Smolagents - Enhance Multi-Agent Order Management and MongoDB Integration Notebooks

[Pash10g]

Description:

This PR introduces significant enhancements to two Jupyter notebooks: smolagents_multi-agent_micro_agents.ipynb and smolagents_hf_with_mongodb.ipynb. These updates aim to improve the functionality, usability, and integration of multi-agent systems with MongoDB.

Changes:

1. smolagents_multi-agent_micro_agents.ipynb:

   • Setup and Dependencies:
     • Added installation commands for required dependencies (smolagents, pymongo, litellm).
   • Import Dependencies:
     • Imported necessary libraries and initialized the LLM model.
   • Database Connection:
     • Established a connection to MongoDB using MongoClient.
   • Agent Tools Definitions:
     • Defined tools for checking stock, updating stock, creating orders, and updating delivery status.
   • Main Order Management System:
     • Created a class OrderManagementSystem to orchestrate agents for inventory, orders, and delivery management.
   • Sample Data:
     • Added functionality to insert sample products into the MongoDB database.
   • Testing the System:
     • Provided a test case to process sample orders and verify the system's functionality.

2. smolagents_hf_with_mongodb.ipynb:

   • Setup and Dependencies:
     • Added installation commands for required dependencies (pymongo, smolagents).
   • MongoDB Atlas Setup:
     • Provided detailed instructions for setting up MongoDB Atlas and obtaining the connection string.
   • Loading the Dataset:
     • Specified the use of the Airbnb dataset from Hugging Face.
   • Defining Tools:
     • Created tools for executing aggregation pipelines and sampling documents from MongoDB.
   • Agent Integration:
     • Integrated ToolCallingAgent with tools for data aggregation and sampling.
   • Example Usage:
     • Demonstrated how to use the agent to query supported countries in the rentals collection and aggregate data.

Improvements:

• Robust Tool Design:
  • Enhanced error handling and feedback mechanisms.
  • Excluded embedding fields from queries to improve performance and readability.
• Enhanced Query Handling:
  • Added initial projection stages in aggregation pipelines to remove embedding fields.
• Improved User Experience:
  • Clearer tool documentation and example usage.
• Practical Application:
  • Demonstrated practical applications for analyzing data within MongoDB Atlas using an LLM-powered agent.

Future Development:

• Expanded Toolset:
  • Implement additional tools for data manipulation and complex analytics.
• Advanced Query Generation:
  • Refine LLM's ability to generate accurate and efficient MongoDB queries.
• Visualization Capabilities:
  • Integrate data visualization libraries for better result presentation.
• Security Enhancements:
  • Incorporate environment variable management for sensitive credentials.

This PR significantly enhances the functionality and usability of the multi-agent order management system and MongoDB integration, providing a robust foundation for further development and practical applications.