MongoDB Smolagents Multi Agent Micro Agents

Smolagents Multi Agent Micro Agents

agentsartificial-intelligencellmsmongodb-genai-showcasenotebooksgenerative-airag

alph-notebooks/mongodb-genai-showcase / smolagents_multi-agent_micro_agents.ipynb

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Multi-Agent Order Management System with MongoDB

This notebook implements a multi-agent system for managing product orders, inventory, and deliveries using:

smolagents for agent management
MongoDB for data persistence
DeepSeek Chat as the LLM model

Setup

First, let's install required dependencies:

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Import Dependencies

Import all required libraries and setup the LLM model:

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/usr/local/lib/python3.10/dist-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:
* 'fields' has been removed
  warnings.warn(message, UserWarning)

Database Connection Class

Create a MongoDB connection manager:

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Agent Tools Defenitions

Define tools for each agent type:

[5]

[6]

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Main Order Management System

Define the main system class that orchestrates all agents:

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Adding Sample Data

To test the system, you might want to add some sample products to MongoDB:

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Sample products added successfully!

Testing the System

Let's test our system with a sample order:

[16]

Orders processing result: Here’s the response to your request:

---

### **Processed Orders and Inventory Update**

1. **Orders Created**:
   - **Order 1**:
     - **Products**:
       - `prod1`: 2 units
       - `prod2`: 1 unit
     - **Delivery Address**: `123 Main St`
     - **Order ID**: `677b8a9ff033af3a53c9a75a`
   - **Order 2**:
     - **Products**:
       - `prod3`: 3 units
     - **Delivery Address**: `456 Elm St`
     - **Order ID**: `677b8aa3f033af3a53c9a75c`

2. **Inventory Updated**:
   - **`prod1` (Laptop)**:
     - Initial stock: 6 units
     - Subtracted: 2 units
     - New stock: 4 units
   - **`prod2` (Smartphone)**:
     - Initial stock: 13 units
     - Subtracted: 1 unit
     - New stock: 12 units
   - **`prod3` (Headphones)**:
     - Initial stock: 24 units
     - Subtracted: 3 units
     - New stock: 21 units

3. **Delivery Status**:
   - Both orders have been marked as **"in_transit"** and are ready for delivery.

---

### **Summary**:
- The orders have been successfully processed.
- The inventory has been updated to reflect the subtracted quantities.
- The delivery status for both orders is now **"in_transit"**.

Let me know if you need further assistance! 😊

Conclusions

In this notebook, we have successfully implemented a multi-agent order management system using smolagents and MongoDB. We defined various tools for managing inventory, creating orders, and updating delivery statuses. We also created a main system class to orchestrate these agents and tested the system with sample data and orders.

This approach demonstrates the power of combining agent-based systems with robust data persistence solutions like MongoDB to create scalable and efficient order management systems.