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92 lines
2.5 KiB
YAML
92 lines
2.5 KiB
YAML
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
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description: 'Helicone AI Gateway provider comparison'
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providers:
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# Basic usage with different providers through Helicone AI Gateway
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- id: helicone:openai/gpt-4o-mini
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label: 'OpenAI via Helicone Gateway'
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config:
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temperature: 0.7
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max_tokens: 500
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- id: helicone:anthropic/claude-sonnet-4-6
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label: 'Anthropic via Helicone Gateway'
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config:
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temperature: 0.7
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max_tokens: 500
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- id: helicone:groq/llama-3.1-8b-instant
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label: 'Groq via Helicone Gateway'
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config:
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temperature: 0.7
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max_tokens: 500
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prompts:
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- |
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You are a helpful AI assistant. Please answer the following question concisely and accurately.
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Question: {{question}}
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Provide a clear, informative response.
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tests:
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- description: 'Basic question answering'
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vars:
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question: 'What is machine learning?'
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assert:
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- type: contains
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value: 'algorithm'
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- type: contains
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value: 'data'
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- type: llm-rubric
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value: 'Response accurately explains machine learning concepts'
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- description: 'Creative writing task'
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vars:
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question: 'Write a short story about a robot learning to paint in exactly 3 sentences.'
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assert:
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- type: llm-rubric
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value: 'Story is exactly 3 sentences long'
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- type: llm-rubric
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value: 'Story is creative and engaging'
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- type: contains
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value: 'robot'
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- description: 'Technical explanation'
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vars:
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question: 'Explain the difference between supervised and unsupervised learning.'
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assert:
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- type: contains
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value: 'supervised'
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- type: contains
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value: 'unsupervised'
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- type: llm-rubric
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value: 'Explanation clearly distinguishes between the two types of learning'
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- description: 'Math problem solving'
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vars:
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question: 'If a train travels 60 miles per hour for 2.5 hours, how far does it travel?'
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assert:
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- type: contains
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value: '150'
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- type: llm-rubric
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value: 'Calculation is correct and clearly explained'
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- description: 'Code explanation'
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vars:
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question: 'What does this Python code do: `[x**2 for x in range(10)]`?'
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assert:
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- type: contains
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value: 'list comprehension'
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- type: contains
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value: 'square'
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- type: llm-rubric
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value: 'Explanation is accurate and includes the output'
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defaultTest:
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options:
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# Helicone provides built-in cost tracking
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# Enable cost tracking for comparison
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includeMetrics: true
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