Ai shopping concierge dev #63
Conversation
…n, negotiation, and AP2 checkout - Add WhatsApp Business API integration for customer chat - Implement AI-powered product curation engine with Google AI - Add dynamic pricing and negotiation capabilities - Enhance checkout optimizer with payment, currency conversion, and settlement - Add real-time analytics and performance monitoring - Create comprehensive deployment infrastructure (Docker, K8s, CI/CD) - Add multi-cloud deployment support (AWS, GCP, Azure) - Implement repository setup and sync automation scripts - Add complete documentation and setup guides - Support both development and production environments This extends the AP2 protocol with intelligent shopping assistance while maintaining full compatibility and sync with upstream changes.
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Summary of ChangesHello @AnkitaParakh, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request establishes the foundational development for an AI Shopping Concierge, transforming the AP2 protocol into a complete, intelligent e-commerce platform. The changes focus on building out core AI functionalities for personalized shopping experiences, enabling multi-channel customer engagement, and providing extensive tooling for seamless deployment and operational management across various cloud environments. Highlights
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Code Review
This pull request introduces a comprehensive AI Shopping Concierge application, including extensive documentation, deployment configurations for Docker and Kubernetes, automation scripts, and several advanced Python modules for curation, negotiation, analytics, and checkout optimization. The overall structure and implementation are very impressive and follow best practices like multi-stage Docker builds, non-root users, health checks, and a modular architecture.
However, there are several areas for improvement. There is some redundancy and inconsistency in the documentation and scripts. A critical issue is a missing import in the negotiation engine that will cause a runtime error. There are also some security concerns in the Kubernetes and Docker Compose production configurations, such as hardcoded credentials and excessive container privileges. I've provided specific comments and suggestions to address these points.
| DB_PASSWORD: cG9zdGdyZXM= # postgres | ||
| REDIS_PASSWORD: cmVkaXM= # redis | ||
| GOOGLE_AI_API_KEY: "" | ||
| WHATSAPP_ACCESS_TOKEN: "" | ||
| WHATSAPP_VERIFY_TOKEN: "" | ||
| WHATSAPP_PHONE_NUMBER_ID: "" | ||
| AP2_MERCHANT_ID: "" | ||
| AP2_API_KEY: "" | ||
| STRIPE_API_KEY: "" | ||
| PAYPAL_CLIENT_ID: "" | ||
| PAYPAL_CLIENT_SECRET: "" | ||
| SECRET_KEY: "" | ||
| SENTRY_DSN: "" | ||
| GRAFANA_ADMIN_USER: YWRtaW4= # admin | ||
| GRAFANA_ADMIN_PASSWORD: YWRtaW4= # admin |
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Hardcoding secrets, even if they are base64-encoded default values, is a significant security risk in a production manifest. These values can be easily decoded. It is strongly recommended to use a proper secret management solution. You should inject these secrets at runtime using Kubernetes Secrets populated from a secure pipeline, or a dedicated secret management tool like HashiCorp Vault, AWS Secrets Manager, or Google Secret Manager.
| fail2ban: | ||
| image: lscr.io/linuxserver/fail2ban:latest | ||
| environment: | ||
| PUID: 1000 | ||
| PGID: 1000 | ||
| TZ: UTC | ||
| volumes: | ||
| - fail2ban_data:/config | ||
| - ./logs:/logs:ro | ||
| - ./deployment/fail2ban:/config/fail2ban:ro | ||
| cap_add: | ||
| - NET_ADMIN | ||
| - NET_RAW | ||
| network_mode: host | ||
| restart: unless-stopped |
There was a problem hiding this comment.
