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		<title>Natural Language to SQL is so easy now</title>
		<link>https://matthewtichenor.com/2024/07/04/natural-language-to-sql-is-so-easy-now/</link>
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		<dc:creator><![CDATA[admin]]></dc:creator>
		<pubDate>Thu, 04 Jul 2024 16:53:53 +0000</pubDate>
				<category><![CDATA[Blog Posts]]></category>
		<category><![CDATA[Agents]]></category>
		<category><![CDATA[ChatGPT]]></category>
		<category><![CDATA[Kaggle]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[SQL]]></category>
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					<description><![CDATA[<p>Since my last post I spent a bit of time learning about Large Language Models (LLMs). And while I don&#8217;t [&#8230;]</p>
<p>The post <a href="https://matthewtichenor.com/2024/07/04/natural-language-to-sql-is-so-easy-now/">Natural Language to SQL is so easy now</a> appeared first on <a href="https://matthewtichenor.com">matthewtichenor.com</a>.</p>
]]></description>
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<p class="">Since my last post I spent a bit of time learning about Large Language Models (LLMs). And while I don&#8217;t consider myself to be an expert (possibly because of my own standards), I can say that working with LLMs is very easy.</p>



<p class="">Back in the days before Open AI released ChatGPT, I remember when natural language to SQL was a difficult sequence to sequence problem. Well, I can say that&#8217;s definitely not the case anymore.</p>



<p class="">In the langchain documents, there is a very clear <a href="https://python.langchain.com/v0.1/docs/use_cases/sql/agents/">example</a> of using their library to create a SQL agent.</p>



<p class="">For those of you unfamiliar with the topic, langchain is a framework for developing LLM applications, and a LLM agent is a specially prompted LLM that takes actions in the environment (e.g. querying a database, searching Google, etc.) to achieve a goal. The ReACT prompt is an example of one these special prompts that turns LLMs into intelligent agents.</p>



<p class="">Anyways, I took the langchain example and decided to create a SQL agent that answers questions about Pokémon, using the data found in <a href="https://www.kaggle.com/">Kaggle</a>. Here&#8217;s a <a href="https://github.com/mwtichen/sql-agent/blob/main/sql-agent.ipynb">link</a> to myGitHub repo with my SQL agent.</p>



<p class="">As always thanks for reading. In my next post, I may talk about my experiences with the <a href="https://www.educative.io/">Educative.io</a> platform.</p>
<p>The post <a href="https://matthewtichenor.com/2024/07/04/natural-language-to-sql-is-so-easy-now/">Natural Language to SQL is so easy now</a> appeared first on <a href="https://matthewtichenor.com">matthewtichenor.com</a>.</p>
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