Azure, AI, SP, .NET, Microsoft - Knowledge Base

Agent Builder from Open AI is a game changer! Using this tool, you can easily create custom ai agent without writing a single line of code. Below the flow was built using Agent Builder.  Agent is taking input and based on the search query, agent is going to search from different MCP unit i.e. Google-drive, or SharePoint, or even using websearch.  Simple to create and FAST to implement the Buisness Workflow! Below the generated the back-end code. Go for Agent Builder :) import { hostedMcpTool, Agent, AgentInputItem, Runner, withTrace } from "@openai/agents"; import { OpenAI } from "openai"; import { runGuardrails } from "@openai/guardrails"; import { z } from "zod"; // Tool definitions const mcp = hostedMcpTool({   serverLabel: "sharepoint",   allowedTools: [     "fetch",     "get_profile",     "get_site",     "list_recent_documents",     "search"   ],   authorization: "https://gr...

Great things to see first time using OpenAI - I can see Good Morning message in my native Bengali language in Windows Command prompt! Below the artifacts you need to install before run the code pip install langchain pip install langchain-openai pip install langgraph And, you need a key from  https://smith.langchain.com /  Finally, the code goes below... import getpass import os from langchain_openai import ChatOpenAI from langchain.schema import HumanMessage from langchain_core.prompts import ChatPromptTemplate if not os.environ.get( "OPENAI_API_KEY" ):   os.environ[ "OPENAI_API_KEY" ] = getpass.getpass( "YOUR OPENAPI KEY GOES HERE" ) chat = ChatOpenAI(model= "gpt-4o-mini" , temperature= 0.7 ) system_template = "Translate the following from English into {language}" prompt_template = ChatPromptTemplate.from_messages(     [( "system" , system_template), ( "user" , "{text}" )] ) #You can change lan...

Microsoft Fabric comes up with multiple capabilities/wings and one of it is Data Enginnering where you brings your data to next generation AI. In Data Enginnering platform, you are going to load your data, perform operation on your data to process it, and finally display your finetune data in nice way using Power BI capabilities. Tables are Files hold your data in Data Enginnering landscape.  Table will allow to hold data in table structure format while you can upload your data file using csv/json/parquet. Upload option is there to upload your file(s) into Microsoft Fabric.              Files uploading in Lakehouse Files uploaded in Lakehouse Simple way to display record is to create one Notebook and drag the file there, Fabric will create the code for you :) Click on the table data, and options are there to display the data in different format. For example, you can view your data in chart format (like bar/chart/pie).   Bar Format Pie Format...

Pandas is one of the best framework in Data Science world. Below is one of the quick session on pandas, it is mainly focused on hands-on activity. Hope you will enjoy this and please feel free to post your comments/suggestions/questions.

This is python session, developed keeping in mind for Data Science applications. Please feel free ask your questions/comments here. Thank you for watching the session!