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	<li><a href="#load-your-knowledge-base" id="toc-load-your-knowledge-base" class="nav-link active" data-scroll-target="#load-your-knowledge-base">Load your knowledge base</a></li>
	<li><a href="#retriever---embeddings" id="toc-retriever---embeddings" class="nav-link" data-scroll-target="#retriever---embeddings">1. Retriever - embeddings 🗂️</a>
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	<li><a href="#split-the-documents-into-chunks" id="toc-split-the-documents-into-chunks" class="nav-link" data-scroll-target="#split-the-documents-into-chunks">1.1 Split the documents into chunks</a></li>
	<li><a href="#building-the-vector-database" id="toc-building-the-vector-database" class="nav-link" data-scroll-target="#building-the-vector-database">1.2 Building the vector database</a></li>
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	<h1 class="title">Advanced RAG</h1>
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	<p>This notebook demonstrates how you can build an advanced RAG (Retrieval Augmented Generation) for answering a user’s question about a specific knowledge base (here, the HuggingFace documentation), using LangChain.</p>
	<p>For an introduction to RAG, you can check <a href="../notebooks/rag_zephyr_langchain.html">this other cookbook</a>!</p>
	<p>RAG systems are complex, with many moving parts: here a RAG diagram, where we noted in blue all possibilities for system enhancement:</p>
	<p><img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_workflow.png" height="700"></p>
	<div class="callout callout-style-default callout-note callout-titled">
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	<p>💡 As you can see, there are many steps to tune in this architecture: tuning the system properly will yield significant performance gains.</p>
	</div>
	</div>
	<p>In this notebook, we will take a look into many of these blue notes to see how to tune your RAG system and get the best performance.</p>
	<p><strong>Let’s dig into the model building!</strong> First, we install the required model dependancies.</p>
	<div id="7d75abe1" class="cell" data-execution_count="1">
	<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>q torch transformers transformers accelerate bitsandbytes langchain sentence<span class="op">-</span>transformers faiss<span class="op">-</span>gpu openpyxl pacmap</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="2a33957c" class="cell" data-execution_count="2">
	<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>reload_ext dotenv</span>
	<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>dotenv</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="d3507708" class="cell" data-execution_count="3">
	<div class="sourceCode cell-code" id="annotated-cell-3"><pre class="sourceCode python code-annotation-code code-with-copy code-annotated"><code class="sourceCode python"><span id="annotated-cell-3-1"><a href="#annotated-cell-3-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tqdm.notebook <span class="im">import</span> tqdm</span>
	<span id="annotated-cell-3-2"><a href="#annotated-cell-3-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
	<span id="annotated-cell-3-3"><a href="#annotated-cell-3-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> typing <span class="im">import</span> Optional, List, Tuple</span>
	<span id="annotated-cell-3-4"><a href="#annotated-cell-3-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datasets <span class="im">import</span> Dataset</span>
	<span id="annotated-cell-3-5"><a href="#annotated-cell-3-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
	<span id="annotated-cell-3-6"><a href="#annotated-cell-3-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="annotated-cell-3-7"><a href="#annotated-cell-3-7" aria-hidden="true" tabindex="-1"></a>pd.set_option(</span>
	<button class="code-annotation-anchor" data-target-cell="annotated-cell-3" data-target-annotation="1">1</button><span id="annotated-cell-3-8" class="code-annotation-target"><a href="#annotated-cell-3-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"display.max_colwidth"</span>, <span class="va">None</span></span>
	<span id="annotated-cell-3-9"><a href="#annotated-cell-3-9" aria-hidden="true" tabindex="-1"></a>) </span><div class="code-annotation-gutter-bg"></div><div class="code-annotation-gutter"></div></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	<div class="cell-annotation">
	<dl class="code-annotation-container-hidden code-annotation-container-grid">
	<dt data-target-cell="annotated-cell-3" data-target-annotation="1">1</dt>
	<dd>
	<span data-code-cell="annotated-cell-3" data-code-lines="8" data-code-annotation="1">This will be helpful when visualizing retriever outputs</span>
	</dd>
	</dl>
	</div>
	</div>
	<section id="load-your-knowledge-base" class="level3">
	<h3 class="anchored" data-anchor-id="load-your-knowledge-base">Load your knowledge base</h3>
	<div id="acfee5b8" class="cell" data-execution_count="4">
	<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> datasets</span>
	<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>ds <span class="op">=</span> datasets.load_dataset(<span class="st">"m-ric/huggingface_doc"</span>, split<span class="op">=</span><span class="st">"train"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="40339fbc" class="cell" data-execution_count="5">
	<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.docstore.document <span class="im">import</span> Document <span class="im">as</span> LangchainDocument</span>
	<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>RAW_KNOWLEDGE_BASE <span class="op">=</span> [</span>
	<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> LangchainDocument(page_content<span class="op">=</span>doc[<span class="st">"text"</span>], metadata<span class="op">=</span>{<span class="st">"source"</span>: doc[<span class="st">"source"</span>]})</span>
	<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> doc <span class="kw">in</span> tqdm(ds)</span>
	<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	</section>
	<section id="retriever---embeddings" class="level1">
	<h1>1. Retriever - embeddings 🗂️</h1>
	<p>The <strong>retriever acts like an internal search engine</strong>: given the user query, it returns a few relevant snippets from your knowledge base.</p>
	<p>These snippets will then be fed to the Reader Model to help it generate its answer.</p>
	<p>So <strong>our objective here is, given a user question, to find the most snippets from our knowledge base to answer that question.</strong></p>
	<p>This is a wide objective, it leaves open some questions. How many snippets should we retrieve? This parameter will be named <code>top_k</code>.</p>
	<p>How long should these snippets be? This is called the <code>chunk size</code>. There’s no one-size-fits-all answers, but here are a few elements: - 🔀 Your <code>chunk size</code> is allowed to vary from one snippet to the other. - Since there will always be some noise in your retrieval, increasing the <code>top_k</code> increases the chance to get relevant elements in your retrieved snippets. 🎯 Shooting more arrows increases your probability to hit your target. - Meanwhile, the summed length of your retrieved documents should not be too high: for instance, for most current models 16k tokens will probably drown your Reader model in information due to <a href="https://huggingface.co/papers/2307.03172">Lost-in-the-middle phenomenon</a>. 🎯 Give your reader model only the most relevant insights, not a huge pile of books!</p>
