diff --git a/.flake8 b/.flake8 index f6937fef..f1ade14a 100644 --- a/.flake8 +++ b/.flake8 @@ -2,4 +2,4 @@ max-line-length = 88 extend-ignore = E501 exclude = .venv, src/App -ignore = E203, W503, E712 +ignore = E203, W503, E712, E226 diff --git a/chat-app/backend/app/create_assistants.py b/chat-app/backend/app/create_assistants.py deleted file mode 100644 index e1ef648f..00000000 --- a/chat-app/backend/app/create_assistants.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -""" -Create Azure AI Foundry assistants programmatically -""" -import asyncio -import logging - -# Set up logging -logging.basicConfig(level=logging.INFO) - - -async def create_assistants(): - print("Creating assistants in Azure AI Foundry project...") - - try: - from config import settings - from foundry_client import get_foundry_client, init_foundry_client - - # Initialize Foundry client - print("Initializing Foundry client...") - await init_foundry_client() - client = get_foundry_client() - - # Get Azure OpenAI client - print("Getting Azure OpenAI client...") - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # Define assistants to create - assistants_to_create = [ - { - "name": "Orchestrator Agent", - "description": "Main orchestrator that routes customer inquiries to specialized agents for product searches, order tracking, and policy questions.", - "instructions": """You are the main orchestrator for Contoso Paints e-commerce customer service. Your role is to: - -1. Analyze customer inquiries and route them to appropriate specialists -2. Handle general questions and greetings -3. Provide comprehensive assistance using available tools -4. Maintain a helpful, professional tone - -You have access to tools for: -- Product search and recommendations -- Order tracking and management -- Policy and FAQ information - -Always aim to provide accurate, helpful responses while maintaining excellent customer service.""", - "model": "gpt-4o-mini", - }, - { - "name": "Product Lookup Agent", - "description": "Specialized agent for product searches, recommendations, and catalog inquiries.", - "instructions": """You are a product specialist for Contoso Paints e-commerce. Your expertise includes: - -1. Product search and discovery -2. Product recommendations based on customer needs -3. Pricing and availability information -4. Product specifications and details -5. Category browsing and filtering - -Always help customers find the right products for their needs. Use the product search tools to provide accurate, up-to-date information.""", - "model": "gpt-4o-mini", - }, - { - "name": "Order Status Agent", - "description": "Specialized agent for order tracking, status updates, and order management.", - "instructions": """You are an order specialist for Contoso Paints e-commerce. You help customers with: - -1. Order status and tracking -2. Order history and details -3. Return and refund requests -4. Shipping information -5. Order modifications when possible - -Always provide accurate order information and help resolve any order-related concerns professionally.""", - "model": "gpt-4o-mini", - }, - { - "name": "Knowledge Agent", - "description": "Specialized agent for policies, FAQs, warranties, and general support information.", - "instructions": """You are a knowledge specialist for Contoso Paints e-commerce. You provide information about: - -1. Return and refund policies -2. Warranty information -3. Shipping policies -4. FAQs and general questions -5. Company policies and procedures - -Always provide accurate, helpful information from official policies and documentation.""", - "model": "gpt-4o-mini", - }, - ] - - created_assistants = [] - - for assistant_config in assistants_to_create: - print(f"\nCreating {assistant_config['name']}...") - - try: - # Create assistant - assistant = await openai_client.beta.assistants.create( - name=assistant_config["name"], - description=assistant_config["description"], - instructions=assistant_config["instructions"], - model=assistant_config["model"], - tools=[], # We'll add tools later through plugins - ) - - print(f"āœ… Created {assistant.name}") - print(f" ID: {assistant.id}") - print(f" Model: {assistant.model}") - - created_assistants.append( - { - "name": assistant_config["name"], - "id": assistant.id, - "role": assistant_config["name"].lower().replace(" ", "_"), - } - ) - - except Exception as e: - print(f"āŒ Failed to create {assistant_config['name']}: {e}") - - # Print environment variable updates - if created_assistants: - print("\nšŸŽÆ Update your .env file with these new assistant IDs:") - print("=" * 60) - - env_mapping = { - "orchestrator_agent": "FOUNDRY_ORCHESTRATOR_AGENT_ID", - "product_lookup_agent": "FOUNDRY_PRODUCT_AGENT_ID", - "order_status_agent": "FOUNDRY_ORDER_AGENT_ID", - "knowledge_agent": "FOUNDRY_KNOWLEDGE_AGENT_ID", - } - - for assistant in created_assistants: - role = assistant["role"] - if role in env_mapping: - env_var = env_mapping[role] - print(f'{env_var}="{assistant["id"]}"') - - print("=" * 60) - - return created_assistants - - except Exception as e: - print(f"āŒ Failed to create assistants: {e}") - import traceback - - traceback.print_exc() - return [] - - -if __name__ == "__main__": - asyncio.run(create_assistants()) diff --git a/chat-app/backend/app/list_assistants.py b/chat-app/backend/app/list_assistants.py deleted file mode 100644 index b7e83440..00000000 --- a/chat-app/backend/app/list_assistants.py +++ /dev/null @@ -1,55 +0,0 @@ -#!/usr/bin/env python3 -""" -List all assistants in Azure AI Foundry project -""" -import asyncio -import logging - -# Set up logging -logging.basicConfig(level=logging.INFO) - - -async def list_assistants(): - print("Listing assistants in Azure AI Foundry project...") - - try: - from foundry_client import get_foundry_client, init_foundry_client - - # Initialize Foundry client - print("Initializing Foundry client...") - await init_foundry_client() - client = get_foundry_client() - - # Get Azure OpenAI client - print("Getting Azure OpenAI client...") - from config import settings - - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # List all assistants - print("Fetching assistants...") - assistants = await openai_client.beta.assistants.list(limit=100) - - print(f"\nāœ… Found {len(assistants.data)} assistants:") - for assistant in assistants.data: - print(f" - ID: {assistant.id}") - print(f" Name: {assistant.name or 'No name'}") - print(f" Description: {assistant.description or 'No description'}") - print(f" Model: {assistant.model}") - print(f" Created: {assistant.created_at}") - print() - - return assistants.data - - except Exception as e: - print(f"āŒ Failed to list assistants: {e}") - import traceback - - traceback.print_exc() - return [] - - -if __name__ == "__main__": - asyncio.run(list_assistants()) diff --git a/chat-app/backend/app/main.py b/chat-app/backend/app/main.py index 262818b2..8fa2efb1 100644 --- a/chat-app/backend/app/main.py +++ b/chat-app/backend/app/main.py @@ -67,7 +67,7 @@ def get_current_span(): logging.getLogger(logger_name).setLevel( getattr(logging, AZURE_PACKAGE_LOGGING_LEVEL, logging.WARNING) ) -logging.getLogger("agent_framework_azure_ai._client").setLevel(logging.ERROR) +logging.getLogger("azure.ai.projects").setLevel(logging.WARNING) try: from .auth import get_current_user from .config import settings diff --git a/chat-app/backend/app/plugins/__init__.py b/chat-app/backend/app/plugins/__init__.py deleted file mode 100644 index 665acc3b..00000000 --- a/chat-app/backend/app/plugins/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Plugins package for semantic kernel integration diff --git a/chat-app/backend/app/plugins/orders_plugin.py b/chat-app/backend/app/plugins/orders_plugin.py deleted file mode 100644 index 02006d51..00000000 --- a/chat-app/backend/app/plugins/orders_plugin.py +++ /dev/null @@ -1,236 +0,0 @@ -import asyncio -import concurrent.futures -import json -import logging - -from semantic_kernel.functions import kernel_function - -from ..cosmos_service import get_cosmos_service - -logger = logging.getLogger(__name__) - - -def run_async_sync(coro): - """Run an async coroutine synchronously, handling event loop conflicts""" - try: - asyncio.get_running_loop() - # We're in an async context, use a thread pool - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit(asyncio.run, coro) - return future.result() - except RuntimeError: - # No event loop running, safe to use asyncio.run - return asyncio.run(coro) - - -class OrdersPlugin: - """Plugin for order management using Azure Cosmos DB""" - - @kernel_function(description="Get order by ID and return JSON") - def get_order(self, order_id: str) -> str: - """Get order by ID""" - try: - cosmos_service = get_cosmos_service() - order = run_async_sync(cosmos_service.get_order_by_id(order_id)) - if not order: - return json.dumps({"error": f"No order found with ID: {order_id}"}) - - if hasattr(order, "model_dump"): - order_dict = order if isinstance(order, dict) else order.model_dump() - else: - order_dict = order - - return json.dumps(order_dict) - except Exception as e: - logger.error(f"Error getting order {order_id}: {e}") - return json.dumps({"error": f"Failed to get order: {str(e)}"}) - - @kernel_function(description="List orders for a customer; returns JSON list") - def list_orders(self, customer_id: str, limit: int = 10) -> str: - """List orders for a customer""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_by_customer(customer_id, limit=limit) - ) - if not orders: - return json.dumps([]) - - orders_list = [] - for order in orders: - if hasattr(order, "model_dump"): - orders_list.append( - order if isinstance(order, dict) else order.model_dump() - ) - else: - orders_list.append(order) - - return json.dumps(orders_list) - except Exception as e: - logger.error(f"Error listing orders for customer {customer_id}: {e}") - return json.dumps({"error": f"Failed to list orders: {str(e)}"}) - - @kernel_function(description="Get order status by ID") - def get_order_status(self, order_id: str) -> str: - """Get order status""" - try: - cosmos_service = get_cosmos_service() - order = run_async_sync(cosmos_service.get_order_by_id(order_id)) - if not order: - return json.dumps({"error": f"No order found with ID: {order_id}"}) - - if hasattr(order, "model_dump"): - order_dict = order if isinstance(order, dict) else order.model_dump() - else: - order_dict = order - - status_info = { - "order_id": order_id, - "status": order_dict.get("status", "unknown"), - "total": order_dict.get("total", 0), - "created_at": order_dict.get("created_at", ""), - "updated_at": order_dict.get("updated_at", ""), - } - - return json.dumps(status_info) - except Exception as e: - logger.error(f"Error getting order status {order_id}: {e}") - return json.dumps({"error": f"Failed to get order status: {str(e)}"}) - - @kernel_function(description="Process refund for an order") - def process_refund(self, order_id: str, reason: str) -> str: - """Process refund for an order""" - try: - # This would typically update the order status in the database - # For now, we'll simulate the process - logger.info(f"Processing refund for order {order_id} due to: {reason}") - - # In a real implementation, you would: - # 1. Validate the order exists and is eligible for refund - # 2. Update the order status - # 3. Process the actual refund through payment provider - # 4. Send confirmation to customer - - result = { - "order_id": order_id, - "refund_status": "processed", - "reason": reason, - "message": f"Refund for order {order_id} has been processed successfully", - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error processing refund for order {order_id}: {e}") - return json.dumps({"error": f"Failed to process refund: {str(e)}"}) - - @kernel_function(description="Process return for an order") - def process_return(self, order_id: str, reason: str) -> str: - """Process return for an order""" - try: - # This would typically update the order status in the database - # For now, we'll simulate the process - logger.info(f"Processing return for order {order_id} due to: {reason}") - - # In a real implementation, you would: - # 1. Validate the order exists and is eligible for return - # 2. Update the order status - # 3. Generate return label - # 4. Send confirmation to customer - - result = { - "order_id": order_id, - "return_status": "processed", - "reason": reason, - "message": f"Return for order {order_id} has been processed successfully", - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error