AI聊AI2026.1.25---飲店開發新飲品創建設計AI Agent (人工智慧代理人)程式碼範例

 AI聊AI2026.1.25---飲店開發新飲品創建設計AI Agent (人工智慧代理人)程式碼範例

Q:飲店開發新飲品創建設計AI Agent (人工智慧代理人)程式碼範例

AI:

設計AI代理商來幫助飲品店開發新飲品，真是個絕妙的主意！讓我們來詳細分析如何建立這樣的代理，重點介紹其核心元件和一個概念性的Python程式碼範例。

新飲料開發的AI代理

理想情況下，飲料開發的AI代理應該能夠利用各種AI技術來分析趨勢、推薦食材組合、預測顧客偏好，甚至模擬風味特徵。以下是其潛在功能的細分以及一個簡化的程式碼範例。

核心能力

資料收集與分析：

市場趨勢：從社交媒體、美食部落格、餐廳菜單和行業報告中抓取數據，以識別新興口味、流行成分和流行飲料類型（例如健康、功能性、異國情調）。

客戶回饋：分析銷售數據、客戶評論和調查結果，了解現有飲料中哪些受歡迎、客戶喜歡或不喜歡什麼以及他們可能在尋找什麼。

成分資料庫：維護一個全面的成分資料庫，包括其風味特徵（甜、酸、苦、鮮味）、營養資訊、常見搭配和可用性。

創意生成：

組合探索：基於分析的數據，代理可以建議新的成分組合，或許使用推薦系統或生成模型等技術。

風味搭配邏輯：實施關於不同風味如何相互作用的規則或知識圖（例如，柑橘與薄荷，生薑與蜂蜜）。

約束滿足：考慮成本、成分可用性、目標客戶人口統計和期望的健康益處等限制。

預測和模擬：

受歡迎程度預測：使用根據歷史銷售和趨勢資料訓練的機器學習模型來預測新飲料概念的潛在受歡迎程度。

風味特徵模擬：雖然複雜，但基本版本可以涉及為風味屬性（甜度、酸度）分配數值評分，並將它們組合起來，從而預測整體風味特徵。高級版本可能使用神經網絡，甚至化學分析。

反饋循環和優化：

味覺測試整合：如果可能的話，整合實際味覺測試的回饋來完善代理的建議。

迭代改進：代理商從成功和不成功的飲料發布中學習，隨著時間的推移不斷改進其建議。

概念 Python 程式碼範例

此範例高度簡化，重點在於智能體根據一些基本規則和資料建議食材組合的邏輯。在實際場景中，每項功能都需要更複雜的AI模型和資料處理。

Python

import random

from collections import defaultdict

class BeverageAI_Agent:

    def __init__(self):

        self.ingredient_database = self._load_ingredient_data()

        self.market_trends = self._load_market_trends()

        self.customer_preferences = self._load_customer_preferences()

    def _load_ingredient_data(self):

        # In a real system, this would come from a database or API.

        # Example: {ingredient: {flavor_profile: [], type: [], pairing_notes: []}}

        return {

            "lemon": {"flavor": ["sour", "citrus"], "type": "fruit", "pairings": ["mint", "ginger", "berry", "honey"]},

            "mint": {"flavor": ["fresh", "aromatic"], "type": "herb", "pairings": ["lemon", "cucumber", "lime"]},

            "strawberry": {"flavor": ["sweet", "fruity"], "type": "fruit", "pairings": ["basil", "mint", "cream"]},

            "ginger": {"flavor": ["spicy", "aromatic"], "type": "spice", "pairings": ["lemon", "honey", "turmeric"]},

            "green tea": {"flavor": ["earthy", "bitter"], "type": "base", "pairings": ["lemon", "honey", "mint"]},

            "black tea": {"flavor": ["bold", "astringent"], "type": "base", "pairings": ["milk", "sugar", "lemon"]},

            "honey": {"flavor": ["sweet"], "type": "sweetener", "pairings": ["lemon", "ginger", "tea"]},

