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import json
from typing import Dict, Any, List, Optional
from openai import OpenAI
from config import OPENAI_API_KEY, OPENAI_MODEL
from mcp_client import MCPClient
from auth import AuthHandler
class SupportAgent:
"""Customer support agent with MCP tool integration."""
def __init__(self, mcp_client: MCPClient, auth_handler: AuthHandler):
# Initialize OpenAI client
self.client = OpenAI(api_key=OPENAI_API_KEY)
self.model = OPENAI_MODEL
self.mcp_client = mcp_client
self.auth_handler = auth_handler
# Initialize MCP connection
self.mcp_client.initialize()
# Define available tools
self.tools = self._define_tools()
def _define_tools(self) -> List[Dict[str, Any]]:
"""Define tool schemas for OpenAI function calling."""
return [
{
"type": "function",
"function": {
"name": "list_products",
"description": "List products with optional filters by category or active status",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Filter by category (e.g., 'Computers', 'Monitors', 'Printers')"
},
"is_active": {
"type": "boolean",
"description": "Filter by active status"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "get_product",
"description": "Get detailed product information by SKU",
"parameters": {
"type": "object",
"properties": {
"sku": {
"type": "string",
"description": "Product SKU (e.g., 'COM-0001', 'MON-0054')"
}
},
"required": ["sku"]
}
}
},
{
"type": "function",
"function": {
"name": "search_products",
"description": "Search products by name or description keyword",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search term (case-insensitive, partial match)"
}
},
"required": ["query"]
}
}
},
{
"type": "function",
"function": {
"name": "get_customer",
"description": "Get customer information by customer ID. Requires authentication.",
"parameters": {
"type": "object",
"properties": {
"customer_id": {
"type": "string",
"description": "Customer UUID"
}
},
"required": ["customer_id"]
}
}
},
{
"type": "function",
"function": {
"name": "list_orders",
"description": "List orders with optional filters. Requires authentication.",
"parameters": {
"type": "object",
"properties": {
"customer_id": {
"type": "string",
"description": "Filter by customer UUID"
},
"status": {
"type": "string",
"description": "Filter by status: draft, submitted, approved, fulfilled, cancelled"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "get_order",
"description": "Get detailed order information including items. Requires authentication.",
"parameters": {
"type": "object",
"properties": {
"order_id": {
"type": "string",
"description": "Order UUID"
}
},
"required": ["order_id"]
}
}
},
{
"type": "function",
"function": {
"name": "create_order",
"description": "Create a new order with items. Requires authentication.",
"parameters": {
"type": "object",
"properties": {
"customer_id": {
"type": "string",
"description": "Customer UUID"
},
"items": {
"type": "array",
"description": "List of order items",
"items": {
"type": "object",
"properties": {
"sku": {"type": "string"},
"quantity": {"type": "integer"},
"unit_price": {"type": "string"},
"currency": {"type": "string", "default": "USD"}
},
"required": ["sku", "quantity", "unit_price"]
}
}
},
"required": ["customer_id", "items"]
}
}
}
]
def _requires_auth(self, tool_name: str) -> bool:
"""Check if tool requires authentication."""
auth_required_tools = ["get_customer", "list_orders", "get_order", "create_order"]
return tool_name in auth_required_tools
def _get_customer_id(self, session_id: str) -> Optional[str]:
"""Get customer_id from authenticated session."""
if not self.auth_handler.is_authenticated(session_id):
return None
return self.auth_handler.get_customer_id(session_id)
def process_message(self, session_id: str, user_message: str, conversation_history: List[Dict[str, str]]) -> str:
"""Process user message and return response."""