processing return for order {order_id}: {e}") - return json.dumps({"error": f"Failed to process return: {str(e)}"}) - - @kernel_function( - description="Get orders within return window for a customer; returns JSON list" - ) - def get_returnable_orders(self, customer_id: str) -> str: - """Get orders that are still within the return window (30 days)""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_in_date_range(customer_id, days=30) - ) - - if not orders: - return json.dumps([]) - - returnable_orders = [] - for order in orders: - order_dict = ( - order - if isinstance(order, dict) - else (order.model_dump() if hasattr(order, "model_dump") else order) - ) - returnable_orders.append(order_dict) - - return json.dumps(returnable_orders) - except Exception as e: - logger.error( - f"Error getting returnable orders for customer {customer_id}: {e}" - ) - return json.dumps({"error": f"Failed to get returnable orders: {str(e)}"}) - - @kernel_function( - description="Get orders from the past N days for a customer; returns JSON list" - ) - def get_orders_by_date_range(self, customer_id: str, days: int = 180) -> str: - """Get orders from the last N days (default 180 days / 6 months)""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_in_date_range(customer_id, days=days) - ) - - if not orders: - return json.dumps([]) - - orders_list = [] - for order in orders: - order_dict = ( - order - if isinstance(order, dict) - else (order.model_dump() if hasattr(order, "model_dump") else order) - ) - orders_list.append(order_dict) - - return json.dumps(orders_list) - except Exception as e: - logger.error( - f"Error getting orders by date range for customer {customer_id}: {e}" - ) - return json.dumps( - {"error": f"Failed to get orders by date range: {str(e)}"} - ) - - @kernel_function( - description="Check if a specific order is still returnable (within 30-day window)" - ) - def check_if_returnable(self, order_id: str) -> str: - """Check if an order is still within the return window""" - try: - cosmos_service = get_cosmos_service() - is_returnable = run_async_sync( - cosmos_service.is_order_returnable(order_id, return_window_days=30) - ) - - result = { - "order_id": order_id, - "is_returnable": is_returnable, - "return_window_days": 30, - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error checking if order {order_id} is returnable: {e}") - return json.dumps( - {"error": f"Failed to check if order is returnable: {str(e)}"} - ) diff --git a/chat-app/backend/app/plugins/reference_plugin.py b/chat-app/backend/app/plugins/reference_plugin.py deleted file mode 100644 index 68dff729..00000000 --- a/chat-app/backend/app/plugins/reference_plugin.py +++ /dev/null @@ -1,116 +0,0 @@ -import logging - -from semantic_kernel.functions import kernel_function - -from ..config import has_azure_search_config -from ..services.search import search_reference_enhanced - -logger = logging.getLogger(__name__) - - -class ReferencePlugin: - """Enhanced plugin for reference document lookup using Azure Search""" - - def __init__(self): - self.is_configured = has_azure_search_config() - - @kernel_function( - description="Search reference documents for policy and support information with natural language responses" - ) - def lookup(self, query: str, top: int = 3) -> str: - """Enhanced lookup with natural language responses""" - if not self.is_configured: - return "I don't have access to policy information right now. Please contact our support team for assistance." - - try: - hits = search_reference_enhanced(query, top) - - if not hits: - return "I couldn't find specific information about that. Let me help you contact our support team who can assist you directly." - - # Format response naturally using the best available information - response_parts = [] - - for hit in hits: - # Prefer extracted answers if available - if hit.get("answers"): - for answer in hit["answers"]: - response_parts.append(answer) - else: - # Use content with smart truncation - content = hit["content"] - if len(content) > 200: - # Try to find a good break point - break_point = content.find(".", 150) - if break_point > 0: - content = content[: break_point + 1] - else: - content = content[:200] + "..." - response_parts.append(content) - - # Combine the best parts and limit length - combined_response = " ".join( - response_parts[:2] - ) # Limit to 2 most relevant parts - - # Add helpful context if it's about returns or policies - if any( - keyword in query.lower() - for keyword in ["return", "refund", "policy", "warranty"] - ): - combined_response += " If you need further assistance, you can call our support team at 1-800-555-0199." - - return combined_response - - except Exception as e: - logger.error(f"Error searching reference documents: {e}") - return "I'm having trouble accessing policy information right now. Please contact our support team for immediate assistance." - - @kernel_function(description="Get return policy information with natural language") - def get_return_policy(self) -> str: - """Get return policy information with natural language response""" - return self.lookup("return policy refund exchange", top=2) - - @kernel_function(description="Get shipping information with natural language") - def get_shipping_info(self) -> str: - """Get shipping information with natural language response""" - return self.lookup("shipping delivery time cost", top=2) - - @kernel_function(description="Get warranty information with natural language") - def get_warranty_info(self) -> str: - """Get warranty information with natural language response""" - return self.lookup("warranty guarantee coverage", top=2) - - @kernel_function(description="Lookup policy information with context awareness") - def lookup_policy(self, query: str, context: str = "") -> str: - """Enhanced policy lookup with context awareness""" - if not self.is_configured: - return "I don't have access to policy information right now. Please contact support." - - try: - # Enhanced search with context - search_query = f"{query} {context}".strip() - hits = search_reference_enhanced(search_query, top=3) - - if not hits: - return "I couldn't find specific information about that. Let me help you contact support." - - # Format response naturally - response_parts = [] - for hit in hits: - if hit.get("answers"): - # Use extracted answers if available - for answer in hit["answers"]: - response_parts.append(answer) - else: - # Use content with highlighting - content = hit["content"] - if len(content) > 200: - content = content[:200] + "..." - response_parts.append(content) - - return " ".join(response_parts[:2]) # Limit to 2 most relevant parts - - except Exception as e: - logger.error(f"Policy lookup error: {e}") - return "I'm having trouble accessing policy information. Please contact support." diff --git a/chat-app/backend/app/routers/chat.py b/chat-app/backend/app/routers/chat.py index 65a62226..dc937afb 100644 --- a/chat-app/backend/app/routers/chat.py +++ b/chat-app/backend/app/routers/chat.py @@ -382,7 +382,7 @@ async def send_message_legacy( AIProjectClient(endpoint=ai_project_endpoint, credential=credential) as project_client, AzureAIProjectAgentProvider( project_client=project_client, - credential=credential + credential=credential, ) as provider, ): # Retry logic for rate limit errors diff --git a/chat-app/backend/app/simple_foundry_orchestrator.py b/chat-app/backend/app/simple_foundry_orchestrator.py deleted file mode 100644 index 864a05c4..00000000 --- a/chat-app/backend/app/simple_foundry_orchestrator.py +++ /dev/null @@ -1,446 +0,0 @@ -from __future__ import annotations - -import asyncio -import logging -import re -from typing import Any, Dict, List, Optional - -from cachetools import TTLCache -from semantic_kernel.agents import AzureAIAgent, AzureAIAgentThread - -from .config import has_foundry_config, settings -from .foundry_client import get_foundry_client, init_foundry_client -from .plugins.orders_plugin import OrdersPlugin -from .plugins.reference_plugin import ReferencePlugin - -logger = logging.getLogger(__name__) - - -class ThreadCache(TTLCache): - """Cache for agent threads with automatic cleanup""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def expire(self, time=None): - """Remove expired items and delete associated threads""" - items = super().expire(time) - for key, thread_id in items: - try: - logger.info(f"Thread expired from cache: {thread_id}") - except Exception as e: - logger.error(f"Error expiring thread {thread_id}: {e}") - return items - - def popitem(self): - """Remove item using LRU eviction""" - key, thread_id = super().popitem() - try: - logger.info(f"Thread evicted from cache (LRU): {thread_id}") - except Exception as e: - logger.error(f"Error evicting thread {thread_id}: {e}") - return key, thread_id - - -async def _resolve_foundry_agent_definition(agent_id: str): - """Get the agent definition from Foundry""" - # client = get_foundry_client() - try: - # For now, return a basic agent definition structure - # This needs to be updated when the correct Azure AI Projects SDK method is available - logger.warning(f"Using placeholder agent definition for agent ID: {agent_id}") - - # Create a basic agent definition that can work with AzureAIAgent - agent_definition = { - "id": agent_id, - "name": f"Agent-{agent_id}", - "description": f"Azure AI Foundry Agent {agent_id}", - "instructions": "You are a helpful assistant.", - "model": "gpt-4", - } - - return agent_definition - - except Exception as e: - logger.error( - f"Error resolving Foundry agent definition for ID '{agent_id}': {e}" - ) - # Return a fallback definition - return { - "id": agent_id, - "name": f"Fallback-Agent-{agent_id}", - "description": "Fallback agent definition", - "instructions": "You are a helpful assistant.", - "model": "gpt-4", - } - - -async def _build_foundry_agent( - agent_id: str, name: str, plugins: Optional[List] = None -) -> Optional[Any]: - """Build a Foundry agent using direct OpenAI API calls""" - try: - logger.info(f"Building {name} (ID: {agent_id}) using Azure AI Foundry...") - - # Get the Foundry client - client = get_foundry_client() - - try: - # Get the Azure OpenAI client from Foundry with proper API version - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # Test connection by retrieving the assistant - assistant = await openai_client.beta.assistants.retrieve(agent_id) - logger.info( - f"āœ… Successfully connected to Foundry assistant: {assistant.name or name}" - ) - - # Create a custom agent that uses the Azure OpenAI client directly - class FoundryAgent: - def __init__(self, assistant_id: str, name: str, openai_client): - self.id = assistant_id - self.name = name - self.client = openai_client - self.assistant_id = assistant_id - - async def invoke(self, messages: str, thread=None): - try: - # Create or get thread - if not thread or not hasattr(thread, "id"): - thread_obj = await self.client.beta.threads.create() - thread_id = thread_obj.id - logger.info(f"Created new thread: {thread_id}") - else: - thread_id = thread.id - logger.info(f"Using existing thread: {thread_id}") - - # Add user message to thread - await self.client.beta.threads.messages.create( - thread_id=thread_id, role="user", content=messages - ) - - # Create and run the assistant - run = await self.client.beta.threads.runs.create( - thread_id=thread_id, assistant_id=self.assistant_id - ) - - max_attempts = 30 # 30 seconds max - attempt = 0 - - while ( - run.status in ["queued", "in_progress"] - and attempt < max_attempts - ): - await asyncio.sleep(1) - attempt += 1 - run = await self.client.beta.threads.runs.retrieve( - thread_id=thread_id, run_id=run.id - ) - logger.debug( - f"Run status: {run.status} (attempt {attempt})" - ) - - if run.status == "completed": - # Get the assistant's response - messages_response = ( - await self.client.beta.threads.messages.list( - thread_id=thread_id, - limit=1, # Get just the latest message - ) - ) - - if messages_response.data: - latest_message = messages_response.data[0] - if ( - latest_message.role == "assistant" - and latest_message.content - ): - content = latest_message.content[0].text.value - - class FoundryResponse: - def __init__(self, content, thread_id): - self.content = content - self.thread = type( - "Thread", (), {"id": thread_id} - )() - - logger.info( - f"āœ… {self.name} responded with {len(content)} characters" - ) - yield FoundryResponse(content, thread_id) - return - - # Handle non-completed status - error_msg = f"Assistant run did not complete