Nicholas Riegel
2933ff8f4b
Created 5 feature files covering blog generation, scheduling, LLM provider selection, SEO population, and notification emails. These files define the Gherkin scenarios that pytest-bdd will generate test functions for. - blog_generation.feature: On-demand generation with auto-publish toggle - blog_scheduling.feature: Scheduled cron slot execution - llm_provider_selection.feature: Provider dispatch and error handling - seo_population.feature: SEO metadata and tag assignment - notification_email.feature: Email notifications after generation Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
30 lines
1.3 KiB
Gherkin
30 lines
1.3 KiB
Gherkin
Feature: LLM provider selection
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As a content system
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I want to route LLM calls to different providers
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So that I can use multiple AI services
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Scenario Outline: Different providers can generate blog content
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Given provider is "<provider>" and model is "<model>"
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And the Anthropic API key is configured in Settings
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When the LLM router is called with a prompt
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Then returns a non-empty string response
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And the generation log records the correct LLM provider
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And the generation log records the correct LLM model
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Examples:
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| provider | model |
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| anthropic | claude-sonnet-4-20250514 |
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| openai | gpt-4o |
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| gemini | gemini-2.0-flash |
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Scenario: Ollama provider generates blog content using local model
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Given the Ollama base URL is "http://localhost:11434"
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And provider is "ollama" and model is "mistral"
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When the LLM router is called with a prompt
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Then returns a non-empty string response
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And the router calls http://localhost:11434/api/chat
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Scenario: Unknown provider raises configuration error
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Given provider is "unknown_provider" and model is "some-model"
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When the LLM router is called with a prompt
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Then a UserError is raised with message containing "not configured"
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