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<h1 class="title">Simple RAG</h1>
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<div id="78116675" 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 accelerate bitsandbytes transformers sentence<span class="op">-</span>transformers faiss<span class="op">-</span>gpu</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div id="be6b2c06" 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>pip install <span class="op">-</span>q langchain</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>If running in Google Colab, you may need to run this cell to make sure you’re using UTF-8 locale to install LangChain</p>
<div id="4dc3a73a" class="cell" data-execution_count="3">
<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> locale</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>locale.getpreferredencoding <span class="op">=</span> <span class="kw">lambda</span>: <span class="st">"UTF-8"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<section id="prepare-the-data" class="level2">
<h2 class="anchored" data-anchor-id="prepare-the-data">Prepare the data</h2>
<p>In this example, we’ll load all of the issues (both open and closed) from <a href="https://github.com/huggingface/peft">PEFT library’s repo</a>.</p>
<p>First, you need to acquire a <a href="https://github.com/settings/tokens?type=beta">GitHub personal access token</a> to access the GitHub API.</p>
<div id="99d8d506" class="cell" data-execution_count="4">
<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> getpass <span class="im">import</span> getpass</span>
<span id="annotated-cell-3-2"><a href="#annotated-cell-3-2" aria-hidden="true" tabindex="-1"></a></span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-3" data-target-annotation="1">1</button><span id="annotated-cell-3-3" class="code-annotation-target"><a href="#annotated-cell-3-3" aria-hidden="true" tabindex="-1"></a>ACCESS_TOKEN <span class="op">=</span> getpass(<span class="st">"YOUR_GITHUB_PERSONAL_TOKEN"</span>)</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>
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<dt data-target-cell="annotated-cell-3" data-target-annotation="1">1</dt>
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<span data-code-cell="annotated-cell-3" data-code-lines="3" data-code-annotation="1">You can also use an environment variable to store your token.</span>
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<p>Next, we’ll load all of the issues in the <a href="https://github.com/huggingface/peft">huggingface/peft</a> repo: - By default, pull requests are considered issues as well, here we chose to exclude them from data with by setting <code>include_prs=False</code> - Setting <code>state = "all"</code> means we will load both open and closed issues.</p>
<div id="4aba18cd" 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.document_loaders <span class="im">import</span> GitHubIssuesLoader</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>loader <span class="op">=</span> GitHubIssuesLoader(</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    repo<span class="op">=</span><span class="st">"huggingface/peft"</span>,</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>    access_token<span class="op">=</span>ACCESS_TOKEN,</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    include_prs<span class="op">=</span><span class="va">False</span>,</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>    state<span class="op">=</span><span class="st">"all"</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>docs <span class="op">=</span> loader.load()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>The content of individual GitHub issues may be longer than what an embedding model can take as input. If we want to embed all of the available content, we need to chunk the documents into appropriately sized pieces.</p>
<p>The most common and straightforward approach to chunking is to define a fixed size of chunks and whether there should be any overlap between them. Keeping some overlap between chunks allows us to preserve some semantic context between the chunks.</p>
<p>Other approaches are typically more involved and take into account the documents’ structure and context. For example, one may want to split a document based on sentences or paragraphs, or create chunks based on the</p>
<p>The fixed-size chunking, however, works well for most common cases, so that is what we’ll do here.</p>
<div id="1ee02e26" class="cell" data-execution_count="6">
<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> langchain.text_splitter <span class="im">import</span> CharacterTextSplitter</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>splitter <span class="op">=</span> CharacterTextSplitter(chunk_size<span class="op">=</span><span class="dv">512</span>, chunk_overlap<span class="op">=</span><span class="dv">30</span>)</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>chunked_docs <span class="op">=</span> splitter.split_documents(docs)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="create-the-embeddings-retriever" class="level2">
