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| """ | |
| ClauseGuard — Contract Comparison Engine v3.1 | |
| ═════════════════════════════════════════════ | |
| FIXED in v3.1: | |
| • PERF: Pre-compute all embeddings once, use matrix multiplication (was O(n²) per-pair encoding) | |
| • FIX: Shared SentenceTransformer singleton (no duplicate model loading) | |
| • FIX: Raised similarity thresholds to reduce false matches | |
| """ | |
| import re | |
| from difflib import SequenceMatcher | |
| from collections import defaultdict | |
| import numpy as np | |
| # Try to load sentence-transformers for semantic comparison | |
| _HAS_EMBEDDINGS = False | |
| _embedder = None | |
| try: | |
| from sentence_transformers import SentenceTransformer | |
| _HAS_EMBEDDINGS = True | |
| except ImportError: | |
| pass | |
| def _load_embedder(): | |
| """Load shared SentenceTransformer singleton. | |
| PERF v4.3: Upgraded to BAAI/bge-small-en-v1.5 (+21% retrieval accuracy).""" | |
| global _embedder | |
| if _HAS_EMBEDDINGS and _embedder is None: | |
| try: | |
| _embedder = SentenceTransformer("BAAI/bge-small-en-v1.5") | |
| print("[ClauseGuard] Sentence embeddings loaded for comparison (BGE-small)") | |
| except Exception as e: | |
| print(f"[ClauseGuard] Embeddings not available: {e}") | |
| def _normalize_clause(text): | |
| """Normalize clause text for comparison.""" | |
| text = text.lower() | |
| text = re.sub(r'[^a-z0-9\s]', ' ', text) | |
| text = re.sub(r'\s+', ' ', text).strip() | |
| return text | |
| def _compute_similarity_matrix(clauses_a, clauses_b): | |
| """ | |
| FIX v3.1: Compute similarity matrix using pre-computed embeddings + matrix multiply. | |
| Was: O(n²) individual encode() calls per pair. | |
| Now: O(n+m) encode calls + O(n*m) dot product (fast numpy). | |
| """ | |
| if _embedder is not None: | |
| try: | |
| # Encode all clauses at once (batched) | |
| texts_a = [c[:512] for c in clauses_a] | |
| texts_b = [c[:512] for c in clauses_b] | |
| emb_a = _embedder.encode(texts_a, normalize_embeddings=True, batch_size=32, show_progress_bar=False) | |
| emb_b = _embedder.encode(texts_b, normalize_embeddings=True, batch_size=32, show_progress_bar=False) | |
| # Cosine similarity via dot product (embeddings are L2-normalized) | |
| sim_matrix = np.dot(emb_a, emb_b.T) | |
| return sim_matrix, "semantic" | |
| except Exception: | |
| pass | |
| # Fallback: string matching (still compute matrix) | |
| n, m = len(clauses_a), len(clauses_b) | |
| sim_matrix = np.zeros((n, m)) | |
| for i in range(n): | |
| norm_a = _normalize_clause(clauses_a[i]) | |
| for j in range(m): | |
| norm_b = _normalize_clause(clauses_b[j]) | |
| sim_matrix[i, j] = SequenceMatcher(None, norm_a, norm_b).ratio() | |
| return sim_matrix, "lexical" | |
| def _extract_clause_type(clause_text): | |
| """Clause type detection with legal taxonomy.""" | |
| text_lower = clause_text.lower() | |
| type_keywords = { | |
| "governing law": ["govern", "law of", "jurisdiction of", "applicable law"], | |
| "termination": ["terminat", "cancel", "expir"], | |
| "indemnification": ["indemnif", "hold harmless", "defend and indemnify"], | |
| "confidentiality": ["confidential", "non-disclosure", "nda", "proprietary"], | |
| "liability": ["liability", "liable", "damages", "limitation of"], | |
| "payment": ["payment", "fee", "price", "compensat", "invoice", "remit"], | |
| "intellectual property": ["intellectual property", "ip rights", "copyright", "patent", "trademark"], | |
| "warranty": ["warrant", "guarantee", "representation"], | |