            "milk": {"flavor": ["creamy"], "type": "dairy", "pairings": ["coffee", "tea", "chocolate"]},

            "basil": {"flavor": ["herbal", "anise"], "type": "herb", "pairings": ["strawberry", "lemon"]},

            "cucumber": {"flavor": ["fresh", "watery"], "type": "vegetable", "pairings": ["mint", "lime"]},

        }

    def _load_market_trends(self):

        # Simulated trends - in reality, from web scraping or market research.

        return {

            "popular_flavors": ["citrus", "berry", "herbal", "spicy"],

            "trending_bases": ["tea", "sparkling water"],

            "popular_addons": ["fresh fruit", "botanicals"],

            "current_customer_interest": ["refreshing", "healthy", "unique"],

        }

    def _load_customer_preferences(self):

        # Simulated preferences based on sales data or surveys.

        # Example: {flavor_preference: likelihood, ingredient_preference: likelihood}

        return {

            "sweet_drinks": 0.7,

            "sour_drinks": 0.4,

            "tea_based": 0.6,

            "fruit_heavy": 0.8,

            "mint_lover": 0.5,

        }

    def analyze_trends_and_preferences(self):

        print("--- Analyzing Trends and Preferences ---")

        print(f"Current Popular Flavors: {', '.join(self.market_trends['popular_flavors'])}")

        print(f"Trending Bases: {', '.join(self.market_trends['trending_bases'])}")

        print(f"Customer Preference for Sweet Drinks: {self.customer_preferences['sweet_drinks'] * 100}%")

        print("\nInsight: Customers are leaning towards refreshing, healthy, and unique fruit-heavy tea-based drinks with citrus or berry notes.")

    def generate_drink_concept(self, num_suggestions=3):

        print("\n--- Generating New Drink Concepts ---")

        suggested_drinks = []

        # Prioritize ingredients based on current trends and customer preferences

        priority_ingredients = []

        for flavor in self.market_trends['popular_flavors']:

            for ing, data in self.ingredient_database.items():

                if flavor in data['flavor']:

                    priority_ingredients.append(ing)

        # Add ingredients based on customer preferences

        if self.customer_preferences.get("fruit_heavy", 0) > 0.6:

            priority_ingredients.extend([ing for ing, data in self.ingredient_database.items() if data['type'] == 'fruit'])

        if self.customer_preferences.get("tea_based", 0) > 0.5:

            priority_ingredients.extend([ing for ing, data in self.ingredient_database.items() if data['type'] == 'base' and 'tea' in ing])

        # Remove duplicates and ensure diversity

        priority_ingredients = list(set(priority_ingredients))

        random.shuffle(priority_ingredients)

        for _ in range(num_suggestions):

            base_ingredient = random.choice([ing for ing, data in self.ingredient_database.items() if data['type'] == 'base' or ing in self.market_trends['trending_bases']])

            

            # Select 1-2 complementary ingredients based on pairings and trends

            complementary_options = [

                ing for ing in self.ingredient_database[base_ingredient]['pairings']

                if ing in priority_ingredients or random.random() < 0.3 # Add some randomness for novelty

            ]

            

            if not complementary_options:

                complementary_options = [ing for ing in priority_ingredients if ing != base_ingredient]

            num_complements = random.randint(1, min(2, len(complementary_options)))

            selected_complements = random.sample(complementary_options, num_complements)

            drink_name_parts = [base_ingredient.replace(' ', '_').title()] + [comp.replace(' ', '_').title() for comp in selected_complements]

            drink_name = " ".join(drink_name_parts) + " Infusion" # A simple naming convention

            suggested_drinks.append({

                "name": drink_name,

                "ingredients": [base_ingredient] + selected_complements,

                "notes": self._generate_notes(base_ingredient, selected_complements)

            })

        

        for i, drink in enumerate(suggested_drinks):

            print(f"\nSuggestion {i+1}: **{drink['name']}**")

            print(f"  Ingredients: {', '.join([ing.title() for ing in drink['ingredients']])}")

            print(f"  Notes: {drink['notes']}")

        

        return suggested_drinks

    def _generate_notes(self, base, complements):

        notes = []

        for comp in complements:

            if comp in self.ingredient_database[base]['pairings']:

                notes.append(f"Classic pairing of {base.title()} and {comp.title()}.")

            else:

                notes.append(f"A unique twist with {comp.title()} to explore new flavors.")

