AI Legal Services & Contract Analysis with Chinese LLMs

# Contract clause extraction and risk flagging import requests contract_review = { "contract_type": "Software Licensing Agreement", "counterparty": "Overseas SaaS Vendor (Delaware-incorporated)", "contract_text_excerpt": "Section 7.2: Licensor shall not be liable for any indirect, consequential, or punitive damages... Section 12.1: This Agreement shall be governed by the laws of the State of California... Section 15.3: Licensor may terminate this Agreement with 30 days written notice for any reason...", "client_position": "Licensee (Chinese enterprise)", "annual_contract_value_usd": 250000, "standard_template_clauses": [ "Governing law should be PRC or Hong Kong for enforceability", "Termination for convenience should require 90 days notice", "Liability cap should be at least 12 months of fees", "Data residency clause required for PRC data protection law" ] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a contract review AI. Extract key clauses, flag risks, compare against standard templates, and suggest redlines. Focus on enforceability in Chinese courts and data protection compliance."}, {"role": "user", "content": f"Review this contract: {json.dumps(contract_review, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])

# M&A due diligence document summary and risk flagging import requests dd_document = { "document_type": "Employment Agreement - CTO", "target_company": "Shenzhen AI Chip Startup", "key_excerpts": "1. CTO holds 8% equity vesting over 4 years. 2. Non-compete covers all of China for 24 months post-termination. 3. IP assignment clause: all inventions during employment assigned to company. 4. CTO may terminate with 3 months notice. 5. No change-of-control acceleration clause.", "deal_context": "Acquirer plans to integrate target R&D team. CTO is critical technical leader. Deal value: 180M yuan.", "risk_areas": ["key_person retention", "IP ownership", "non-compete enforceability"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are an M&A due diligence AI. Analyze legal documents, extract material issues, assess deal risks, and recommend follow-up questions. Focus on Chinese labor law and contract enforceability."}, {"role": "user", "content": f"Analyze due diligence document: {json.dumps(dd_document, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# PIPL compliance gap analysis for a company import requests compliance_check = { "company": "Online Education Platform", "users": "2.3M registered, 180K active daily", "data_collected": ["name, phone, ID number (for minors)", "learning behavior data", "facial recognition for attendance", "payment information"], "current_practices": [ "Privacy policy last updated 2024", "No dedicated data protection officer", "User consent obtained at registration only", "Data stored on Alibaba Cloud (domestic)", "No data retention schedule defined" ], "regulations_to_check": ["PIPL (Personal Information Protection Law)", "DSL (Data Security Law)", "Minors Protection Law (online)"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a regulatory compliance AI specializing in Chinese data protection laws. Analyze company practices against regulations, identify gaps, estimate severity, and recommend remediation steps."}, {"role": "user", "content": f"Check compliance: {json.dumps(compliance_check, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])

# Legal research memo on trade secret misappropriation import requests legal_research = { "jurisdiction": "PRC (Shenzhen Intermediate Court)", "legal_issue": "Trade secret misappropriation by former employee who joined competitor", "facts_summary": "Former VP of Engineering left Company A, joined direct competitor Company B. Company A alleges VP took customer lists, pricing strategy, and proprietary algorithm designs. No non-compete in employment contract.", "relevant_law": "Anti-Unfair Competition Law Article 9, Civil Code Article 123", "case_law_to_analyze": [ "Shenzhen Court (2024): Huawei v. former employees - established standard for trade secret identification", "SPC Guiding Case No. 16: Broad definition of trade secrets in tech sector", "Beijing Court (2025): No non-compete does not preclude trade secret protection" ], "desired_output": "Research memo with case summaries, applicable legal standards, strengths/weaknesses of position, and recommended arguments" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a legal research AI. Analyze case law, summarize holdings, identify relevant legal standards, and draft professional research memos. Cite specific articles and precedents accurately."}, {"role": "user", "content": f"Generate research memo: {json.dumps(legal_research, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])

# Employment contract draft generation import requests contract_params = { "document_type": "Employment Contract", "jurisdiction": "PRC (Shanghai)", "employer": "Shanghai Tech Solutions Co., Ltd.", "employee": {"position": "Senior Software Engineer", "probation_months": 3}, "terms": { "salary_yuan_month": 35000, "contract_duration_years": 3, "working_hours": "9:00-18:00, 5 days/week", "annual_leave_days": 10, "social_insurance": "Standard Shanghai rates" }, "special_clauses": [ "Non-compete: 12 months, limited to Shanghai, 50% salary compensation", "IP assignment: all work product during employment", "Confidentiality: indefinite duration", "Stock options: 10,000 units vesting over 4 years" ], "language": "Bilingual (Chinese primary, English reference)" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are a legal document drafting AI. Generate professional contracts compliant with Chinese labor law. Include all mandatory clauses and clearly structure optional provisions. Flag clauses requiring legal review."}, {"role": "user", "content": f"Draft employment contract: {json.dumps(contract_params, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])

# Litigation risk assessment and settlement recommendation import requests litigation_case = { "case_type": "Breach of commercial contract", "plaintiff": "Guangdong Manufacturer", "defendant": "Trading Company (Hong Kong)", "claim_amount_yuan": 4500000, "facts": "Defendant placed order for 500 units, paid 30% deposit. Manufacturer completed production. Defendant refused delivery citing 'quality issues' but never provided inspection report. Goods sitting in warehouse for 8 months.", "evidence_strength": { "written_contract": "Strong - signed, clear terms", "quality_inspection": "Neutral - no third-party inspection", "communication_records": "Strong - WeChat and email trail", "witnesses": "Weak - no independent witnesses" }, "jurisdiction": "Shenzhen International Arbitration Court", "similar_cases": "3 comparable cases in SZIAC (2023-2025): plaintiff won 2, defendant won 1. Average award: 72% of claimed amount." } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a litigation strategy AI. Assess case strengths and weaknesses, predict outcomes, estimate costs and timelines, and recommend settlement ranges and negotiation tactics."}, {"role": "user", "content": f"Assess litigation case: {json.dumps(litigation_case, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])