The fail2ban service is configured with network_mode: host and adds NET_ADMIN and NET_RAW capabilities. This configuration breaks container isolation and grants the container elevated privileges on the host's network stack, which poses a significant security risk. Please review this configuration carefully. If fail2ban is essential, ensure it is properly firewalled and monitored. Consider alternative security approaches that do not require such high privileges.
| # Copyright 2025 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # https://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """WhatsApp Business API integration for the AI Shopping Agent. | ||
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| This module provides a seamless integration between WhatsApp Business API | ||
| and the AP2 shopping agent, enabling customers to shop through WhatsApp chat. | ||
| """ | ||
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| import asyncio | ||
| import json | ||
| import logging | ||
| from datetime import datetime, timezone | ||
| from typing import Any, Dict, List, Optional | ||
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| import aiohttp | ||
| from fastapi import FastAPI, HTTPException, Request | ||
| from pydantic import BaseModel, Field | ||
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| from roles.shopping_agent.agent import root_agent | ||
| from common.system_utils import get_env_var | ||
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| logger = logging.getLogger(__name__) | ||
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| class WhatsAppMessage(BaseModel): | ||
| """WhatsApp message structure.""" | ||
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| from_number: str = Field(..., alias="from") | ||
| to_number: str = Field(..., alias="to") | ||
| message_type: str = Field(..., alias="type") | ||
| text: Optional[str] = None | ||
| media_url: Optional[str] = None | ||
| media_type: Optional[str] = None | ||
| timestamp: datetime | ||
| message_id: str | ||
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| class WhatsAppContact(BaseModel): | ||
| """WhatsApp contact information.""" | ||
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| phone_number: str | ||
| name: Optional[str] = None | ||
| profile_name: Optional[str] = None | ||
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| class WhatsAppWebhookEvent(BaseModel): | ||
| """WhatsApp webhook event structure.""" | ||
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| entry: List[Dict[str, Any]] | ||
| object: str | ||
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| class CustomerSession: | ||
| """Manages individual customer shopping sessions.""" | ||
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| def __init__(self, phone_number: str): | ||
| self.phone_number = phone_number | ||
| self.session_id = f"whatsapp_{phone_number}_{int(datetime.now().timestamp())}" | ||
| self.conversation_history: List[Dict[str, Any]] = [] | ||
| self.shopping_context: Dict[str, Any] = {} | ||
| self.last_activity = datetime.now(timezone.utc) | ||
| self.cart_items: List[Dict[str, Any]] = [] | ||
| self.customer_preferences: Dict[str, Any] = {} | ||
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| def add_message(self, message: str, sender: str): | ||
| """Add a message to conversation history.""" | ||
| self.conversation_history.append({ | ||
| "timestamp": datetime.now(timezone.utc).isoformat(), | ||
| "sender": sender, | ||
| "message": message | ||
| }) | ||
| self.last_activity = datetime.now(timezone.utc) | ||
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| class WhatsAppShoppingAgent: | ||
| """Main WhatsApp shopping agent integration.""" | ||
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| def __init__(self): | ||
| self.whatsapp_token = get_env_var("WHATSAPP_BUSINESS_TOKEN") | ||
| self.whatsapp_phone_id = get_env_var("WHATSAPP_PHONE_NUMBER_ID") | ||
| self.webhook_verify_token = get_env_var("WHATSAPP_WEBHOOK_VERIFY_TOKEN") | ||
| self.base_url = f"https://graph.facebook.com/v18.0/{self.whatsapp_phone_id}" | ||
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| # Session management | ||
| self.active_sessions: Dict[str, CustomerSession] = {} | ||
| self.session_timeout = 3600 # 1 hour timeout | ||
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| # Agent integration | ||
| self.shopping_agent = root_agent | ||
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| async def send_whatsapp_message( | ||
| self, | ||
| to_number: str, | ||
| message: str, | ||
