Search for a command to run...
Principal Program Manager, Microsoft Fabric CAT helping users and organizations build scalable, insightful, secure solutions. Blogs, opinions are my own and do not represent my employer.
No comments yet. Be the first to comment.
Last week Microsoft released an open-source text embedding model called Harrier in three sizes- 270M, 0.6B and 27B. I have been testing it in my RAG pipeline and so far it has crushed all my metrics.
At FabCon Atlanta last week, the updated notebook Copilot for data engineering and data science was announced. It brings agentic capabilities to the Copilot and is much more intelligent and Fabric-awa
Using Fabric data agent Python SDK
Use workspace monitoring to monitor MCP server use
For Power BI Copilot and Data agents with semantic models, you must use Prep Data for AI configuration to ground the responses in the context added in Prep for AI. In this blog, I will show you how you can use the Power BI remote MCP server to get the configuration.
Below I call the GetSemanticModelSchema tool from the hosted MCP to get the Prep for AI configurations. Run below in a Fabric notebook.
import httpx
import json
async def get_semantic_model_schema(model_id):
"""Fetch and parse the semantic model prep for ai config from Power BI MCP server.
Author : Sandeep Pawar | Fabric.guru
"""
MCP_SERVER_URL = "https://api.fabric.microsoft.com/v1/mcp/powerbi"
token = notebookutils.credentials.getToken("pbi")
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
}
payload = {
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "GetSemanticModelSchema",
"arguments": {"artifactId": model_id}
}
}
def parse_sse_response(text):
for line in text.split('\n'):
if line.startswith('data: '):
return json.loads(line[6:])
return {}
async with httpx.AsyncClient(timeout=120.0) as client:
response = await client.post(MCP_SERVER_URL, headers=headers, json=payload)
data = parse_sse_response(response.text)
parsed = json.loads(data['result']['content'][0]['text'])
return {
"name": parsed['semanticModel']['Name'],
"tables": parsed['schema']['Tables'],
"relationships": parsed['schema']['ActiveRelationships'],
"custom_instructions": parsed['schema']['CustomInstructions'],
"verified_answers": parsed['schema']['VerifiedAnswers']
}
# retrieve
prep4ai = await get_semantic_model_schema("<semantic_model_guid>")
Here you have it. Note the verified answer. This is often a confusion, what does the Verified Answer actually store ? As you can see it includes the projections - the tables, columns, measures, filters used in the visual and not the DAX query of the visual !
You can use this to programmatically check, verify, monitor the configurations or make it part of your best practices analyzer for AI.