Simple Notebook Guide - Questionnaire to Contract

# Load example contracts from ops/
examples_path = project_root / "apps/backend/ops/few_shot_examples/contract_generation_examples.json"
 
with open(examples_path) as f:
    examples = json.load(f)
 
print("📚 EXAMPLE CONTRACTS (what AI learns from):")
print("="*80)
 
for i, example in enumerate(examples['examples'][:2], 1):
    print(f"\nEXAMPLE {i}: {example['scenario']}")
    print(f"   League: {example['input_context']['league_name']}")
    print(f"   Tier: {example['input_context']['tier']}")
    print(f"   Annual Fee: ${example['input_context']['annual_fee']:,}")
    print(f"\n   Contract Sections:")
    for section_name, section_content in example['output_sections'].items():
        print(f"      • {section_content['section_title']}")

# Load output schema
from apps.backend.schemas.questionnaire_extraction import LeagueQuestionnaire
 
schema = LeagueQuestionnaire.model_json_schema()
 
print("\n📊 OUTPUT SCHEMA (structure AI must follow):")
print("="*80)
print(json.dumps({
    "title": schema['title'],
    "required_fields": schema.get('required', []),
    "total_properties": len(schema.get('properties', {}))
}, indent=2))

# Assemble the complete prompt
prompt = f"""
You are a contract generation specialist.
 
LEARN FROM THESE EXAMPLES:
{json.dumps(examples['examples'][:2], indent=2)}
 
OUTPUT MUST MATCH THIS SCHEMA:
{json.dumps(schema, indent=2)}
 
NOW GENERATE CONTRACT FOR:
{json.dumps(new_league_data, indent=2)}
"""
 
print("\n🔧 ASSEMBLED PROMPT (sent to AI):")
print("="*80)
print(prompt[:500] + "...\n[truncated for display]")

# Imports
import sys, json
from pathlib import Path
sys.path.insert(0, str(Path.cwd().parent.parent.parent))
 
from apps.backend.services.questionnaire_processor.league_processors.contract_generator import ContractGenerator
from apps.backend.schemas.questionnaire_extraction import LeagueQuestionnaire

print("="*80)
print("STEP 1: LOAD EXAMPLE CONTRACTS (what AI learns from)")
print("="*80)
 
examples_path = project_root / "apps/backend/ops/few_shot_examples/contract_generation_examples.json"
with open(examples_path) as f:
    contract_examples = json.load(f)
 
# Display examples
for example in contract_examples['examples'][:2]:
    print(f"\n📄 {example['scenario']}")
    print(json.dumps(example, indent=2)[:300] + "...")

print("\n" + "="*80)
print("STEP 2: LOAD OUTPUT SCHEMA (structure AI must follow)")
print("="*80)
 
schema = LeagueQuestionnaire.model_json_schema()
print(f"Schema has {len(schema['properties'])} top-level fields")
print(json.dumps(schema, indent=2)[:500] + "...")

print("\n" + "="*80)
print("STEP 3: PROCESS YOUR QUESTIONNAIRE")
print("="*80)
 
# Use existing extractor (NO new logic!)
from apps.backend.services.questionnaire_processor.league_processors.questionnaire_extractor import QuestionnaireExtractor
 
extractor = QuestionnaireExtractor()
league_data = extractor.extract(your_pdf_file)
 
print(f"Extracted data for: {league_data['league_name']}")

print("\n" + "="*80)
print("STEP 4: BUILD PROMPT (examples + schema + your data)")
print("="*80)
 
assembled_prompt = f"""
EXAMPLES:
{json.dumps(contract_examples['examples'], indent=2)}
 
SCHEMA:
{json.dumps(schema, indent=2)}
 
YOUR DATA:
{json.dumps(league_data, indent=2)}
 
Generate contract following examples and schema.
"""
 
print("Prompt size:", len(assembled_prompt), "characters")
print(assembled_prompt[:400] + "...\n[truncated]")

print("\n" + "="*80)
print("STEP 5: GENERATE CONTRACT (using existing generator)")
print("="*80)
 
# Use existing generator (NO new logic!)
generator = ContractGenerator()
contract = generator.generate(league_data)
 
print(f"✅ Contract generated!")
print(contract[:500] + "...")