# Get authentication status
is_authenticated = self.auth_handler.is_authenticated(session_id)
customer_email = self.auth_handler.get_email(session_id) if is_authenticated else None
# Check if message is about authentication
if "email" in user_message.lower() and "pin" in user_message.lower():
# Try to extract email and PIN from message
# This is a simple approach - in production, use structured input
return "To authenticate, please provide your email and PIN in the format: 'email: your@email.com, pin: 1234'"
# Check if query might need authentication
order_keywords = ["order", "purchase", "buy", "my orders", "order history", "track order", "place order"]
needs_auth = any(keyword in user_message.lower() for keyword in order_keywords)
if needs_auth and not is_authenticated:
return "To access your orders, I need to verify your identity. Please provide your email and PIN in this format: 'email: your@email.com, pin: 1234'"
# Process with LLM (all API calls are automatically logged in OpenAI Platform under Logs → Completions)
response_text = self._process_with_llm(session_id, user_message, conversation_history, is_authenticated, customer_email)
return response_text
def _process_with_llm(self, session_id: str, user_message: str, conversation_history: List[Dict[str, str]], is_authenticated: bool, customer_email: Optional[str]) -> str:
"""Internal method to process message with LLM."""
# Build system message with authentication status
auth_status = "authenticated" if is_authenticated else "not authenticated"
system_content = """You are a helpful customer support agent for a computer products company.
You can help customers with:
- Product inquiries (browsing, searching, getting details) - no authentication needed
- Order management (viewing orders, order status, placing orders) - requires authentication
Current session status: """ + auth_status
if customer_email:
system_content += f"\nAuthenticated customer: {customer_email}"
system_content += """
IMPORTANT INSTRUCTIONS:
- When a customer asks to see/list/show their orders, use the list_orders tool directly
- When a customer asks about a specific order, use the get_order tool
- The customer_id is already set for authenticated sessions - you don't need to provide it
- Be friendly, professional, and helpful. Provide clear, concise answers."""
messages = [
{
"role": "system",
"content": system_content
}
]
# Add conversation history
messages.extend(conversation_history)
# Add current user message
messages.append({"role": "user", "content": user_message})
try:
# Call OpenAI with tool calling
# Note: For standard OpenAI Python SDK, API calls appear in Logs -> Completions
# The Traces tab is for OpenAI Agents SDK (JavaScript/TypeScript)
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
tools=self.tools,
tool_choice="auto"
)
message = response.choices[0].message
# Handle tool calls
if message.tool_calls:
tool_results = []
for tool_call in message.tool_calls:
tool_name = tool_call.function.name
tool_args = json.loads(tool_call.function.arguments)
# Check authentication for order-related tools
if self._requires_auth(tool_name) and not self.auth_handler.is_authenticated(session_id):
tool_results.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_name,
"content": "Authentication required. Please provide your email and PIN."
})
continue
# Inject customer_id for order-related tools
if self._requires_auth(tool_name):
customer_id = self._get_customer_id(session_id)
if customer_id:
# ALWAYS replace customer_id with the authenticated UUID
# Don't trust what the LLM provides - it may provide email instead
if tool_name in ["list_orders", "get_customer", "create_order"]:
tool_args["customer_id"] = customer_id
else:
# If customer_id is not available, don't call the tool
# This prevents using email as customer_id
tool_results.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_name,
"content": "Error: Customer ID not found. Please re-authenticate."
})
continue
# Call MCP tool
try:
result = self.mcp_client.call_tool(tool_name, tool_args)
# Extract text content from result
if "content" in result and len(result["content"]) > 0:
content = result["content"][0].get("text", str(result))
elif "structuredContent" in result:
content = result["structuredContent"].get("result", str(result))
else:
content = str(result)
tool_results.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_name,
"content": content
})
except Exception as e:
tool_results.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_name,
"content": f"Error: {str(e)}"
})
# Get final response with tool results
messages.append(message)
messages.extend(tool_results)
# Final response - automatically traced
final_response = self.client.chat.completions.create(
model=self.model,
messages=messages
)
return final_response.choices[0].message.content
else:
return message.content
except Exception as e:
return f"I apologize, but I encountered an error: {str(e)}. Please try again."
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