ID: 001

🛒 Target Superstore AI Assistant

An AI-powered shopping assistant built with Streamlit, LangGraph, and LangChain for the fictional US retail chain Target Superstore.

The assistant supports:

• 🔍 Product search and filtering
• 🛍️ Cart management
• 👤 User loyalty lookup
• 💰 Final price calculation with loyalty points
• ✅ Human-in-the-loop approval for cart additions
• 🤖 Tool-calling AI agent using LangGraph
• 💬 Conversational UI with Streamlit

---

🚀 Features

✅ AI Shopping Assistant

Users can chat naturally in English or Urdu to:

• Search products
• Add/remove products from cart
• View cart contents
• Checkout
• Ask pricing questions

---

✅ Human-in-the-Loop (HITL) Approval

Before adding products to the cart, the system pauses execution and asks for explicit approval using LangGraph interrupts.

Example flow:

1. User says:

   Add Nestle Milk Pack

2. AI triggers add_to_cart

3. Graph pauses with:

   • Approve ✅
   • Reject ❌

4. Execution resumes after user decision

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✅ Tool-Based Architecture

The assistant uses LangChain tools:

Tool	Description
get_product_info	Product search/filtering
get_user_data	Retrieve customer profile
calculate_final_price	Loyalty discount calculation
add_to_cart	Add item with approval workflow
view_cart	Show cart
remove_from_cart	Remove item from cart

---

🏗️ Tech Stack

• Python
• Streamlit
• LangGraph
• LangChain
• Pandas
• OpenAI / LLM Provider
• dotenv

---

📂 Project Structure

project/
│
├── app.py
├── llm.py
├── users_data.json
├── superstore_products.xlsx
├── .env
├── requirements.txt
└── README.md

---

⚙️ Installation

1. Clone Repository

git clone <your_repo_url>
cd <project_name>

---

2. Create Virtual Environment

python -m venv venv

Activate it:

Mac/Linux

source venv/bin/activate

Windows

venv\Scripts\activate

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3. Install Dependencies

pip install -r requirements.txt

---

🔐 Environment Variables

Create a .env file:

OPENAI_API_KEY=your_api_key_here

You may also configure your custom LLM provider inside llm.py.

---

▶️ Run the App

streamlit run app.py

---

🧠 How It Works

LangGraph Workflow

User Input
    ↓
LLM Node
    ↓
Tool Decision
    ↓
Tool Execution
    ↓
Interrupt (if approval needed)
    ↓
Resume Execution
    ↓
Assistant Response

---

🛍️ Cart Workflow

Add Product

User → "Add product X"
       ↓
AI calls add_to_cart
       ↓
Interrupt triggered
       ↓
User approves/rejects
       ↓
Cart updated

---

📊 Product Search

The assistant supports filtering by:

• Product Name
• Product ID
• Brand
• Category
• Subcategory
• Price Range
• Tags

Example queries:

Show me imported chocolates
Find organic products under 1000 PKR
Search for Nestle products

---

👤 User Data Example

get_user_data(user_id) returns:

{
  "user_id": "U1001",
  "name": "Ali Khan",
  "loyalty_points": 2500,
  "tier": "Gold"
}

---

💰 Loyalty Price Calculation

calculate_final_price(price=5000, loyalty_points=1200)

Output:

Discount applied: 1200 PKR.
Final price: 3800 PKR.

---

🧩 Key LangGraph Concepts Used

StateGraph

Defines the agent workflow graph.

---

ToolNode

Executes LangChain tools automatically.

---

interrupt()

Pauses execution for human approval.

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Command(resume=...)

Resumes graph execution after approval/rejection.

---

InMemorySaver

Persists conversation state across Streamlit reruns.

---

🎨 UI Components

Main Chat Area

• User/assistant conversation
• Approval prompts
• Tool responses

---

Sidebar Cart

Displays:

• Cart items
• Quantity
• Total price

---

📌 Example Commands

Show me snacks under 500 PKR
Add P1001 to cart
View my cart
Remove P1001
Checkout

---

🔒 System Prompt Rules

The AI strictly follows rules such as:

• Only one cart tool per turn
• Approval required for add-to-cart
• No approval for remove-from-cart
• Checkout triggered only on explicit request

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🛠️ Future Improvements

• Persistent database storage
• Real payment integration
• Inventory updates
• Voice assistant support
• Multi-user authentication
• Recommendation engine
• Order history
• WhatsApp integration

---

📦 Example Requirements

streamlit
pandas
langchain
langgraph
python-dotenv
openpyxl

---

📸 Demo Flow

User: Add Milk Pack

AI:
Approval Required
[Approve] [Reject]

User clicks Approve

AI:
Added Milk Pack x1 to cart.
Cart total: PKR 450

---

🤝 License

MIT License

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🙌 Acknowledgements

Built with:

• Streamlit
• LangChain
• LangGraph
• Pandas