        

        # Add notes based on general trends

        if any(f in self.market_trends['popular_flavors'] for f in [self.ingredient_database[i]['flavor'] for i in [base] + complements]):

             notes.append("Aligns with current popular flavor trends.")

        

        if "refreshing" in self.market_trends['current_customer_interest'] and (base in ["lemon", "cucumber"] or any(f in ["fresh", "citrus"] for f_list in [self.ingredient_database[i]['flavor'] for i in [base] + complements] for f in f_list)):

            notes.append("Likely to be refreshing, meeting customer demand.")

        return " ".join(notes)

    def predict_popularity(self, drink_concept):

        print(f"\n--- Predicting Popularity for: {drink_concept['name']} ---")

        # A very simplistic prediction model

        popularity_score = 0

        ingredients_in_concept = drink_concept['ingredients']

        for ingredient in ingredients_in_concept:

            # Boost score for popular ingredients

            if ingredient in self.market_trends['popular_addons'] or \

               any(f in self.market_trends['popular_flavors'] for f in self.ingredient_database[ingredient]['flavor']):

                popularity_score += 0.2

            # Consider customer preferences

            if ingredient == "lemon" and self.customer_preferences.get("sour_drinks", 0) > 0.3:

                popularity_score += 0.1

            if ingredient == "strawberry" and self.customer_preferences.get("fruit_heavy", 0) > 0.7:

                popularity_score += 0.15

            if ingredient == "green tea" and self.customer_preferences.get("tea_based", 0) > 0.5:

                popularity_score += 0.1

        # Check for alignment with overall customer interest

        if "refreshing" in drink_concept['notes'] and "refreshing" in self.market_trends['current_customer_interest']:

            popularity_score += 0.2

        

        # Cap the score for a more realistic range

        popularity_score = min(1.0, popularity_score) 

        print(f"Predicted Popularity Score (0-1): {popularity_score:.2f}")

        return popularity_score

# --- Running the AI Agent ---

if __name__ == "__main__":

    agent = BeverageAI_Agent()

    # Step 1: Analyze current data

    agent.analyze_trends_and_preferences()

    # Step 2: Generate new drink concepts

    new_drinks = agent.generate_drink_concept(num_suggestions=3)

    # Step 3: Predict popularity for the generated concepts

    for drink in new_drinks:

        agent.predict_popularity(drink)

    print("\n--- Next Steps ---")

    print("Consider conducting small-scale taste tests for the highest-scoring concepts.")

    print("Gather feedback to refine the agent's models and ingredient database.")

如何擴展此代理

更複雜的資料來源：與即時社交媒體 API（例如 Twitter、Instagram）、電子商務平台（用於銷售資料）和食品產業研究資料庫整合。

自然語言處理 (NLP)：使用 NLP 分析客戶評論、社交媒體貼文和美食博客，以提取情感、識別新興成分並理解風味描述。

高級推薦系統：採用協同過濾或基於內容的過濾技術，根據過去成功的組合或類似的客戶偏好來推薦食材。

生成對抗網路 (GAN) 或變分自動編碼器 (VAE)：對於高度先進的場景，它們可能會根據學習的模式生成新的成分組合甚至「配方」。

強化學習：代理可以透過反覆試驗進行學習，對受歡迎的飲料獲得正強化，對不受歡迎的飲料獲得負強化，不斷優化其策略。

使用者介面 (UI)：為飲料店老闆建立一個簡單的 Web 介面，以便輸入標準、查看建議和提供回饋。

成本和供應鏈整合：考慮原料成本、可用性和供應商資訊，以確保建議切實可行。

營養分析：自動計算建議飲品的營養資訊。

這個AI代理框架為徹底改變飲料店的菜單創新方式提供了一個堅實的起點！