| message_type: str = "text" | ||
| ) -> bool: | ||
| """Send a message via WhatsApp Business API.""" | ||
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| headers = { | ||
| "Authorization": f"Bearer {self.whatsapp_token}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| payload = { | ||
| "messaging_product": "whatsapp", | ||
| "to": to_number, | ||
| "type": message_type, | ||
| "text": {"body": message} | ||
| } | ||
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| try: | ||
| async with aiohttp.ClientSession() as session: | ||
| async with session.post( | ||
| f"{self.base_url}/messages", | ||
| headers=headers, | ||
| json=payload | ||
| ) as response: | ||
| if response.status == 200: | ||
| logger.info(f"Message sent successfully to {to_number}") | ||
| return True | ||
| else: | ||
| logger.error(f"Failed to send message: {await response.text()}") | ||
| return False | ||
| except Exception as e: | ||
| logger.error(f"Error sending WhatsApp message: {e}") | ||
| return False | ||
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| async def send_interactive_message( | ||
| self, | ||
| to_number: str, | ||
| header: str, | ||
| body: str, | ||
| buttons: List[Dict[str, str]] | ||
| ) -> bool: | ||
| """Send an interactive message with buttons.""" | ||
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| headers = { | ||
| "Authorization": f"Bearer {self.whatsapp_token}", | ||
| "Content-Type": "application/json" | ||
| } | ||
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| interactive_buttons = [] | ||
| for i, button in enumerate(buttons): | ||
| interactive_buttons.append({ | ||
| "type": "reply", | ||
| "reply": { | ||
| "id": f"btn_{i}", | ||
| "title": button["title"] | ||
| } | ||
| }) | ||
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| payload = { | ||
| "messaging_product": "whatsapp", | ||
| "to": to_number, | ||
| "type": "interactive", | ||
| "interactive": { | ||
| "type": "button", | ||
| "header": {"type": "text", "text": header}, | ||
| "body": {"text": body}, | ||
| "action": {"buttons": interactive_buttons} | ||
| } | ||
| } | ||
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| try: | ||
| async with aiohttp.ClientSession() as session: | ||
| async with session.post( | ||
| f"{self.base_url}/messages", | ||
| headers=headers, | ||
| json=payload | ||
| ) as response: | ||
| return response.status == 200 | ||
| except Exception as e: | ||
| logger.error(f"Error sending interactive message: {e}") | ||
| return False | ||
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| def get_or_create_session(self, phone_number: str) -> CustomerSession: | ||
| """Get existing session or create new one.""" | ||
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| # Clean up expired sessions | ||
| self._cleanup_expired_sessions() | ||
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| if phone_number not in self.active_sessions: | ||
| self.active_sessions[phone_number] = CustomerSession(phone_number) | ||
| logger.info(f"Created new session for {phone_number}") | ||
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| return self.active_sessions[phone_number] | ||
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| def _cleanup_expired_sessions(self): | ||
| """Remove expired sessions.""" | ||
| current_time = datetime.now(timezone.utc) | ||
| expired_sessions = [] | ||
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| for phone_number, session in self.active_sessions.items(): | ||
| time_diff = (current_time - session.last_activity).total_seconds() | ||
| if time_diff > self.session_timeout: | ||
| expired_sessions.append(phone_number) | ||
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| for phone_number in expired_sessions: | ||
| del self.active_sessions[phone_number] | ||
| logger.info(f"Expired session for {phone_number}") | ||
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| async def process_incoming_message(self, webhook_data: Dict[str, Any]) -> bool: | ||
| """Process incoming WhatsApp message.""" | ||
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| try: | ||
| for entry in webhook_data.get("entry", []): | ||
| for change in entry.get("changes", []): | ||
| if change.get("field") == "messages": | ||
| messages = change.get("value", {}).get("messages", []) | ||
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| for message in messages: | ||
| await self._handle_message(message) | ||
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| return True | ||
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| except Exception as e: | ||
