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https://github.com/hyparam/csv-validator
CSV validator for Guardrails AI
https://github.com/hyparam/csv-validator
ai csv guardrails
Last synced: 2 months ago
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CSV validator for Guardrails AI
- Host: GitHub
- URL: https://github.com/hyparam/csv-validator
- Owner: hyparam
- License: apache-2.0
- Created: 2024-01-30T18:20:52.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-27T19:35:57.000Z (8 months ago)
- Last Synced: 2024-05-28T05:12:42.699Z (8 months ago)
- Topics: ai, csv, guardrails
- Language: Python
- Homepage:
- Size: 125 KB
- Stars: 4
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Guardrails CSV Validator
[](https://opensource.org/licenses/Apache-2-0)
|||
|---| --- |
| Developed by | Hyperparam |
| Date of development | Feb 15, 2024 |
| Validator type | Format |
| Blog | |
| License | Apache 2 |
| Input/Output | Output |
## Description
### Intended Use
A CSV validator for [Guardrails AI](https://www.guardrailsai.com/).
This validator checks for various CSV issues such as mismatched column lengths, or mismatched quote delimiters.
### Requirements
* Dependencies:
- guardrails-ai>=0.4.0
## Installation
```bash
$ guardrails hub install hub://hyparam/csv_validator
```
## Usage Examples
### Validating string output via Python
In this example, we apply the validator to a string output generated by an LLM.
```python
# Import Guard and Validator
from guardrails.hub import CsvMatch
from guardrails import Guard
# Setup Guard
guard = Guard().use(
CsvMatch
)
guard.validate("name,email\njohn,[email protected]\njane,[email protected]") # Validator passes
guard.validate("name,email\njohn\njane,[email protected]") # Validator fails
```
### Validating JSON output via Python
In this example, we apply the validator to a string field of a JSON output generated by an LLM.
```python
# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import CsvMatch
from guardrails import Guard
# Initialize Validator
val = CsvMatch()
# Create Pydantic BaseModel
class DbBackup(BaseModel):
db_name: str
data: str = Field(validators=[val])
# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=DbBackup)
# Run LLM output generating JSON through guard
guard.parse("""
{
"db_name": "USERS",
"data": "name,email\njohn,[email protected]\njane,[email protected]"
}
""")
```
# API Reference
**`__init__(self, on_fail="noop")`**
Initializes a new instance of the CsvMatch class.
**Parameters**
- **`delimiter`** *(str)*: String delimiter for csv. Defaults to `,`.
- **`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:
1. This method should not be called directly by the user. Instead, invoke `guard.parse(...)` where this method will be called internally for each associated Validator.
2. When invoking `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.