five-agent architecture base

.gitignore

@@ -6,6 +6,8 @@
 app-logs.zip
 LogFiles
 deploy.log
+__pycache__
+*.log 
 
 # .tfstate files
 *.tfstate

TROUBLESHOOTING.md

@@ -347,7 +347,7 @@ terraform apply
 
 <!-- START BADGE -->
 <div align="center">
-  <img src="https://img.shields.io/badge/Total%20views-1543-limegreen" alt="Total views">
-  <p>Refresh Date: 2025-11-25</p>
+  <img src="https://img.shields.io/badge/Total%20views-1557-limegreen" alt="Total views">
+  <p>Refresh Date: 2025-11-28</p>
 </div>
 <!-- END BADGE -->

src/DATA_PIPELINE.md

@@ -277,7 +277,7 @@ az search index show-statistics \
 
 <!-- START BADGE -->
 <div align="center">
-  <img src="https://img.shields.io/badge/Total%20views-1543-limegreen" alt="Total views">
-  <p>Refresh Date: 2025-11-25</p>
+  <img src="https://img.shields.io/badge/Total%20views-1557-limegreen" alt="Total views">
+  <p>Refresh Date: 2025-11-28</p>
 </div>
 <!-- END BADGE -->

src/Dockerfile

@@ -13,4 +13,5 @@ COPY . .
 EXPOSE 8000
 
 # Run the application
-CMD ["uvicorn", "chat_app:app", "--host", "0.0.0.0", "--port", "8000"]
+# Multi-agent entrypoint (falls back internally if multi disabled)
+CMD ["uvicorn", "chat_app_multi_agent:app", "--host", "0.0.0.0", "--port", "8000"]

src/app/agents/__init__.py

@@ -0,0 +1 @@
+# Agent initialization module

src/app/agents/agent_initializer.py

@@ -0,0 +1,23 @@
+"""Local agent initializer stub.
+
+This replaces remote Microsoft Foundry agent creation with deterministic
+pseudo agent IDs for environments where the Agents API is unavailable.
+It preserves the AGENT_ID: output pattern used by Terraform provisioner
+scripts so existing parsing logic continues to work.
+"""
+import os
+
+def initialize_local_agent(env_var_name: str, name: str) -> str:
+    pseudo_id = f"asst_local_{env_var_name}".replace('-', '_')
+    # Persist to environment for current process (optional)
+    os.environ[env_var_name] = pseudo_id
+    print("=" * 60)
+    print(f"Local pseudo agent ready: {name}")
+    print(f"Environment Variable: {env_var_name}")
+    print(f"AGENT_ID:{pseudo_id}")
+    print("=" * 60)
+    return pseudo_id
+
+# Backwards compatibility name
+def initialize_agent(**kwargs):  # type: ignore
+    return initialize_local_agent(kwargs.get("env_var_name", "unknown"), kwargs.get("name", "Unnamed Agent"))