<h2 class="anchored" data-anchor-id="create-the-embeddings-retriever">Create the embeddings + retriever</h2>
<p>Now that the docs are all of the appropriate size, we can create a database with their embeddings.</p>
<p>To create document chunk embeddings we’ll use the <code>HuggingFaceEmbeddings</code> and the <a href="https://huggingface.co./BAAI/bge-base-en-v1.5"><code>BAAI/bge-base-en-v1.5</code></a> embeddings model. To create the vector database, we’ll use <code>FAISS</code>, a library developed by Facebook AI. This library offers efficient similarity search and clustering of dense vectors, which is what we need here. FAISS is currently one of the most used libraries for NN search in massive datasets.</p>
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<p>There are many other embeddings models available on the Hub, and you can keep an eye on the best performing ones by checking the <a href="https://huggingface.co./spaces/mteb/leaderboard">Massive Text Embedding Benchmark (MTEB) Leaderboard</a>.</p>
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<p>We’ll access both the embeddings model and FAISS via LangChain API.</p>
<div id="3342a691" class="cell" data-execution_count="7">
<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.vectorstores <span class="im">import</span> FAISS</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.embeddings <span class="im">import</span> HuggingFaceEmbeddings</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>db <span class="op">=</span> FAISS.from_documents(chunked_docs,</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>                          HuggingFaceEmbeddings(model_name<span class="op">=</span><span class="st">'BAAI/bge-base-en-v1.5'</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>We need a way to return(retrieve) the documents given an unstructured query. For that, we’ll use the <code>as_retriever</code> method using the <code>db</code> as a backbone: - <code>search_type="similarity"</code> means we want to perform similarity search between the query and documents - <code>search_kwargs={'k': 4}</code> instructs the retriever to return top 4 results.</p>
<div id="28bd25f2" class="cell" data-execution_count="8">
<div class="sourceCode cell-code" id="annotated-cell-7"><pre class="sourceCode python code-annotation-code code-with-copy code-annotated"><code class="sourceCode python"><span id="annotated-cell-7-1"><a href="#annotated-cell-7-1" aria-hidden="true" tabindex="-1"></a>retriever <span class="op">=</span> db.as_retriever(</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-7" data-target-annotation="1">1</button><span id="annotated-cell-7-2" class="code-annotation-target"><a href="#annotated-cell-7-2" aria-hidden="true" tabindex="-1"></a>    search_type<span class="op">=</span><span class="st">"similarity"</span>,</span>
<span id="annotated-cell-7-3"><a href="#annotated-cell-7-3" aria-hidden="true" tabindex="-1"></a>    search_kwargs<span class="op">=</span>{<span class="st">'k'</span>: <span class="dv">4</span>}</span>
<span id="annotated-cell-7-4"><a href="#annotated-cell-7-4" 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-7" data-target-annotation="1">1</dt>
<dd>
<span data-code-cell="annotated-cell-7" data-code-lines="2,3" data-code-annotation="1">The ideal search type is context dependent, and you should experiment to find the best one for your data.</span>
</dd>
</dl>
</div>
</div>
<p>The vector database and retriever are now set up, next we need to set up the next piece of the chain - the model.</p>
</section>
<section id="load-quantized-model" class="level2">
<h2 class="anchored" data-anchor-id="load-quantized-model">Load quantized model</h2>
<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. To make inference faster, we will load the quantized version of the model:</p>
<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>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>
<div id="e5288d87" 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">import</span> torch</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>model_name <span class="op">=</span> <span class="st">'HuggingFaceH4/zephyr-7b-beta'</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>bnb_config <span class="op">=</span> BitsAndBytesConfig(</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>    load_in_4bit<span class="op">=</span><span class="va">True</span>,</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>    bnb_4bit_use_double_quant<span class="op">=</span><span class="va">True</span>,</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>    bnb_4bit_quant_type<span class="op">=</span><span class="st">"nf4"</span>,</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>    bnb_4bit_compute_dtype<span class="op">=</span>torch.bfloat16</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></span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> AutoModelForCausalLM.from_pretrained(model_name, quantization_config<span class="op">=</span>bnb_config)</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>tokenizer <span class="op">=</span> AutoTokenizer.from_pretrained(model_name)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="setup-the-llm-chain" class="level2">