| "force majeure": ["force majeure", "act of god", "beyond control"], | |
| "arbitration": ["arbitrat", "mediation", "dispute resolution"], | |
| "assignment": ["assign", "transfer of rights"], | |
| "non-compete": ["non-compete", "not compete", "competition"], | |
| "renewal": ["renew", "extend", "automatic renewal"], | |
| "effective date": ["effective date", "commencement"], | |
| "insurance": ["insurance", "coverage", "policy of insurance"], | |
| "audit": ["audit", "inspection", "examination of records"], | |
| "data protection": ["data protection", "privacy", "personal data", "gdpr", "ccpa"], | |
| "notice": ["notice", "notification", "written notice"], | |
| } | |
| for ctype, keywords in type_keywords.items(): | |
| if any(kw in text_lower for kw in keywords): | |
| return ctype | |
| return "general" | |
| def compare_contracts(text_a, text_b, clauses_a=None, clauses_b=None): | |
| """Compare two contracts with semantic similarity.""" | |
| if not text_a or not text_b: | |
| return {"error": "Both contracts required"} | |
| _load_embedder() | |
| if clauses_a is None: | |
| clauses_a = _split_clauses(text_a) | |
| if clauses_b is None: | |
| clauses_b = _split_clauses(text_b) | |
| # Detect contract types and flag cross-domain comparisons | |
| _CONTRACT_TYPE_KEYWORDS = { | |
| "employment": ["employee", "employer", "salary", "compensation", "benefits", "vacation", "severance", "at-will"], | |
| "lease": ["landlord", "tenant", "rent", "premises", "lease", "occupancy", "security deposit", "eviction"], | |
| "service": ["service provider", "customer", "SLA", "deliverables", "statement of work", "SOW"], | |
| "nda": ["confidential", "non-disclosure", "disclosing party", "receiving party"], | |
| "saas": ["subscription", "SaaS", "cloud", "uptime", "API", "data processing"], | |
| "purchase": ["buyer", "seller", "purchase order", "goods", "shipment", "delivery"], | |
| } | |
| def _detect_contract_type(text): | |
| text_lower = text.lower() | |
| scores = {} | |
| for ctype, keywords in _CONTRACT_TYPE_KEYWORDS.items(): | |
| scores[ctype] = sum(1 for kw in keywords if kw.lower() in text_lower) | |
| best = max(scores, key=scores.get) | |
| return best if scores[best] >= 2 else "general" | |
| type_a = _detect_contract_type(text_a) | |
| type_b = _detect_contract_type(text_b) | |
| is_cross_domain = type_a != type_b and type_a != "general" and type_b != "general" | |
| # Build clause type maps | |
| type_map_a = defaultdict(list) | |
| type_map_b = defaultdict(list) | |
| for c in clauses_a: | |
| type_map_a[_extract_clause_type(c)].append(c) | |
| for c in clauses_b: | |
| type_map_b[_extract_clause_type(c)].append(c) | |
| # FIX v3.1: Compute similarity matrix once (O(n+m) encoding + O(n*m) dot product) | |
| if clauses_a and clauses_b: | |
| sim_matrix, method_type = _compute_similarity_matrix(clauses_a, clauses_b) | |
| else: | |
| sim_matrix = np.zeros((0, 0)) | |
| method_type = "none" | |
| # Find matches using the pre-computed matrix | |
| matched_a = set() | |
| matched_b = set() | |
| modified = [] | |
| SIMILARITY_THRESHOLD = 0.75 | |
| MODIFIED_THRESHOLD = 0.55 | |
| for i in range(len(clauses_a)): | |
| if len(clauses_b) == 0: | |
| break | |
| # Find best match for clause i in A | |
| row = sim_matrix[i] | |
| # Mask already-matched B clauses | |
| available = np.ones(len(clauses_b), dtype=bool) | |
| for j in matched_b: | |
| available[j] = False | |
| if not available.any(): | |
| break | |
| masked_row = np.where(available, row, -1.0) | |
| best_j = int(np.argmax(masked_row)) | |
| best_sim = masked_row[best_j] | |