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	</div>
	<div class="callout-body-container callout-body">
	<p>In this notebook, we use Langchain library since <strong>it offers a huge variety of options for vector databases and allows us to keep document metadata throughout the processing</strong>.</p>
	</div>
	</div>
	<section id="split-the-documents-into-chunks" class="level3">
	<h3 class="anchored" data-anchor-id="split-the-documents-into-chunks">1.1 Split the documents into chunks</h3>
	<ul>
	<li>In this part, <strong>we split the documents from our knowledge base into smaller chunks</strong> which will be the snippets on which the reader LLM will base its answer.</li>
	<li>The goal is to prepare a collection of <strong>semantically relevant snippets</strong>. So their size should be adapted to precise ideas: too small will truncate ideas, too large will dilute them.</li>
	</ul>
	<div class="callout callout-style-default callout-tip callout-titled">
	<div class="callout-header d-flex align-content-center">
	<div class="callout-icon-container">
	<i class="callout-icon"></i>
	</div>
	<div class="callout-title-container flex-fill">
	Tip
	</div>
	</div>
	<div class="callout-body-container callout-body">
	<p>💡 Many options exist for text splitting: splitting on words, on sentence boundaries, recursive chunking that processes documents in a tree-like way to preserve structure information… To learn more about chunking, I recommend you read <a href="https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb">this great notebook</a> by Greg Kamradt.</p>
	</div>
	</div>
	<ul>
	<li><strong>Recursive chunking</strong> breaks down the text into smaller parts step by step using a given list of separators sorted from the most important to the least important separator. If the first split doesn’t give the right size or shape chunks, the method repeats itself on the new chunks using a different separator. For instance with the list of separators <code>["\n\n", "\n", ".", ""]</code>:
	<ul>
	<li>The method will first break down the document wherever there is a double line break <code>"\n\n"</code>.</li>
	<li>Resulting documents will be split again on simple line breaks <code>"\n"</code>, then on sentence ends <code>"."</code>.</li>
	<li>And finally, if some chunks are still too big, they will be split whenever they overflow the maximum size.</li>
	</ul></li>
	<li>With this method, the global structure is well preserved, at the expense of getting slight variations in chunk size.</li>
	</ul>
	<blockquote class="blockquote">
	<p><a href="https://huggingface.co/spaces/A-Roucher/chunk_visualizer">This space</a> lets you visualize how different splitting options affect the chunks you get.</p>
	</blockquote>
	<p>🔬 Let’s experiment a bit with chunk sizes, beginning with an arbitrary size, and see how splits work. We use Langchain’s implementation of recursive chunking with <code>RecursiveCharacterTextSplitter</code>. - Parameter <code>chunk_size</code> controls the length of individual chunks: this length is counted by default as the number of characters in the chunk. - Parameter <code>chunk_overlap</code> lets adjacent chunks get a bit of overlap on each other. This reduces the probability that an idea could be cut in half by the split between two adjacent chunks. We ~arbitrarily set this to 1/10th of the chunk size, you could try different values!</p>
	<div id="7a7ac95d" class="cell" data-execution_count="6">
	<div class="sourceCode cell-code" id="annotated-cell-6"><pre class="sourceCode python code-annotation-code code-with-copy code-annotated"><code class="sourceCode python"><span id="annotated-cell-6-1"><a href="#annotated-cell-6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.text_splitter <span class="im">import</span> RecursiveCharacterTextSplitter</span>
	<span id="annotated-cell-6-2"><a href="#annotated-cell-6-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="annotated-cell-6-3"><a href="#annotated-cell-6-3" aria-hidden="true" tabindex="-1"></a><span class="co"># We use a hierarchical list of separators specifically tailored for splitting Markdown documents</span></span>
	<span id="annotated-cell-6-4"><a href="#annotated-cell-6-4" aria-hidden="true" tabindex="-1"></a><span class="co"># This list is taken from LangChain's MarkdownTextSplitter class.</span></span>
	<span id="annotated-cell-6-5"><a href="#annotated-cell-6-5" aria-hidden="true" tabindex="-1"></a>MARKDOWN_SEPARATORS <span class="op">=</span> [</span>
	<span id="annotated-cell-6-6"><a href="#annotated-cell-6-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n</span><span class="st">#{1,6} "</span>,</span>
	<span id="annotated-cell-6-7"><a href="#annotated-cell-6-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"```</span><span class="ch">\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-8"><a href="#annotated-cell-6-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n\\</span><span class="st"></span><span class="ch">\\</span><span class="st"></span><span class="ch">\\</span><span class="st">*+</span><span class="ch">\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-9"><a href="#annotated-cell-6-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n</span><span class="st">---+</span><span class="ch">\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-10"><a href="#annotated-cell-6-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n</span><span class="st">___+</span><span class="ch">\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-11"><a href="#annotated-cell-6-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-12"><a href="#annotated-cell-6-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"</span><span class="ch">\n</span><span class="st">"</span>,</span>
	<span id="annotated-cell-6-13"><a href="#annotated-cell-6-13" aria-hidden="true" tabindex="-1"></a> <span class="st">" "</span>,</span>
	<span id="annotated-cell-6-14"><a href="#annotated-cell-6-14" aria-hidden="true" tabindex="-1"></a> <span class="st">""</span>,</span>
	<span id="annotated-cell-6-15"><a href="#annotated-cell-6-15" aria-hidden="true" tabindex="-1"></a>]</span>
	<span id="annotated-cell-6-16"><a href="#annotated-cell-6-16" aria-hidden="true" tabindex="-1"></a></span>
	<span id="annotated-cell-6-17"><a href="#annotated-cell-6-17" aria-hidden="true" tabindex="-1"></a>text_splitter <span class="op">=</span> RecursiveCharacterTextSplitter(</span>
	<button class="code-annotation-anchor" data-target-cell="annotated-cell-6" data-target-annotation="1">1</button><span id="annotated-cell-6-18" class="code-annotation-target"><a href="#annotated-cell-6-18" aria-hidden="true" tabindex="-1"></a> chunk_size<span class="op">=</span><span class="dv">1000</span>,</span>
	<button class="code-annotation-anchor" data-target-cell="annotated-cell-6" data-target-annotation="2">2</button><span id="annotated-cell-6-19" class="code-annotation-target"><a href="#annotated-cell-6-19" aria-hidden="true" tabindex="-1"></a> chunk_overlap<span class="op">=</span><span class="dv">100</span>,</span>
	<button class="code-annotation-anchor" data-target-cell="annotated-cell-6" data-target-annotation="3">3</button><span id="annotated-cell-6-20" class="code-annotation-target"><a href="#annotated-cell-6-20" aria-hidden="true" tabindex="-1"></a> add_start_index<span class="op">=</span><span class="va">True</span>,</span>
	<button class="code-annotation-anchor" data-target-cell="annotated-cell-6" data-target-annotation="4">4</button><span id="annotated-cell-6-21" class="code-annotation-target"><a href="#annotated-cell-6-21" aria-hidden="true" tabindex="-1"></a> strip_whitespace<span class="op">=</span><span class="va">True</span>,</span>