<h2 class="anchored" data-anchor-id="setup-the-llm-chain">Setup the LLM chain</h2>
<p>Finally, we have all the pieces we need to set up the LLM chain.</p>
<p>First, create a text_generation pipeline using the loaded model and its tokenizer.</p>
<p>Next, create a prompt template - this should follow the format of the model, so if you substitute the model checkpoint, make sure to use the appropriate formatting.</p>
<div id="389798fe" class="cell" data-execution_count="10">
<div class="sourceCode cell-code" id="annotated-cell-9"><pre class="sourceCode python code-annotation-code code-with-copy code-annotated"><code class="sourceCode python"><span id="annotated-cell-9-1"><a href="#annotated-cell-9-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.llms <span class="im">import</span> HuggingFacePipeline</span>
<span id="annotated-cell-9-2"><a href="#annotated-cell-9-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain.prompts <span class="im">import</span> PromptTemplate</span>
<span id="annotated-cell-9-3"><a href="#annotated-cell-9-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> pipeline</span>
<span id="annotated-cell-9-4"><a href="#annotated-cell-9-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> langchain_core.output_parsers <span class="im">import</span> StrOutputParser</span>
<span id="annotated-cell-9-5"><a href="#annotated-cell-9-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-6"><a href="#annotated-cell-9-6" aria-hidden="true" tabindex="-1"></a>text_generation_pipeline <span class="op">=</span> pipeline(</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="1">1</button><span id="annotated-cell-9-7" class="code-annotation-target"><a href="#annotated-cell-9-7" aria-hidden="true" tabindex="-1"></a>    model<span class="op">=</span>model,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="2">2</button><span id="annotated-cell-9-8" class="code-annotation-target"><a href="#annotated-cell-9-8" aria-hidden="true" tabindex="-1"></a>    tokenizer<span class="op">=</span>tokenizer,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="3">3</button><span id="annotated-cell-9-9" class="code-annotation-target"><a href="#annotated-cell-9-9" aria-hidden="true" tabindex="-1"></a>    task<span class="op">=</span><span class="st">"text-generation"</span>,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="4">4</button><span id="annotated-cell-9-10" class="code-annotation-target"><a href="#annotated-cell-9-10" aria-hidden="true" tabindex="-1"></a>    temperature<span class="op">=</span><span class="fl">0.2</span>,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="5">5</button><span id="annotated-cell-9-11" class="code-annotation-target"><a href="#annotated-cell-9-11" aria-hidden="true" tabindex="-1"></a>    do_sample<span class="op">=</span><span class="va">True</span>,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="6">6</button><span id="annotated-cell-9-12" class="code-annotation-target"><a href="#annotated-cell-9-12" aria-hidden="true" tabindex="-1"></a>    repetition_penalty<span class="op">=</span><span class="fl">1.1</span>,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="7">7</button><span id="annotated-cell-9-13" class="code-annotation-target"><a href="#annotated-cell-9-13" aria-hidden="true" tabindex="-1"></a>    return_full_text<span class="op">=</span><span class="va">True</span>,</span>
<button class="code-annotation-anchor" data-target-cell="annotated-cell-9" data-target-annotation="8">8</button><span id="annotated-cell-9-14" class="code-annotation-target"><a href="#annotated-cell-9-14" aria-hidden="true" tabindex="-1"></a>    max_new_tokens<span class="op">=</span><span class="dv">400</span>,</span>
<span id="annotated-cell-9-15"><a href="#annotated-cell-9-15" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="annotated-cell-9-16"><a href="#annotated-cell-9-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-17"><a href="#annotated-cell-9-17" aria-hidden="true" tabindex="-1"></a>llm <span class="op">=</span> HuggingFacePipeline(pipeline<span class="op">=</span>text_generation_pipeline)</span>
<span id="annotated-cell-9-18"><a href="#annotated-cell-9-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-19"><a href="#annotated-cell-9-19" aria-hidden="true" tabindex="-1"></a>prompt_template <span class="op">=</span> <span class="st">"""</span></span>
<span id="annotated-cell-9-20"><a href="#annotated-cell-9-20" aria-hidden="true" tabindex="-1"></a><span class="st">&lt;|system|&gt;</span></span>
<span id="annotated-cell-9-21"><a href="#annotated-cell-9-21" aria-hidden="true" tabindex="-1"></a><span class="st">Answer the question based on your knowledge. Use the following context to help:</span></span>
<span id="annotated-cell-9-22"><a href="#annotated-cell-9-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-23"><a href="#annotated-cell-9-23" aria-hidden="true" tabindex="-1"></a><span class="sc">{context}</span></span>
<span id="annotated-cell-9-24"><a href="#annotated-cell-9-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-25"><a href="#annotated-cell-9-25" aria-hidden="true" tabindex="-1"></a><span class="st">&lt;/s&gt;</span></span>
