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Copy file name to clipboardExpand all lines: README.md
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@@ -21,7 +21,7 @@ It leverages Azure Open AI Service and Azure AI Search, to identify relevant doc
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This example focuses on a generic use case - chat with your own data, generate a document template using your own data, and exporting the document in a docx format.
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The sample data is sourced from 1) generic AI-generated promissory notes and 2) a select set of research and grants published on PubMed and NIH.
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The sample data is sourced from generic AI-generated promissory notes.
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The documents are intended for use as sample data only.
Copy file name to clipboardExpand all lines: docs/TRANSPARENCY_FAQ.md
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This solution accelerator is an open-source GitHub Repository to help create AI assistants using Azure Open AI Service and Azure AI Search. This can be used by anyone looking for reusable architecture and code snippets to build AI assistants with their own enterprise data. The repository showcases a generic scenario of a user who wants to generate a document template based on a sample set of data.
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-### What can Build your own copilot - Generic Solution Accelerator do?
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The sample solution included focuses on a generic use case - chat with your own data, generate a document template using your own data, and exporting the document in a docx format. The sample data is sourced from 1) generic AI-generated promissory notes and 2) a select set of research and grants published on PubMed and NIH. The documents are intended for use as sample data only. The sample solution takes user input in text format and returns LLM responses in text format up to 800 tokens. It uses prompt flow to search data from AI search vector store, summarize the retrieved documents with Azure Open AI.
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The sample solution included focuses on a generic use case - chat with your own data, generate a document template using your own data, and exporting the document in a docx format. The sample data is sourced from generic AI-generated promissory notes. The documents are intended for use as sample data only. The sample solution takes user input in text format and returns LLM responses in text format up to 800 tokens. It uses prompt flow to search data from AI search vector store, summarize the retrieved documents with Azure Open AI.
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-### What is/are Build your own copilot - Generic Solution Accelerator’s intended use(s)?
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This repository is to be used only as a solution accelerator following the open-source license terms listed in the GitHub repository. The example scenario’s intended purpose is to help users generate a document template to perform their work more efficiently.
Intro='The proposal will include the following sections:',
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Closing='Does this look good? If so, you can **generate the document** now. You can also ask me to **add an item** or **change the order of the sections**.',
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JSONParseError='Could not create a template, please try again and ask for a document type.',
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JSONParseError='I was unable to find content related to your query and could not generate a template. Please try again.',
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JSONStructureError='Unable to render the sections within the template. Please try again.'
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