What is Gbbai Semantickernel Using Mcptools

gbbai-semantickernel-using-mcptools is a Semantic Kernel Agent orchestrator that utilizes MCP Tools through Azure API Management, enabling the integration of various tools with large language models (LLMs) via the Model Context Protocol.

Use cases

Use cases include developing AI-driven applications that require semantic understanding, automating workflows using LLMs, and enhancing data processing capabilities through tool integration.

How to use

To use gbbai-semantickernel-using-mcptools, set up a Python environment, install the required dependencies, and deploy the necessary Azure resources using the Azure Developer CLI (azd). Follow the instructions in the README for detailed steps.

Key features

Key features include the ability to plug and play tools with LLMs, end-to-end authentication and authorization via Azure API Management, and the management of OAuth 2.0 tokens for backend tools.

Where to use

gbbai-semantickernel-using-mcptools can be used in fields such as artificial intelligence, natural language processing, and cloud computing, particularly where integration of various tools with LLMs is required.

Semantic Kernel Agent Orchestrator using MCP Tools via Azure API Management

Model Context Protocol with Azure API Management to enable plug & play of
tools to LLMs

🔧 Prerequisites

• azd, used to deploy all Azure resources and assets used in this sample.
• PowerShell Core pwsh if using Windows
• Python 3.11
• An Azure Subscription with Contributor permissions
• Sign in to Azure with Azure CLI
• VS Code installed with the Jupyter notebook extension enabled

Architecture

• Model Context Protocol servers runing behind with Azure API Management to enable plug & play of tools to LLMs. The API Management can ensure end-to-end authentication and authorization, using credential manager manager for managing OAuth 2.0 tokens to backend tools and client token validation [TO BE IMPLEMENTED].

Instructions

1. Python Environment Setup
   

   python3.11 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   

2. Create the infrastructure
   
   This sample uses azd and a bicep template to deploy all Azure resources, including Azure AI Search.

   • Login to your Azure account: azd auth login

   • Create an environment: azd env new

   • Run azd up.

   • Choose your Azure subscription.
   • Enter a region for the resources.

   The deployment creates multiple Azure resources and runs multiple jobs. It takes several minutes to complete. The deployment is complete when you get a command line notification stating “SUCCESS: Your up workflow to provision and deploy to Azure completed.”

3. Running the Notebook with the Orchestrator
   
   Open the notebook orchestrator-model-context-protocol and execute it to see the orchestrator in action.

4. Delete the Resources
   
   You can delete the infrastruture created before by using azd down --purge