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Find samples using AI Agents search capabilities 🚀
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</div>
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<divclassName={styles.sampleCount}>
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{isSampleCountLoading ? 'Finding how many samples are available...' : (<span>There are <Linkhref="https://aka.ms/sqlai-samples"target="_blank">{sampleCount} samples</Link> in the database.</span>)}
This search engine uses AI Agents to find samples from the <Linkhref="https://aka.ms/sqlai-samples"target="_blank">Azure SQL Database Samples repository</Link> using a RAG pattern with structured output.
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<ul>
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<li>The searched text is given to an <strong>AI Agent</strong> that decide the best tool to use to answer the question, either using similarity search or generating and executing a SQL query</li>
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<li>Similiarity search across all available resources is done using the newly introduced <Linkhref='https://devblogs.microsoft.com/azure-sql/exciting-announcement-public-preview-of-native-vector-support-in-azure-sql-database/'target="_blank">vector support in Azure SQL Database</Link>.</li>
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<li>Results are then passed to a GPT-4o model to generate a sample summary and thoughts with a defined <Linkhref='https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/structured-outputs?tabs=rest'target="_blank">structured output</Link>.</li>
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<li><strong>Semantic caching</strong> is used to improve the performance of the search engine and reduce LLM calls costs.</li>
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</ul>
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If you want to have more details and get the source code of this sample, just ask about "this agentic AI sample" or "this website sample". Read more about creating AI apps with Azure SQL here: <Linkhref="https://aka.ms/sqlai"target="_blank">https://aka.ms/sqlai</Link>
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</div>
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</TeachingPopoverBody>
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<TeachingPopoverFooterprimary="Got it"/>
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</TeachingPopoverSurface>
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</TeachingPopover>
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<ButtononClick={handleGoToGithub}target="_blank">Go to GitHub Repo</Button>
This search engine uses AI Agents to find samples from the <Linkhref="https://aka.ms/sqlai-samples"target="_blank">Azure SQL Database Samples repository</Link> using a RAG pattern with structured output.
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<ul>
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<li>The searched text is given to an <strong>AI Agent</strong> that decide the best tool to use to answer the question, either using similarity search or generating and executing a SQL query</li>
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<li>Similiarity search across all available resources is done using the newly introduced <Linkhref='https://devblogs.microsoft.com/azure-sql/exciting-announcement-public-preview-of-native-vector-support-in-azure-sql-database/'target="_blank">vector support in Azure SQL Database</Link>.</li>
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<li>Results are then passed to a GPT-4o model to generate a sample summary and thoughts with a defined <Linkhref='https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/structured-outputs?tabs=rest'target="_blank">structured output</Link>.</li>
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<li><strong>Semantic caching</strong> is used to improve the performance of the search engine and reduce LLM calls costs.</li>
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</ul>
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If you want to have more details and get the source code of this sample, just ask about "this agentic AI sample" or "this website sample". Read more about creating AI apps with Azure SQL here: <Linkhref="https://aka.ms/sqlai"target="_blank">https://aka.ms/sqlai</Link>
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</div>
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</TeachingPopoverBody>
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<TeachingPopoverFooterprimary="Got it"/>
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</TeachingPopoverSurface>
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</TeachingPopover>
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<ButtononClick={handleGoToGithub}target="_blank">Go to GitHub Repo</Button>
Find samples using AI Agents search capabilities 🚀
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</div>
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<divclassName={styles.sampleCount}>
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{isSampleCountLoading ? 'Finding how many samples are available...' : (<span>There are <Linkhref="/samples">{sampleCount} samples</Link> in the database.</span>)}
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