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import os
import time

from scale_gp import SGPClient

client = SGPClient(api_key=api_key)

safety_evaluation_dataset = client.evaluation_datasets.create(
    account_id=account_id,
    name="safety_evaluation_dataset_test",
    schema_type="GENERATION",
    type="safety",
    harms_list=["toxicity", "profanity"],
    advanced_config={"emotions": ["angry", "sad", "grumpy"]},
)
print(safety_evaluation_dataset)

generation_job = client.evaluation_datasets.generation_jobs.create(
    evaluation_dataset_id=safety_evaluation_dataset.id,
    num_test_cases=3,
    group_by_artifact_id=False,
    harms_list=["toxicity", "profanity"],
    advanced_config={
        "emotions": ["angry", "sad", "grumpy"],
        "moods": ["Indicative", "Imperative", "Interrogative"],
        "methods": [
            "Asking obliquely with obscure slang or niche cultural references",
            "Making a leading statement that tries to trigger completion",
            "Framing as part of a fictional story",
        ],
        "tones": ["Formal", "Normal", "Drunk Text"],
    },
)
while True:
    generation_job = client.evaluation_datasets.generation_jobs.retrieve(
        generation_job_id=generation_job.generation_job_id,
        evaluation_dataset_id=safety_evaluation_dataset.id
    )
    if generation_job.status == "Pending":
        print("generating test cases...")
        time.sleep(5)
    else:
        break
print(generation_job)
# view autogenerated test cases
test_cases = client.evaluation_datasets.autogenerated_draft_test_cases.list(
    evaluation_dataset_id=safety_evaluation_dataset.id
)
print(test_cases.itmes)
for test_case in test_cases.items:
    client.evaluation_datasets.autogenerated_draft_test_cases.approve(
        evaluation_dataset_id=safety_evaluation_dataset.id,
        autogenerated_draft_test_case_id=test_case.id,
    )

published_dataset_response = client.evaluation_datasets.publish(
    evaluation_dataset_id=safety_evaluation_dataset.id,
)

EvaluationDataset(
    id='6cd3aa30-57e7-4cb4-9315-d90ed487442d',
    account_id='66049ada2fc77c99ef015be7',
    created_at=datetime.datetime(2024, 9, 26, 20, 10, 57, 183391),
    created_by_user_id='42a5c8af-f698-43d0-923e-ba70102a2887',
    draft=None,
    name='safety_evaluation_dataset_test',
    schema_type='GENERATION',
    updated_at=datetime.datetime(2024, 9, 26, 20, 10, 57, 183391),
    archived_at=None,
    evaluation_dataset_metadata={
        'harms_list': [
            'toxicity',
            'profanity'
        ]
    },
    knowledge_base_id=None,
    out_of_date=None,
    vendor=None
)
Follow the instructions in the Quickstart Guide to setup the SGP ClientFetch a Knowledge Base ID from: https://egp.dashboard.scale.com/knowledge-bases
from scale_gp import SGPClient

client = SGPClient(api_key=api_key)
For safety evaluation datasets, a generation job workflow is created to generate test cases. You must additionally define a harms list, which include topics that you want to test that your application or model handles properly. Advanced configs for emotions, moods, methods, tones can also be provided

safety_evaluation_dataset = client.evaluation_datasets.create(
    account_id=account_id,
    name="safety_evaluation_dataset_test",
    schema_type="GENERATION",
    type="safety",
    harms_list=["toxicity", "profanity"],
    advanced_config={"emotions": ["angry", "sad", "grumpy"]},
)
Start the generation job. This job will generate test cases based on the provided harms list and advanced configs.

