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    <title>Rag on 4sh.dev</title>
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      <title>RAG Agents with LangChain and LangGraph</title>
      <link>/posts/2024/an-opinionated-rag-agent/</link>
      <pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;&lt;a href=&#34;https://arxiv.org/abs/2310.11511&#34;&gt;Self-RAG&lt;/a&gt; (Self-Reflective Retrieval-Augmented Generation) is a framework that
combines the benefits of
retrieval-augmented generation (RAG) with self-reflection, allowing large language models (LLMs) to adaptively retrieve
passages on-demand and generate more accurate responses.&lt;/p&gt;
&lt;p&gt;Langgraph recently published
a &lt;a href=&#34;https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/&#34;&gt;blog post&lt;/a&gt; on &lt;em&gt;Self-RAG&lt;/em&gt; with their
own implementation.&lt;/p&gt;
&lt;p&gt;I really like the idea of validating the retrieved information. In that past I have noticed that, while vector databases
will return documents that usually match the input query string, some may contain irrelevant content.
Having the ability to validate and correct this is a great feature for any RAG setup.&lt;/p&gt;</description>
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