successfully. Status: {run.status}" - if run.status == "failed": - error_msg += f". Error: {getattr(run, 'last_error', 'Unknown error')}" - - logger.error(error_msg) - - class ErrorResponse: - def __init__(self, content): - self.content = content - self.thread = None - - yield ErrorResponse( - f"Sorry, I encountered an issue: {error_msg}" - ) - - except Exception as invoke_error: - logger.error( - f"Error invoking {self.name}: {invoke_error}", exc_info=True - ) - - class InvokeErrorResponse: - def __init__(self, content): - self.content = content - self.thread = None - - yield InvokeErrorResponse( - f"I encountered an error while processing your request: {str(invoke_error)}" - ) - - foundry_agent = FoundryAgent(agent_id, name, openai_client) - logger.info(f"āœ… Successfully created Foundry agent: {name}") - return foundry_agent - - except Exception as e: - logger.error(f"Failed to create Foundry agent {name}: {e}", exc_info=True) - # No fallback - return None to indicate failure - return None - - except Exception as e: - logger.error(f"Error building Foundry agent '{name}' with ID '{agent_id}': {e}") - return None - - -class SimpleFoundryOrchestrator: - """ - Simple orchestrator that routes to the appropriate Foundry agent. - Uses Semantic Kernel AzureAIAgent like the reference implementation. - Includes thread caching for better performance. - """ - - def __init__(self): - self.agents: Dict[str, AzureAIAgent] = {} - self.is_configured = has_foundry_config() - self.thread_cache: ThreadCache = ThreadCache(maxsize=1000, ttl=3600.0) - - if not self.is_configured: - logger.warning( - "Foundry not configured. Set AZURE_FOUNDRY_ENDPOINT and agent IDs" - ) - - @classmethod - async def create(cls) -> "SimpleFoundryOrchestrator": - """Initialize the orchestrator and create all agents""" - self = cls() - - if not self.is_configured: - logger.warning( - "Simple Foundry orchestrator will not initialize (not configured)" - ) - return self - - try: - logger.info("Initializing Simple Foundry orchestrator...") - logger.info(f" - Foundry endpoint: {settings.azure_foundry_endpoint}") - logger.info( - f" - Orchestrator agent ID: {settings.foundry_orchestrator_agent_id}" - ) - logger.info(f" - Order agent ID: {settings.foundry_order_agent_id}") - logger.info( - f" - Knowledge agent ID: {settings.foundry_knowledge_agent_id}" - ) - - # Initialize Foundry client first - logger.info("Initializing Foundry client...") - await init_foundry_client() - logger.info("āœ… Foundry client initialized") - - # Build the main orchestrator agent if configured - if settings.foundry_orchestrator_agent_id: - logger.info("Building OrchestratorAgent...") - orchestrator_agent = await _build_foundry_agent( - agent_id=settings.foundry_orchestrator_agent_id, - name="OrchestratorAgent", - plugins=[OrdersPlugin(), ReferencePlugin()], - ) - if orchestrator_agent: - self.agents["OrchestratorAgent"] = orchestrator_agent - logger.info("āœ… Added OrchestratorAgent with all plugins") - else: - logger.warning("āŒ Failed to create OrchestratorAgent") - else: - logger.warning("āš ļø No OrchestratorAgent ID configured") - - # Build agents with their plugins attached - if settings.foundry_order_agent_id: - logger.info("Building OrderStatusAgent...") - order_agent = await _build_foundry_agent( - agent_id=settings.foundry_order_agent_id, - name="OrderStatusAgent", - plugins=[OrdersPlugin()], - ) - if order_agent: - self.agents["OrderStatusAgent"] = order_agent - logger.info("āœ… Added OrderStatusAgent with OrdersPlugin") - else: - logger.warning("āŒ Failed to create OrderStatusAgent") - else: - logger.warning("āš ļø No OrderStatusAgent ID configured") - - if settings.foundry_knowledge_agent_id: - logger.info("Building KnowledgeAgent...") - knowledge_agent = await _build_foundry_agent( - agent_id=settings.foundry_knowledge_agent_id, - name="KnowledgeAgent", - plugins=[ReferencePlugin()], - ) - if knowledge_agent: - self.agents["KnowledgeAgent"] = knowledge_agent - logger.info("āœ… Added KnowledgeAgent with ReferencePlugin") - else: - logger.warning("āŒ Failed to create KnowledgeAgent") - else: - logger.warning("āš ļø No KnowledgeAgent ID configured") - - if not self.agents: - logger.error("āŒ No agents were successfully built!") - self.is_configured = False - return self - - logger.info( - f"āœ… Simple Foundry orchestrator initialized with {len(self.agents)} agents" - ) - return self - - except Exception as e: - logger.error( - f"āŒ Failed to initialize Simple Foundry orchestrator: {e}", - exc_info=True, - ) - self.is_configured = False - return self - - def _determine_target_agent(self, user_text: str) -> str: - query_lower = user_text.lower() - - order_patterns = [ - r"\b(order|tracking|shipment|invoice)\b", - r"\b(where is my|order status|order #)\b", - ] - policy_patterns = [ - r"\b(return|refund|exchange|policy|warranty)\b", - r"\b(problem|issue|complaint|damaged|leaking)\b", - r"\b(ship|delivery|shipping|track)\b", - r"\b(help|support|contact|customer service)\b", - r"\b(guarantee|coverage|defect)\b", - ] - - order_score = sum(1 for pattern in order_patterns if re.search(pattern, query_lower)) - policy_score = sum(1 for pattern in policy_patterns if re.search(pattern, query_lower)) - - if order_score > 0 and "OrderStatusAgent" in self.agents: - logger.info("Routing to OrderStatusAgent") - return "OrderStatusAgent" - if policy_score > 0 and "KnowledgeAgent" in self.agents: - logger.info("Routing to KnowledgeAgent") - return "KnowledgeAgent" - if "OrchestratorAgent" in self.agents: - logger.info("Default routing to OrchestratorAgent") - return "OrchestratorAgent" - available_agents = list(self.agents.keys()) - if available_agents: - return available_agents[0] - logger.error("No agents available for routing") - return "OrchestratorAgent" - - async def respond( - self, - user_text: str, - conversation_id: Optional[str] = None, - history: List[Dict[str, str]] | None = None, - ) -> Dict[str, Any]: - """Respond using the determined agent with thread caching""" - if not self.is_configured: - logger.error("Simple Foundry orchestrator not configured") - return { - "error": "Simple Foundry orchestrator not configured", - "text": "I'm sorry, the AI service is not properly configured.", - } - - try: - # Determine which agent to use - target_agent_name = self._determine_target_agent(user_text) - agent = self.agents.get(target_agent_name) - - if not agent: - logger.error(f"Agent {target_agent_name} not available") - return { - "error": f"Agent {target_agent_name} not available", - "text": f"I'm sorry, the {target_agent_name} is not currently available.", - } - - logger.info(f"Using {target_agent_name} for query: {user_text[:50]}...") - - # Get or create thread for this conversation (thread caching) - thread = None - if conversation_id: - cache_key = f"{conversation_id}_{target_agent_name}" - thread_id = self.thread_cache.get(cache_key) - if thread_id: - thread = AzureAIAgentThread( - client=agent.client, thread_id=thread_id - ) - logger.info(f"Reusing cached thread: {thread_id}") - - # Use the agent's invoke method with thread caching - response_content = "" - async for message in agent.invoke(messages=user_text, thread=thread): - if hasattr(message, "content") and message.content: - response_content += str(message.content) - - # Cache the thread for future use - if conversation_id and hasattr(message, "thread") and message.thread: - cache_key = f"{conversation_id}_{target_agent_name}" - self.thread_cache[cache_key] = message.thread.id - - if response_content: - logger.info( - f"{target_agent_name} response length: {len(response_content)} chars" - ) - return { - "messages": [response_content], - "awaiting_user": response_content.strip().endswith("?"), - "text": response_content, - } - else: - logger.error(f"No response from {target_agent_name}") - return { - "error": "No response from agent", - "text": "I'm sorry, I couldn't get a response from the agent.", - } - - except Exception as e: - logger.error(f"Error in Simple Foundry orchestrator: {e}", exc_info=True) - return { - "error": f"Failed to generate response: {str(e)}", - "text": "I'm sorry, I encountered an error trying to process your request.", - } - - async def shutdown(self): - """Cleanup if needed""" - logger.info("Simple Foundry orchestrator shutdown") - - -_simple_foundry_orchestrator_instance: SimpleFoundryOrchestrator | None = None - - -async def get_simple_foundry_orchestrator() -> SimpleFoundryOrchestrator: - global _simple_foundry_orchestrator_instance - if _simple_foundry_orchestrator_instance is None: - _simple_foundry_orchestrator_instance = await SimpleFoundryOrchestrator.create() - return _simple_foundry_orchestrator_instance diff --git a/chat-app/backend/app/utils/foundry_agent_utils.py b/chat-app/backend/app/utils/foundry_agent_utils.py index 3ddc588e..565bd081 100644 --- a/chat-app/backend/app/utils/foundry_agent_utils.py +++ b/chat-app/backend/app/utils/foundry_agent_utils.py @@ -13,7 +13,7 @@ async def call_foundry_agent( azure_client_id: Optional[str] = None, ) -> str: try: - from agent_framework_azure_ai import AzureAIProjectAgentProvider + from agent_framework.azure import AzureAIProjectAgentProvider from azure.ai.projects.aio import AIProjectClient try: diff --git a/chat-app/backend/requirements.txt b/chat-app/backend/requirements.txt index 74afbed5..c8d70e75 100644 --- a/chat-app/backend/requirements.txt +++ b/chat-app/backend/requirements.txt @@ -10,7 +10,7 @@ agent-framework-azure-ai==1.0.0rc6 agent-framework-core==1.0.0rc6 azure-identity==1.25.3 azure-search-documents==11.7.0b2 -azure-ai-projects==2.1.0 +azure-ai-projects>=2.3.0 azure-cosmos==4.15.0 azure-ai-voicelive==1.2.0b5 diff --git a/chat-app/frontend/package-lock.json b/chat-app/frontend/package-lock.json index 35eee446..486ba5e8 100644 --- a/chat-app/frontend/package-lock.json +++ b/chat-app/frontend/package-lock.json @@ -86,12 +86,13 @@ } }, "node_modules/@babel/code-frame": { - "version": "7.29.0", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz", - "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==", + "version": "7.29.7", + "resolved": "https://ms-feed-2.pkgs.visualstudio.com/1es-public/_packaging/npm-public/npm/registry/@babel/code-frame/-/code-frame-7.29.7.tgz", + "integrity": "sha1-8vu/6ofESiFZDsUVt3iywm2IZuc=", "dev": true, + "license": "MIT", "dependencies": { - "@babel/helper-validator-identifier": "^7.28.5", + "@babel/helper-validator-identifier": "^7.29.7", 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"resolved": "https://ms-feed-17.pkgs.visualstudio.com/1es-public/_packaging/npm-public/npm/registry/zod/-/zod-4.4.3.tgz", + "integrity": "sha1-toDxcohdGLvr8hqDTqJeVaG781Y=", + "license": "MIT", "funding": { "url": "https://github.com/sponsors/colinhacks" } }, "node_modules/zwitch": { "version": "2.0.4", - "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", - "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", + "resolved": "https://ms-feed-2.pkgs.visualstudio.com/1es-public/_packaging/npm-public/npm/registry/zwitch/-/zwitch-2.0.4.tgz", + "integrity": "sha1-yCfUsKy3b8PmhaTG7CkC1RBw6dc=", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" diff --git a/infra/scripts/post-provision/agent_scripts/01_create_agents.py b/infra/scripts/post-provision/agent_scripts/01_create_agents.py index 70920e69..e368623f 100644 --- a/infra/scripts/post-provision/agent_scripts/01_create_agents.py +++ b/infra/scripts/post-provision/agent_scripts/01_create_agents.py @@ -4,14 +4,7 @@ from pathlib import Path from azure.ai.projects.aio import AIProjectClient -from azure.ai.projects.models import ( - AISearchIndexResource, - AzureAISearchTool, - AzureAISearchToolResource, - ConnectionType, - FunctionTool, - PromptAgentDefinition, -) +from azure.ai.projects.models import ConnectionType from agent_framework.azure import AzureAIProjectAgentProvider from azure.identity.aio import AzureCliCredential from dotenv import load_dotenv @@ -19,7 +12,10 @@ load_dotenv() sys.path.insert(0, str(Path(__file__).resolve().parents[4])) -from scenarios.scenario_loader import load_agent_instructions, load_manifest, resolve_scenario, catalog_tool_name, policy_tool_name +from scenarios.scenario_loader import ( # noqa: E402 + load_agent_instructions, load_manifest, resolve_scenario, + catalog_tool_name, policy_tool_name, +) p = argparse.ArgumentParser() p.add_argument("--ai_project_endpoint", required=True) diff --git a/infra/scripts/post-provision/agent_scripts/requirements.txt b/infra/scripts/post-provision/agent_scripts/requirements.txt