<span id="annotated-cell-9-26"><a href="#annotated-cell-9-26" aria-hidden="true" tabindex="-1"></a><span class="st">&lt;|user|&gt;</span></span>
<span id="annotated-cell-9-27"><a href="#annotated-cell-9-27" aria-hidden="true" tabindex="-1"></a><span class="sc">{question}</span></span>
<span id="annotated-cell-9-28"><a href="#annotated-cell-9-28" aria-hidden="true" tabindex="-1"></a><span class="st">&lt;/s&gt;</span></span>
<span id="annotated-cell-9-29"><a href="#annotated-cell-9-29" aria-hidden="true" tabindex="-1"></a><span class="st">&lt;|assistant|&gt;</span></span>
<span id="annotated-cell-9-30"><a href="#annotated-cell-9-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-31"><a href="#annotated-cell-9-31" aria-hidden="true" tabindex="-1"></a><span class="st"> """</span></span>
<span id="annotated-cell-9-32"><a href="#annotated-cell-9-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-33"><a href="#annotated-cell-9-33" aria-hidden="true" tabindex="-1"></a>prompt <span class="op">=</span> PromptTemplate(</span>
<span id="annotated-cell-9-34"><a href="#annotated-cell-9-34" aria-hidden="true" tabindex="-1"></a>    input_variables<span class="op">=</span>[<span class="st">"context"</span>, <span class="st">"question"</span>],</span>
<span id="annotated-cell-9-35"><a href="#annotated-cell-9-35" aria-hidden="true" tabindex="-1"></a>    template<span class="op">=</span>prompt_template,</span>
<span id="annotated-cell-9-36"><a href="#annotated-cell-9-36" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="annotated-cell-9-37"><a href="#annotated-cell-9-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-9-38"><a href="#annotated-cell-9-38" aria-hidden="true" tabindex="-1"></a>llm_chain <span class="op">=</span> prompt <span class="op">|</span> llm <span class="op">|</span> StrOutputParser()</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-9" data-target-annotation="1">1</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="7" data-code-annotation="1">The pre-trained model for text generation.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="2">2</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="8" data-code-annotation="2">Tokenizer to preprocess input text and postprocess generated output.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="3">3</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="9" data-code-annotation="3">Specifies the task as text generation.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="4">4</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="10" data-code-annotation="4">Controls the randomness in the output generation. Lower values make the output more deterministic.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="5">5</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="11" data-code-annotation="5">Enables sampling to introduce randomness in the output generation.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="6">6</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="12" data-code-annotation="6">Penalizes repetition in the output to encourage diversity.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="7">7</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="13" data-code-annotation="7">Returns the full generated text including the input prompt.</span>
</dd>
<dt data-target-cell="annotated-cell-9" data-target-annotation="8">8</dt>
<dd>
<span data-code-cell="annotated-cell-9" data-code-lines="14" data-code-annotation="8">Limits the maximum number of new tokens generated.</span>
</dd>
</dl>
</div>
</div>
<p>Note: <em>You can also use <code>tokenizer.apply_chat_template</code> to convert a list of messages (as dicts: <code>{'role': 'user', 'content': '(...)'}</code>) into a string with the appropriate chat format.</em></p>
<p>Finally, we need to combine the <code>llm_chain</code> with the retriever to create a RAG chain. We pass the original question through to the final generation step, as well as the retrieved context docs:</p>
<div id="2ad1978e" class="cell" data-execution_count="11">
<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="im">from</span> langchain_core.runnables <span class="im">import</span> RunnablePassthrough</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>retriever <span class="op">=</span> db.as_retriever()</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>rag_chain <span class="op">=</span> (</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> {<span class="st">"context"</span>: retriever, <span class="st">"question"</span>: RunnablePassthrough()}</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>    <span class="op">|</span> llm_chain</span>
<span id="cb8-8"><a href="#cb8-8" 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="compare-the-results" class="level2">
<h2 class="anchored" data-anchor-id="compare-the-results">Compare the results</h2>
<p>Let’s see the difference RAG makes in generating answers to the library-specific questions.</p>
<div id="aa570a95" class="cell" data-execution_count="12">
<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>question <span class="op">=</span> <span class="st">"How do you combine multiple adapters?"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>First, let’s see what kind of answer we can get with just the model itself, no context added:</p>
