gemini-vision-extractlisted

# Gemini Vision Extract Use this skill when you have an image and need structured fields out of it - line items off a receipt, totals off an invoice, values off a chart - rather than a paragraph of description. The Gemini call is constrained with `response_mime_type=application/json` plus a Pydantic `response_schema`, so the output parses into a typed object every time. ## When to invoke - User says: "read this receipt", "parse the invoice image", "OCR into structured data", "extract fields from a screenshot". - Code in the conversation feeds an image to an LLM and then regex-scrapes prose for values. ## When NOT to invoke - The source is already machine-readable text/HTML/PDF-with-text - extract directly, no vision model needed. - You need pixel-perfect OCR of dense documents at scale; a dedicated document-AI service may fit better. ## Concrete example User input: ```text Pull merchant, date, and total out of these receipt photos into JSON. ``` Output: ```python from vision_extract import Receipt, extract # GEMINI_API_KEY in the environment data = extract("receipt.png", schema=Receipt) print(data.model_dump_json(indent=2)) # {"merchant": "Corner Cafe", "date": "2026-06-06", "total": 4.5, "currency": "USD", "items": [...]} ``` ## Pattern to apply 1. Define a Pydantic model with optional fields and pass it as `response_schema`; make absent fields nullable. 2. Set `response_mime_type=application/json` so the model returns parseable JSON, not commentary. 3. Read `