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# integration-helicone (Helicone AI Gateway)
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You can run this example with:
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```bash
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npx promptfoo@latest init --example integration-helicone
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cd integration-helicone
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```
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This example demonstrates how to use the Helicone AI Gateway provider in promptfoo to route requests through a self-hosted Helicone AI Gateway instance for unified provider access.
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## What This Example Shows
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- **Unified Interface**: Use the same OpenAI-compatible syntax to access multiple providers
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- **Load Balancing**: Smart routing based on provider availability and performance
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- **Self-Hosted Gateway**: Full control over your LLM routing infrastructure
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- **Provider Comparison**: Compare responses from different providers through a single interface
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- **Flexible Configuration**: Easy switching between providers and models
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## Prerequisites
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1. **Helicone AI Gateway**: A running instance (we'll start one locally)
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2. **API Keys**: You'll need at least one provider API key:
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- OpenAI API key (recommended)
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- Anthropic API key (optional)
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- Groq API key (optional)
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## Setup
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1. **Set Environment Variables**:
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```bash
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# Set your provider API keys
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export OPENAI_API_KEY=your_openai_api_key_here
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export ANTHROPIC_API_KEY=your_anthropic_api_key_here # Optional
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export GROQ_API_KEY=your_groq_api_key_here # Optional
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```
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2. **Start Helicone AI Gateway**:
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```bash
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# In a separate terminal, start the gateway
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npx @helicone/ai-gateway@latest
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```
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The gateway will start on `http://localhost:8080` by default.
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3. **Install promptfoo** (if you haven't already):
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```bash
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npm install -g promptfoo
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```
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## Running the Example
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From this directory, run:
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```bash
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promptfoo eval
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```
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This will:
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- Send the same prompts to all three providers through the Helicone AI Gateway
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- Compare responses and performance across providers
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- Generate a detailed comparison report
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- Show differences in model capabilities and response patterns
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## What Happens
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1. **Request Routing**: Each request is sent to the local Helicone AI Gateway at `http://localhost:8080`
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2. **Provider Selection**: The gateway routes each request to the appropriate provider (OpenAI, Anthropic, or Groq)
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3. **Unified Interface**: All providers use the same OpenAI-compatible request/response format
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4. **Response Comparison**: promptfoo compares the responses from each provider
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## Gateway Features
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The Helicone AI Gateway provides several powerful features:
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- **Load Balancing**: Automatic routing to the fastest/most reliable provider
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- **Caching**: Built-in response caching to reduce costs and improve latency
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- **Rate Limiting**: Configurable rate limits to prevent abuse
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- **Observability**: Optional integration with Helicone's observability platform
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- **Self-Hosted**: Full control over your infrastructure and data
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## Configuration Details
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The example configuration includes:
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### Provider Setup
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```yaml
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providers:
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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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```
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### Key Features Demonstrated
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1. **Unified Interface**: All providers use the same `helicone:provider/model` format
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2. **OpenAI Compatibility**: Standard OpenAI parameters work across all providers
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3. **Easy Switching**: Change providers by simply updating the model name
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4. **Local Gateway**: All requests go through your self-hosted gateway instance
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## Customization
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You can modify the configuration to:
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1. **Add More Providers**: Include any providers supported by your Helicone AI Gateway
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2. **Change Models**: Specify different models using the `provider/model` format
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3. **Custom Gateway**: Point to a different Helicone AI Gateway instance
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4. **Router Configuration**: Use custom routers for different environments
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### Example with Custom Gateway and Router
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```yaml
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providers:
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- id: helicone:openai/gpt-4o
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config:
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baseUrl: http://my-gateway.company.com:8080
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router: production
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temperature: 0.5
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```
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## Advanced Features
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### Using Different Gateway Endpoints
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Route to different environments using routers:
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```yaml
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providers:
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- id: helicone:openai/gpt-4o
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config:
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router: production
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- id: helicone:openai/gpt-4o-mini
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config:
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router: development
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```
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### Custom Gateway Configuration
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If you're running your own Helicone AI Gateway with custom configuration:
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```yaml
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providers:
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- id: helicone:custom-provider/custom-model
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config:
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baseUrl: http://localhost:9000
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headers:
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Custom-Header: value
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```
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## Troubleshooting
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### Common Issues
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1. **Authentication Error**: Verify your `HELICONE_API_KEY` is correct
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2. **Provider API Key Missing**: Ensure you have valid API keys for the providers you're testing
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3. **No Data in Dashboard**: Check that requests are successfully completing
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### Debug Mode
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For detailed request logging:
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```bash
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LOG_LEVEL=debug promptfoo eval
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```
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## Learn More
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- [Helicone Documentation](https://docs.helicone.ai/)
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- [promptfoo Helicone Provider Guide](/docs/providers/helicone/)
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- [promptfoo Documentation](https://promptfoo.dev/docs/)
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## Next Steps
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1. **Explore the Dashboard**: Review the analytics in your Helicone dashboard
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2. **Set Up Alerts**: Configure cost and usage alerts in Helicone
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3. **Optimize Costs**: Use caching and rate limiting to reduce expenses
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4. **Scale Testing**: Add more providers and test cases for comprehensive evaluation
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@@ -0,0 +1,91 @@
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# 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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