| logger.error(f"Error processing webhook data: {e}") | ||
| return False | ||
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| async def _handle_message(self, message: Dict[str, Any]): | ||
| """Handle individual WhatsApp message.""" | ||
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| phone_number = message.get("from") | ||
| message_type = message.get("type") | ||
| timestamp = datetime.fromtimestamp(int(message.get("timestamp")), timezone.utc) | ||
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| if not phone_number: | ||
| logger.error("No phone number in message") | ||
| return | ||
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| # Get or create customer session | ||
| session = self.get_or_create_session(phone_number) | ||
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| # Extract message content | ||
| message_text = "" | ||
| if message_type == "text": | ||
| message_text = message.get("text", {}).get("body", "") | ||
| elif message_type == "interactive": | ||
| # Handle button responses | ||
| button_reply = message.get("interactive", {}).get("button_reply", {}) | ||
| message_text = button_reply.get("title", "") | ||
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| if not message_text: | ||
| await self.send_whatsapp_message( | ||
| phone_number, | ||
| "Sorry, I can only handle text messages right now. How can I help you shop today?" | ||
| ) | ||
| return | ||
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| # Add to conversation history | ||
| session.add_message(message_text, "customer") | ||
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| # Process with shopping agent | ||
| agent_response = await self._get_agent_response(session, message_text) | ||
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| # Send response back to customer | ||
| if agent_response: | ||
| await self.send_whatsapp_message(phone_number, agent_response) | ||
| session.add_message(agent_response, "agent") | ||
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| async def _get_agent_response(self, session: CustomerSession, message: str) -> str: | ||
| """Get response from the shopping agent.""" | ||
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| try: | ||
| # Prepare context for the agent | ||
| context = { | ||
| "customer_phone": session.phone_number, | ||
| "session_id": session.session_id, | ||
| "conversation_history": session.conversation_history[-5:], # Last 5 messages | ||
| "shopping_context": session.shopping_context, | ||
| "channel": "whatsapp" | ||
| } | ||
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| # TODO: Integrate with actual shopping agent | ||
| # For now, return a simple response | ||
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| # Check for common shopping intents | ||
| message_lower = message.lower() | ||
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| if any(word in message_lower for word in ["hi", "hello", "hey", "start"]): | ||
| return ("👋 Hi! I'm your AI shopping assistant. I can help you find products, " | ||
| "compare prices, create bundles, and complete your purchase right here in WhatsApp!\n\n" | ||
| "What are you looking to buy today? For example:\n" | ||
| "• 'I need a new phone'\n" | ||
| "• 'Show me winter jackets'\n" | ||
| "• 'Find me a laptop under $1000'") | ||
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| elif any(word in message_lower for word in ["buy", "shop", "looking for", "need", "want"]): | ||
| # Extract product intent | ||
| return (f"Great! I'll help you find '{message}'. " | ||
| f"Let me search our catalog for the best options...\n\n" | ||
| f"🔍 *Searching for products...*\n\n" | ||
| f"I found several great options! Would you like me to:\n" | ||
| f"1️⃣ Show you the top 3 recommendations\n" | ||
| f"2️⃣ Filter by price range\n" | ||
| f"3️⃣ Show bundle deals\n\n" | ||
| f"Just reply with 1, 2, or 3!") | ||
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| elif message_lower in ["1", "2", "3"]: | ||
| if message_lower == "1": | ||
| return self._generate_product_recommendations() | ||
| elif message_lower == "2": | ||
| return "💰 What's your budget range?\n\n• Under $50\n• $50-$200\n• $200-$500\n• Over $500\n\nJust tell me your range!" | ||
| elif message_lower == "3": | ||
| return self._generate_bundle_offers() | ||
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| else: | ||
| return ("I understand you're interested in shopping! Let me help you find what you need. " | ||
| "Could you tell me more specifically what you're looking for?") | ||
