From Prompt

from trusttest.dataset_builder.single_prompt import SinglePromptDatasetBuilder, DatasetItem
from trusttest.probes.dataset import PromptDatasetProbe
from trusttest.evaluation_contexts import ExpectedResponseContext
from trusttest.targets.http import HttpTarget, PayloadConfig
from trusttest.evaluators.llm_judges import CorrectnessEvaluator
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluation_scenarios import EvaluationScenario

# Configure target
target = HttpTarget(
    url="https://your-model-endpoint.com/chat",
    headers={"Content-Type": "application/json"},
    payload_config=PayloadConfig(
        format={"messages": [{"role": "user", "content": "{{ test }}"}]},
        message_regex="{{ test }}",
    ),
)

# Create dataset builder
builder = SinglePromptDatasetBuilder(
    instructions="""
    Generate questions that a customer might ask a support chatbot for an e-commerce platform.
    
    Include questions about:
    - Order status and tracking
    - Returns and refunds
    - Product information
    - Account management
    - Shipping options
    
    Each question should be realistic and varied.
    """,
    examples=[
        DatasetItem(
            question="Where is my order #12345?",
            context=ExpectedResponseContext(
                expected_response="I can help you track your order. Please provide your order number and I'll look up the current status."
            ),
        ),
        DatasetItem(
            question="How do I return a defective item?",
            context=ExpectedResponseContext(
                expected_response="To return a defective item, go to your Orders page, select the item, and click 'Return'. We'll provide a prepaid shipping label."
            ),
        ),
    ],
    context_type=ExpectedResponseContext,
    language="English",
    num_items=50,
)

# Create probe
probe = PromptDatasetProbe(target=target, dataset_builder=builder)

# Generate test set
test_set = probe.get_test_set()

# Evaluate
evaluator = CorrectnessEvaluator()
suite = EvaluatorSuite(evaluators=[evaluator])
scenario = EvaluationScenario(evaluator_suite=suite)

results = scenario.evaluate(test_set)
results.display_summary()

from typing import Sequence
from trusttest.dataset_builder.single_prompt import SinglePromptDatasetBuilder, DatasetItem
from trusttest.evaluation_contexts import ExpectedResponseContext

class DomainSpecificBuilder(SinglePromptDatasetBuilder[ExpectedResponseContext]):
    """Generate domain-specific functional tests."""
    
    def __init__(
        self,
        domain: str,
        num_items: int = 20,
    ) -> None:
        super().__init__(
            instructions=f"""
            Generate realistic questions for a {domain} assistant.
            
            Questions should:
            - Be specific to the {domain} domain
            - Vary in complexity (simple to complex)
            - Cover different aspects of the domain
            - Be phrased as real users would ask them
            """,
            examples=[
                DatasetItem(
                    question=f"What is the most important thing to know about {domain}?",
                    context=ExpectedResponseContext(
                        expected_response=f"A comprehensive overview of key {domain} concepts."
                    ),
                ),
            ],
            context_type=ExpectedResponseContext,
            language="English",
            num_items=num_items,
        )
        self.domain = domain

    async def _build_batch_instructions(
        self,
        batch_size: int,
        previous_questions: Sequence[str],
    ) -> str:
        base = f"""
        Generate {batch_size} questions for the {self.domain} domain.
        
        Make them diverse and realistic.
        """
        
        if previous_questions:
            return f"{base}\n\nAvoid these already generated questions:\n{list(previous_questions)}"
        return base


# Use custom builder
builder = DomainSpecificBuilder(domain="healthcare", num_items=30)
probe = PromptDatasetProbe(target=target, dataset_builder=builder)

# ❌ Too vague
instructions = "Generate customer questions"

# ✅ Specific and detailed
instructions = """
Generate questions that customers of a SaaS project management tool might ask.

Focus on:
- Feature discovery ("How do I...")
- Troubleshooting ("Why isn't X working...")
- Best practices ("What's the best way to...")

Questions should be:
- Realistic and natural-sounding
- Specific to project management
- Varied in complexity
"""

examples = [
    # Simple factual question
    DatasetItem(
        question="How do I create a new project?",
        context=ExpectedResponseContext(
            expected_response="Click the '+' button in the top right..."
        ),
    ),
    # Troubleshooting question
    DatasetItem(
        question="Why can't I see my team member's tasks?",
        context=ExpectedResponseContext(
            expected_response="This might be due to permission settings..."
        ),
    ),
    # Best practice question
    DatasetItem(
        question="What's the best way to organize sprints?",
        context=ExpectedResponseContext(
            expected_response="We recommend starting with 2-week sprints..."
        ),
    ),
]

from trusttest.dataset_builder.conversation import ConversationDatasetBuilder

builder = ConversationDatasetBuilder(
    instructions="""
    Generate multi-turn customer support conversations.
    
    Conversations should:
    - Start with a customer issue
    - Include follow-up questions
    - End with resolution
    """,
    num_turns=3,
    num_conversations=20,
)

probe = PromptDatasetProbe(target=target, dataset_builder=builder)