<div id="3c1688aa" class="cell" data-execution_count="13">
<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>llm_chain.invoke({<span class="st">"context"</span>:<span class="st">""</span>, <span class="st">"question"</span>: question})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>As you can see, the model interpreted the question as one about physical computer adapters, while in the context of PEFT, “adapters” refer to LoRA adapters. Let’s see if adding context from GitHub issues helps the model give a more relevant answer:</p>
<div id="57388c24" class="cell" data-execution_count="14">
<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>rag_chain.invoke(question)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>As we can see, the added context, really helps the exact same model, provide a much more relevant and informed answer to the library-specific question.</p>
<p>Notably, combining multiple adapters for inference has been added to the library, and one can find this information in the documentation, so for the next iteration of this RAG it may be worth including documentation embeddings.</p>


</section>

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      allowHTML: true,
      maxWidth: 500,
      delay: 100,
      arrow: false,
      appendTo: function(el) {
          return el.parentElement;
      },
      interactive: true,
      interactiveBorder: 10,
      theme: 'quarto',
      placement: 'bottom-start',
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    if (contentFn) {
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    }
    if (onTriggerFn) {
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    }
    if (onUntriggerFn) {
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    }
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    const ref = noterefs[i];
    tippyHover(ref, function() {
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      let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
      try { href = new URL(href).hash; } catch {}
      const id = href.replace(/^#\/?/, "");
      const note = window.document.getElementById(id);
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        return note.innerHTML;
      } else {
        return "";
      }
    });
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            break;
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          window.Quarto.typesetMath(container);
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      }
      if (window.Quarto?.typesetMath) {
        window.Quarto.typesetMath(note);
      }
      if (note.classList.contains("callout")) {
        return note.outerHTML;
      } else {
        return note.innerHTML;
      }
    }
  }
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    const xref = xrefs[i];
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      instance.disable();
      let url = xref.getAttribute('href');
      let hash = undefined; 
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      } else {
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      }
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        const note = window.document.getElementById(id);
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            const html = processXRef(id, note.cloneNode(true));
            instance.setContent(html);
          } finally {
            instance.enable();
            instance.show();
          }
        } else {
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          .then(res => res.text())
          .then(html => {
            const parser = new DOMParser();
            const htmlDoc = parser.parseFromString(html, "text/html");
            const note = htmlDoc.getElementById(id);
            if (note !== null) {
              const html = processXRef(id, note);
              instance.setContent(html);
            } 
          }).finally(() => {
            instance.enable();
            instance.show();
          });
        }
      } else {
        // See if we can fetch a full url (with no hash to target)
        // This is a special case and we should probably do some content thinning / targeting
        fetch(url)
        .then(res => res.text())
        .then(html => {
          const parser = new DOMParser();
          const htmlDoc = parser.parseFromString(html, "text/html");
          const note = htmlDoc.querySelector('main.content');
          if (note !== null) {
            // This should only happen for chapter cross references
            // (since there is no id in the URL)
            // remove the first header
            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
              note.children[0].remove();
            }
            const html = processXRef(null, note);
            instance.setContent(html);
          } 
        }).finally(() => {
          instance.enable();
          instance.show();
        });
      }
    }, function(instance) {
    });
  }
      let selectedAnnoteEl;
      const selectorForAnnotation = ( cell, annotation) => {
        let cellAttr = 'data-code-cell="' + cell + '"';
