | None = None
)

A RelationalAI model is made up of objects, which represent real-world entities and are categorized by type, and rules that describe objects and the relationships between them. The Model class is used to create, manage, and query models.

Parameters#

Name	Type	Description
`name`	`str`	The name of the model. Must be at least three characters. May only contain letters, numbers, and underscores.
`format`	`str`	Optional format for query responses. Default is `"default"`, which stores results as a pandas DataFrame. Use `"snowpark"` to get results as a Snowpark DataFrame.
`profile`	`str` or `None`	Optional name of the configuration profile to use when creating the model. If `None`, the currently active profile is used.
`connection`	`Session` or `None`	Optional Snowpark Session object to use when creating the model. If `None`, connection details in the configuration profile set by the `profile` parameter are used. Ignored in Azure-based projects.

Attributes#

Name	Type	Description
`.name`	`str`	The name of the model.

Methods#

Name	Description	Returns
`.Type()`	Creates a `Type` for categorizing objects in the model.	`Type`
`.rule()`	Adds a rule to the model.	`Context`
`.query()`	Queries the model.	`Context`
`.export()`	Exports a Python function as a Snowflake stores procedure. Used as a decorator.	`Callable`

.rule() and .query() are Python context managers and must be used in a with statement. The with block defines the scope of the rule or query and is where you define the rule or query logic using the RelationalAI query-builder syntax.

The following methods create contexts for expressing conditional logic in rules and queries and must be called from inside a .rule() or .query() context’s with block:

Name	Description	Returns
`.found()`	Checks that a subquery has at least one result.	`Context`
`.not_found()`	Checks that a subquery has no results.	`Context`
`.match()`	Matches objects to one or more subqueries. Similar to a Python `match` or a SQL `CASE` statment.	`Context`
`.case()`	Creates a subquery inside of a .`match()` context.	`Context`

Example#

#import relationalai as rai

# Create a new model named "people."
model = rai.Model("people")

# Get the Person table from the 'sandbox.public' schema in Snowflake.
Person = model.Type("Person", source="sandbox.public.person")

# Create an Adult type in the model.
Adult = model.Type("Adult")

# The model.rule() context manager is used to define rules in the model.
# This rule adds all people over 18 to the Adult type.
with model.rule():
    person = Person()
    person.age >= 18
    person.set(Adult)

# The model.query() context manager is used to query the model.
# This query returns the names of all adults in the model.
with model.query() as select:
    adult = Adult()
    response = select(adult.name)

print(response.results)
# Output:
#     name
# 0  Alice
# 1    Bob

IMPORTANT

Before you can access a Snowflake table in a model, such as the sandbox.public.person table in the preceding example, you must import the table using the rai imports:stream CLI command or the RelationalAI SQL Library. Your Snowflake user must have the correct privileges to create a stream on the table. Contact your Snowflake administrator if you need help importing a Snowflake table.

By default, query results are returned as a pandas DataFrame, which downloads to your local machine and can be slow down large datasets. To keep results in Snowflake and avoid downloads, use format="snowpark" when creating the model:

## NOTE: The format parameter must be passed as a keyword argument.
model = rai.Model("people", format="snowpark")

# ...

with model.query() as select:
    adult = Adult()
    response = select(adult.name)

# The response is a Snowpark DataFrame.
print(response.results)
# Output:
# <snowflake.snowpark.dataframe.DataFrame object at 0x1313d3590>

# To view the contents of the Snowpark DataFrame, use the .show() method.
response.results.show()
# Output:
# ----------
# |"NAME"  |
# ----------
# |Bob     |
# |Alice   |
# ----------

# To save results to a table in Snowflake, use the .write() method.
response.results.write.save_as_table("sandbox.public.adults")

Refer the Snowflake documentation for details on working with Snowpark DataFrames.

You may also export a Python function as a Snowflake stored procedure using the .export() method, allowing Snowflake SQL users to interact with the model:

## Export a function to the `sandbox.public` schema in Snowflake.
# The stored procedure is named "get_adults_by_age".
# NOTE: Type hints are required for the function parameters and return values.

from typing import Tuple

@model.export("sandbox.public")
def get_adults_by_age(age: int) -> Tuple[str, int]:
    adult = Adult(age=age)
    return adult.name, adult.age

# The stored procedure can be called in Snowflake SQL:
# CALL sandbox.public.get_adults_by_age(21);

Anything you can do in a query context can be done in an exported function, including advanced logic and graph analytics. See .export() for more details.

To use a configuration profile other than the active profile, pass the profile name to the profile parameter when creating the model:

## NOTE: The profile name must be passed as a keyword argument.
model = rai.Model("people", profile="my_profile")

To use a Snowpark Session other than the one specified in the configuration profile, pass a Session object to the connection parameter when creating the model:

#from snowflake.snowpark import Session

connection_parameters = {
    "user": <USER>,
    "password": <PASSWORD>,
    "account": <ACCOUNT>,
    "warehouse": <WAREHOUSE>,
    "database": <DATABASE>,
    "schema": <SCHEMA>
)

conn = Session.builder.configs(connection_parameters).create()

# NOTE: The connection object must be passed as a keyword argument.
model = rai.Model("people", connection=conn)

See the Snowflake docs for more details on creating Snowpark Session objects.