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		<title>Retriever on Pythonflow</title>
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			<managingEditor>alicandonmez90@gmail.com (flowfelis)</managingEditor>
		
		
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			<lastBuildDate>Fri, 26 Jun 2026 07:00:00 +0100</lastBuildDate>
		
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				<title>LangBot Day 9: The RAG Pipeline — Retriever Meets Generator</title>
				<link>https://pythonflow.com/posts/langbot-day-9-rag-pipeline/</link>
				<pubDate>Fri, 26 Jun 2026 07:00:00 +0100</pubDate><author>alicandonmez90@gmail.com (flowfelis)</author>
				<guid>https://pythonflow.com/posts/langbot-day-9-rag-pipeline/</guid>
				<description>&lt;p&gt;&lt;strong&gt;Recap:&lt;/strong&gt; On Day 8, LangBot gained a long-term memory — a vector store (Pinecone) filled with document embeddings. You can ask &lt;code&gt;/search home office stipend&lt;/code&gt; and get relevant chunks back. But that is half the picture. You still have to type &lt;code&gt;/search&lt;/code&gt;, read raw chunks, and synthesize the answer yourself. Today we close the loop: the retriever feeds into the chatbot automatically, so you ask &lt;em&gt;&amp;ldquo;How much is the home office stipend?&amp;rdquo;&lt;/em&gt; and LangBot answers from your documents — no slash command, no manual lookup.&lt;/p&gt;</description>
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