AI Hospitality, Hotels & Travel with Chinese LLMs

# Personalized 7-day Japan itinerary generation import requests traveler_profile = { "destination": "Japan (Tokyo, Kyoto, Osaka)", "travel_dates": "2026-10-01 to 2026-10-07", "travelers": {"adults": 2, "children": 1, "child_age": 8}, "budget_level": "mid-range (hotel 600-1000 yuan/night)", "interests": ["temples", "street food", "anime culture", "nature", "shopping"], "dietary": ["one vegetarian adult"], "mobility": "prefers walking + public transit, max 15k steps/day", "previous_visits": "First time to Japan", "special_requests": ["child-friendly activities", "avoid crowds where possible"] } 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 an expert travel planner specializing in East Asian destinations. Create detailed day-by-day itineraries with specific venues, timing, transport, and dining recommendations. Include practical tips and contingency plans."}, {"role": "user", "content": f"Create itinerary: {json.dumps(traveler_profile, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])

# Dynamic pricing recommendation for hotel import requests pricing_context = { "hotel": {"name": "West Lake Boutique Hotel", "stars": 4, "rooms": 68, "location": "Hangzhou, near West Lake"}, "target_date": "2026-10-01", "current_occupancy_forecast": "45% (30 days out)", "competitor_rates_yuan": {"Grand Hyatt": 1280, "Courtyard": 680, "local_boutique": 520}, "current_rate_yuan": 780, "local_events": ["West Lake Music Festival (Oct 1-3)", "National Day holiday peak"], "weather_forecast": "Sunny, 24C, ideal for outdoor activities", "historical_data": {"same_date_last_year_occupancy": "92%", "same_date_last_year_adr": 850}, "booking_pace": "15% behind last year at same point", "target_occupancy": "85%" } 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 hotel revenue management AI. Recommend optimal rates, channel strategies, and promotional tactics to maximize RevPAR while meeting occupancy targets. Consider competitor positioning."}, {"role": "user", "content": f"Recommend pricing strategy: {json.dumps(pricing_context, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Hotel concierge chatbot with guest context import requests guest_context = { "guest": {"name": "Mr. Chen", "loyalty_tier": "Gold", "language": "Chinese", "stay_nights": 3}, "room": "Deluxe Ocean View, Room 1205", "preferences": ["prefers quiet rooms", "interested in local seafood", "early riser"], "current_query": "What restaurants do you recommend for tonight? I'm celebrating my anniversary.", "hotel_services": [ "Beachfront seafood restaurant (reservation required)", "Rooftop bar with live jazz", "In-room dining (24h)", "Spa couples package", "Private beach dinner setup" ], "local_attractions": ["Sunset cruise", "Night market", "Beachside lantern festival"] } 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 luxury hotel concierge AI. Provide warm, personalized recommendations. Upsell hotel services naturally. Know local attractions and can make suggestions based on guest preferences and occasion."}, {"role": "user", "content": f"Guest inquiry: {json.dumps(guest_context, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Review sentiment analysis and response drafting import requests review_data = { "platform": "Ctrip", "rating": 3, "guest_type": "business traveler", "review_text": "Room was clean and location is great. But WiFi was extremely slow during my video calls, and the breakfast buffet had very limited vegetarian options. Front desk staff was friendly but check-in took 20 minutes.", "hotel_response_tone": "apologetic, solution-oriented, warm", "previous_similar_reviews_count": 4 } 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 hotel reputation management AI. Analyze review sentiment, extract actionable issues, and draft professional, empathetic responses in Chinese. Suggest operational improvements."}, {"role": "user", "content": f"Analyze and respond to review: {json.dumps(review_data, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Room assignment optimization with guest preferences import requests room_assignment = { "arriving_guests": [ {"reservation_id": "R-8842", "guest": "Ms. Liu", "tier": "Platinum", "preferences": ["high floor", "quiet", "away from elevator"], "room_type": "King Suite"}, {"reservation_id": "R-8843", "guest": "Family Wang", "tier": "Standard", "preferences": ["near elevator", "adjoining rooms"], "room_type": "Twin Deluxe", "children": 2} ], "available_rooms": [ {"room": "1501", "type": "King Suite", "floor": 15, "view": "city", "distance_to_elevator_m": 25}, {"room": "1203", "type": "Twin Deluxe", "floor": 12, "view": "garden", "distance_to_elevator_m": 5, "adjoining": "1205"} ], "departing_today": ["1203", "1205"], "housekeeping_shift": "8 staff, 10:00-16:00" } 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 hotel operations AI. Optimize room assignments to maximize guest satisfaction and housekeeping efficiency. Consider loyalty tier, preferences, and operational constraints."}, {"role": "user", "content": f"Optimize room assignments: {json.dumps(room_assignment, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Corporate conference planning assistant import requests event_request = { "event_type": "Corporate annual sales conference", "attendees": 180, "dates": "2026-11-15 to 2026-11-17", "budget_yuan": 350000, "requirements": { "meeting_rooms": ["Main ballroom (200 pax)", "3 breakout rooms (30 pax each)"], "catering": "2 coffee breaks + 2 lunches + 1 gala dinner", "av_equipment": ["LED wall", "wireless mics x6", "live streaming setup"], "accommodation": "90 rooms (2 nights)", "transport": "Airport pickup for 30 VIPs" }, "dietary_restrictions": ["15 vegetarian", "5 halal", "3 gluten-free"], "special_requests": ["Team building activity", "Photographer", "Welcome gift bags"] } 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 an event planning AI for hotels. Generate detailed proposals with vendor recommendations, timelines, budget breakdowns, and contingency plans. Consider Chinese business event customs."}, {"role": "user", "content": f"Plan this event: {json.dumps(event_request, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])