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This article shows how to set up a data agent in microsoft fabric using a lakehouse as the example data source 📘 what is a fabric data agent? We first create and populate a lakehouse, then create a fabric data agent and add the lakehouse to it.
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The table below shows which data sources currently support example queries in the data agent This approach dramatically reduces the time and effort required to build intelligent data agents These examples help guide the agent’s query generation process by providing patterns and context.
With a data agent in microsoft fabric, you can create conversational ai experiences that answer questions about data stored in lakehouses, warehouses, power bi semantic models, and kql databases in fabric.
Using the new evaluate_few_shot_examples () function, you can validate each natural language/sql pair and receive a detailed summary of which examples passed or failed. You'll learn how to define the agent's objective, prioritize data sources, incorporate key terminology, and provide query logic for common scenarios These configuration tips help ensure the agent performs reliably across diverse data environments and user needs. Example queries, also known as few shot examples, are used by data agent tools to improve the quality of generated queries
They allow creators to pass example query logic that the agent can reference when forming a response. Data agent in microsoft fabric is a new microsoft fabric feature that allows you to build your own conversational q&a systems using generative ai A fabric data agent makes data insights more accessible and actionable for everyone in your organization. Use these instructions to guide how the agent reasons through questions, selects data sources, and formats its responses
When iterating on agent instructions
Specify which data sources to use for particular types of questions and in what order of priority. In this blog, i’ll walk you through how i created a fabric data agent using github copilot agent mode in visual studio code