        let lineAttr = 'data-code-annotation="' +  annotation + '"';
        const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
        return selector;
      }
      const selectCodeLines = (annoteEl) => {
        const doc = window.document;
        const targetCell = annoteEl.getAttribute("data-target-cell");
        const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
        const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
        const lines = annoteSpan.getAttribute("data-code-lines").split(",");
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        })
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        let height = null;
        let parent = null;
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            height = el.offsetHeight;
            parent = el.parentElement.parentElement;
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            height = bottom - top;
          }
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            div.style.height = height + 4 + "px";
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            }
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          selectedAnnoteEl = annoteEl;
        }
      };
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        elementsIds.forEach((elId) => {
          const div = window.document.getElementById(elId);
          if (div) {
            div.remove();
          }
        });
        selectedAnnoteEl = undefined;
      };
        // Handle positioning of the toggle
    window.addEventListener(
      "resize",
      throttle(() => {
        elRect = undefined;
        if (selectedAnnoteEl) {
          selectCodeLines(selectedAnnoteEl);
        }
      }, 10)
    );
    function throttle(fn, ms) {
    let throttle = false;
    let timer;
      return (...args) => {
        if(!throttle) { // first call gets through
            fn.apply(this, args);
            throttle = true;
        } else { // all the others get throttled
            if(timer) clearTimeout(timer); // cancel #2
            timer = setTimeout(() => {
              fn.apply(this, args);
              timer = throttle = false;
            }, ms);
        }
      };
    }
      const annoteTargets = window.document.querySelectorAll('.code-annotation-anchor');
      for (let i=0; i<annoteTargets.length; i++) {
        const annoteTarget = annoteTargets[i];
        const targetCell = annoteTarget.getAttribute("data-target-cell");
        const targetAnnotation = annoteTarget.getAttribute("data-target-annotation");
        const contentFn = () => {
          const content = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
          if (content) {
            const tipContent = content.cloneNode(true);
            tipContent.classList.add("code-annotation-tip-content");
            return tipContent.outerHTML;
          }
        }
        const config = {
          allowHTML: true,
          content: contentFn,
          onShow: (instance) => {
            selectCodeLines(instance.reference);
            instance.reference.classList.add('code-annotation-active');
            window.tippy.hideAll();
          },
          onHide: (instance) => {
            unselectCodeLines();
            instance.reference.classList.remove('code-annotation-active');
          },
          maxWidth: 300,
          delay: [50, 0],
          duration: [200, 0],
          offset: [5, 10],
          arrow: true,
          appendTo: function(el) {
            return el.parentElement.parentElement.parentElement;
          },
          interactive: true,
          interactiveBorder: 10,
          theme: 'quarto',
          placement: 'right',
          popperOptions: {
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              options: {
                flipVariations: false, // true by default
                allowedAutoPlacements: ['right'],
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              },
            },
            {
              name: 'preventOverflow',
              options: {
                mainAxis: false,
                altAxis: false
              }
            }
            ]        
          }      
        };
        window.tippy(annoteTarget, config); 
      }
  const findCites = (el) => {
    const parentEl = el.parentElement;
    if (parentEl) {
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      if (cites) {
        return {
          el,
          cites: cites.split(' ')
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    const ref = bibliorefs[i];
    const citeInfo = findCites(ref);
    if (citeInfo) {
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        var popup = window.document.createElement('div');
        citeInfo.cites.forEach(function(cite) {
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          citeDiv.classList.add('csl-entry');
          var biblioDiv = window.document.getElementById('ref-' + cite);
          if (biblioDiv) {
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          }
          popup.appendChild(citeDiv);
        });
        return popup.innerHTML;
      });
    }
  }
});
</script>
</div> <!-- /content -->




</body></html>