cookbook/data_labeling/_16_document_extraction/TEST_LOG.md
Tested 2026-07-18 against gemini-3.5-flash, agno 2.7.4.
Status: PASS
Description: Extracts document-level metadata (title, cuisine, language, recipe_count) from the public ThaiRecipes.pdf into a typed RecipeBook via output_schema. Exercises PDF file input by URL plus structured output on a Gemini model.
Result: Returned RecipeBook(title='Thai SELECT Cookbook', cuisine='Thai', language='English', recipe_count=10). Single model call, 7449 total tokens, ~3.9s.
Status: PASS
Description: Same metadata extraction with each field wrapped in a ConfidentField (value plus Literal high/medium/low confidence), exercising nested models and Literal enums inside the structured-output schema.
Result: All four fields populated with confidence "high": title 'Thai SELECT Cookbook', cuisine 'Thai', language 'English', recipe_count '10'. ~6.0s model call.
Status: PASS
Description: Extracts book metadata plus a nested List[Recipe] (name, course, prep_time_minutes), the line-item extraction shape, from the same PDF.
Result: Returned title 'Thai SELECT COOKBOOK', cuisine 'Thai', and 10 recipes including 'Pad Thai Goong Sod' (prep 15), 'Tom Kha Gai' (prep 10), and 'Gluai Buat Chi' (prep 10); all course fields null, consistent with the document not labeling courses. ~9.4s model call.