mitzen

We can quickly configure vscode to use github mcp by creating a fille under .vscode/mcp.json. This file should contain the followings:- {   " inputs " : [     {       " type " : " promptString " ,       " id " : " github_token " ,       " description " : " GitHub Personal Access Token " ,       " password " : true     }   ],   " servers " : {     " github " : {       " command " : " docker " ,       " args " : [         " run " , " -i " , " --rm " ,         " -e " , " GITHUB_PERSONAL_ACCESS_TOKEN " ,         " ghcr.io/github/github-mcp-server "       ],       " env " : {         " GITHUB_PERSONAL_ACCESS_TOKEN " : " ${input:github_token} "       }     }   } } And then you are good to prompt away

To enable RAG in our agent, we need Azure foundry embedding model and a gpt-4-o model to test it out. This will empower our agent with inhouse knowledge.  Lets deploy our Azure Foundry instance first. Pretty straight forward. Ensure we have deployed 2 model - embedding-3-small as shown below We also need a storage account where we place our document to be indexed by Azure AI Search later.  Next, we will create an Azure AI Search service. This will allow us to index document in our storage account. We can also configure a schedule if we wanted to:-  Then we can import our document to build up our knowledge base.  Here, we will configure our vector searches and now you know why we are creating the embedding model. Next we will setup our agent and then add knowlege to it. So we create an agent.  Under the section, knowledge (on the left tab), that's where we provide a knowledge and index to it. Once we have configure it, we can prompt our agent accordingly.