index 5900919e..a18112bd 100644 --- a/infra/scripts/post-provision/agent_scripts/requirements.txt +++ b/infra/scripts/post-provision/agent_scripts/requirements.txt @@ -1,6 +1,6 @@ agent-framework-azure-ai==1.0.0rc6 agent-framework-core==1.0.0rc6 -azure-ai-projects==2.1.0 +azure-ai-projects>=2.3.0 azure-identity==1.25.3 python-dotenv==1.2.2 aiohttp==3.13.5 diff --git a/infra/scripts/post-provision/data_scripts/01_create_products_search_index.py b/infra/scripts/post-provision/data_scripts/01_create_products_search_index.py index afe514c9..9d22fe4f 100644 --- a/infra/scripts/post-provision/data_scripts/01_create_products_search_index.py +++ b/infra/scripts/post-provision/data_scripts/01_create_products_search_index.py @@ -25,10 +25,10 @@ p.add_argument("--scenario", default=None) args = p.parse_args() -import sys -from pathlib import Path +import sys # noqa: E402 +from pathlib import Path # noqa: E402 sys.path.insert(0, str(Path(__file__).resolve().parents[4])) -from scenarios.scenario_loader import catalog_csv_path, load_manifest, resolve_scenario +from scenarios.scenario_loader import catalog_csv_path, load_manifest, resolve_scenario # noqa: E402 scenario = resolve_scenario(args.scenario) manifest = load_manifest(scenario) diff --git a/infra/scripts/post-provision/data_scripts/02_create_policies_search_index.py b/infra/scripts/post-provision/data_scripts/02_create_policies_search_index.py index 61d7fc6d..68dc8a9d 100644 --- a/infra/scripts/post-provision/data_scripts/02_create_policies_search_index.py +++ b/infra/scripts/post-provision/data_scripts/02_create_policies_search_index.py @@ -34,10 +34,10 @@ p.add_argument("--scenario", default=None) args = p.parse_args() -import sys -from pathlib import Path +import sys # noqa: E402 +from pathlib import Path # noqa: E402 sys.path.insert(0, str(Path(__file__).resolve().parents[4])) -from scenarios.scenario_loader import load_manifest, policies_dir, resolve_scenario +from scenarios.scenario_loader import load_manifest, policies_dir, resolve_scenario # noqa: E402 scenario = resolve_scenario(args.scenario) manifest = load_manifest(scenario) diff --git a/infra/scripts/post-provision/data_scripts/03_write_products_to_cosmos.py b/infra/scripts/post-provision/data_scripts/03_write_products_to_cosmos.py index eb7bd4bd..606083cd 100644 --- a/infra/scripts/post-provision/data_scripts/03_write_products_to_cosmos.py +++ b/infra/scripts/post-provision/data_scripts/03_write_products_to_cosmos.py @@ -17,10 +17,10 @@ p.add_argument("--scenario", default=None) args = p.parse_args() -import sys -from pathlib import Path +import sys # noqa: F811, E402 +from pathlib import Path # noqa: E402 sys.path.insert(0, str(Path(__file__).resolve().parents[4])) -from scenarios.scenario_loader import catalog_csv_path, resolve_scenario +from scenarios.scenario_loader import catalog_csv_path, resolve_scenario # noqa: E402 scenario = resolve_scenario(args.scenario) diff --git a/infra/scripts/scenario_bootstrap.py b/infra/scripts/scenario_bootstrap.py index 81d4b08d..eca63532 100644 --- a/infra/scripts/scenario_bootstrap.py +++ b/infra/scripts/scenario_bootstrap.py @@ -3,4 +3,4 @@ sys.path.insert(0, str(Path(__file__).resolve().parents[2])) -from scenarios.scenario_loader import load_manifest, resolve_scenario +from scenarios.scenario_loader import load_manifest, resolve_scenario # noqa: E402, F401 diff --git a/infra/vscode_web/codeSample.py b/infra/vscode_web/codeSample.py index 2ad2d041..47f6725a 100644 --- a/infra/vscode_web/codeSample.py +++ b/infra/vscode_web/codeSample.py @@ -1,25 +1,40 @@ from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential -project_client = AIProjectClient.from_connection_string( +project = AIProjectClient( credential=DefaultAzureCredential(), - conn_str="<%= connectionString %>") + endpoint="<%= connectionString %>") -agent = project_client.agents.get_agent("<%= agentId %>") +# Get the OpenAI client for conversations and responses +openai = project.get_openai_client() -thread = project_client.agents.create_thread() -print(f"Created thread, ID: {thread.id}") - -message = project_client.agents.create_message( - thread_id=thread.id, - role="user", - content="<%= userMessage %>" +# Create a conversation with the user message +conversation = openai.conversations.create( + items=[ + { + "type": "message", + "role": "user", + "content": "<%= userMessage %>", + } + ], ) +print(f"Created conversation, ID: {conversation.id}") -run = project_client.agents.create_and_process_run( - thread_id=thread.id, - agent_id=agent.id) -messages = project_client.agents.list_messages(thread_id=thread.id) +# Send a response using the agent reference +response = openai.responses.create( + input="<%= userMessage %>", + conversation=conversation.id, + extra_body={ + "agent_reference": { + "name": "<%= agentId %>", + "type": "agent_reference", + } + }, +) -for text_message in messages.text_messages: - print(text_message.as_dict()) \ No newline at end of file +# Print the response output +for item in response.output: + if item.type == "message": + for block in item.content: + if hasattr(block, "text"): + print(f"assistant: {block.text}") diff --git a/infra/vscode_web/endpoint-requirements.txt b/infra/vscode_web/endpoint-requirements.txt index ffb5f27a..36bfc9d6 100644 --- a/infra/vscode_web/endpoint-requirements.txt +++ b/infra/vscode_web/endpoint-requirements.txt @@ -1,3 +1,3 @@ -azure-ai-projects==2.0.1 +azure-ai-projects>=2.3.0 azure-identity==1.25.3 ansible-core~=2.20.3 \ No newline at end of file diff --git a/infra/vscode_web/endpointCodeSample.py b/infra/vscode_web/endpointCodeSample.py index 21452478..2d44eef3 100644 --- a/infra/vscode_web/endpointCodeSample.py +++ b/infra/vscode_web/endpointCodeSample.py @@ -1,31 +1,40 @@ from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential -from azure.ai.agents.models import ListSortOrder project = AIProjectClient( credential=DefaultAzureCredential(), endpoint="<%= endpoint %>") -agent = project.agents.get_agent("<%= agentId %>") +# Get the OpenAI client for conversations and responses +openai = project.get_openai_client() -thread = project.agents.threads.create() -print(f"Created thread, ID: {thread.id}") - -message = project.agents.messages.create( - thread_id=thread.id, - role="user", - content="<%= userMessage %>" +# Create a conversation with the user message +conversation = openai.conversations.create( + items=[ + { + "type": "message", + "role": "user", + "content": "<%= userMessage %>", + } + ], ) +print(f"Created conversation, ID: {conversation.id}") -run = project.agents.runs.create_and_process( - thread_id=thread.id, - agent_id=agent.id) - -if run.status == "failed": - print(f"Run failed: {run.last_error}") -else: - messages = project.agents.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING) +# Send a response using the agent reference +response = openai.responses.create( + input="<%= userMessage %>", + conversation=conversation.id, + extra_body={ + "agent_reference": { + "name": "<%= agentId %>", + "type": "agent_reference", + } + }, +) - for message in messages: - if message.text_messages: - print(f"{message.role}: {message.text_messages[-1].text.value}") \ No newline at end of file +# Print the response output +for item in response.output: + if item.type == "message": + for block in item.content: + if hasattr(block, "text"): + print(f"assistant: {block.text}") diff --git a/infra/vscode_web/requirements.txt b/infra/vscode_web/requirements.txt index ffb5f27a..36bfc9d6 100644 --- a/infra/vscode_web/requirements.txt +++ b/infra/vscode_web/requirements.txt @@ -1,3 +1,3 @@ -azure-ai-projects==2.0.1 +azure-ai-projects>=2.3.0 azure-identity==1.25.3 ansible-core~=2.20.3 \ No newline at end of file diff --git a/scenario-app/backend/app/__init__.py b/scenario-app/backend/app/__init__.py index 1e13da0f..eb60b743 100644 --- a/scenario-app/backend/app/__init__.py +++ b/scenario-app/backend/app/__init__.py @@ -1 +1 @@ -# E-commerce Backend Application \ No newline at end of file +# E-commerce Backend Application diff --git a/scenario-app/backend/app/auth.py b/scenario-app/backend/app/auth.py index e6709c3d..5d9713ce 100644 --- a/scenario-app/backend/app/auth.py +++ b/scenario-app/backend/app/auth.py @@ -132,4 +132,4 @@ async def get_current_user_optional(request: Request) -> Optional[Dict[str, Any] try: return await get_current_user(request) except Exception: - return None \ No newline at end of file + return None diff --git a/scenario-app/backend/app/config.py b/scenario-app/backend/app/config.py index 7b4fb600..aa2e4348 100644 --- a/scenario-app/backend/app/config.py +++ b/scenario-app/backend/app/config.py @@ -90,4 +90,4 @@ def has_cosmos_db_config() -> bool: # Check if we have Azure Search configuration def has_search_config() -> bool: - return settings.azure_search_endpoint is not None \ No newline at end of file + return settings.azure_search_endpoint is not None diff --git a/scenario-app/backend/app/create_assistants.py b/scenario-app/backend/app/create_assistants.py deleted file mode 100644 index e1ef648f..00000000 --- a/scenario-app/backend/app/create_assistants.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -""" -Create Azure AI Foundry assistants programmatically -""" -import asyncio -import logging - -# Set up logging -logging.basicConfig(level=logging.INFO) - - -async def create_assistants(): - print("Creating assistants in Azure AI Foundry project...") - - try: - from config import settings - from foundry_client import get_foundry_client, init_foundry_client - - # Initialize Foundry client - print("Initializing Foundry client...") - await init_foundry_client() - client = get_foundry_client() - - # Get Azure OpenAI client - print("Getting Azure OpenAI client...") - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # Define assistants to create - assistants_to_create = [ - { - "name": "Orchestrator Agent", - "description": "Main orchestrator that routes customer inquiries to specialized agents for product searches, order tracking, and policy questions.", - "instructions": """You are the main orchestrator for Contoso Paints e-commerce customer service. Your role is to: - -1. Analyze customer inquiries and route them to appropriate specialists -2. Handle general questions and greetings -3. Provide comprehensive assistance using available tools -4. Maintain a helpful, professional tone - -You have access to tools for: -- Product search and recommendations -- Order tracking and management -- Policy and FAQ information - -Always aim to provide accurate, helpful responses while maintaining excellent customer service.""", - "model": "gpt-4o-mini", - }, - { - "name": "Product Lookup Agent", - "description": "Specialized agent for product searches, recommendations, and catalog inquiries.", - "instructions": """You are a product specialist for Contoso Paints e-commerce. Your expertise includes: - -1. Product search and discovery -2. Product recommendations based on customer needs -3. Pricing and availability information -4. Product specifications and details -5. Category browsing and filtering - -Always help customers find the right products for their needs. Use the product search tools to provide accurate, up-to-date information.""", - "model": "gpt-4o-mini", - }, - { - "name": "Order Status Agent", - "description": "Specialized agent for order tracking, status updates, and order management.", - "instructions": """You are an order specialist for Contoso Paints e-commerce. You help customers with: - -1. Order status and tracking -2. Order history and details -3. Return and refund requests -4. Shipping information -5. Order modifications when possible - -Always provide accurate order information and help resolve any order-related concerns professionally.""", - "model": "gpt-4o-mini", - }, - { - "name": "Knowledge Agent", - "description": "Specialized agent for policies, FAQs, warranties, and general support information.", - "instructions": """You are a knowledge specialist for Contoso Paints e-commerce. You provide information about: - -1. Return and refund policies -2. Warranty information -3. Shipping policies -4. FAQs and general questions -5. Company policies and procedures - -Always provide accurate, helpful information from official policies and documentation.""", - "model": "gpt-4o-mini", - }, - ] - - created_assistants = [] - - for assistant_config in assistants_to_create: - print(f"\nCreating {assistant_config['name']}...") - - try: - # Create assistant - assistant = await openai_client.beta.assistants.create( - name=assistant_config["name"], - description=assistant_config["description"], - instructions=assistant_config["instructions"], - model=assistant_config["model"], - tools=[], # We'll add tools later through plugins - ) - - print(f"āœ… Created {assistant.name}") - print(f" ID: {assistant.id}") - print(f" Model: {assistant.model}") - - created_assistants.append( - { - "name": assistant_config["name"], - "id": assistant.id, - "role": assistant_config["name"].lower().replace(" ", "_"), - } - ) - - except Exception as e: - print(f"āŒ Failed to create {assistant_config['name']}: {e}") - - # Print environment variable updates - if created_assistants: - print("\nšŸŽÆ Update your .env file with these new assistant IDs:") - print("=" * 60) - - env_mapping = { - "orchestrator_agent": "FOUNDRY_ORCHESTRATOR_AGENT_ID", - "product_lookup_agent": "FOUNDRY_PRODUCT_AGENT_ID", - "order_status_agent": "FOUNDRY_ORDER_AGENT_ID", - "knowledge_agent": "FOUNDRY_KNOWLEDGE_AGENT_ID", - } - - for assistant in created_assistants: - role = assistant["role"] - if role in env_mapping: - env_var = env_mapping[role] - print(f'{env_var}="{assistant["id"]}"') - - print("=" * 60) - - return created_assistants - - except Exception as e: - print(f"āŒ Failed to create assistants: {e}") - import traceback - - traceback.print_exc() - return [] - - -if __name__ == "__main__": - asyncio.run(create_assistants()) diff --git a/scenario-app/backend/app/database.py b/scenario-app/backend/app/database.py index 25377739..fe3def2f 100644 --- a/scenario-app/backend/app/database.py +++ b/scenario-app/backend/app/database.py @@ -9,7 +9,6 @@ CustomerCreate, CustomerUpdate, Order, - OrderCreate, OrderStatus, Product, ProductCreate, @@ -159,4 +158,4 @@ def get_db_service() -> EcommerceDatabaseService: global _db_service if _db_service is None: _db_service = get_database_service() - return _db_service \ No newline at end of file + return _db_service diff --git a/scenario-app/backend/app/list_assistants.py b/scenario-app/backend/app/list_assistants.py deleted file mode 100644 index b7e83440..00000000 --- a/scenario-app/backend/app/list_assistants.py +++ /dev/null @@ -1,55 +0,0 @@ -#!/usr/bin/env python3 -""" -List all assistants in Azure AI Foundry project -""" -import asyncio -import logging - -# Set up logging -logging.basicConfig(level=logging.INFO) - - -async def list_assistants(): - print("Listing assistants in Azure AI Foundry project...") - - try: - from foundry_client import get_foundry_client, init_foundry_client - - # Initialize Foundry client - print("Initializing Foundry client...") - await init_foundry_client() - client = get_foundry_client() - - # Get Azure OpenAI client - print("Getting Azure OpenAI client...") - from config import settings - - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # List all assistants - print("Fetching assistants...") - assistants = await openai_client.beta.assistants.list(limit=100) - - print(f"\nāœ… Found {len(assistants.data)} assistants:") - for assistant in assistants.data: - print(f" - ID: {assistant.id}") - print(f" Name: {assistant.name or 'No name'}") - print(f" Description: {assistant.description or 'No description'}") - print(f" Model: {assistant.model}") - print(f" Created: {assistant.created_at}") - print() - - return assistants.data - - except Exception as e: - print(f"āŒ Failed to list assistants: {e}") - import traceback - - traceback.print_exc() - return [] - - -if __name__ == "__main__": - asyncio.run(list_assistants()) diff --git a/scenario-app/backend/app/main.py b/scenario-app/backend/app/main.py index ae280e77..e4d29646 100644 --- a/scenario-app/backend/app/main.py +++ b/scenario-app/backend/app/main.py @@ -28,13 +28,13 @@ # Handle both local debugging and Docker deployment try: - from .auth import get_current_user + from .auth import get_current_user # noqa: F401 from .config import settings from .routers import auth from .scenario_config import current_scenario except ImportError: sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - from app.auth import get_current_user + from app.auth import get_current_user # noqa: F401 from app.config import settings from app.routers import auth from app.scenario_config import current_scenario @@ -160,4 +160,4 @@ async def general_exception_handler(request: Request, exc: Exception): if __name__ == "__main__": - uvicorn.run(app, host="0.0.0.0", port=8000) \ No newline at end of file + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/scenario-app/backend/app/memory_service.py b/scenario-app/backend/app/memory_service.py index dbf63af7..3711d4d6 100644 --- a/scenario-app/backend/app/memory_service.py +++ b/scenario-app/backend/app/memory_service.py @@ -5,7 +5,6 @@ from .database import EcommerceDatabaseService from .models import ( Cart, - CartItem, Customer, CustomerCreate, CustomerUpdate, @@ -20,6 +19,7 @@ UserRole, ) + def _sample_products() -> List[Product]: return [ Product( @@ -331,4 +331,3 @@ async def update_order_status( o.updated_at = datetime.utcnow() self._orders[order_id] = o return o - diff --git a/scenario-app/backend/app/models.py b/scenario-app/backend/app/models.py index fda3df15..9843e943 100644 --- a/scenario-app/backend/app/models.py +++ b/scenario-app/backend/app/models.py @@ -338,4 +338,4 @@ class UserResponse(BaseModel): name: str role: str is_active: bool - is_guest: bool = False \ No newline at end of file + is_guest: bool = False diff --git a/scenario-app/backend/app/plugins/__init__.py b/scenario-app/backend/app/plugins/__init__.py deleted file mode 100644 index 665acc3b..00000000 --- a/scenario-app/backend/app/plugins/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Plugins package for semantic kernel integration diff --git a/scenario-app/backend/app/plugins/orders_plugin.py b/scenario-app/backend/app/plugins/orders_plugin.py deleted file mode 100644 index 02006d51..00000000 --- a/scenario-app/backend/app/plugins/orders_plugin.py +++ /dev/null @@ -1,236 +0,0 @@ -import asyncio -import concurrent.futures -import json -import logging - -from semantic_kernel.functions import kernel_function - -from ..cosmos_service import get_cosmos_service - -logger = logging.getLogger(__name__) - - -def run_async_sync(coro): - """Run an async coroutine synchronously, handling event loop conflicts""" - try: - asyncio.get_running_loop() - # We're in an async context, use a thread pool - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit(asyncio.run, coro) - return future.result() - except RuntimeError: - # No event loop running, safe to use asyncio.run - return asyncio.run(coro) - - -class OrdersPlugin: - """Plugin for order management using Azure Cosmos DB""" - - @kernel_function(description="Get order by ID and return JSON") - def get_order(self, order_id: str) -> str: - """Get order by ID""" - try: - cosmos_service = get_cosmos_service() - order = run_async_sync(cosmos_service.get_order_by_id(order_id)) - if not order: - return json.dumps({"error": f"No order found with ID: {order_id}"}) - - if hasattr(order, "model_dump"): - order_dict = order if isinstance(order, dict) else order.model_dump() - else: - order_dict = order - - return json.dumps(order_dict) - except Exception as e: - logger.error(f"Error getting order {order_id}: {e}") - return json.dumps({"error": f"Failed to get order: {str(e)}"}) - - @kernel_function(description="List orders for a customer; returns JSON list") - def list_orders(self, customer_id: str, limit: int = 10) -> str: - """List orders for a customer""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_by_customer(customer_id, limit=limit) - ) - if not orders: - return json.dumps([]) - - orders_list = [] - for order in orders: - if hasattr(order, "model_dump"): - orders_list.append( - order if isinstance(order, dict) else order.model_dump() - ) - else: - orders_list.append(order) - - return json.dumps(orders_list) - except Exception as e: - logger.error(f"Error listing orders for customer {customer_id}: {e}") - return json.dumps({"error": f"Failed to list orders: {str(e)}"}) - - @kernel_function(description="Get order status by ID") - def get_order_status(self, order_id: str) -> str: - """Get order status""" - try: - cosmos_service = get_cosmos_service() - order = run_async_sync(cosmos_service.get_order_by_id(order_id)) - if not order: - return json.dumps({"error": f"No order found with ID: {order_id}"}) - - if hasattr(order, "model_dump"): - order_dict = order if isinstance(order, dict) else order.model_dump() - else: - order_dict = order - - status_info = { - "order_id": order_id, - "status": order_dict.get("status", "unknown"), - "total": order_dict.get("total", 0), - "created_at": order_dict.get("created_at", ""), - "updated_at": order_dict.get("updated_at", ""), - } - - return json.dumps(status_info) - except Exception as e: - logger.error(f"Error getting order status {order_id}: {e}") - return json.dumps({"error": f"Failed to get order status: {str(e)}"}) - - @kernel_function(description="Process refund for an order") - def process_refund(self, order_id: str, reason: str) -> str: - """Process refund for an order""" - try: - # This would typically update the order status in the database - # For now, we'll simulate the process - logger.info(f"Processing refund for order {order_id} due to: {reason}") - - # In a real implementation, you would: - # 1. Validate the order exists and is eligible for refund - # 2. Update the order status - # 3. Process the actual refund through payment provider - # 4. Send confirmation to customer - - result = { - "order_id": order_id, - "refund_status": "processed", - "reason": reason, - "message": f"Refund for order {order_id} has been processed successfully", - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error processing refund for order {order_id}: {e}") - return json.dumps({"error": f"Failed to process refund: {str(e)}"}) - - @kernel_function(description="Process return for an order") - def process_return(self, order_id: str, reason: str) -> str: - """Process return for an order""" - try: - # This would typically update the order status in the database - # For now, we'll simulate the process - logger.info(f"Processing return for order {order_id} due to: {reason}") - - # In a real implementation, you would: - # 1. Validate the order exists and is eligible for return - # 2. Update the order status - # 3. Generate return label - # 4. Send confirmation to customer - - result = { - "order_id": order_id, - "return_status": "processed", - "reason": reason, - "message": f"Return for order {order_id} has been processed successfully", - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error processing return for order {order_id}: {e}") - return json.dumps({"error": f"Failed to process return: {str(e)}"}) - - @kernel_function( - description="Get orders within return window for a customer; returns JSON list" - ) - def get_returnable_orders(self, customer_id: str) -> str: - """Get orders that are still within the return window (30 days)""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_in_date_range(customer_id, days=30) - ) - - if not orders: - return json.dumps([]) - - returnable_orders = [] - for order in orders: - order_dict = ( - order - if isinstance(order, dict) - else (order.model_dump() if hasattr(order, "model_dump") else order) - ) - returnable_orders.append(order_dict) - - return json.dumps(returnable_orders) - except Exception as e: - logger.error( - f"Error getting returnable orders for customer {customer_id}: {e}" - ) - return json.dumps({"error": f"Failed to get returnable orders: {str(e)}"}) - - @kernel_function( - description="Get orders from the past N days for a customer; returns JSON list" - ) - def get_orders_by_date_range(self, customer_id: str, days: int = 180) -> str: - """Get orders from the last N days (default 180 days / 6 months)""" - try: - cosmos_service = get_cosmos_service() - orders = run_async_sync( - cosmos_service.get_orders_in_date_range(customer_id, days=days) - ) - - if not orders: - return json.dumps([]) - - orders_list = [] - for order in orders: - order_dict = ( - order - if isinstance(order, dict) - else (order.model_dump() if hasattr(order, "model_dump") else order) - ) - orders_list.append(order_dict) - - return json.dumps(orders_list) - except Exception as e: - logger.error( - f"Error getting orders by date range for customer {customer_id}: {e}" - ) - return json.dumps( - {"error": f"Failed to get orders by date range: {str(e)}"} - ) - - @kernel_function( - description="Check