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| except Exception as e: | ||
| logger.error(f"Error getting agent response: {e}") | ||
| return "Sorry, I encountered an error. Please try again!" | ||
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| def _generate_product_recommendations(self) -> str: | ||
| """Generate mock product recommendations with negotiation options.""" | ||
| return ( | ||
| "🛍️ *Top 3 Recommendations:*\n\n" | ||
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| "1️⃣ **Premium Wireless Headphones**\n" | ||
| "💰 $199.99 ~~$249.99~~\n" | ||
| "⭐ 4.8/5 stars | Free shipping\n" | ||
| "🎵 Noise cancelling, 30hr battery\n\n" | ||
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| "2️⃣ **Smart Fitness Watch**\n" | ||
| "💰 $299.99\n" | ||
| "⭐ 4.6/5 stars | 2-day delivery\n" | ||
| "❤️ Heart rate, GPS, waterproof\n\n" | ||
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| "3️⃣ **Bluetooth Speaker Bundle**\n" | ||
| "💰 $89.99 for 2 speakers!\n" | ||
| "⭐ 4.7/5 stars | Limited time offer\n" | ||
| "🔊 360° sound, 20hr battery each\n\n" | ||
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| "💬 Reply with the number to select, or:\n" | ||
| "💸 'Negotiate 1' to discuss pricing\n" | ||
| "📦 'Bundle deal' for combo offers\n" | ||
| "🔄 'More options' to see alternatives" | ||
| ) | ||
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| def _generate_bundle_offers(self) -> str: | ||
| """Generate bundle offers with negotiation.""" | ||
| return ( | ||
| "🎁 *Special Bundle Deals:*\n\n" | ||
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| "📱 **Tech Bundle** - Save $100!\n" | ||
| "• Wireless Headphones\n" | ||
| "• Phone Case\n" | ||
| "• Wireless Charger\n" | ||
| "💰 $179.99 (was $279.99)\n\n" | ||
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| "🏃 **Fitness Bundle** - Save $75!\n" | ||
| "• Fitness Watch\n" | ||
| "• Bluetooth Earbuds\n" | ||
| "• Gym Bag\n" | ||
| "💰 $324.99 (was $399.99)\n\n" | ||
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| "🏠 **Home Audio Bundle** - Save $50!\n" | ||
| "• 2x Bluetooth Speakers\n" | ||
| "• Smart Display\n" | ||
| "• Streaming Device\n" | ||
| "💰 $249.99 (was $299.99)\n\n" | ||
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| "💬 Interested? Reply:\n" | ||
| "• Bundle name to select\n" | ||
| "• 'Custom bundle' to create your own\n" | ||
| "• 'Negotiate' + bundle name to discuss pricing" | ||
| ) | ||
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| # FastAPI app for webhook handling | ||
| app = FastAPI(title="WhatsApp Shopping Agent", version="1.0.0") | ||
| whatsapp_agent = WhatsAppShoppingAgent() | ||
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| @app.get("/webhook") | ||
| async def verify_webhook(request: Request): | ||
| """Verify WhatsApp webhook.""" | ||
| hub_mode = request.query_params.get("hub.mode") | ||
| hub_token = request.query_params.get("hub.verify_token") | ||
| hub_challenge = request.query_params.get("hub.challenge") | ||
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| if hub_mode == "subscribe" and hub_token == whatsapp_agent.webhook_verify_token: | ||
| logger.info("Webhook verified successfully") | ||
| return int(hub_challenge) | ||
| else: | ||
| logger.error("Webhook verification failed") | ||
| raise HTTPException(status_code=403, detail="Forbidden") | ||
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| @app.post("/webhook") | ||
| async def handle_webhook(request: Request): | ||
| """Handle incoming WhatsApp messages.""" | ||
| try: | ||
| body = await request.json() | ||
| logger.info(f"Received webhook: {json.dumps(body, indent=2)}") | ||
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| success = await whatsapp_agent.process_incoming_message(body) | ||
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| if success: | ||
| return {"status": "ok"} | ||
| else: | ||
| raise HTTPException(status_code=500, detail="Processing failed") | ||
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| except Exception as e: | ||
| logger.error(f"Webhook handling error: {e}") | ||
| raise HTTPException(status_code=500, detail="Internal server error") | ||
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| @app.get("/health") | ||
| async def health_check(): | ||
| """Health check endpoint.""" | ||
| return {"status": "healthy", "service": "WhatsApp Shopping Agent"} | ||
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| if __name__ == "__main__": | ||
| import uvicorn | ||
| uvicorn.run(app, host="0.0.0.0", port=8000) No newline at end of file |
There was a problem hiding this comment.
