Amazon_Shopping_Assistant

This Shopping Assistant, developed using the LangGraph framework, facilitates product search, recommendations, and shopping cart management in a conversational setting. The assistant integrates LangGraph’s tools for smooth and responsive interactions, allowing users to find products, manage their shopping cart, and inquire about order and purchase-related information. also, tools are interacting with database in the backend for product search, order status, delivery time retrieval etc.

Assistant Capabilities

1. Product Search and Recommendations:

   • The assistant responds to product-related inquiries, including product specifications, price, and availability from database.
   • A recommendation system suggests related or alternative products based on user queries, enhancing the shopping experience with personalized options.

2. Cart Management:

   • Users can add items to their shopping cart or remove them during the conversation, with the assistant prompting confirmations for each cart action.
   • The assistant provides live updates on the items in the cart, showing the product details and quantity.

3. Order and Purchase Queries:

   • The assistant responds to questions regarding checkout, delivery times, payment options, and order status.
   • For a smooth transaction experience, the assistant offers follow-up support on order status and estimated delivery.

Conversational Flow

1. Multi-Turn Conversations:

   • The assistant handles multi-turn interactions, remembering the context of user queries and follow-up questions.
   • It manages interruptions and seamlessly resumes the conversation, ensuring a natural user experience.

2. Structured Response Flow:

   • The response flow includes:
     • Human Message: Initial query from the user.
     • AI Message: AI’s response based on the query.
     • Tool Message: Specific tool action, if applicable (e.g., adding an item to the cart).

Testing and Validation

1. Scenario Testing:

   • The assistant was tested across various shopping scenarios to validate accuracy and reliability in conversation flow, including:
     • Complex queries involving multiple products.
     • Out-of-stock scenarios.
     • Invalid or ambiguous queries.

2. Edge Case Handling:

   • Documented common edge cases (e.g., items out of stock, unclear queries), ensuring the assistant gracefully manages these situations without disrupting the conversation.

Dataset Information

1. Dataset Source:
   • The dataset was sourced from Kaggle, and pre-processing steps were applied to prepare it for use, including:
     • Removing rows with null values.
     • Modifying product title length for better readability, as the dataset initially only included descriptions.
   • The data is stored in an PostreSQL database.
2. Functionalities in assistant_tools.py:
   • Key functionalities include:
     • search_products: Searches products based on user queries.
     • add_to_cart: Adds selected items to the cart.
     • remove_from_cart: Removes items from the cart.
     • view_cart: Displays items in the cart.
     • checkout: Processes the checkout.
     • get_payment_options, get_order_status, get_delivery_time: Provides payment options, order status, and delivery information.

Output Screenshots

Screenshots documenting the assistant's interactions and key functionalities are stored in the "Output Screenshot" folder for reference.

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Steps to Run

Environment Setup

1. Create a .env file in the project root directory with the following content:

   DB_HOST = <Your_Database_Host>
   DB_PORT = <Your_Database_Port>
   DB_DATABASE = <Your_Database_Name>
   DB_USER = <Your_Database_Username>
   DB_PASSWORD = <Your_Database_Password>
   GROQ_API_KEY = <Your_Groq_API_Key>
   
   

2. Install PostgreSQL:

   • Ensure PostgreSQL is installed on your system.
   • For installation, visit the PostgreSQL official site.

3. Database Setup:

   • Make sure you have the following information ready:
     • Database Host: DB_HOST
     • Database Port: DB_PORT
     • Database Name: DB_DATABASE
     • Username: DB_USER
     • Password: DB_PASSWORD

4. Log in to PostgreSQL: Open a terminal and connect to PostgreSQL as the desired user. For example:

   psql -U <username>

   Replace <username> with your PostgreSQL username.

5. Create a New Database: Create the database where you will restore the backup:

   CREATE DATABASE <database_name>;

   Replace <database_name> with the desired database name.

6. Exit PostgreSQL: Type \q to exit the PostgreSQL shell:

   \q

7. Restore the Backup: Use the psql command-line tool to restore the backup from the backup.sql file:

   psql -U <username> -d <database_name> -f backup.sql

   Replace:

   • <username>: Your PostgreSQL username.
   • <database_name>: The name of the database created in step 2.
   • backup.sql: Path to the provided SQL backup file.

8. Install Dependencies:

• Run the following command to install all dependencies:

  pip install -r requirements.txt

• (The requirements.txt file includes all necessary packages, including langchain, langchain-groq, langgraph, python-dotenv, and groq.)

9. Run the Assistant:

   • Start the assistant by executing:

     python main.py

10. Interact with the Assistant:

• Enter shopping-related queries to initiate conversations with the assistant.
• Example query: "Hi, I am looking for a gaming monitor" will prompt the assistant to display available mugs.

11. Cart Management:

• Select a product type, and then ask the assistant to add it to the cart.
• The assistant provides real-time cart updates, allowing users to view, modify, or remove items from the cart as needed.

12. Checkout and Order Support:

• Proceed with checkout options, including reviewing cart items, modifying quantities, and finalizing the purchase.
• The assistant also responds to order-related questions, including delivery status, payment methods, and estimated arrival times.