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Hey
I am new to Fabric IQ and was trying out the tutorial on ms learn.
https://learn.microsoft.com/en-us/fabric/iq/ontology/tutorial-4-create-data-agent
on step 4, i have created a data agent.
When i prompt the agent, i get error: please see below.
{"entitySelector":{"query":"MATCH (store:Store)-[:has]->(sale:SaleEvent)<-[:soldIn]-(product:Products)\nRETURN store.StoreId AS store_id, product.ProductId AS product_id, store.StoreName AS store_name, product.ProductName AS product_name","queryType":"GQL"}}
Please can anyone explain what is going wrong here?
thanks
Solved! Go to Solution.
Great @msprog , then we should review the query and analyze why the model is not interpreting it correctly.
As a final step, if needed, we could provide additional instructions to the agent.
If you were working with a Lakehouse, you could also include example questions with queries in the agent, so that if something similar comes up, it can take it into account during interpretation.
Hi @msprog ,
if this helped you, I’d appreciate it if you could mark it as the accepted solution and give it a like 👍
This helps others in the community find the answer more easily.
Thanks! 🙌
Hi @msprog
Quick question: even if you're following the Fabric IQ and agents tutorial, are you using a paid Fabric capacity or a trial one?
It won’t work with a trial capacity. In fact, the agent prerequisites specify that a paid capacity is required.
Also, please check in the admin portal that Fabric Agents and Graph features are enabled.
Hope this helps.
Let me know if you’ve managed to solve it or if you’re still facing issues.
Best regards,
@arabalca it is only this prompt that is failing - i suspect something wrong with the query the agent has constructed.
other prompts worked for e.g. below
Great @msprog , then we should review the query and analyze why the model is not interpreting it correctly.
As a final step, if needed, we could provide additional instructions to the agent.
If you were working with a Lakehouse, you could also include example questions with queries in the agent, so that if something similar comes up, it can take it into account during interpretation.
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