	<span id="annotated-cell-6-22"><a href="#annotated-cell-6-22" aria-hidden="true" tabindex="-1"></a> separators<span class="op">=</span>MARKDOWN_SEPARATORS,</span>
	<span id="annotated-cell-6-23"><a href="#annotated-cell-6-23" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="annotated-cell-6-24"><a href="#annotated-cell-6-24" aria-hidden="true" tabindex="-1"></a></span>
	<span id="annotated-cell-6-25"><a href="#annotated-cell-6-25" aria-hidden="true" tabindex="-1"></a>docs_processed <span class="op">=</span> []</span>
	<span id="annotated-cell-6-26"><a href="#annotated-cell-6-26" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> doc <span class="kw">in</span> RAW_KNOWLEDGE_BASE:</span>
	<span id="annotated-cell-6-27"><a href="#annotated-cell-6-27" aria-hidden="true" tabindex="-1"></a> docs_processed <span class="op">+=</span> text_splitter.split_documents([doc])</span><div class="code-annotation-gutter-bg"></div><div class="code-annotation-gutter"></div></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	<div class="cell-annotation">
	<dl class="code-annotation-container-hidden code-annotation-container-grid">
	<dt data-target-cell="annotated-cell-6" data-target-annotation="1">1</dt>
	<dd>
	<span data-code-cell="annotated-cell-6" data-code-lines="18" data-code-annotation="1">The maximum number of characters in a chunk: we selected this value arbitrally</span>
	</dd>
	<dt data-target-cell="annotated-cell-6" data-target-annotation="2">2</dt>
	<dd>
	<span data-code-cell="annotated-cell-6" data-code-lines="19" data-code-annotation="2">The number of characters to overlap between chunks</span>
	</dd>
	<dt data-target-cell="annotated-cell-6" data-target-annotation="3">3</dt>
	<dd>
	<span data-code-cell="annotated-cell-6" data-code-lines="20" data-code-annotation="3">If <code>True</code>, includes chunk’s start index in metadata</span>
	</dd>
	<dt data-target-cell="annotated-cell-6" data-target-annotation="4">4</dt>
	<dd>
	<span data-code-cell="annotated-cell-6" data-code-lines="21" data-code-annotation="4">If <code>True</code>, strips whitespace from the start and end of every document</span>
	</dd>
	</dl>
	</div>
	</div>
	<p>We also have to keep in mind that when embedding documents, we will use an embedding model that has accepts a certain maximum sequence length <code>max_seq_length</code>.</p>
	<p>So we should make sure that our chunk sizes are below this limit, because any longer chunk will be truncated before processing, thus losing relevancy.</p>
	<div id="174a0a05" class="cell" data-execution_count="7">
	<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sentence_transformers <span class="im">import</span> SentenceTransformer</span>
	<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co"># To get the value of the max sequence_length, we will query the underlying `SentenceTransformer` object used in the RecursiveCharacterTextSplitter.</span></span>
	<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(</span>
	<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="ss">f"Model's maximum sequence length: </span><span class="sc">{</span>SentenceTransformer(<span class="st">'thenlper/gte-small'</span>)<span class="sc">.</span>max_seq_length<span class="sc">}</span><span class="ss">"</span></span>
	<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> AutoTokenizer</span>
	<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>tokenizer <span class="op">=</span> AutoTokenizer.from_pretrained(<span class="st">"thenlper/gte-small"</span>)</span>
	<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>lengths <span class="op">=</span> [<span class="bu">len</span>(tokenizer.encode(doc.page_content)) <span class="cf">for</span> doc <span class="kw">in</span> tqdm(docs_processed)]</span>
	<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the distrubution of document lengths, counted as the number of tokens</span></span>
	<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> pd.Series(lengths).hist()</span>
	<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of document lengths in the knowledge base (in count of tokens)"</span>)</span>
	<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>👀 As you can see, <strong>the chunk lengths are not aligned with our limit of 512 tokens</strong>, and some documents are above the limit, thus some part of them will be lost in truncation! - So we should change the <code>RecursiveCharacterTextSplitter</code> class to count length in number of tokens instead of number of characters. - Then we can choose a specific chunk size, here we would choose a lower threshold than 512: - smaller documents could allow the split to focus more on specific ideas. - But too small chunks would split sentences in half, thus losing meaning again: the proper tuning is a matter of balance.</p>
	<div id="2fa8715e" class="cell" data-execution_count="8">
	<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.text_splitter <span class="im">import</span> RecursiveCharacterTextSplitter</span>
	<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> AutoTokenizer</span>
	<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>EMBEDDING_MODEL_NAME <span class="op">=</span> <span class="st">"thenlper/gte-small"</span></span>
	<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> split_documents(</span>
	<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> chunk_size: <span class="bu">int</span>,</span>
	<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> knowledge_base: List[LangchainDocument],</span>
	<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a> tokenizer_name: Optional[<span class="bu">str</span>] <span class="op">=</span> EMBEDDING_MODEL_NAME,</span>
	<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>) <span class="op">-></span> List[LangchainDocument]:</span>
	<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a> <span class="co">"""</span></span>
	<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a><span class="co"> Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.</span></span>
	<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a><span class="co"> """</span></span>
	<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a> text_splitter <span class="op">=</span> RecursiveCharacterTextSplitter.from_huggingface_tokenizer(</span>
	<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a> AutoTokenizer.from_pretrained(tokenizer_name),</span>
	<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a> chunk_size<span class="op">=</span>chunk_size,</span>
	<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a> chunk_overlap<span class="op">=</span><span class="bu">int</span>(chunk_size <span class="op">/</span> <span class="dv">10</span>),</span>
	<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a> add_start_index<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a> strip_whitespace<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a> separators<span class="op">=</span>MARKDOWN_SEPARATORS,</span>
	<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a> )</span>
	<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a> docs_processed <span class="op">=</span> []</span>
	<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> doc <span class="kw">in</span> knowledge_base:</span>
	<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a> docs_processed <span class="op">+=</span> text_splitter.split_documents([doc])</span>
