Nicholas Riegel
8469ef8b33
- Split monolithic blog_generation.feature into separate files per feature: * blog_generation.feature: On-demand AI blog generation (3 scenarios) * blog_scheduling.feature: Scheduled posts (2 scenarios) * llm_provider_selection.feature: Multi-provider routing (6 scenarios) * seo_population.feature: SEO field population (1 scenario) * notification_email.feature: Post-generation emails (2 scenarios) Total: 14 BDD scenarios covering all major workflows - Extended test_bdd_steps.py from 363 to 472 lines with new step definitions: * Added no_email_sent() for draft post email suppression verification * Added email_contains_title() for email content validation * Added email_contains_social_copy() for platform copy verification * Added blog_post_has_tags(), blog_post_has_tag() for tag verification * Added blog_post_has_social_copy(), at_least_one_platform_enabled() * Added log_has_correct_provider(), log_has_correct_model() * Added log_trigger_source(), generation_duration_recorded() Follows pytest-bdd best practices: one feature per file, each with dedicated scenarios and step definitions. All 14 scenarios now have complete step coverage. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
46 lines
2 KiB
Gherkin
46 lines
2 KiB
Gherkin
# =================================================================
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# Multi-provider LLM routing
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# =================================================================
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Feature: Multi-provider LLM routing
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As an ITSulu admin
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I want to choose which LLM provider generates each blog post
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So that I can control cost, quality, and availability
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Background:
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Given the Anthropic API key is configured in Settings
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And the blog "ITSulu Insights" exists in Odoo
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Scenario: Anthropic provider generates blog content
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Given provider is "anthropic" and model is "claude-sonnet-4-20250514"
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When the LLM router is called with a prompt
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Then the router calls the Anthropic /v1/messages endpoint
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And returns a non-empty string response
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Scenario: OpenAI provider generates blog content
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Given provider is "openai" and model is "gpt-4o"
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When the LLM router is called with a prompt
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Then the router calls the OpenAI /v1/chat/completions endpoint
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And returns a non-empty string response
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Scenario: Gemini provider generates blog content
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Given provider is "gemini" and model is "gemini-2.0-flash"
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When the LLM router is called with a prompt
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Then the router calls the Google Gemini API endpoint
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And returns a non-empty string response
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Scenario: Ollama provider generates blog content using local model
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Given provider is "ollama" and model is "mistral"
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And the Ollama base URL is "http://localhost:11434"
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When the LLM router is called with a prompt
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Then the router calls http://localhost:11434/api/chat
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And returns a non-empty string response
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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 "provider not configured"
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Scenario: Token usage is recorded in generation log
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Given provider is "anthropic" and model is "claude-sonnet-4-20250514"
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When the LLM router is called with a prompt
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Then the generation log record contains tokens_used > 0
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