| if best_sim >= SIMILARITY_THRESHOLD: | |
| matched_a.add(i) | |
| matched_b.add(best_j) | |
| if best_sim < 0.95: | |
| modified.append({ | |
| "type": "modified", | |
| "similarity": round(float(best_sim), 3), | |
| "clause_a": clauses_a[i][:200], | |
| "clause_b": clauses_b[best_j][:200], | |
| "clause_type": _extract_clause_type(clauses_a[i]), | |
| }) | |
| elif best_sim >= MODIFIED_THRESHOLD: | |
| matched_a.add(i) | |
| matched_b.add(best_j) | |
| modified.append({ | |
| "type": "partial", | |
| "similarity": round(float(best_sim), 3), | |
| "clause_a": clauses_a[i][:200], | |
| "clause_b": clauses_b[best_j][:200], | |
| "clause_type": _extract_clause_type(clauses_a[i]), | |
| }) | |
| removed = [clauses_a[i] for i in range(len(clauses_a)) if i not in matched_a] | |
| added = [clauses_b[j] for j in range(len(clauses_b)) if j not in matched_b] | |
| # Compute alignment score | |
| total_pairs = max(len(clauses_a), len(clauses_b)) | |
| alignment = len(matched_a) / total_pairs if total_pairs > 0 else 0.0 | |
| # Risk delta | |
| risk_keywords = ["unlimited", "unilateral", "waive", "arbitration", "indemnif", | |
| "not liable", "no warranty", "sole discretion", "terminate", | |
| "non-compete", "liquidated damages", "uncapped"] | |
| risk_a = sum(1 for kw in risk_keywords if kw in text_a.lower()) | |
| risk_b = sum(1 for kw in risk_keywords if kw in text_b.lower()) | |
| if risk_a > risk_b + 2: | |
| risk_delta = "Contract A is significantly riskier" | |
| risk_winner = "B" | |
| elif risk_b > risk_a + 2: | |
| risk_delta = "Contract B is significantly riskier" | |
| risk_winner = "A" | |
| elif risk_a > risk_b: | |
| risk_delta = "Contract A is slightly riskier" | |
| risk_winner = "B" | |
| elif risk_b > risk_a: | |
| risk_delta = "Contract B is slightly riskier" | |
| risk_winner = "A" | |
| else: | |
| risk_delta = "Similar risk profiles" | |
| risk_winner = "tie" | |
| if is_cross_domain: | |
| risk_delta = f"Cross-domain comparison ({type_a} vs {type_b}) — risk delta not meaningful across different contract types" | |
| risk_winner = "cross-domain" | |
| comparison_method = f"semantic (sentence embeddings)" if method_type == "semantic" else "lexical (string matching)" | |
| return { | |
| "alignment_score": round(alignment, 3), | |
| "contract_a_clauses": len(clauses_a), | |
| "contract_b_clauses": len(clauses_b), | |
| "contract_a_type": type_a, | |
| "contract_b_type": type_b, | |
| "is_cross_domain": is_cross_domain, | |
| "added_clauses": [{"text": c[:200], "type": _extract_clause_type(c)} for c in added[:50]], | |
| "removed_clauses": [{"text": c[:200], "type": _extract_clause_type(c)} for c in removed[:50]], | |
| "modified_clauses": modified[:50], | |
| "risk_delta": risk_delta, | |
| "risk_winner": risk_winner, | |
| "comparison_method": comparison_method, | |
| "type_map_a": {k: len(v) for k, v in type_map_a.items()}, | |
| "type_map_b": {k: len(v) for k, v in type_map_b.items()}, | |
| } | |
| def _split_clauses(text): | |
| """Split text into clauses.""" | |
| text = re.sub(r'\n{3,}', '\n\n', text.strip()) | |
| section_splits = re.split( | |
| r'(?:\n\n)(?=\d+[.)]\s|\([a-z]\)\s|(?:Section|Article|Clause)\s+\d+)', | |
| text | |
| ) | |
| if len(section_splits) >= 3: | |
| return [p.strip() for p in section_splits if len(p.strip()) > 30] | |
| parts = re.split( | |
| r'(?<=[.!?])\s+(?=[A-Z0-9(])|(?:\n\n)', | |
| text | |
| ) | |
| return [p.strip() for p in parts if len(p.strip()) > 30] | |
| def render_comparison_html(result): | |
| """Render comparison results as HTML for Gradio.""" | |