	<span id="cb6-27"><a href="#cb6-27" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a> <span class="co"># Remove duplicates</span></span>
	<span id="cb6-29"><a href="#cb6-29" aria-hidden="true" tabindex="-1"></a> unique_texts <span class="op">=</span> {}</span>
	<span id="cb6-30"><a href="#cb6-30" aria-hidden="true" tabindex="-1"></a> docs_processed_unique <span class="op">=</span> []</span>
	<span id="cb6-31"><a href="#cb6-31" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> doc <span class="kw">in</span> docs_processed:</span>
	<span id="cb6-32"><a href="#cb6-32" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> doc.page_content <span class="kw">not</span> <span class="kw">in</span> unique_texts:</span>
	<span id="cb6-33"><a href="#cb6-33" aria-hidden="true" tabindex="-1"></a> unique_texts[doc.page_content] <span class="op">=</span> <span class="va">True</span></span>
	<span id="cb6-34"><a href="#cb6-34" aria-hidden="true" tabindex="-1"></a> docs_processed_unique.append(doc)</span>
	<span id="cb6-35"><a href="#cb6-35" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-36"><a href="#cb6-36" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> docs_processed_unique</span>
	<span id="cb6-37"><a href="#cb6-37" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-38"><a href="#cb6-38" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-39"><a href="#cb6-39" aria-hidden="true" tabindex="-1"></a>docs_processed <span class="op">=</span> split_documents(</span>
	<span id="cb6-40"><a href="#cb6-40" aria-hidden="true" tabindex="-1"></a> <span class="dv">512</span>, <span class="co"># We choose a chunk size adapted to our model</span></span>
	<span id="cb6-41"><a href="#cb6-41" aria-hidden="true" tabindex="-1"></a> RAW_KNOWLEDGE_BASE,</span>
	<span id="cb6-42"><a href="#cb6-42" aria-hidden="true" tabindex="-1"></a> tokenizer_name<span class="op">=</span>EMBEDDING_MODEL_NAME,</span>
	<span id="cb6-43"><a href="#cb6-43" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb6-44"><a href="#cb6-44" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-45"><a href="#cb6-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's visualize the chunk sizes we would have in tokens from a common model</span></span>
	<span id="cb6-46"><a href="#cb6-46" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> AutoTokenizer</span>
	<span id="cb6-47"><a href="#cb6-47" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb6-48"><a href="#cb6-48" aria-hidden="true" tabindex="-1"></a>tokenizer <span class="op">=</span> AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)</span>
	<span id="cb6-49"><a href="#cb6-49" aria-hidden="true" tabindex="-1"></a>lengths <span class="op">=</span> [<span class="bu">len</span>(tokenizer.encode(doc.page_content)) <span class="cf">for</span> doc <span class="kw">in</span> tqdm(docs_processed)]</span>
	<span id="cb6-50"><a href="#cb6-50" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> pd.Series(lengths).hist()</span>
	<span id="cb6-51"><a href="#cb6-51" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of document lengths in the knowledge base (in count of tokens)"</span>)</span>
	<span id="cb6-52"><a href="#cb6-52" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>➡️ Now the chunk length distribution looks better!</p>
	</section>
	<section id="building-the-vector-database" class="level3">
	<h3 class="anchored" data-anchor-id="building-the-vector-database">1.2 Building the vector database</h3>
	<p>We want to compute the embeddings for all the chunks of our knowledge base: to learn more on sentence embeddings, we recommend reading <a href="https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/">this guide</a>.</p>
	<section id="how-does-retrieval-work" class="level4">
	<h4 class="anchored" data-anchor-id="how-does-retrieval-work">How does retrieval work ?</h4>
	<p>Once the chunks are all embedded, we store them into a vector database. When the user types in a query, it gets embedded by the same model previously used, and a similarity search returns the closest documents from the vector database.</p>
	<p>The technical challenge is thus, given a query vector, to quickly find the nearest neighbours of this vector in the vector database. To do this, we need to choose two things: a distance, and a search algorithm to find the nearest neighbors quickly within a database of thousands of records.</p>
	<section id="nearest-neighbor-search-algorithm" class="level5">
	<h5 class="anchored" data-anchor-id="nearest-neighbor-search-algorithm">Nearest Neighbor search algorithm</h5>
	<p>There are plentiful choices for the nearest neighbor search algorithm: we go with Facebook’s <a href="https://github.com/facebookresearch/faiss">FAISS</a>, since FAISS is performant enough for most use cases, and it is well known thus widely implemented.</p>
	</section>
	<section id="distances" class="level5">
	<h5 class="anchored" data-anchor-id="distances">Distances</h5>
	<p>Regarding distances, you can find a good guide <a href="https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/#distance-between-embeddings">here</a>. In short:</p>
	<ul>
	<li><strong>Cosine similarity</strong> computes similarity between two vectors as the cosinus of their relative angle: it allows us to compare vector directions are regardless of their magnitude. Using it requires to normalize all vectors, to rescale them into unit norm.</li>
	<li><strong>Dot product</strong> takes into account magnitude, with the sometimes undesirable effect that increasing a vector’s length will make it more similar to all others.</li>
	<li><strong>Euclidean distance</strong> is the distance between the ends of vectors.</li>
	</ul>
	<p>You can try <a href="https://developers.google.com/machine-learning/clustering/similarity/check-your-understanding">this small exercise</a> to check your understanding of these concepts. But once vectors are normalized, <a href="https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use">the choice of a specific distance does not matter much</a>.</p>
	<p>Our particular model works well with cosine similarity, so choose this distance, and we set it up both in the Embedding model, and in the <code>distance_strategy</code> argument of our FAISS index. With cosine similarity, we have to normalize our embeddings.</p>
	<div class="callout callout-style-default callout-warning callout-titled">
	<div class="callout-header d-flex align-content-center">
	<div class="callout-icon-container">
	<i class="callout-icon"></i>
	</div>
	<div class="callout-title-container flex-fill">
	Warning
	</div>
	</div>
	<div class="callout-body-container callout-body">
	<p>🚨👇 The cell below takes a few minutes to run on A10G!</p>
	</div>
	</div>
	<div id="938830be" class="cell" data-execution_count="9">
	<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.vectorstores <span class="im">import</span> FAISS</span>
	<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain_community.embeddings <span class="im">import</span> HuggingFaceEmbeddings</span>
	<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain_community.vectorstores.utils <span class="im">import</span> DistanceStrategy</span>
	<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>embedding_model <span class="op">=</span> HuggingFaceEmbeddings(</span>
	<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> model_name<span class="op">=</span>EMBEDDING_MODEL_NAME,</span>
	<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> multi_process<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> model_kwargs<span class="op">=</span>{<span class="st">"device"</span>: <span class="st">"cuda"</span>},</span>