src/app/agents/agent_processor.py

@@ -0,0 +1,174 @@
+"""
+Agent processor for handling interactions with Microsoft Foundry agents.
+Includes MCP (Model Context Protocol) integration for tool calling.
+"""
+import os
+import json
+from typing import List, Dict, Any
+try:
+    from azure.ai.projects import AIProjectClient  # type: ignore
+    from azure.identity import DefaultAzureCredential  # type: ignore
+    _REMOTE_AVAILABLE = True
+except Exception:
+    _REMOTE_AVAILABLE = False
+
+
+def create_function_tool_for_agent(agent_name: str) -> List[Dict[str, Any]]:
+    """
+    Create function tools for a specific agent using MCP.
+
+    Args:
+        agent_name: Name of the agent (e.g., 'interior_designer', 'inventory_agent')
+
+    Returns:
+        List of function tool definitions
+    """
+    # Placeholder for MCP tool integration
+    # In production, this would connect to MCP servers to get available tools
+    tools = []
+
+    # Define tools based on agent type
+    if agent_name == "interior_designer":
+        tools.append({
+            "type": "function",
+            "function": {
+                "name": "create_image",
+                "description": "Create or modify images based on user requirements",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "prompt": {"type": "string", "description": "Image generation prompt"},
+                        "path": {"type": "string", "description": "Path to existing image (optional)"}
+                    },
+                    "required": ["prompt"]
+                }
+            }
+        })
+
+    elif agent_name == "inventory_agent":
+        tools.append({
+            "type": "function",
+            "function": {
+                "name": "inventory_check",
+                "description": "Check inventory levels for products",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "product_dict": {
+                            "type": "object",
+                            "description": "Dictionary mapping product names to product IDs"
+                        }
+                    },
+                    "required": ["product_dict"]
+                }
+            }
+        })
+
+    elif agent_name == "customer_loyalty":
+        tools.append({
+            "type": "function",
+            "function": {
+                "name": "customer_loyalty_check",
+                "description": "Check customer loyalty status and calculate discount",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "customer_id": {"type": "string", "description": "Customer ID"}
+                    },
+                    "required": ["customer_id"]
+                }
+            }
+        })
+
+    elif agent_name == "cora":
+        # Cora (shopper agent) might have general query tools
+        tools.append({
+            "type": "function",
+            "function": {
+                "name": "search_products",
+                "description": "Search for products in catalog",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {"type": "string", "description": "Search query"}
+                    },
+                    "required": ["query"]
+                }
+            }
+        })
+
+    return tools
+
+
+class AgentProcessor:
+    """Handles communication with Microsoft Foundry agents"""
+
+    def __init__(self, agent_id: str, project_endpoint: str = None):
+        """
+        Initialize agent processor.
+
+        Args:
+            agent_id: The agent ID from Microsoft Foundry
+            project_endpoint: Optional project endpoint (reads from env if not provided)
+        """
+        self.agent_id = agent_id
+        self.project_endpoint = project_endpoint or os.environ.get("AZURE_AI_AGENT_ENDPOINT")
+
+        if not self.project_endpoint or not _REMOTE_AVAILABLE:
+            raise ValueError("Remote agent support unavailable (endpoint or SDK missing)")
+        self.client = AIProjectClient(endpoint=self.project_endpoint, credential=DefaultAzureCredential())
+
+    def run_conversation_with_text_stream(
+        self,
+        user_message: str,
+        conversation_history: List[Dict[str, str]] = None,
+        additional_context: Dict[str, Any] = None
+    ):
+        """
+        Run a conversation with the agent and stream the response.
+
+        Args:
+            user_message: The user's message
+            conversation_history: Optional conversation history
+            additional_context: Additional context to provide to the agent
+
+        Yields:
+            Chunks of the agent's response
+        """
+        try:
+            # Create a thread for this conversation
+            thread = self.client.agents.create_thread()
+
+            # Build the message content
+            message_content = user_message
+            if additional_context:
+                message_content = f"Context: {json.dumps(additional_context)}\n\nUser: {user_message}"
+
+            # Add message to thread
+            self.client.agents.create_message(
+                thread_id=thread.id,
+                role="user",
+                content=message_content
+            )
+
+            # Run the agent
+            run = self.client.agents.create_and_process_run(
+                thread_id=thread.id,
+                assistant_id=self.agent_id
+            )
+
+            # Get messages
+            messages = self.client.agents.list_messages(thread_id=thread.id)
+
+            # Find the assistant's response
+            for message in messages:
+                if message.role == "assistant":
+                    for content in message.content:
+                        if hasattr(content, 'text'):
+                            yield content.text.value
+
+            # Clean up
+            self.client.agents.delete_thread(thread.id)
+
+        except Exception as e:
+            yield f"Error communicating with agent: {str(e)}"

