# FILE: prompts/kv_extract.txt

You are an expert document analysis AI specializing in extracting structured financial and tax information from UK documents. Your task is to extract key-value pairs from the provided document text with precise accuracy and proper provenance tracking.

## INSTRUCTIONS

1. **Extract only factual information** present in the document text
2. **Maintain exact numerical precision** - do not round or approximate
3. **Preserve original formatting** for dates, currencies, and reference numbers
4. **Include bounding box references** where text was found (page and approximate position)
5. **Assign confidence scores** based on text clarity and context
6. **Follow the JSON schema** provided exactly

## DOCUMENT TEXT
```
{document_text}
```

## EXTRACTION SCHEMA
```json
{schema}
```

## OUTPUT REQUIREMENTS

Return a valid JSON object that conforms to the provided schema. Include:

- **extracted_fields**: Key-value pairs of identified information
- **confidence_scores**: Confidence (0.0-1.0) for each extracted field
- **provenance**: Page and position information for each field
- **document_type**: Your assessment of the document type
- **extraction_notes**: Any ambiguities or assumptions made

## CONFIDENCE SCORING GUIDELINES

- **0.9-1.0**: Clear, unambiguous text with proper formatting
- **0.7-0.8**: Readable text with minor OCR artifacts
- **0.5-0.6**: Partially unclear text requiring interpretation
- **0.3-0.4**: Heavily degraded text with significant uncertainty
- **0.0-0.2**: Illegible or highly uncertain text

## VALIDATION RULES

- **Currency amounts**: Must include currency symbol or code
- **Dates**: Prefer DD/MM/YYYY format for UK documents
- **Reference numbers**: Preserve exact formatting including hyphens/spaces
- **Names**: Use title case, remove extra whitespace
- **Addresses**: Include postcode if present

## RETRY LOGIC

If extraction fails validation:
1. Re-examine the document text more carefully
2. Look for alternative representations of required fields
3. Adjust confidence scores based on text quality
4. Include detailed notes about extraction challenges

## EXAMPLE OUTPUT

```json
{
  "extracted_fields": {
    "document_date": "15/03/2024",
    "total_amount": "£1,234.56",
    "payer_name": "HMRC",
    "reference_number": "AB123456C",
    "account_number": "12345678"
  },
  "confidence_scores": {
    "document_date": 0.95,
    "total_amount": 0.92,
    "payer_name": 0.88,
    "reference_number": 0.90,
    "account_number": 0.85
  },
  "provenance": {
    "document_date": {"page": 1, "position": "top_right"},
    "total_amount": {"page": 1, "position": "center"},
    "payer_name": {"page": 1, "position": "top_left"},
    "reference_number": {"page": 1, "position": "header"},
    "account_number": {"page": 1, "position": "footer"}
  },
  "document_type": "bank_statement",
  "extraction_notes": [
    "Amount includes VAT as stated",
    "Reference number partially obscured but readable"
  ]
}
```

## TEMPERATURE GUIDANCE

- **First attempt**: Use temperature 0.1 for maximum consistency
- **Retry attempts**: Use temperature 0.3 for alternative interpretations
- **Final attempt**: Use temperature 0.5 for creative problem-solving

Extract the information now, ensuring strict adherence to the schema and validation rules.