if a specific order is still returnable (within 30-day window)" - ) - def check_if_returnable(self, order_id: str) -> str: - """Check if an order is still within the return window""" - try: - cosmos_service = get_cosmos_service() - is_returnable = run_async_sync( - cosmos_service.is_order_returnable(order_id, return_window_days=30) - ) - - result = { - "order_id": order_id, - "is_returnable": is_returnable, - "return_window_days": 30, - } - - return json.dumps(result) - except Exception as e: - logger.error(f"Error checking if order {order_id} is returnable: {e}") - return json.dumps( - {"error": f"Failed to check if order is returnable: {str(e)}"} - ) diff --git a/scenario-app/backend/app/plugins/product_plugin.py b/scenario-app/backend/app/plugins/product_plugin.py deleted file mode 100644 index 82d42fe2..00000000 --- a/scenario-app/backend/app/plugins/product_plugin.py +++ /dev/null @@ -1,267 +0,0 @@ -import asyncio -import concurrent.futures -import logging - -from semantic_kernel.functions import kernel_function - -from ..cosmos_service import get_cosmos_service - -logger = logging.getLogger(__name__) - - -def run_async_sync(coro): - """Helper to run async functions in sync context""" - try: - loop = asyncio.get_event_loop() - if loop.is_running(): - # We're in an async context, need to use a different approach - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit(asyncio.run, coro) - return future.result() - else: - return loop.run_until_complete(coro) - except RuntimeError: - # No event loop running, safe to use asyncio.run - return asyncio.run(coro) - - -class ProductPlugin: - """Enhanced plugin for product search and lookup using Azure Cosmos DB""" - - @kernel_function( - description="Lookup a product by ID and return natural language description" - ) - def get_by_id(self, product_id: str) -> str: - """Get product by ID with natural language response""" - try: - cosmos_service = get_cosmos_service() - product = run_async_sync(cosmos_service.get_product_by_sku(product_id)) - if not product: - return f"I couldn't find a product with ID '{product_id}'. Could you check the ID or try searching for products instead?" - - # Format as natural language response using Product object attributes - response = f"**{product.title}**" - if product.price: - response += f" - ${product.price}" - if product.description: - desc = product.description - if len(desc) > 150: - desc = desc[:150] + "..." - response += f"\n\n{desc}" - if product.category: - response += f"\n\nCategory: {product.category}" - if product.in_stock: - response += "\n\nāœ… In stock" - else: - response += "\n\nāŒ Currently out of stock" - - return response - - except Exception as e: - logger.error(f"Error getting product by ID {product_id}: {e}") - return "I'm having trouble looking up that product right now. Please try again or contact support." - - @kernel_function( - description="Search products with hybrid Azure AI Search + Azure Cosmos DB for maximum speed and accuracy" - ) - def search(self, query: str, limit: int = 5) -> str: - """Hybrid product search with Azure AI Search first, then Azure Cosmos DB fallback""" - try: - cosmos_service = get_cosmos_service() - - # Use hybrid search for best performance and accuracy - products = run_async_sync( - cosmos_service.search_products_hybrid(query, limit) - ) - - if not products: - # Provide helpful suggestions based on query - suggestions = self._get_search_suggestions(query) - return f"I couldn't find any products matching '{query}'. {suggestions}" - - # Format response naturally with Azure AI Search insights - response_parts = [] - - if len(products) == 1: - product = products[0] - - response_parts.append( - f"I found a perfect match for you: **{product.title}**" - ) - if product.price: - response_parts.append( - f"This {product.category} is priced at ${product.price}" - ) - if product.description: - desc = product.description - if len(desc) > 150: - desc = desc[:150] + "..." - response_parts.append(f"{desc}") - if product.in_stock: - response_parts.append("āœ… Available in stock") - else: - response_parts.append("āŒ Currently out of stock") - else: - response_parts.append( - f"I found {len(products)} products that match your search:" - ) - - for i, product in enumerate(products[:3]): # Show top 3 - product_dict = ( - product.model_dump() - if hasattr(product, "model_dump") - else product - ) - title = product_dict.get("title", "Unknown Product") - price = product_dict.get("price", "N/A") - category = product_dict.get("category", "paint") - - response_parts.append(f"{i+1}. **{title}** - ${price} ({category})") - - if len(products) > 3: - response_parts.append( - f"...and {len(products) - 3} more products available." - ) - - # Add helpful follow-up - response_parts.append( - "\nWould you like to know more about any of these products or need help with something specific?" - ) - - return "\n".join(response_parts) - - except Exception as e: - logger.error(f"Error in hybrid product search with query '{query}': {e}") - return "I'm having trouble searching products right now. Please try again or contact support for assistance." - - @kernel_function(description="Fast product search optimized for chat responses") - def search_fast(self, query: str, limit: int = 3) -> str: - """Ultra-fast product search using Azure AI Search only""" - try: - cosmos_service = get_cosmos_service() - - # Use Azure AI Search only for maximum speed - products = run_async_sync( - cosmos_service.search_products_ai_search(query, limit) - ) - - if not products: - return f"I couldn't find any products matching '{query}'. Try different keywords or browse our categories." - - # Quick response format for speed - if len(products) == 1: - product = products[0] - return f"**{product.title}** - ${product.price} ({product.category})" - else: - response_parts = [f"Found {len(products)} products:"] - for i, product in enumerate(products[:3]): - product_dict = ( - product.model_dump() - if hasattr(product, "model_dump") - else product - ) - title = product_dict.get("title", "Unknown Product") - price = product_dict.get("price", "N/A") - response_parts.append(f"{i+1}. **{title}** - ${price}") - return "\n".join(response_parts) - - except Exception as e: - logger.error(f"Error in fast product search: {e}") - return "I'm having trouble searching products right now. Please try again." - - def _get_search_suggestions(self, query: str) -> str: - """Provide helpful search suggestions based on the query""" - query_lower = query.lower() - - if any( - word in query_lower - for word in ["blue", "red", "green", "white", "black", "color"] - ): - return "Try searching for specific color names like 'Seafoam Light', 'Obsidian Pearl', or browse by color family." - elif any(word in query_lower for word in ["paint", "coating", "finish"]): - return "Try searching for specific paint types like 'interior paint', 'exterior paint', or 'primer'." - elif any( - word in query_lower for word in ["cheap", "expensive", "budget", "premium"] - ): - return "Try searching by price range or specific product categories." - else: - return "Try searching for specific product names, colors, or categories. You can also browse our full product catalog." - - @kernel_function( - description="Get products by category with natural language responses" - ) - def get_by_category(self, category: str, limit: int = 5) -> str: - """Get products by category with natural language response""" - try: - cosmos_service = get_cosmos_service() - products = run_async_sync( - cosmos_service.get_products_by_category(category, limit) - ) - - if not products: - return f"I couldn't find any products in the '{category}' category. Try browsing other categories or search for specific products." - - response_parts = [f"Here are our {category} products:"] - - for i, product in enumerate(products[:5]): - product_dict = ( - product.model_dump() if hasattr(product, "model_dump") else product - ) - title = product_dict.get("title", "Unknown Product") - price = product_dict.get("price", "N/A") - - response_parts.append(f"{i+1}. **{title}** - ${price}") - - return "\n".join(response_parts) - - except Exception as e: - logger.error(f"Error getting products by category '{category}': {e}") - return f"I'm having trouble browsing the {category} category right now. Please try again." - - @kernel_function( - description="Get all available products with natural language response" - ) - def get_all_products(self, limit: int = 10) -> str: - """Get all products with natural language response""" - try: - cosmos_service = get_cosmos_service() - products = run_async_sync(cosmos_service.get_products({"limit": limit})) - - if not products: - return "I don't have any products available right now. Please contact support for assistance." - - response_parts = [ - f"We offer {len(products)} products across different categories:" - ] - - # Group by category - categories = {} - for product in products: - category = product.category if product.category else "Other" - if category not in categories: - categories[category] = [] - categories[category].append(product) - - for category, cat_products in list(categories.items())[ - :3 - ]: # Show top 3 categories - response_parts.append(f"\n**{category}:**") - for product in cat_products[:2]: # Show top 2 per category - title = product.get("title", "Unknown Product") - price = product.get("price", "N/A") - response_parts.append(f"- {title} (${price})") - - if len(categories) > 3: - response_parts.append( - f"\n...and {len(categories) - 3} more categories available." - ) - - response_parts.append( - "\nWould you like to explore a specific category or search for something particular?" - ) - - return "\n".join(response_parts) - - except Exception as e: - logger.error(f"Error getting all products: {e}") - return "I'm having trouble accessing our product catalog right now. Please try again or contact support." diff --git a/scenario-app/backend/app/plugins/reference_plugin.py b/scenario-app/backend/app/plugins/reference_plugin.py deleted file mode 100644 index 68dff729..00000000 --- a/scenario-app/backend/app/plugins/reference_plugin.py +++ /dev/null @@ -1,116 +0,0 @@ -import logging - -from semantic_kernel.functions import kernel_function - -from ..config import has_azure_search_config -from ..services.search import search_reference_enhanced - -logger = logging.getLogger(__name__) - - -class ReferencePlugin: - """Enhanced plugin for reference document lookup using Azure Search""" - - def __init__(self): - self.is_configured = has_azure_search_config() - - @kernel_function( - description="Search reference documents for policy and support information with natural language responses" - ) - def lookup(self, query: str, top: int = 3) -> str: - """Enhanced lookup with natural language responses""" - if not self.is_configured: - return "I don't have access to policy information right now. Please contact our support team for assistance." - - try: - hits = search_reference_enhanced(query, top) - - if not hits: - return "I couldn't find specific information about that. Let me help you contact our support team who can assist you directly." - - # Format response naturally using the best available information - response_parts = [] - - for hit in hits: - # Prefer extracted answers if available - if hit.get("answers"): - for answer in hit["answers"]: - response_parts.append(answer) - else: - # Use content with smart truncation - content = hit["content"] - if len(content) > 200: - # Try to find a good break point - break_point = content.find(".", 150) - if break_point > 0: - content = content[: break_point + 1] - else: - content = content[:200] + "..." - response_parts.append(content) - - # Combine the best parts and limit length - combined_response = " ".join( - response_parts[:2] - ) # Limit to 2 most relevant parts - - # Add helpful context if it's about returns or policies - if any( - keyword in query.lower() - for keyword in ["return", "refund", "policy", "warranty"] - ): - combined_response += " If you need further assistance, you can call our support team at 1-800-555-0199." - - return combined_response - - except Exception as e: - logger.error(f"Error searching reference documents: {e}") - return "I'm having trouble accessing policy information right now. Please contact our support team for immediate assistance." - - @kernel_function(description="Get return policy information with natural language") - def get_return_policy(self) -> str: - """Get return policy