src/app/agents/agents_config.py

@@ -0,0 +1,25 @@
+"""Domain-specific agent instruction consolidation.
+
+Uses Cora (shopper) prompt as baseline and applies lightweight
+specialization for each additional domain.
+"""
+import os
+
+PROMPTS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'prompts')
+
+def _read(name: str) -> str:
+    path = os.path.join(PROMPTS_DIR, name)
+    if os.path.exists(path):
+        with open(path, 'r', encoding='utf-8') as f:
+            return f.read().strip()
+    return ""
+
+BASE_CORA = _read('ShopperAgentPrompt.txt') or "You are Cora, a helpful shopping assistant for Zava DIY."
+
+AGENT_INSTRUCTIONS = {
+    'cora': BASE_CORA,
+    'interior_design': _read('InteriorDesignAgentPrompt.txt') or "Provide interior design guidance tied to product suggestions.",
+    'inventory': _read('InventoryAgentPrompt.txt') or "Report mock inventory status with concise JSON if feasible.",
+    'customer_loyalty': _read('CustomerLoyaltyAgentPrompt.txt') or "Determine a loyalty discount (default 10%).",
+    'cart_management': _read('CartManagerAgentPrompt.txt') or "Maintain a cart list; support 'add <item>' and 'remove <item>'."
+}

src/app/agents/agents_state.json

@@ -0,0 +1,27 @@
+{
+  "cora": {
+    "id": "asst_xPTPWSqJrKKTxhweUZMwXxNb",
+    "hash": "ec1323afb9692d92de14373b05eb60026bb3738f5fb3303976f74d5c40536092",
+    "status": "created"
+  },
+  "interior_designer": {
+    "id": "asst_6YVlwwf9MRv91UCEZhKXYggr",
+    "hash": "0fbee2d10b87eee5a9d0bd87a9d7af60f327c9093edd47d8e6683505db5aabba",
+    "status": "created"
+  },
+  "inventory_agent": {
+    "id": "asst_cFo2Nh5ncBmw9ZimlOkzBITv",
+    "hash": "87deafceb6532b78ef075dd0e084909c4177286a0808cb9755c9a289f0076ba3",
+    "status": "created"
+  },
+  "customer_loyalty": {
+    "id": "asst_4yiWqVI0AyJ46yKFwpnNjRrS",
+    "hash": "1e0ffd8c5b4dac8cd247b90966b513f89b5c1c366edd476ca164d27b86dc276c",
+    "status": "created"
+  },
+  "cart_manager": {
+    "id": "asst_QChARJsqcQrWKLRhOUt7ojOG",
+    "hash": "bd3985311b2b5e0d4d88ae4583c5c3b36113d6ddbbee67e6d36924f34292ccab",
+    "status": "created"
+  }
+}

src/app/agents/cartManagerAgent_initializer.py

@@ -0,0 +1,22 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+from agent_initializer import initialize_agent
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+from dotenv import load_dotenv
+from agent_initializer import initialize_local_agent
+
+load_dotenv()
+
+PROMPT_TARGET = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'prompts', 'CartManagerAgentPrompt.txt')
+if os.path.exists(PROMPT_TARGET):
+    with open(PROMPT_TARGET, 'r', encoding='utf-8') as f:
+        _ = f.read()
+
+initialize_local_agent(env_var_name="cart_manager", name="Cart Manager Agent")
+initialize_agent(

src/app/agents/customerLoyaltyAgent_initializer.py

@@ -0,0 +1,22 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+from agent_initializer import initialize_agent
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+from dotenv import load_dotenv
+from agent_initializer import initialize_local_agent
+
+load_dotenv()
+
+PROMPT_TARGET = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'prompts', 'CustomerLoyaltyAgentPrompt.txt')
+if os.path.exists(PROMPT_TARGET):
+    with open(PROMPT_TARGET, 'r', encoding='utf-8') as f:
+        _ = f.read()
+
+initialize_local_agent(env_var_name="customer_loyalty", name="Customer Loyalty Agent")
+initialize_agent(