	<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> encode_kwargs<span class="op">=</span>{<span class="st">"normalize_embeddings"</span>: <span class="va">True</span>}, <span class="co"># set True for cosine similarity</span></span>
	<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>KNOWLEDGE_VECTOR_DATABASE <span class="op">=</span> FAISS.from_documents(</span>
	<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> docs_processed, embedding_model, distance_strategy<span class="op">=</span>DistanceStrategy.COSINE</span>
	<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>👀 To visualize the search for the closest documents, let’s project our embeddings from 384 dimensions down to 2 dimensions using PaCMAP.</p>
	<div class="callout callout-style-default callout-note callout-titled">
	<div class="callout-header d-flex align-content-center">
	<div class="callout-icon-container">
	<i class="callout-icon"></i>
	</div>
	<div class="callout-title-container flex-fill">
	Note
	</div>
	</div>
	<div class="callout-body-container callout-body">
	<p>💡 We chose PaCMAP rather than other techniques such as t-SNE or UMAP, since <a href="https://www.nature.com/articles/s42003-022-03628-x#Abs1">it is efficient (preserves local and global structure), robust to initialization parameters and fast</a>.</p>
	</div>
	</div>
	<div id="5ed5a830" class="cell" data-execution_count="10">
	<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># embed a user query in the same space</span></span>
	<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>user_query <span class="op">=</span> <span class="st">"How to create a pipeline object?"</span></span>
	<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>query_vector <span class="op">=</span> embedding_model.embed_query(user_query)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="206f15a0" class="cell" data-execution_count="11">
	<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pacmap</span>
	<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
	<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
	<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>embedding_projector <span class="op">=</span> pacmap.PaCMAP(</span>
	<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> n_components<span class="op">=</span><span class="dv">2</span>, n_neighbors<span class="op">=</span><span class="va">None</span>, MN_ratio<span class="op">=</span><span class="fl">0.5</span>, FP_ratio<span class="op">=</span><span class="fl">2.0</span>, random_state<span class="op">=</span><span class="dv">1</span></span>
	<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>embeddings_2d <span class="op">=</span> [</span>
	<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a> <span class="bu">list</span>(KNOWLEDGE_VECTOR_DATABASE.index.reconstruct_n(idx, <span class="dv">1</span>)[<span class="dv">0</span>])</span>
	<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> idx <span class="kw">in</span> <span class="bu">range</span>(<span class="bu">len</span>(docs_processed))</span>
	<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>] <span class="op">+</span> [query_vector]</span>
	<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a><span class="co"># fit the data (The index of transformed data corresponds to the index of the original data)</span></span>
	<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>documents_projected <span class="op">=</span> embedding_projector.fit_transform(np.array(embeddings_2d), init<span class="op">=</span><span class="st">"pca"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="249c7a6b" class="cell" data-execution_count="12">
	<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame.from_dict(</span>
	<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a> [</span>
	<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a> {</span>
	<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"x"</span>: documents_projected[i, <span class="dv">0</span>],</span>
	<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"y"</span>: documents_projected[i, <span class="dv">1</span>],</span>
	<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"source"</span>: docs_processed[i].metadata[<span class="st">"source"</span>].split(<span class="st">"/"</span>)[<span class="dv">1</span>],</span>
	<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"extract"</span>: docs_processed[i].page_content[:<span class="dv">100</span>] <span class="op">+</span> <span class="st">"..."</span>,</span>
	<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"symbol"</span>: <span class="st">"circle"</span>,</span>
	<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"size_col"</span>: <span class="dv">4</span>,</span>
	<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a> }</span>
	<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="bu">len</span>(docs_processed))</span>
	<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a> ]</span>
	<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a> <span class="op">+</span> [</span>
	<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a> {</span>
	<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a> <span class="st">"x"</span>: documents_projected[<span class="op">-</span><span class="dv">1</span>, <span class="dv">0</span>],</span>
	<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"y"</span>: documents_projected[<span class="op">-</span><span class="dv">1</span>, <span class="dv">1</span>],</span>
	<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a> <span class="st">"source"</span>: <span class="st">"User query"</span>,</span>
	<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a> <span class="st">"extract"</span>: user_query,</span>
	<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a> <span class="st">"size_col"</span>: <span class="dv">100</span>,</span>
	<span id="cb10-20"><a href="#cb10-20" aria-hidden="true" tabindex="-1"></a> <span class="st">"symbol"</span>: <span class="st">"star"</span>,</span>
	<span id="cb10-21"><a href="#cb10-21" aria-hidden="true" tabindex="-1"></a> }</span>
	<span id="cb10-22"><a href="#cb10-22" aria-hidden="true" tabindex="-1"></a> ]</span>
	<span id="cb10-23"><a href="#cb10-23" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb10-24"><a href="#cb10-24" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb10-25"><a href="#cb10-25" aria-hidden="true" tabindex="-1"></a><span class="co"># visualize the embedding</span></span>
	<span id="cb10-26"><a href="#cb10-26" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> px.scatter(</span>
	<span id="cb10-27"><a href="#cb10-27" aria-hidden="true" tabindex="-1"></a> df,</span>
	<span id="cb10-28"><a href="#cb10-28" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">"x"</span>,</span>
	<span id="cb10-29"><a href="#cb10-29" aria-hidden="true" tabindex="-1"></a> y<span class="op">=</span><span class="st">"y"</span>,</span>
	<span id="cb10-30"><a href="#cb10-30" aria-hidden="true" tabindex="-1"></a> color<span class="op">=</span><span class="st">"source"</span>,</span>
	<span id="cb10-31"><a href="#cb10-31" aria-hidden="true" tabindex="-1"></a> hover_data<span class="op">=</span><span class="st">"extract"</span>,</span>
	<span id="cb10-32"><a href="#cb10-32" aria-hidden="true" tabindex="-1"></a> size<span class="op">=</span><span class="st">"size_col"</span>,</span>
	<span id="cb10-33"><a href="#cb10-33" aria-hidden="true" tabindex="-1"></a> symbol<span class="op">=</span><span class="st">"symbol"</span>,</span>
