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I have created a bot, and provided a sharepoint site as it knowledge base. When I asked questions related a table embedded in Power Point, the bot seems to be able to read data from there, when I asked questions related to an invoice status file in xlsx format, the chatbot also says it doesn't know the answer. How shall I store structured data in this case?
Gen Answers over SharePoint does not currently support XSLX files (see below), but even if it did, its purpose is to generate answers over unstructured data sources, whereas (depending on the use case) an Excel spreadsheet is more like a database. Can you elaborate on the use case?
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-boost-node#supported-content
I have an Excel file, which contains customer name, invoice ID, invoice date, due date, outstanding amount, related sales order id et. I would like chatbot to read the data in Excel, find the relevant line of data, run LLM analysis and generate answers like: the invoice was sent out 2 months ago, it looks it's not paid yet, maybe you should sent a reminder to customer xxx.
What would be the context for the copilot's action? Will users ask about a specific invoice? Search for unpaid invoices?
yes, I would like copilot to search xlsx by invoice ID provided by user. I realize later that if I load the xlsx file to the chatbot, the bot can retrieve information, but there are two problems: 1) the bot only answers correctly when user provides invoice id which is a unique key, also user has to ask very specific questions to get the answer, 2) the xlsx file doesn't get refreshed automatically in this case. So I'm now thinking about loading the xlsx file to Power APP (using one drive for business connector) and link Power App to copilot. Any help on this approach will be appreciated.
Here is some demo which may solve the problem.
(1) Chat with your data using ONLY the Power Platform | LinkedIn
Remy's LinkedIn post is awesome, but keep in mind that like Gen Answers, it relies on a search-based pattern. This is not the best pattern to support analytical questions. For example - "how many invoices are unpaid" is not a question that can be easily answered just by analyzing search results.
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