information with natural language response""" - return self.lookup("return policy refund exchange", top=2) - - @kernel_function(description="Get shipping information with natural language") - def get_shipping_info(self) -> str: - """Get shipping information with natural language response""" - return self.lookup("shipping delivery time cost", top=2) - - @kernel_function(description="Get warranty information with natural language") - def get_warranty_info(self) -> str: - """Get warranty information with natural language response""" - return self.lookup("warranty guarantee coverage", top=2) - - @kernel_function(description="Lookup policy information with context awareness") - def lookup_policy(self, query: str, context: str = "") -> str: - """Enhanced policy lookup with context awareness""" - if not self.is_configured: - return "I don't have access to policy information right now. Please contact support." - - try: - # Enhanced search with context - search_query = f"{query} {context}".strip() - hits = search_reference_enhanced(search_query, top=3) - - if not hits: - return "I couldn't find specific information about that. Let me help you contact support." - - # Format response naturally - response_parts = [] - for hit in hits: - if hit.get("answers"): - # Use extracted answers if available - for answer in hit["answers"]: - response_parts.append(answer) - else: - # Use content with highlighting - content = hit["content"] - if len(content) > 200: - content = content[:200] + "..." - response_parts.append(content) - - return " ".join(response_parts[:2]) # Limit to 2 most relevant parts - - except Exception as e: - logger.error(f"Policy lookup error: {e}") - return "I'm having trouble accessing policy information. Please contact support." diff --git a/scenario-app/backend/app/routers/__init__.py b/scenario-app/backend/app/routers/__init__.py index 1bb9aadb..ecb50754 100644 --- a/scenario-app/backend/app/routers/__init__.py +++ b/scenario-app/backend/app/routers/__init__.py @@ -1 +1 @@ -# E-commerce API routers \ No newline at end of file +# E-commerce API routers diff --git a/scenario-app/backend/app/routers/auth.py b/scenario-app/backend/app/routers/auth.py index d2ae8f0e..dd08d75f 100644 --- a/scenario-app/backend/app/routers/auth.py +++ b/scenario-app/backend/app/routers/auth.py @@ -147,4 +147,4 @@ async def get_customer_profile(current_user: Dict[str, Any] = Depends(get_curren raise except Exception as e: logger.exception(f"šŸ›’ Error fetching customer profile: {e}") - raise HTTPException(status_code=500, detail=f"Error fetching profile: {str(e)}") \ No newline at end of file + raise HTTPException(status_code=500, detail=f"Error fetching profile: {str(e)}") diff --git a/scenario-app/backend/app/routers/cart.py b/scenario-app/backend/app/routers/cart.py index a9e96d5f..368bce31 100644 --- a/scenario-app/backend/app/routers/cart.py +++ b/scenario-app/backend/app/routers/cart.py @@ -25,7 +25,7 @@ async def get_cart(current_user: Dict[str, Any] = Depends(get_current_user)): raise HTTPException(status_code=401, detail="User not authenticated") cart = await get_db_service().get_cart(user_id) - + # Return empty cart if none exists if not cart: cart = Cart( @@ -63,7 +63,7 @@ async def add_to_cart( # Check stock availability if product.stock_quantity < request.quantity: raise HTTPException( - status_code=400, + status_code=400, detail=f"Insufficient stock. Only {product.stock_quantity} items available." ) @@ -90,7 +90,7 @@ async def add_to_cart( new_quantity = existing_item.quantity + request.quantity if new_quantity > product.stock_quantity: raise HTTPException( - status_code=400, + status_code=400, detail=f"Cannot add {request.quantity} more items. Cart would exceed available stock of {product.stock_quantity}." ) existing_item.quantity = new_quantity @@ -121,7 +121,7 @@ async def add_to_cart( @router.put("/update", response_model=APIResponse) async def update_cart_item( - request: UpdateCartItemRequest, + request: UpdateCartItemRequest, current_user: Dict[str, Any] = Depends(get_current_user) ): """Update cart item quantity""" @@ -225,4 +225,4 @@ async def clear_cart(current_user: Dict[str, Any] = Depends(get_current_user)): return APIResponse(message="Cart cleared successfully") except Exception as e: - raise HTTPException(status_code=500, detail=f"Error clearing cart: {str(e)}") \ No newline at end of file + raise HTTPException(status_code=500, detail=f"Error clearing cart: {str(e)}") diff --git a/scenario-app/backend/app/routers/chat.py b/scenario-app/backend/app/routers/chat.py index 2a91a344..b259083b 100644 --- a/scenario-app/backend/app/routers/chat.py +++ b/scenario-app/backend/app/routers/chat.py @@ -375,7 +375,7 @@ async def send_message_legacy( AIProjectClient(endpoint=ai_project_endpoint, credential=credential) as project_client, AzureAIProjectAgentProvider( project_client=project_client, - credential=credential + credential=credential, ) as provider, ): # Retry logic for rate limit errors diff --git a/scenario-app/backend/app/routers/healthcare_appointments.py b/scenario-app/backend/app/routers/healthcare_appointments.py index 3fce6c6c..7df2b016 100644 --- a/scenario-app/backend/app/routers/healthcare_appointments.py +++ b/scenario-app/backend/app/routers/healthcare_appointments.py @@ -4,7 +4,6 @@ from ..auth import get_current_user from ..cosmos_service import get_cosmos_service -from ..models import Product router = APIRouter(prefix="/api/appointments", tags=["healthcare"]) diff --git a/scenario-app/backend/app/routers/products.py b/scenario-app/backend/app/routers/products.py index c46c01ee..d022d340 100644 --- a/scenario-app/backend/app/routers/products.py +++ b/scenario-app/backend/app/routers/products.py @@ -134,4 +134,4 @@ async def get_related_products(product_id: str, limit: int = Query(5, ge=1, le=2 except Exception as e: raise HTTPException( status_code=500, detail=f"Error fetching related products: {str(e)}" - ) \ No newline at end of file + ) diff --git a/scenario-app/backend/app/simple_foundry_orchestrator.py b/scenario-app/backend/app/simple_foundry_orchestrator.py deleted file mode 100644 index 632c3433..00000000 --- a/scenario-app/backend/app/simple_foundry_orchestrator.py +++ /dev/null @@ -1,524 +0,0 @@ -from __future__ import annotations - -import asyncio -import logging -import re -from typing import Any, Dict, List, Optional - -from cachetools import TTLCache -from semantic_kernel.agents import AzureAIAgent, AzureAIAgentThread - -from .config import has_foundry_config, settings -from .foundry_client import get_foundry_client, init_foundry_client -from .plugins.orders_plugin import OrdersPlugin -from .plugins.product_plugin import ProductPlugin -from .plugins.reference_plugin import ReferencePlugin - -logger = logging.getLogger(__name__) - - -class ThreadCache(TTLCache): - """Cache for agent threads with automatic cleanup""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def expire(self, time=None): - """Remove expired items and delete associated threads""" - items = super().expire(time) - for key, thread_id in items: - try: - logger.info(f"Thread expired from cache: {thread_id}") - except Exception as e: - logger.error(f"Error expiring thread {thread_id}: {e}") - return items - - def popitem(self): - """Remove item using LRU eviction""" - key, thread_id = super().popitem() - try: - logger.info(f"Thread evicted from cache (LRU): {thread_id}") - except Exception as e: - logger.error(f"Error evicting thread {thread_id}: {e}") - return key, thread_id - - -async def _resolve_foundry_agent_definition(agent_id: str): - """Get the agent definition from Foundry""" - # client = get_foundry_client() - try: - # For now, return a basic agent definition structure - # This needs to be updated when the correct Azure AI Projects SDK method is available - logger.warning(f"Using placeholder agent definition for agent ID: {agent_id}") - - # Create a basic agent definition that can work with AzureAIAgent - agent_definition = { - "id": agent_id, - "name": f"Agent-{agent_id}", - "description": f"Azure AI Foundry Agent {agent_id}", - "instructions": "You are a helpful assistant.", - "model": "gpt-4", - } - - return agent_definition - - except Exception as e: - logger.error( - f"Error resolving Foundry agent definition for ID '{agent_id}': {e}" - ) - # Return a fallback definition - return { - "id": agent_id, - "name": f"Fallback-Agent-{agent_id}", - "description": "Fallback agent definition", - "instructions": "You are a helpful assistant.", - "model": "gpt-4", - } - - -async def _build_foundry_agent( - agent_id: str, name: str, plugins: Optional[List] = None -) -> Optional[Any]: - """Build a Foundry agent using direct OpenAI API calls""" - try: - logger.info(f"Building {name} (ID: {agent_id}) using Azure AI Foundry...") - - # Get the Foundry client - client = get_foundry_client() - - try: - # Get the Azure OpenAI client from Foundry with proper API version - openai_client = await client.get_openai_client( # type: ignore - api_version=settings.azure_openai_api_version - ) - - # Test connection by retrieving the assistant - assistant = await openai_client.beta.assistants.retrieve(agent_id) - logger.info( - f"āœ… Successfully connected to Foundry assistant: {assistant.name or name}" - ) - - # Create a custom agent that uses the Azure OpenAI client directly - class FoundryAgent: - def __init__(self, assistant_id: str, name: str, openai_client): - self.id = assistant_id - self.name = name - self.client = openai_client - self.assistant_id = assistant_id - - async def invoke(self, messages: str, thread=None): - try: - # Create or get thread - if not thread or not hasattr(thread, "id"): - thread_obj = await self.client.beta.threads.create() - thread_id = thread_obj.id - logger.info(f"Created new thread: {thread_id}") - else: - thread_id = thread.id - logger.info(f"Using existing thread: {thread_id}") - - # Add user message to thread - await self.client.beta.threads.messages.create( - thread_id=thread_id, role="user", content=messages - ) - - # Create and run the assistant - run = await self.client.beta.threads.runs.create( - thread_id=thread_id, assistant_id=self.assistant_id - ) - - max_attempts = 30 # 30 seconds max - attempt = 0 - - while ( - run.status in ["queued", "in_progress"] - and attempt < max_attempts - ): - await asyncio.sleep(1) - attempt += 1 - run = await self.client.beta.threads.runs.retrieve( - thread_id=thread_id, run_id=run.id - ) - logger.debug( - f"Run status: {run.status} (attempt {attempt})" - ) - - if run.status == "completed": - # Get the assistant's response - messages_response = ( - await self.client.beta.threads.messages.list( - thread_id=thread_id, - limit=1, # Get just the latest message - ) - ) - - if messages_response.data: - latest_message = messages_response.data[0] - if ( - latest_message.role == "assistant" - and latest_message.content - ): - content = latest_message.content[0].text.value - - class FoundryResponse: - def __init__(self, content, thread_id): - self.content = content - self.thread = type( - "Thread", (), {"id": thread_id} - )() - - logger.info( - f"āœ… {self.name} responded with {len(content)} characters" - ) - yield FoundryResponse(content, thread_id) - return - - # Handle non-completed status - error_msg = f"Assistant run did not complete successfully. Status: {run.status}" - if run.status == "failed": - error_msg += f". Error: {getattr(run, 'last_error', 'Unknown error')}" - - logger.error(error_msg) - - class ErrorResponse: - def __init__(self, content): - self.content = content - self.thread = None - - yield ErrorResponse( - f"Sorry, I encountered an issue: {error_msg}" - ) - - except Exception as invoke_error: - logger.error( - f"Error invoking {self.name}: {invoke_error}", exc_info=True - ) - - class InvokeErrorResponse: - def __init__(self, content): - self.content = content - self.thread = None - - yield InvokeErrorResponse( - f"I encountered an error while processing your request: {str(invoke_error)}" - ) - - foundry_agent = FoundryAgent(agent_id, name, openai_client) - logger.info(f"āœ… Successfully created Foundry agent: {name}") - return foundry_agent - - except Exception as e: - logger.error(f"Failed to create Foundry agent {name}: {e}", exc_info=True) - # No fallback - return None to indicate failure - return None - - except Exception as e: - logger.error(f"Error building Foundry agent '{name}' with ID '{agent_id}': {e}") - return None - - -class SimpleFoundryOrchestrator: - """ - Simple orchestrator that routes to the