This file defines a WhatsAppShoppingAgent and a complete FastAPI application. This seems to duplicate logic found in unified_chat_manager.py, which also appears to be the main entry point. Having two separate FastAPI apps handling WhatsApp webhooks is redundant and confusing.
It's recommended to consolidate the WhatsApp-specific logic into the WhatsAppAdapter within unified_chat_manager.py and make unified_chat_manager.py the single entry point for all chat channels. This will create a cleaner, more maintainable architecture.
| RUN if [ -d ".git" ]; then \ | ||
| git submodule update --init --recursive; \ | ||
| fi |
There was a problem hiding this comment.
Running git submodule update inside the Docker build is not a best practice for production images. It requires the .git directory to be part of the build context, which increases the image size and couples the build process to Git. A better approach is to handle submodule checkout in your CI/CD pipeline before starting the Docker build. This keeps the image clean and focused on the application code.
| createdb ai_shopping_agent | ||
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| # Run migrations (if using Django/SQLAlchemy) | ||
| python manage.py migrate |
There was a problem hiding this comment.
This guide mentions python manage.py migrate, which is a Django command. However, other documentation (COMPLETE_SETUP_GUIDE.md) and the project structure suggest that alembic is used for database migrations with SQLAlchemy. To ensure consistency and avoid confusion, please update this to use the correct alembic command.
| python manage.py migrate | |
| python -m alembic upgrade head |
| postgres_data: | ||
| driver: local | ||
| driver_opts: | ||
| type: none | ||
| o: bind | ||
| device: /data/postgres | ||
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| redis_data: | ||
| driver: local | ||
| driver_opts: | ||
| type: none | ||
| o: bind | ||
| device: /data/redis | ||
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| prometheus_data: | ||
| driver: local | ||
| driver_opts: | ||
| type: none | ||
| o: bind | ||
| device: /data/prometheus | ||
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| grafana_data: | ||
| driver: local | ||
| driver_opts: | ||
| type: none | ||
| o: bind | ||
| device: /data/grafana | ||
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| fail2ban_data: | ||
| driver: local |
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Using the local driver with bind mounts to host paths (e.g., device: /data/postgres) for persistent data in production makes the setup less portable and dependent on the host's directory structure. It's generally better to use Docker-managed named volumes. This allows Docker to manage the storage location, which simplifies management and improves portability across different environments.
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| # AI Shopping Concierge - Deployment Guide | |||
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There appear to be multiple deployment guides in this pull request (DEPLOYMENT_GUIDE.md, COMPLETE_SETUP_GUIDE.md, and this file). This creates redundancy and can lead to confusion and maintenance overhead. It would be best to consolidate them into a single, comprehensive guide in a clear location.
| kubectl apply -f namespace.yaml | ||
| kubectl apply -f configmap.yaml | ||
| kubectl apply -f postgres.yaml | ||
| kubectl apply -f redis.yaml |
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The command kubectl apply -f redis.yaml is mentioned, but the guide does not provide the contents for a redis.yaml file, unlike the other Kubernetes manifests. This omission will cause the setup to fail. Please include the manifest for Redis.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Thanks for submitting this PR. I've noticed this change is not using the core Agent Payments Protocol (AP2) objects, such as the IntentMandate or CartMandate Verifiable Credentials (VCs). AP2 relies on these cryptographically signed mandates for essential security and authorization. Could you please clarify the intent or necessity of this PR, and explain how it contributes to the protocol without incorporating these required AP2 objects? Thanks! |
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