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Evaluation framework for contract drafting.
Scores: clause completeness, playbook compliance, missing key terms,
invented legal terms, business usefulness, internal consistency,
risk flag accuracy, citation/source support.
"""
import json
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from drafting_engine import ContractDraftingEngine, DraftingContext, DraftedContract
from playbook import get_required_clauses, get_risk_flags
@dataclass
class EvalResult:
task_id: str
contract_type: str
scores: Dict[str, float]
total_score: float
details: Dict[str, Any]
class EvalRunner:
def __init__(self, engine: ContractDraftingEngine):
self.engine = engine
self.rubric_weights = {
"clause_completeness": 0.20,
"playbook_compliance": 0.15,
"missing_key_terms": 0.15,
"invented_legal_terms": 0.10,
"business_usefulness": 0.10,
"internal_consistency": 0.10,
"risk_flag_accuracy": 0.10,
"citation_support": 0.10,
}
def evaluate_task(self, task: Dict[str, Any]) -> EvalResult:
ctx = DraftingContext(**task["context"])
contract = self.engine.draft(ctx)
scores = {}
scores["clause_completeness"] = self._score_clause_completeness(contract, task)
scores["playbook_compliance"] = self._score_playbook_compliance(contract, task)
scores["missing_key_terms"] = self._score_missing_key_terms(contract, task)
scores["invented_legal_terms"] = self._score_invented_terms(contract)
scores["business_usefulness"] = self._score_business_usefulness(contract, task)
scores["internal_consistency"] = self._score_internal_consistency(contract)
scores["risk_flag_accuracy"] = self._score_risk_flag_accuracy(contract, task)
scores["citation_support"] = self._score_citation_support(contract)
total = sum(scores[k] * self.rubric_weights[k] for k in scores)
return EvalResult(
task_id=task["task_id"],
contract_type=ctx.contract_type,
scores=scores,
total_score=total,
details={"contract": contract},
)
def _score_clause_completeness(self, contract: DraftedContract, task: Dict) -> float:
required = set(get_required_clauses(contract.contract_type))
present = {c.clause_name for c in contract.clauses}
if not required:
return 1.0
return len(present & required) / len(required)
def _score_playbook_compliance(self, contract: DraftedContract, task: Dict) -> float:
position = contract.context.party_position
compliant = 0
total = 0
for c in contract.clauses:
fallback = c.clause_text.lower()
if position == "pro_company":
if "cap" in fallback or "company" in fallback:
compliant += 1
elif position == "balanced":
if "mutual" in fallback or "each party" in fallback:
compliant += 1
elif position == "pro_counterparty":
if "broad" in fallback or "customer" in fallback:
compliant += 1
total += 1
return compliant / total if total > 0 else 0.0
def _score_missing_key_terms(self, contract: DraftedContract, task: Dict) -> float:
gold_terms = set(task.get("gold_key_terms", []))
text = " ".join(c.clause_text.lower() for c in contract.clauses)
found = sum(1 for term in gold_terms if term.lower() in text)
return found / len(gold_terms) if gold_terms else 1.0
def _score_invented_terms(self, contract: DraftedContract) -> float:
placeholders = 0
total = len(contract.clauses)
for c in contract.clauses:
if "[placeholder" in c.clause_text.lower() or "[insert" in c.clause_text.lower():
placeholders += 1
# Score = 1 - fraction of placeholders
return max(0.0, 1.0 - (placeholders / total if total > 0 else 0))
def _score_business_usefulness(self, contract: DraftedContract, task: Dict) -> float:
constraints = task["context"].get("business_constraints", [])
text = " ".join(c.clause_text.lower() for c in contract.clauses)
met = sum(1 for cons in constraints if cons.lower() in text)
return met / len(constraints) if constraints else 1.0
def _score_internal_consistency(self, contract: DraftedContract) -> float:
notes = contract.verifier_notes
warnings = [n for n in notes if n.startswith("WARNING")]
missing = [n for n in notes if n.startswith("MISSING")]
score = 1.0
score -= 0.1 * len(warnings)
score -= 0.2 * len(missing)
return max(0.0, score)
def _score_risk_flag_accuracy(self, contract: DraftedContract, task: Dict) -> float:
expected_flags = set(task.get("expected_risk_flags", []))
actual_flags = {f["flag"] for f in contract.risk_flags}
if not expected_flags:
return 1.0
tp = len(expected_flags & actual_flags)
fp = len(actual_flags - expected_flags)
fn = len(expected_flags - actual_flags)
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
if precision + recall == 0:
return 0.0
return 2 * precision * recall / (precision + recall)
def _score_citation_support(self, contract: DraftedContract) -> float:
sourced = 0
for c in contract.clauses:
if c.retrieved_clauses and len(c.retrieved_clauses) > 0:
sourced += 1
return sourced / len(contract.clauses) if contract.clauses else 0.0
def run_suite(self, tasks: List[Dict[str, Any]]) -> List[EvalResult]:
return [self.evaluate_task(t) for t in tasks]
def report(self, results: List[EvalResult]) -> str:
lines = ["# Evaluation Report", ""]
avg_total = sum(r.total_score for r in results) / len(results) if results else 0
lines.append(f"Average Total Score: {avg_total:.3f}")
lines.append("")
for dim in self.rubric_weights:
avg = sum(r.scores[dim] for r in results) / len(results) if results else 0
lines.append(f"- {dim}: {avg:.3f}")
lines.append("")
for r in results:
lines.append(f"## {r.task_id} ({r.contract_type})")
lines.append(f"Total: {r.total_score:.3f}")
for dim, score in r.scores.items():
lines.append(f" {dim}: {score:.3f}")
lines.append("")
return "\n".join(lines)
# ---------------------------------------------------------------------------
# Gold tasks for evaluation
# ---------------------------------------------------------------------------
GOLD_TASKS: List[Dict[str, Any]] = [
{
"task_id": "saas_pro_company_001",
"context": {
"contract_type": "saas_agreement",
"party_position": "pro_company",
"deal_context": "Enterprise SaaS platform for financial analytics. Customer is a mid-size bank.",
"business_constraints": ["SOC 2 Type II", "annual billing", "99.9% uptime"],
"governing_law": "Delaware",
"company_name": "FinAnalytics Inc",
"counterparty_name": "MidSize Bank",
},
"gold_key_terms": ["limitation of liability", "indemnification", "data protection", "SLA", "termination"],
"expected_risk_flags": ["NO_CAP", "NO_DPA"],
},
{
"task_id": "nda_balanced_001",
"context": {
"contract_type": "nda",
"party_position": "balanced",
"deal_context": "Mutual NDA for M&A discussions between two tech companies.",
"business_constraints": ["3 year term", "mutual obligations", "return of information"],
"governing_law": "California",
"company_name": "TechCorp A",
"counterparty_name": "TechCorp B",
},
"gold_key_terms": ["confidential information", "receiving party", "return", "remedies", "no license"],
"expected_risk_flags": [],
},
{
"task_id": "msa_pro_counterparty_001",
"context": {
"contract_type": "msa",
"party_position": "pro_counterparty",
"deal_context": "Professional services MSA for software implementation.",
"business_constraints": ["fixed fee", "IP ownership by customer", "30-day payment"],
"governing_law": "New York",
"company_name": "Implementor LLC",
"counterparty_name": "Enterprise Client",
},
"gold_key_terms": ["scope of work", "intellectual property", "warranty", "limitation of liability", "termination"],
"expected_risk_flags": ["NO_MUTUALITY", "BROAD_SCOPE"],
},
{
"task_id": "dpa_balanced_001",
"context": {
"contract_type": "dpa",
"party_position": "balanced",
"deal_context": "GDPR DPA for SaaS provider processing EU personal data.",
"business_constraints": ["GDPR compliant", "subprocessor list", "audit rights"],
"governing_law": "Ireland",
"company_name": "CloudProvider",
"counterparty_name": "EU Controller",
},
"gold_key_terms": ["controller", "processor", "subprocessors", "security measures", "data return"],
"expected_risk_flags": ["NO_DPA", "UNRESTRICTED_SUBPROCESSORS"],
},
{
"task_id": "consulting_balanced_001",
"context": {
"contract_type": "consulting_agreement",
"party_position": "balanced",
"deal_context": "Strategy consulting engagement for market entry.",
"business_constraints": ["hourly billing", "work for hire", "non-solicitation"],
"governing_law": "Delaware",
"company_name": "Strategy Partners",
"counterparty_name": "StartupCo",
},
"gold_key_terms": ["services", "compensation", "intellectual property", "independent contractor", "confidentiality"],
"expected_risk_flags": [],
},
]
def main():
from drafting_engine import ContractDraftingEngine
from clause_retriever import build_retriever_from_hf_datasets
print("Building retriever...")
retriever = build_retriever_from_hf_datasets()
engine = ContractDraftingEngine(retriever=retriever)
runner = EvalRunner(engine)
print("Running evaluation suite...")
results = runner.run_suite(GOLD_TASKS)
report = runner.report(results)
print(report)
with open("eval_report.md", "w") as f:
f.write(report)
if __name__ == "__main__":
main()
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