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‎.env‎

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‎.github/workflows/pr_qc.yml‎

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name: Pull Request Quality Checks
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on:
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pull_request:
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types: [ opened, synchronize ]
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branches: [ main ]
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jobs:
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run-qa:
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runs-on: ubuntu-latest
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steps:
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- name: Check out repository code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: 3.11
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- name: Run qa
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run: |
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pip install ".[dev]"
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make qa

‎.gitignore‎

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.python-version
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__pycache__/
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build
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*.egg-info
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.venv
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.pytest_cache
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.ruff_cache
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.vscode
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.idea

‎CONTRIBUTING.md‎

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# Guardrails Validator Template
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## How to create a Guardrails Validator
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- On the top right of the page, click "Use this template", select "create a new repository" and set a name for the package. See [Naming Conventions](#naming-conventions) below.
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- Clone down the new repository.
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- Modify the class in [validator/main.py](validator/main.py) with source code for the new validator
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- Make sure that the class still inherits from `Validator` and has the `register_validator` annotation.
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- Set the `name` in the `register_validator` to the name of the repo prefixed with your org as a namespace and set the appropriate data type.
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- Change [validator/__init__.py](validator/__init__.py) to your new Validator classname instead of ValidatorTemplate
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- Perform a self install with `make dev` or `pip install -e ".[dev]"`
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- Locally test the validator with the [test instructions below](#testing-and-using-your-validator)
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- Modify the README and follow the Validator Card format; you can find an example [here](https://github.com/guardrails-ai/lowercase/blob/main/README.md)
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* Note: This package uses a pyproject.toml file, on first run, run `make dev` to pull down and install all dependencies
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### Naming Conventions
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1. Avoid using `is` and `bug`
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2. Use snake_case: i.e. `_` to separate words. e.g. valid_address
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3. For the description of the repo, write one sentence that says what the validator does; should be the same as the description in the pydoc string.
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4. When annotating the class use the `{namespace}/{validator_name}` pattern: e.g. `@register_validator(name=“guardrails/valid_address”)`
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### Testing and using your validator
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- Open [test/test-validator.py](test/test-validator.py) to test your new validator
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- Import your new validator and modify `ValidatorTestObject` accordingly
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- Modify the TEST_OUTPUT and TEST_FAIL_OUTPUT accordingly
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- Run `python test/test-validator.py` via terminal, make sure the returned output reflects the input object
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- Write advanced tests for failures, etc.
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## Upload your validator to the validator hub
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- Update the [pyproject.toml](pyproject.toml) file and make necessary changes as follows:
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- Update the `name` field to the name of your validator
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- Update the `description` field to a short description of your validator
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- Update the `authors` field to your name and email
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- Add/update the `dependencies` field to include all dependencies your validator needs.
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- If there are are any post-installation steps such as downloading tokenizers, logging into huggingface etc., update the [post-install.py](validator/post-install.py) file accordingly.
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- You can add additional files to the [validator](validator) directory, but don't rename any existing files/directories.
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- e.g. Add any environment variables (without the values, just the keys) to the [.env](.env) file.
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- Ensure that there are no other dependencies or any additional steps required to run your validator.

‎LICENSE‎

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Apache License
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‎Makefile‎

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dev:
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pip install -e ".[dev]"
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lint:
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ruff check .
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test:
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pytest ./tests
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type:
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pyright validator
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qa:
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make lint
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make type
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make test

‎README.md‎

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# Overview
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| Developed by | Guardrails AI |
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| --- | --- |
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| Date of development | Feb 15, 2024 |
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| Validator type | Format |
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| Blog | |
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| License | Apache 2 |
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| Input/Output | Output |
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## Description
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### Intended Use
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This validator is a template for creating other validators, but for demonstrative purposes it ensures that a generated output is the literal `pass`.
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### Requirements
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* Dependencies:
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- guardrails-ai>=0.4.0
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* Foundation model access keys:
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- OPENAI_API_KEY
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## Installation
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```bash
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$ guardrails hub install hub://guardrails/validator_template
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```
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## Usage Examples
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### Validating string output via Python
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In this example, we apply the validator to a string output generated by an LLM.
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```python
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# Import Guard and Validator
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from guardrails.hub import ValidatorTemplate
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from guardrails import Guard
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# Setup Guard
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guard = Guard().use(
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ValidatorTemplate
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)
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guard.validate("pass") # Validator passes
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guard.validate("fail") # Validator fails
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```
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### Validating JSON output via Python
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In this example, we apply the validator to a string field of a JSON output generated by an LLM.
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```python
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# Import Guard and Validator
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from pydantic import BaseModel, Field
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from guardrails.hub import ValidatorTemplate
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from guardrails import Guard
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# Initialize Validator
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val = ValidatorTemplate()
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# Create Pydantic BaseModel
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class Process(BaseModel):
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process_name: str
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status: str = Field(validators=[val])
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# Create a Guard to check for valid Pydantic output
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guard = Guard.from_pydantic(output_class=Process)
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# Run LLM output generating JSON through guard
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guard.parse("""
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{
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"process_name": "templating",
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"status": "pass"
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}
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""")
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```
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# API Reference
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**`__init__(self, on_fail="noop")`**
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<ul>
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Initializes a new instance of the ValidatorTemplate class.
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**Parameters**
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- **`arg_1`** *(str)*: A placeholder argument to demonstrate how to use init arguments.
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- **`arg_2`** *(str)*: Another placeholder argument to demonstrate how to use init arguments.
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- **`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.
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</ul>
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<br/>
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**`validate(self, value, metadata) -> ValidationResult`**
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<ul>
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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.
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Note:
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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.
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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.
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**Parameters**
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- **`value`** *(Any)*: The input value to validate.
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- **`metadata`** *(dict)*: A dictionary containing metadata required for validation. Keys and values must match the expectations of this validator.
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| Key | Type | Description | Default |
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| --- | --- | --- | --- |
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| `key1` | String | Description of key1's role. | N/A |
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</ul>

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