	<span id="cb10-34"><a href="#cb10-34" aria-hidden="true" tabindex="-1"></a> color_discrete_map<span class="op">=</span>{<span class="st">"User query"</span>: <span class="st">"black"</span>},</span>
	<span id="cb10-35"><a href="#cb10-35" aria-hidden="true" tabindex="-1"></a> width<span class="op">=</span><span class="dv">1000</span>,</span>
	<span id="cb10-36"><a href="#cb10-36" aria-hidden="true" tabindex="-1"></a> height<span class="op">=</span><span class="dv">700</span>,</span>
	<span id="cb10-37"><a href="#cb10-37" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb10-38"><a href="#cb10-38" aria-hidden="true" tabindex="-1"></a>fig.update_traces(</span>
	<span id="cb10-39"><a href="#cb10-39" aria-hidden="true" tabindex="-1"></a> marker<span class="op">=</span><span class="bu">dict</span>(opacity<span class="op">=</span><span class="dv">1</span>, line<span class="op">=</span><span class="bu">dict</span>(width<span class="op">=</span><span class="dv">0</span>, color<span class="op">=</span><span class="st">"DarkSlateGrey"</span>)), selector<span class="op">=</span><span class="bu">dict</span>(mode<span class="op">=</span><span class="st">"markers"</span>)</span>
	<span id="cb10-40"><a href="#cb10-40" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb10-41"><a href="#cb10-41" aria-hidden="true" tabindex="-1"></a>fig.update_layout(</span>
	<span id="cb10-42"><a href="#cb10-42" aria-hidden="true" tabindex="-1"></a> legend_title_text<span class="op">=</span><span class="st">"<b>Chunk source</b>"</span>,</span>
	<span id="cb10-43"><a href="#cb10-43" aria-hidden="true" tabindex="-1"></a> title<span class="op">=</span><span class="st">"<b>2D Projection of Chunk Embeddings via PaCMAP</b>"</span>,</span>
	<span id="cb10-44"><a href="#cb10-44" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb10-45"><a href="#cb10-45" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p><img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/PaCMAP_embeddings.png" height="700"></p>
	<p>➡️ On the graph above, you can see a spatial representation of the kowledge base documents. As the vector embeddings represent the document’s meaning, their closeness in meaning should be reflected in their embedding’s closeness.</p>
	<p>The user query’s embedding is also shown : we want to find the <code>k</code> document that have the closest meaning, thus we pick the <code>k</code> closest vectors.</p>
	<p>In the LangChain vector database implementation, this search operation is performed by the method <code>vector_database.similarity_search(query)</code>.</p>
	<p>Here is the result:</p>
	<div id="47319e72" class="cell" data-execution_count="13">
	<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"</span><span class="ch">\n</span><span class="ss">Starting retrieval for </span><span class="sc">{</span>user_query<span class="op">=</span><span class="sc">}</span><span class="ss">..."</span>)</span>
	<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>retrieved_docs <span class="op">=</span> KNOWLEDGE_VECTOR_DATABASE.similarity_search(query<span class="op">=</span>user_query, k<span class="op">=</span><span class="dv">5</span>)</span>
	<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">==================================Top document=================================="</span>)</span>
	<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(retrieved_docs[<span class="dv">0</span>].page_content)</span>
	<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"==================================Metadata=================================="</span>)</span>
	<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(retrieved_docs[<span class="dv">0</span>].metadata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	</section>
	</section>
	</section>
	</section>
	<section id="reader---llm" class="level1">
	<h1>2. Reader - LLM 💬</h1>
	<p>In this part, the <strong>LLM Reader reads the retrieved context to formulate its answer.</strong></p>
	<p>There are actually substeps that can all be tuned: 1. The content of the retrieved documents is aggregated together into the “context”, with many processing options like <em>prompt compression</em>. 2. The context and the user query are aggregated into a prompt then given to the LLM to generate its answer.</p>
	<section id="reader-model" class="level3">
	<h3 class="anchored" data-anchor-id="reader-model">2.1. Reader model</h3>
	<p>The choice of a reader model is important on a few aspects: - the reader model’s <code>max_seq_length</code> must accomodate our prompt, which includes the context output by the retriever call: the context consists in 5 documents of 512 tokens each, so we aim for a context length of 4k tokens at least. - the reader model</p>
	<p>For this example, we chose <a href="https://huggingface.co/HuggingFaceH4/zephyr-7b-beta"><code>HuggingFaceH4/zephyr-7b-beta</code></a>, a small but powerful model.</p>
	<div class="callout callout-style-default callout-note callout-titled">
	<div class="callout-header d-flex align-content-center">
	<div class="callout-icon-container">
	<i class="callout-icon"></i>
	</div>
	<div class="callout-title-container flex-fill">
	Note
	</div>
	</div>
	<div class="callout-body-container callout-body">
	<p>With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the <a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard">Open-source LLM leaderboard</a>.</p>
	</div>
	</div>
	<p>To make inference faster, we will load the quantized version of the model:</p>
	<div id="eaf2beef" class="cell" data-execution_count="14">
	<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> pipeline</span>
	<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> torch</span>
	<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig</span>
	<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>READER_MODEL_NAME <span class="op">=</span> <span class="st">"HuggingFaceH4/zephyr-7b-beta"</span></span>
	<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>bnb_config <span class="op">=</span> BitsAndBytesConfig(</span>
	<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> load_in_4bit<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> bnb_4bit_use_double_quant<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> bnb_4bit_quant_type<span class="op">=</span><span class="st">"nf4"</span>,</span>
	<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a> bnb_4bit_compute_dtype<span class="op">=</span>torch.bfloat16,</span>
	<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config<span class="op">=</span>bnb_config)</span>
	<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>tokenizer <span class="op">=</span> AutoTokenizer.from_pretrained(READER_MODEL_NAME)</span>
	<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>READER_LLM <span class="op">=</span> pipeline(</span>
	<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a> model<span class="op">=</span>model,</span>
	<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a> tokenizer<span class="op">=</span>tokenizer,</span>
	<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a> task<span class="op">=</span><span class="st">"text-generation"</span>,</span>
	<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a> do_sample<span class="op">=</span><span class="va">True</span>,</span>
	<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a> temperature<span class="op">=</span><span class="fl">0.2</span>,</span>
	<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a> repetition_penalty<span class="op">=</span><span class="fl">1.1</span>,</span>
