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chore: import upstream snapshot with attribution
2026-07-13 13:24:08 +08:00

72 lines
2.5 KiB
YAML

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: Max-score assertion for objective output selection
# This example demonstrates how max-score can objectively select the best implementation
# based on weighted scores from multiple assertions (correctness, documentation, efficiency)
prompts:
- 'Generate a Python function to {{task}}'
- 'Write an efficient Python function to {{task}}'
- 'Create a well-documented Python function to {{task}}'
providers:
- openai:o4-mini
- anthropic:claude-sonnet-4-20250514
- google:gemini-2.5-flash
tests:
- vars:
task: 'merge two sorted lists into one sorted list'
assert:
# Correctness test
- type: python
value: |
# Test the merge function
list1 = [1, 3, 5, 7, 9]
list2 = [2, 4, 6, 8, 10]
result = merge_sorted_lists(list1, list2)
assert result == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], f"Expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], got {result}"
# Test with empty lists
assert merge_sorted_lists([], [1, 2, 3]) == [1, 2, 3]
assert merge_sorted_lists([1, 2, 3], []) == [1, 2, 3]
assert merge_sorted_lists([], []) == []
# Documentation test
- type: llm-rubric
value: 'Code includes a clear docstring explaining parameters, return value, and complexity'
# Code structure and efficiency
- type: llm-rubric
value: 'The implementation uses an efficient algorithm (O(m+n) time complexity) and follows Python best practices'
# Max-score selects the best implementation objectively
- type: max-score
value:
weights:
python: 3 # Correctness is most important
llm-rubric: 1.5 # Documentation and code quality weighted together
- vars:
task: 'check if a string is a palindrome (ignoring case and spaces)'
assert:
# Correctness
- type: python
value: |
assert is_palindrome("racecar") == True
assert is_palindrome("A man a plan a canal Panama") == True
assert is_palindrome("race a car") == False
assert is_palindrome("hello") == False
assert is_palindrome("") == True
# Edge case handling
- type: llm-rubric
value: 'Handles edge cases like empty strings and single characters correctly'
# Efficiency
- type: llm-rubric
value: 'Uses an efficient algorithm (O(n) time complexity)'
# Simple max-score (all assertions equally weighted)
- type: max-score