Create 11_ecommerce_chatbot.py

examples/11_ecommerce_chatbot.py

@@ -0,0 +1,200 @@
+"""
+This example demonstrates how to create an e-commerce chatbot that:
+1. Understands customer queries about products
+2. Provides helpful responses with product recommendations
+3. Maintains context through conversation
+4. Returns structured product recommendations
+"""
+
+import asyncio
+from enum import Enum
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+import workflowai
+from workflowai import Model, Run
+
+
+class Role(str, Enum):
+    """Enum representing possible message roles."""
+    USER = "user"
+    ASSISTANT = "assistant"
+
+
+class Product(BaseModel):
+    """Model representing a product recommendation."""
+    name: str = Field(
+        description="Name of the product",
+        examples=["Wireless Noise-Cancelling Headphones", "4K Smart TV"],
+    )
+    price: float = Field(
+        description="Price of the product in USD",
+        examples=[299.99, 799.99],
+        ge=0,
+    )
+    description: str = Field(
+        description="Brief description of the product",
+        examples=[
+            "Premium wireless headphones with active noise cancellation",
+            "65-inch 4K Smart TV with HDR support",
+        ],
+    )
+    rating: Optional[float] = Field(
+        default=None,
+        description="Customer rating out of 5 stars",
+        examples=[4.5, 4.8],
+        ge=0,
+        le=5,
+    )
+    url: Optional[str] = Field(
+        default=None,
+        description="URL to view the product details",
+        examples=["https://example.com/products/wireless-headphones"],
+    )
+
+
+class Message(BaseModel):
+    """Model representing a chat message."""
+    role: Role = Field()
+    content: str = Field(
+        description="The content of the message",
+        examples=[
+            "I'm looking for noise-cancelling headphones for travel",
+            "Based on your requirements, here are some great headphone options...",
+        ],
+    )
+    recommended_products: Optional[List[Product]] = Field(
+        default=None,
+        description="Product recommendations included with this message, if any",
+    )
+
+
+class AssistantMessage(Message):
+    """Model representing a message from the assistant."""
+    role: Role = Role.ASSISTANT
+    content: str = ""
+
+
+class ChatbotOutput(BaseModel):
+    """Output model for the chatbot response."""
+    assistant_message: AssistantMessage = Field(
+        description="The chatbot's response message",
+    )
+
+
+class ChatInput(BaseModel):
+    """Input model containing the user's message and conversation history."""
+    conversation_history: Optional[List[Message]] = Field(
+        default=None,
+        description="Previous messages in the conversation, if any",
+    )
+    user_message: str = Field(
+        description="The current message from the user",
+    )
+
+
+@workflowai.agent(
+    id="ecommerce-chatbot",
+    model=Model.LLAMA_3_3_70B,
+)
+async def get_product_recommendations(chat_input: ChatInput) -> Run[ChatbotOutput]:
+    """
+    Act as a knowledgeable e-commerce shopping assistant.
+
+    Guidelines:
+    1. Understand customer needs and preferences:
+       - Analyze the query for specific requirements (price range, features, etc.)
+       - Consider any context from conversation history
+       - Ask clarifying questions if needed
+
+    2. Provide helpful recommendations:
+       - Suggest 3-5 relevant products that match the criteria
+       - Include a mix of price points when appropriate
+       - Explain why each product is recommended
+
+    3. Maintain a friendly, professional tone:
+       - Be conversational but informative
+       - Highlight key features and benefits
+       - Acknowledge specific customer needs
+
+    4. Product information should be realistic:
+       - Use reasonable prices for the product category
+       - Include accurate descriptions and features
+       - Provide realistic ratings based on typical products
+
+    5. Format the response clearly:
+       - Start with a helpful message addressing the query
+       - Follow with relevant product recommendations
+       - Make it easy to understand the options
+    """
+    ...
+
+
+async def main():
+    # Example 1: Initial query about headphones
+    print("\nExample 1: Looking for headphones")
+    print("-" * 50)
+
+    chat_input = ChatInput(
+        user_message="I'm looking for noise-cancelling headphones for travel. My budget is around $300.",
+    )
+
+    run = await get_product_recommendations(chat_input)
+    print(run)
+
+    # Example 2: Follow-up question with conversation history
+    print("\nExample 2: Follow-up about battery life")
+    print("-" * 50)
+
+    chat_input = ChatInput(
+        user_message="Which one has the best battery life?",
+        conversation_history=[
+            Message(
+                role=Role.USER,
+                content="I'm looking for noise-cancelling headphones for travel. My budget is around $300.",
+            ),
+            run.output.assistant_message,
+        ],
+    )
+
+    run = await get_product_recommendations(chat_input)
+    print(run)
+
+    # Example 3: Specific question about a previously recommended product
+    print("\nExample 3: Question about a specific product")
+    print("-" * 50)
+
+    chat_input = ChatInput(
+        user_message="Tell me more about the noise cancellation features of the first headphone you recommended.",
+        conversation_history=[
+            Message(
+                role=Role.USER,
+                content="I'm looking for noise-cancelling headphones for travel. My budget is around $300.",
+            ),
+            run.output.assistant_message,
+            Message(
+                role=Role.USER,
+                content="Which one has the best battery life?",
+            ),
+            run.output.assistant_message,
+        ],
+    )
+
+    run = await get_product_recommendations(chat_input)
+    print(run)
+
+    # Example 4: Different product category
+    print("\nExample 4: Looking for a TV")
+    print("-" * 50)
+
+    chat_input = ChatInput(
+        user_message="I need a good TV for gaming. My budget is $1000.",
+    )
+
+    run = await get_product_recommendations(chat_input)
+    print(run)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())