When Tavily search results reveal that the OCR'd model number has a
character error, the specs_research prompt asks the LLM to output a
CORRECTED_MODEL_NUMBER line. The agent parses it out, stores it in the
job output, and DraftArticleHandler applies it to the article in
preference to the raw vision value.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add WebSearchInterface + TavilyWebSearch (POST /search, max 5 results)
- SpecsResearchAgent now fetches search results first, injects them as
{{searchResults}} context into the prompt, then calls plain generate()
— no dependency on model-specific web_search tool support
- Update specs_research prompt template (PHP default + DB migration) to
use the new {{searchResults}} variable
- Wire TAVILY_API_KEY env var; register TavilyWebSearch in services.yaml
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The agent was calling generate() — pure model memory — which caused Mistral
to hallucinate specs for older devices (e.g. i5-1135G7 instead of i3-3120M).
generateWithWebSearch() is now used so Mistral queries live sources.
OllamaClientInterface gains generateWithWebSearch(); OllamaClient falls back
to generate() since Ollama has no built-in search tool.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All agent prompts are now stored in app.prompt_templates (migration 20260519000000)
and editable by admins via the new AI Prompts CRUD page. If no DB entry exists
for a key the hardcoded default is used automatically as fallback.
PromptTemplateService renders templates with {{variable}} substitution.
All four agents (SpecsResearch, JsonCoding, EbayText, OllamaVision) use the service.
SpecsResearchAgent now receives the articleType name (e.g. "Laptop") so the
specs prompt is scoped to the correct device category instead of being generic.
SpecsResearchHandler loads the ArticleType from the repository for this purpose.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Messages and handlers for the full AI pipeline:
DraftArticle → Validation → SpecsResearch → PhotoUpload → EbayText →
JsonCoding → PublishToChannel / DeactivateListingMessage / TrackingPush /
UpdateStockOnChannels / OrderReceived.
OllamaClient and OllamaClientInterface provide the base LLM backend.
AI agents (EbayTextAgent, JsonCodingAgent, OllamaVisionAgent,
SpecsResearchAgent) wrap the client with task-specific prompts.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