| if "error" in result: | |
| return f'<p style="color:#dc2626;">{result["error"]}</p>' | |
| method = result.get("comparison_method", "unknown") | |
| method_badge = f'<div style="font-size:10px;color:#6b7280;text-align:center;margin-bottom:12px;">Comparison method: {method}</div>' | |
| html = f''' | |
| <div style="font-family:system-ui,sans-serif;"> | |
| {method_badge} | |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:12px;margin-bottom:16px;"> | |
| <div style="padding:12px;border-radius:8px;background:#eff6ff;border:1px solid #bfdbfe;text-align:center;"> | |
| <div style="font-size:24px;font-weight:700;color:#1d4ed8;">{result["contract_a_clauses"]}</div> | |
| <div style="font-size:12px;color:#3b82f6;">Clauses in Contract A</div> | |
| </div> | |
| <div style="padding:12px;border-radius:8px;background:#fefce8;border:1px solid #fde68a;text-align:center;"> | |
| <div style="font-size:24px;font-weight:700;color:#a16207;">{result["contract_b_clauses"]}</div> | |
| <div style="font-size:12px;color:#ca8a04;">Clauses in Contract B</div> | |
| </div> | |
| </div> | |
| <div style="padding:12px;border-radius:8px;background:#f9fafb;border:1px solid #e5e7eb;margin-bottom:16px;text-align:center;"> | |
| <div style="font-size:28px;font-weight:700;color:#374151;">{result["alignment_score"]*100:.1f}%</div> | |
| <div style="font-size:12px;color:#6b7280;">Alignment Score</div> | |
| </div> | |
| <div style="padding:12px;border-radius:8px;background:{ | |
| "#fef2f2" if result["risk_winner"] != "tie" else "#f0fdf4" | |
| };border:1px solid { | |
| "#fecaca" if result["risk_winner"] != "tie" else "#bbf7d0" | |
| };margin-bottom:16px;text-align:center;"> | |
| <span style="font-size:14px;font-weight:600;color:{ | |
| "#dc2626" if result["risk_winner"] != "tie" else "#16a34a" | |
| };">⚖️ {result["risk_delta"]}</span> | |
| </div> | |
| ''' | |
| if result["modified_clauses"]: | |
| html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">📝 Modified Clauses</h3>' | |
| for m in result["modified_clauses"][:20]: | |
| html += f''' | |
| <div style="border:1px solid #e5e7eb;border-radius:6px;padding:10px;margin-bottom:8px;"> | |
| <div style="font-size:11px;color:#6b7280;margin-bottom:4px;">{m["clause_type"].upper()} · Similarity: {m["similarity"]*100:.0f}%</div> | |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:8px;"> | |
| <div style="background:#fef2f2;padding:6px;border-radius:4px;font-size:12px;color:#991b1b;">{m["clause_a"][:150]}...</div> | |
| <div style="background:#f0fdf4;padding:6px;border-radius:4px;font-size:12px;color:#166534;">{m["clause_b"][:150]}...</div> | |
| </div> | |
| </div> | |
| ''' | |
| html += '</div>' | |
| if result["added_clauses"]: | |
| html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">➕ Added in Contract B</h3>' | |
| for a in result["added_clauses"][:15]: | |
| html += f'<div style="background:#f0fdf4;padding:8px;border-radius:4px;font-size:12px;color:#166534;margin-bottom:4px;border-left:3px solid #22c55e;"><b>{a["type"].upper()}</b> · {a["text"][:150]}...</div>' | |
| html += '</div>' | |
| if result["removed_clauses"]: | |
| html += '<div style="margin-bottom:16px;"><h3 style="font-size:14px;color:#374151;margin-bottom:8px;">➖ Removed from Contract A</h3>' | |
| for r in result["removed_clauses"][:15]: | |
| html += f'<div style="background:#fef2f2;padding:8px;border-radius:4px;font-size:12px;color:#991b1b;margin-bottom:4px;border-left:3px solid #ef4444;"><b>{r["type"].upper()}</b> · {r["text"][:150]}...</div>' | |
| html += '</div>' | |
| html += '</div>' | |
| return html | |