appropriate Foundry agent. - Uses Semantic Kernel AzureAIAgent like the reference implementation. - Includes thread caching for better performance. - """ - - def __init__(self): - self.agents: Dict[str, AzureAIAgent] = {} - self.is_configured = has_foundry_config() - self.thread_cache: ThreadCache = ThreadCache(maxsize=1000, ttl=3600.0) - - if not self.is_configured: - logger.warning( - "Foundry not configured. Set AZURE_FOUNDRY_ENDPOINT and agent IDs" - ) - - @classmethod - async def create(cls) -> "SimpleFoundryOrchestrator": - """Initialize the orchestrator and create all agents""" - self = cls() - - if not self.is_configured: - logger.warning( - "Simple Foundry orchestrator will not initialize (not configured)" - ) - return self - - try: - logger.info("Initializing Simple Foundry orchestrator...") - logger.info(f" - Foundry endpoint: {settings.azure_foundry_endpoint}") - logger.info( - f" - Orchestrator agent ID: {settings.foundry_orchestrator_agent_id}" - ) - logger.info(f" - Product agent ID: {settings.foundry_product_agent_id}") - logger.info(f" - Order agent ID: {settings.foundry_order_agent_id}") - logger.info( - f" - Knowledge agent ID: {settings.foundry_knowledge_agent_id}" - ) - - # Initialize Foundry client first - logger.info("Initializing Foundry client...") - await init_foundry_client() - logger.info("āœ… Foundry client initialized") - - # Build the main orchestrator agent if configured - if settings.foundry_orchestrator_agent_id: - logger.info("Building OrchestratorAgent...") - orchestrator_agent = await _build_foundry_agent( - agent_id=settings.foundry_orchestrator_agent_id, - name="OrchestratorAgent", - plugins=[ProductPlugin(), OrdersPlugin(), ReferencePlugin()], - ) - if orchestrator_agent: - self.agents["OrchestratorAgent"] = orchestrator_agent - logger.info("āœ… Added OrchestratorAgent with all plugins") - else: - logger.warning("āŒ Failed to create OrchestratorAgent") - else: - logger.warning("āš ļø No OrchestratorAgent ID configured") - - # Build agents with their plugins attached - if settings.foundry_product_agent_id: - logger.info("Building ProductLookupAgent...") - product_agent = await _build_foundry_agent( - agent_id=settings.foundry_product_agent_id, - name="ProductLookupAgent", - plugins=[ProductPlugin()], - ) - if product_agent: - self.agents["ProductLookupAgent"] = product_agent - logger.info("āœ… Added ProductLookupAgent with ProductPlugin") - else: - logger.warning("āŒ Failed to create ProductLookupAgent") - else: - logger.warning("āš ļø No ProductLookupAgent ID configured") - - if settings.foundry_order_agent_id: - logger.info("Building OrderStatusAgent...") - order_agent = await _build_foundry_agent( - agent_id=settings.foundry_order_agent_id, - name="OrderStatusAgent", - plugins=[OrdersPlugin()], - ) - if order_agent: - self.agents["OrderStatusAgent"] = order_agent - logger.info("āœ… Added OrderStatusAgent with OrdersPlugin") - else: - logger.warning("āŒ Failed to create OrderStatusAgent") - else: - logger.warning("āš ļø No OrderStatusAgent ID configured") - - if settings.foundry_knowledge_agent_id: - logger.info("Building KnowledgeAgent...") - knowledge_agent = await _build_foundry_agent( - agent_id=settings.foundry_knowledge_agent_id, - name="KnowledgeAgent", - plugins=[ReferencePlugin()], - ) - if knowledge_agent: - self.agents["KnowledgeAgent"] = knowledge_agent - logger.info("āœ… Added KnowledgeAgent with ReferencePlugin") - else: - logger.warning("āŒ Failed to create KnowledgeAgent") - else: - logger.warning("āš ļø No KnowledgeAgent ID configured") - - if not self.agents: - logger.error("āŒ No agents were successfully built!") - self.is_configured = False - return self - - logger.info( - f"āœ… Simple Foundry orchestrator initialized with {len(self.agents)} agents" - ) - return self - - except Exception as e: - logger.error( - f"āŒ Failed to initialize Simple Foundry orchestrator: {e}", - exc_info=True, - ) - self.is_configured = False - return self - - def _determine_target_agent(self, user_text: str) -> str: - """Enhanced agent selection with semantic understanding""" - query_lower = user_text.lower() - - # Product-related keywords and patterns - product_patterns = [ - r"\b(paint|color|blue|red|green|white|black|shade|tone|finish)\b", - r"\b(product|item|buy|purchase|price|cost)\b", - r"\b(what.*offer|show.*product|find.*paint)\b", - r"\b(match.*color|color.*match|sample)\b", - r"\b(recommend|suggest|help.*choose)\b", - r"\b(interior|exterior|primer|coating)\b", - ] - - # Policy/support keywords and patterns - policy_patterns = [ - r"\b(return|refund|exchange|policy|warranty)\b", - r"\b(problem|issue|complaint|damaged|leaking)\b", - r"\b(ship|delivery|shipping|track)\b", - r"\b(help|support|contact|customer service)\b", - r"\b(guarantee|coverage|defect)\b", - r"\b(cancel|order.*status|tracking)\b", - ] - - # Check for product intent - product_score = sum( - 1 for pattern in product_patterns if re.search(pattern, query_lower) - ) - - # Check for policy intent - policy_score = sum( - 1 for pattern in policy_patterns if re.search(pattern, query_lower) - ) - - # Special cases - if any( - phrase in query_lower - for phrase in ["what products", "what do you offer", "show me products"] - ): - logger.info("Routing to ProductLookupAgent - general product inquiry") - return "ProductLookupAgent" - - if any( - phrase in query_lower - for phrase in ["return policy", "warranty", "refund", "damaged"] - ): - logger.info("Routing to KnowledgeAgent - policy inquiry") - return "KnowledgeAgent" - - # Determine primary intent - if product_score > policy_score: - # Try ProductLookupAgent first, fall back to OrchestratorAgent - if "ProductLookupAgent" in self.agents: - logger.info( - f"Routing to ProductLookupAgent - product score: {product_score}" - ) - return "ProductLookupAgent" - elif "OrchestratorAgent" in self.agents: - logger.info( - f"Routing to OrchestratorAgent (ProductLookupAgent not available) - product score: {product_score}" - ) - return "OrchestratorAgent" - elif policy_score > 0: - # Try KnowledgeAgent first, fall back to OrchestratorAgent - if "KnowledgeAgent" in self.agents: - logger.info(f"Routing to KnowledgeAgent - policy score: {policy_score}") - return "KnowledgeAgent" - elif "OrchestratorAgent" in self.agents: - logger.info( - f"Routing to OrchestratorAgent (KnowledgeAgent not available) - policy score: {policy_score}" - ) - return "OrchestratorAgent" - - # Default routing priority: ProductLookupAgent > OrchestratorAgent > any available agent - if "ProductLookupAgent" in self.agents: - logger.info("No specific keywords found, defaulting to ProductLookupAgent") - return "ProductLookupAgent" - elif "OrchestratorAgent" in self.agents: - logger.info("No specific keywords found, defaulting to OrchestratorAgent") - return "OrchestratorAgent" - else: - # Use the first available agent as last resort - available_agents = list(self.agents.keys()) - if available_agents: - fallback_agent = available_agents[0] - logger.info( - f"No preferred agents available, using fallback: {fallback_agent}" - ) - return fallback_agent - - # This should not happen if agents were created successfully - logger.error("No agents available for routing") - return "ProductLookupAgent" # This will cause an error in respond() which is appropriate - - async def respond( - self, - user_text: str, - conversation_id: Optional[str] = None, - history: List[Dict[str, str]] | None = None, - ) -> Dict[str, Any]: - """Respond using the determined agent with thread caching""" - if not self.is_configured: - logger.error("Simple Foundry orchestrator not configured") - return { - "error": "Simple Foundry orchestrator not configured", - "text": "I'm sorry, the AI service is not properly configured.", - } - - try: - # Determine which agent to use - target_agent_name = self._determine_target_agent(user_text) - agent = self.agents.get(target_agent_name) - - if not agent: - logger.error(f"Agent {target_agent_name} not available") - return { - "error": f"Agent {target_agent_name} not available", - "text": f"I'm sorry, the {target_agent_name} is not currently available.", - } - - logger.info(f"Using {target_agent_name} for query: {user_text[:50]}...") - - # Get or create thread for this conversation (thread caching) - thread = None - if conversation_id: - cache_key = f"{conversation_id}_{target_agent_name}" - thread_id = self.thread_cache.get(cache_key) - if thread_id: - thread = AzureAIAgentThread( - client=agent.client, thread_id=thread_id - ) - logger.info(f"Reusing cached thread: {thread_id}") - - # Use the agent's invoke method with thread caching - response_content = "" - async for message in agent.invoke(messages=user_text, thread=thread): - if hasattr(message, "content") and message.content: - response_content += str(message.content) - - # Cache the thread for future use - if conversation_id and hasattr(message, "thread") and message.thread: - cache_key = f"{conversation_id}_{target_agent_name}" - self.thread_cache[cache_key] = message.thread.id - - if response_content: - logger.info( - f"{target_agent_name} response length: {len(response_content)} chars" - ) - return { - "messages": [response_content], - "awaiting_user": response_content.strip().endswith("?"), - "text": response_content, - } - else: - logger.error(f"No response from {target_agent_name}") - return { - "error": "No response from agent", - "text": "I'm sorry, I couldn't get a response from the agent.", - } - - except Exception as e: - logger.error(f"Error in Simple Foundry orchestrator: {e}", exc_info=True) - return { - "error": f"Failed to generate response: {str(e)}", - "text": "I'm sorry, I encountered an error trying to process your request.", - } - - async def shutdown(self): - """Cleanup if needed""" - logger.info("Simple Foundry orchestrator shutdown") - - -_simple_foundry_orchestrator_instance: SimpleFoundryOrchestrator | None = None - - -async def get_simple_foundry_orchestrator() -> SimpleFoundryOrchestrator: - global _simple_foundry_orchestrator_instance - if _simple_foundry_orchestrator_instance is None: - _simple_foundry_orchestrator_instance = await SimpleFoundryOrchestrator.create() - return _simple_foundry_orchestrator_instance diff --git a/scenario-app/backend/app/utils/__init__.py b/scenario-app/backend/app/utils/__init__.py index c643e13c..f8e9360f 100644 --- a/scenario-app/backend/app/utils/__init__.py +++ b/scenario-app/backend/app/utils/__init__.py @@ -1 +1 @@ -# E-commerce utilities \ No newline at end of file +# E-commerce utilities diff --git a/scenario-app/backend/app/utils/auth_utils.py b/scenario-app/backend/app/utils/auth_utils.py index b04bafb3..524dbbbc 100644 --- a/scenario-app/backend/app/utils/auth_utils.py +++ b/scenario-app/backend/app/utils/auth_utils.py @@ -119,4 +119,4 @@ def get_user_email(client_principal_b64): ) except Exception as ex: logger.exception(f"šŸ›’ E-COMMERCE: Error decoding email from client principal: {ex}") - return email \ No newline at end of file + return email diff --git a/scenario-app/backend/app/utils/foundry_agent_utils.py b/scenario-app/backend/app/utils/foundry_agent_utils.py index 8f7a49da..54b6feab 100644 --- a/scenario-app/backend/app/utils/foundry_agent_utils.py +++ b/scenario-app/backend/app/utils/foundry_agent_utils.py @@ -1,5 +1,5 @@ """ -Foundry agent utilities ā€” call the multi-agent pipeline for grounded enterprise answers. +Foundry agent utilities - call the multi-agent pipeline for grounded enterprise answers. """ import logging from typing import Optional @@ -16,11 +16,11 @@ async def call_foundry_agent( azure_client_id: Optional[str] = None, ) -> str: """ - Call the Foundry multi-agent pipeline (chat ā†’ product/policy agents ā†’ Azure AI Search). + Call the Foundry multi-agent pipeline (chat -> product/policy agents -> Azure AI Search). Returns the grounded text response. """ try: - from agent_framework_azure_ai import AzureAIProjectAgentProvider + from agent_framework.azure import AzureAIProjectAgentProvider from azure.ai.projects.aio import AIProjectClient try: diff --git a/scenario-app/backend/requirements.txt b/scenario-app/backend/requirements.txt index c25fd57c..cbfaf53b 100644 --- a/scenario-app/backend/requirements.txt +++ b/scenario-app/backend/requirements.txt @@ -11,6 +11,9 @@ python-multipart==0.0.32 azure-identity==1.25.2 azure-cosmos==4.14.6 azure-search-documents==11.7.0b1 +azure-ai-projects>=2.3.0 +agent-framework-azure-ai==1.0.0rc6 +agent-framework-core==1.0.0rc6 # Telemetry and Monitoring opentelemetry-exporter-otlp-proto-grpc