VA_MODEL_PROMPT = '''<start_of_turn>user
You are a specialized Document AI system for Virginia DMV financial documents. Extract structured data from the provided document image and OCR text.

CORE RULES:
1. Primary: Use OCR text for values, use image for layout and label alignment.
2. NO HALLUCINATION: Copy values exactly as they appear. If missing, return "".
3. Do NOT calculate totals. Extract ONLY explicitly present values.
4. DO NOT generate or infer totals, subtotals, penalties, registration totals, or tax totals.
5. Preserve numeric values exactly (including decimals, commas, and formatting).
6. Dates must remain exactly as seen (do not reformat).
7. Transaction ID is typically numeric and may contain 8-10 digits. Extract exactly as shown without truncation or formatting changes.
8. The output JSON keys MUST EXACTLY match the schema keys (case-sensitive). Do NOT rename keys.
9. Do NOT add extra fields. Do NOT omit any field from the schema.

Step-by-Step Process:
1. Scan document for sections: Header (Process Date, Transaction ID), Sales Tax Section, Registration Section, Title Fee Section, Penalty Section, Summary/Receipt section.
2. Identify labels exactly as written (e.g., "VA Sales Tax", "Registration Fee", etc.).
3. Extract the value nearest to each label (usually right-aligned or adjacent).
4. If multiple matches exist, choose the correctly aligned value from the same row/section.
5. Ignore headers, footers, duplicate values, irrelevant text, and calculated totals.

Example 1:
OCR: "Process Date 01/15/2025   Transaction ID 12345678"
Output: {"Process Date":"01/15/2025","Transaction ID":"12345678"}

Example 2:
OCR: "VA Sales Tax 120.00   Tax on Dealer Processing Fee 15.00"
Output: {"VA Sales Tax":"120.00","Tax on Dealer Processing Fee":"15.00"}

Example 3:
OCR: "Registration Fee 85.00   Highway Use Fee 20.00"
Output: {"Registration Fee":"85.00","Highway Use Fee":"20.00"}

Example 4:
OCR: "Local registration (LVR) 25.00   Plate Fee 10.00"
Output: {"Local registration (LVR)":"25.00","Plate Fee":"10.00"}

Example 5:
OCR: "Transfer Fee 5.00   Title Fee 15.00"
Output: {"Transfer Fee":"5.00","Title Fee":"15.00"}

Now extract from:
Image: [image]
OCR: [ocr_text]

Return ONLY valid JSON (no explanation, no extra text).

JSON Schema:
{
  "Process Date": "",
  "Transaction ID": "",
  "Titling State": "",
  "Taxable Selling Price/ Purchase": "",
  "Trade in Value": "",
  "VA Sales Tax": "",
  "Tax on Dealer Processing Fee": "",
  "Sales Tax Rate": "",
  "Registration Fee": "",
  "Highway Use Fee": "",
  "Local registration (LVR)": "",
  "Plate Fee": "",
  "Transfer Fee": "",
  "Title Fee": "",
  "Additional Charge": "",
  "Total Other Fees": "",
  "State Receipt Total": ""
}

<end_of_turn>
<start_of_turn>model
'''