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Developed by | Numbers Station AI |
---|---|
Date of development | Feb 15, 2024 |
Validator type | Format |
Blog | - |
License | Apache 2 |
Input/Output | Output |
Checks that schema columns are present in a SQL query.
guardrails-ai>=0.4.0
sqlglot
guardrails hub install hub://numbersstation/sql_column_presence
In this example, we apply the validator to a string output generated by an LLM.
# Import Guard and Validator
from guardrails import Guard
from guardrails.hub import SqlColumnPresence
# Setup Guard
guard = Guard().use(SqlColumnPresence, ["name", "breed", "weight"], on_fail="exception")
guard.validate(
"SELECT name, AVG(weight) FROM animals GROUP BY name"
) # Validator passes
try:
guard.validate(
"SELECT name, color, AVG(weight) FROM animals GROUP BY name, color"
) # Validator fails
except Exception as e:
print(e)
Output:
Validation failed for field with errors: Columns [color] not in [weight, name, breed]
In this example, we apply the validator to a string field of a JSON output generated by an LLM.
# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import SqlColumnPresence
from guardrails import Guard
# Initialize Validator
val = SqlColumnPresence(["name", "breed", "weight"])
# Create Pydantic BaseModel
class Report(BaseModel):
name: str
query: str = Field(validators=[val])
# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=Process)
# Run LLM output generating JSON through guard
guard.parse("""
{
"name": "Canine Lookup",
"query": "SELECT name, AVG(weight) FROM animals GROUP BY name"
}
""")
__init__(self, cols, on_fail="noop")
Initializes a new instance of the SqlColumnPresence class.
Parameters
cols
(List[str]): The list of valid columns.on_fail
(str, Callable): The policy to enact when a validator fails. If str
, must be one of reask
, fix
, filter
, refrain
, noop
, exception
or fix_reask
. Otherwise, must be a function that is called when the validator fails.validate(self, value, metadata) -> ValidationResult
Validates the given value
using the rules defined in this validator, relying on the metadata
provided to customize the validation process. This method is automatically invoked by guard.parse(...)
, ensuring the validation logic is applied to the input data.
Note:
guard.parse(...)
where this method will be called internally for each associated Validator.guard.parse(...)
, ensure to pass the appropriate metadata
dictionary that includes keys and values required by this validator. If guard
is associated with multiple validators, combine all necessary metadata into a single dictionary.Parameters
value
(Any): The input value to validate.metadata
(dict): A dictionary containing metadata required for validation. No additional metadata keys are needed for this validator.The validator playground is available to authenticated users. Please log in to use it.