	<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a> return_full_text<span class="op">=</span><span class="va">False</span>,</span>
	<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a> max_new_tokens<span class="op">=</span><span class="dv">500</span>,</span>
	<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="f6f72fd3" class="cell" data-execution_count="15">
	<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>READER_LLM(<span class="st">"What is 4+4? Answer:"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	</section>
	<section id="prompt" class="level3">
	<h3 class="anchored" data-anchor-id="prompt">2.2. Prompt</h3>
	<p>The RAG prompt template below is what we will feed to the Reader LLM: it is important to have it formatted in the Reader LLM’s chat template.</p>
	<p>We give it our context and the user’s question.</p>
	<div id="e28bd1d6" class="cell" data-execution_count="16">
	<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>prompt_in_chat_format <span class="op">=</span> [</span>
	<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> {</span>
	<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"role"</span>: <span class="st">"system"</span>,</span>
	<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> <span class="st">"content"</span>: <span class="st">"""Using the information contained in the context,</span></span>
	<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="st">give a comprehensive answer to the question.</span></span>
	<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="st">Respond only to the question asked, response should be concise and relevant to the question.</span></span>
	<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="st">Provide the number of the source document when relevant.</span></span>
	<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a><span class="st">If the answer cannot be deduced from the context, do not give an answer."""</span>,</span>
	<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> },</span>
	<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> {</span>
	<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"role"</span>: <span class="st">"user"</span>,</span>
	<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"content"</span>: <span class="st">"""Context:</span></span>
	<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a><span class="sc">{context}</span></span>
	<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a><span class="st">---</span></span>
	<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a><span class="st">Now here is the question you need to answer.</span></span>
	<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a><span class="st">Question: </span><span class="sc">{question}</span><span class="st">"""</span>,</span>
	<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a> },</span>
	<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a>]</span>
	<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a>RAG_PROMPT_TEMPLATE <span class="op">=</span> tokenizer.apply_chat_template(</span>
	<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a> prompt_in_chat_format, tokenize<span class="op">=</span><span class="va">False</span>, add_generation_prompt<span class="op">=</span><span class="va">True</span></span>
	<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(RAG_PROMPT_TEMPLATE)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>Let’s test our Reader on our previously retrieved documents!</p>
	<div id="96c062d3" class="cell" data-execution_count="17">
	<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>retrieved_docs_text <span class="op">=</span> [</span>
	<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> doc.page_content <span class="cf">for</span> doc <span class="kw">in</span> retrieved_docs</span>
	<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>] <span class="co"># we only need the text of the documents</span></span>
	<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>context <span class="op">=</span> <span class="st">"</span><span class="ch">\n</span><span class="st">Extracted documents:</span><span class="ch">\n</span><span class="st">"</span></span>
	<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>context <span class="op">+=</span> <span class="st">""</span>.join([<span class="ss">f"Document </span><span class="sc">{</span><span class="bu">str</span>(i)<span class="sc">}</span><span class="ss">:::</span><span class="ch">\n</span><span class="ss">"</span> <span class="op">+</span> doc <span class="cf">for</span> i, doc <span class="kw">in</span> <span class="bu">enumerate</span>(retrieved_docs_text)])</span>
	<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>final_prompt <span class="op">=</span> RAG_PROMPT_TEMPLATE.<span class="bu">format</span>(</span>
	<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a> question<span class="op">=</span><span class="st">"How to create a pipeline object?"</span>, context<span class="op">=</span>context</span>
	<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>)</span>
	<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Redact an answer</span></span>
	<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>answer <span class="op">=</span> READER_LLM(final_prompt)[<span class="dv">0</span>][<span class="st">"generated_text"</span>]</span>
	<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(answer)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	</section>
	<section id="reranking" class="level3">
	<h3 class="anchored" data-anchor-id="reranking">2.3. Reranking</h3>
	<p>A good option for RAG is to retrieve more documents than you want in the end, then rerank the results with a more powerful retrieval model before keeping only the <code>top_k</code>.</p>
	<p>For this, <a href="https://arxiv.org/abs/2112.01488">Colbertv2</a> is a great choice: instead of a bi-encoder like our classical embedding models, it is a cross-encoder that computes more fine-grained interactions between the query tokens and each document’s tokens.</p>
	<p>It is easily usable thanks to <a href="https://github.com/bclavie/RAGatouille">the RAGatouille library</a>.</p>
	<div id="c0298337" class="cell" data-execution_count="18">
	<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> ragatouille <span class="im">import</span> RAGPretrainedModel</span>
	<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>RERANKER <span class="op">=</span> RAGPretrainedModel.from_pretrained(<span class="st">"colbert-ir/colbertv2.0"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	</section>
	</section>
	<section id="assembling-it-all" class="level1">
	<h1>3. Assembling it all!</h1>
	<div id="f7d2b29a" class="cell" data-execution_count="19">
	<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> Pipeline</span>
	<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> answer_with_rag(</span>
	<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> question: <span class="bu">str</span>,</span>
	<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> llm: Pipeline,</span>
	<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> knowledge_index: FAISS,</span>
	<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> reranker: Optional[RAGPretrainedModel] <span class="op">=</span> <span class="va">None</span>,</span>
	<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a> num_retrieved_docs: <span class="bu">int</span> <span class="op">=</span> <span class="dv">30</span>,</span>