generation_job = client.evaluation_datasets.generation_jobs.create(
    evaluation_dataset_id=safety_evaluation_dataset.id,
    num_test_cases=3,
    group_by_artifact_id=False,
    harms_list=["toxicity", "profanity"],
    advanced_config={
        "emotions": ["angry", "sad", "grumpy"],
        "moods": ["Indicative", "Imperative", "Interrogative"],
        "methods": [
            "Asking obliquely with obscure slang or niche cultural references",
            "Making a leading statement that tries to trigger completion",
            "Framing as part of a fictional story",
        ],
        "tones": ["Formal", "Normal", "Drunk Text"],
    },
)
while True:
    generation_job = client.evaluation_datasets.generation_jobs.retrieve(
        generation_job_id=generation_job.generation_job_id,
        evaluation_dataset_id=safety_evaluation_dataset.id
    )
    if generation_job.status == "Pending":
        print("generating test cases...")
        time.sleep(5)
    else:
        break

# view autogenerated test cases
test_cases = client.evaluation_datasets.autogenerated_draft_test_cases.list(
    evaluation_dataset_id=safety_evaluation_dataset.id
)
Before publishing the dataset, review the auto-generated test cases and approve/decline each test case. Publishing is blocked until all test cases are reviewed.

for test_case in test_cases.items:
    client.evaluation_datasets.autogenerated_draft_test_cases.approve(
        evaluation_dataset_id=safety_evaluation_dataset.id,
        autogenerated_draft_test_case_id=test_case.id,
    )
Publishing the dataset allows it to be available for use in evaluations
published_dataset_response = client.evaluation_datasets.publish(
    evaluation_dataset_id=safety_evaluation_dataset.id,
)

import os
import time

from scale_gp import SGPClient

client = SGPClient(api_key=api_key)

safety_evaluation_dataset = client.evaluation_datasets.create(
    account_id=account_id,
    name="safety_evaluation_dataset_test",
    schema_type="GENERATION",
    type="safety",
    harms_list=["toxicity", "profanity"],
    advanced_config={"emotions": ["angry", "sad", "grumpy"]},
)
print(safety_evaluation_dataset)

generation_job = client.evaluation_datasets.generation_jobs.create(
    evaluation_dataset_id=safety_evaluation_dataset.id,
    num_test_cases=3,
    group_by_artifact_id=False,
    harms_list=["toxicity", "profanity"],
    advanced_config={
        "emotions": ["angry", "sad", "grumpy"],
        "moods": ["Indicative", "Imperative", "Interrogative"],
        "methods": [
            "Asking obliquely with obscure slang or niche cultural references",
            "Making a leading statement that tries to trigger completion",
            "Framing as part of a fictional story",
        ],
        "tones": ["Formal", "Normal", "Drunk Text"],
    },
)
while True:
    generation_job = client.evaluation_datasets.generation_jobs.retrieve(
        generation_job_id=generation_job.generation_job_id,
        evaluation_dataset_id=safety_evaluation_dataset.id
    )
    if generation_job.status == "Pending":
        print("generating test cases...")
        time.sleep(5)
    else:
        break
print(generation_job)
# view autogenerated test cases
test_cases = client.evaluation_datasets.autogenerated_draft_test_cases.list(
    evaluation_dataset_id=safety_evaluation_dataset.id
)
print(test_cases.itmes)
for test_case in test_cases.items:
    client.evaluation_datasets.autogenerated_draft_test_cases.approve(
        evaluation_dataset_id=safety_evaluation_dataset.id,
        autogenerated_draft_test_case_id=test_case.id,
    )

published_dataset_response = client.evaluation_datasets.publish(
    evaluation_dataset_id=safety_evaluation_dataset.id,
)

EvaluationDataset(
    id='6cd3aa30-57e7-4cb4-9315-d90ed487442d',
    account_id='66049ada2fc77c99ef015be7',
    created_at=datetime.datetime(2024, 9, 26, 20, 10, 57, 183391),
    created_by_user_id='42a5c8af-f698-43d0-923e-ba70102a2887',
    draft=None,
    name='safety_evaluation_dataset_test',
    schema_type='GENERATION',
    updated_at=datetime.datetime(2024, 9, 26, 20, 10, 57, 183391),
    archived_at=None,
    evaluation_dataset_metadata={
        'harms_list': [
            'toxicity',
            'profanity'
        ]
    },
    knowledge_base_id=None,
    out_of_date=None,
    vendor=None
)
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