Schema Validator¶

Introduction¶

Schema validator enables users to ensure correctness of generated data before uploading to HF or File System.

Key features supported for schema validation are as follows:-

YAML based schema check:- Users can define their schema using YAML config files in the following ways:-
Define a custom schema class inside custom_schemas.py and add it's path in schema key inside schema_config.
Add expected schema config in a list of dict format inside fields key inside schema_config.
Rule based validation support:- Aside from adding validator rules inside custom class, users can choose from validation methods supported(details in additional validation rules section) and add it as a key for a particular field's dict.

Usage Illustration¶

Let's assume we have the following record generated which we want to validate:-

{
        "id": 130426,
        "conversation": [
            {
                "role": "user",
                "content": "I am trying to get the CPU cycles at a specific point in my code."
            },
            {
                "role": "assistant",
                "content": "The `rdtsc` function you're using gives you the number of cycles since the CPU was last reset, which is not what you want in this case."
            }
        ],
        "taxonomy": [
            {
                "category": "Coding",
                "subcategory": ""
            }
        ],
        "annotation_type": [
            "mistral-large"
        ],
        "language": [
            "en"
        ],
        "tags": [
            "glaiveai/glaive-code-assistant-v2",
            "reannotate",
            "self-critique"
        ]
}

For the above record, user can have the following class defined inside custom_schemas.py defining the expected keys and values along with additional validation rules if any.

class CustomUserSchema(BaseModel):
    '''
    This demonstrates an example of a customizable user schema that can be modified or redefined by the end user.
    Below is a sample schema with associated validator methods.
    '''
    id: int
    conversation: list[dict[str,Any]]
    taxonomy: list[dict[str, Any]]
    annotation_type: list[str]
    language: list[str]
    tags: list[str]

    @root_validator(pre=True)
    def check_non_empty_lists(cls, values):
        if not values.get('id'):
            raise ValueError('id cannot be empty')
        return values

Sample YAML configuration to use custom schema defined in custom_schemas.py:-¶

schema_config:
  schema: sygra.validators.custom_schemas.CustomUserSchema

Sample YAML configuration to define schema in YAML:-¶

schema_config:
  fields:
    - name: id
      type: int
      is_greater_than: 99999
    - name: conversation
      type: list[dict[str, any]]
    - name: taxonomy
      type: list[dict[str, any]]
    - name: annotation_type
      type: list[str]
    - name: language
      type: list[str]
    - name: tags
      type: list[str]

Note that fields is expected to be a list of dicts with name and type present in each dict with additional option of providing validation key. In the above example is_greater_than is a validation key shown for demonstration purpose to ensure id key in each record has a value with 6 digits or more.

Additional Validation Rules Supported:-¶

Currently we support the following validation rules that can be directly used by the user:-

is_greater_than: Ensures value present in a given field is greater than value provided by user in schema definition.
is_equal_to: Ensures value present in a given field is exactly same as value provided by user in schema definition.
is_less_than: Ensures value present in a given field is less than value provided by user in schema definition.

More rules will be added in subsequent releases for users to use directly in their schema.

Rules for using schema validation:-¶

Now that we have covered a sample example on how to define schema and use it, here are some rules users have to keep in mind:-

Schema validation is skipped if schema_config key is not present in graph_config.yaml. It is assumed that user doesn't want schema validation to happen, hence we skip validation check in this case.
If schema_config key is present in graph_config.yaml, it is expected that either schema or fields key is present inside schema_config and has been defined correctly. Absence of both or invalid definition of schema path or fields will raise exception.
type defined in either custom_schemas.py or inside fields have to be valid python types. Typo while defining type, for example lisr instead of list will raise invalid type error stopping the pipeline execution, and user has to re-define correctly.