	<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a> num_docs_final: <span class="bu">int</span> <span class="op">=</span> <span class="dv">5</span>,</span>
	<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>) <span class="op">-></span> Tuple[<span class="bu">str</span>, List[LangchainDocument]]:</span>
	<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a> <span class="co"># Gather documents with retriever</span></span>
	<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="st">"=> Retrieving documents..."</span>)</span>
	<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a> relevant_docs <span class="op">=</span> knowledge_index.similarity_search(query<span class="op">=</span>question, k<span class="op">=</span>num_retrieved_docs)</span>
	<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a> relevant_docs <span class="op">=</span> [doc.page_content <span class="cf">for</span> doc <span class="kw">in</span> relevant_docs] <span class="co"># keep only the text</span></span>
	<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a> <span class="co"># Optionally rerank results</span></span>
	<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> reranker:</span>
	<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="st">"=> Reranking documents..."</span>)</span>
	<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a> relevant_docs <span class="op">=</span> reranker.rerank(question, relevant_docs, k<span class="op">=</span>num_docs_final)</span>
	<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a> relevant_docs <span class="op">=</span> [doc[<span class="st">"content"</span>] <span class="cf">for</span> doc <span class="kw">in</span> relevant_docs]</span>
	<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a> relevant_docs <span class="op">=</span> relevant_docs[:num_docs_final]</span>
	<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a> <span class="co"># Build the final prompt</span></span>
	<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a> context <span class="op">=</span> <span class="st">"</span><span class="ch">\n</span><span class="st">Extracted documents:</span><span class="ch">\n</span><span class="st">"</span></span>
	<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a> context <span class="op">+=</span> <span class="st">""</span>.join([<span class="ss">f"Document </span><span class="sc">{</span><span class="bu">str</span>(i)<span class="sc">}</span><span class="ss">:::</span><span class="ch">\n</span><span class="ss">"</span> <span class="op">+</span> doc <span class="cf">for</span> i, doc <span class="kw">in</span> <span class="bu">enumerate</span>(relevant_docs)])</span>
	<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a> final_prompt <span class="op">=</span> RAG_PROMPT_TEMPLATE.<span class="bu">format</span>(question<span class="op">=</span>question, context<span class="op">=</span>context)</span>
	<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a> <span class="co"># Redact an answer</span></span>
	<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="st">"=> Generating answer..."</span>)</span>
	<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a> answer <span class="op">=</span> llm(final_prompt)[<span class="dv">0</span>][<span class="st">"generated_text"</span>]</span>
	<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> answer, relevant_docs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>Let’s see how our RAG pipeline answers a user query.</p>
	<div id="06ccd294" class="cell" data-execution_count="20">
	<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>question <span class="op">=</span> <span class="st">"how to create a pipeline object?"</span></span>
	<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a></span>
	<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>answer, relevant_docs <span class="op">=</span> answer_with_rag(</span>
	<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> question, READER_LLM, KNOWLEDGE_VECTOR_DATABASE, reranker<span class="op">=</span>RERANKER</span>
	<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<div id="f6ece0d2" class="cell" data-execution_count="21">
	<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"==================================Answer=================================="</span>)</span>
	<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"</span><span class="sc">{</span>answer<span class="sc">}</span><span class="ss">"</span>)</span>
	<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"==================================Source docs=================================="</span>)</span>
	<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> i, doc <span class="kw">in</span> <span class="bu">enumerate</span>(relevant_docs):</span>
	<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="ss">f"Document </span><span class="sc">{</span>i<span class="sc">}</span><span class="ss">------------------------------------------------------------"</span>)</span>
	<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(doc)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
	</div>
	<p>✅ We now have a fully functional, performant RAG sytem. That’s it for today! Congratulations for making it to the end 🥳</p>
	</section>
	<section id="to-go-further" class="level1">
	<h1>To go further 🗺️</h1>
	<p>This is not the end of the journey! You can try many steps to improve your RAG system. We recommend doing so in an iterative way: bring small changes to the system and see what improves performance.</p>
	<section id="setting-up-an-evaluation-pipeline" class="level3">
	<h3 class="anchored" data-anchor-id="setting-up-an-evaluation-pipeline">Setting up an evaluation pipeline</h3>
	<ul>
	<li>💬 “You cannot improve the model performance that you do not measure”, said Gandhi… or at least Llama2 told me he said it. Anyway, you should absolutely start by measuring performance: this means building a small evaluation dataset, then monitor the performance of your RAG system on this evaluation dataset.</li>
	</ul>
	</section>
	<section id="improving-the-retriever" class="level3">
	<h3 class="anchored" data-anchor-id="improving-the-retriever">Improving the retriever</h3>
	<p>🛠️ <strong>You can use these options to tune the results:</strong></p>
	<ul>
	<li>Tune the chunking method:
	<ul>
	<li>Size of the chunks</li>
	<li>Method: split on different separators, use <a href="https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker">semantic chunking</a>…</li>
	</ul></li>
	<li>Change the embedding model</li>
	</ul>
	<p>👷‍♀️ <strong>More could be considered:</strong> - Try another chunking method, like semantic chunking - Change the index used (here, FAISS) - Query expansion: reformulate the user query in slightly different ways to retrieve more documents.</p>
	</section>
	<section id="improving-the-reader" class="level3">
	<h3 class="anchored" data-anchor-id="improving-the-reader">Improving the reader</h3>
	<p>🛠️ <strong>Here you can try the following options to improve results:</strong> - Tune the prompt - Switch reranking on/off - Choose a more powerful reader model</p>
	<p>💡 <strong>Many options could be considered here to further improve the results:</strong> - Compress the retrieved context to keep only the most relevant parts to answer the query. - Extend the RAG system to make it more user-friendly: - cite source - make conversational</p>


	</section>
	</section>

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	const parser = new DOMParser();
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	</script>
	</div> <!-- /content -->




	</body></html>