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<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">π€ What is this library for?</a></li>
<li class="toctree-l1"><a class="reference internal" href="install.html">βοΈ Installation and configuration</a></li>
<li class="toctree-l1"><a class="reference internal" href="quickstart.html">β‘ Quickstart</a></li>
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<p class="caption" role="heading"><span class="caption-text">Example notebooks</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notebooks/01_promptmeteo_usage.html">Promptmeteo Usage - Save and Load Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks/03_sentiment_analysis.html">Sentiment Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks/04_test_flant5_classification_prompts.html">Flan-t5-Small - Test Classification Prompts</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks/05_test_openai_classification_prompts.html">OpenAI - Test Classification Prompts</a></li>
<li class="toctree-l1"><a class="reference internal" href="notebooks/06_test_openai_api-generation.html">OpenAI - Test API Generation</a></li>
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<section id="what-is-this-library-for">
<h1>π€ What is this library for?<a class="headerlink" href="#what-is-this-library-for" title="Permalink to this heading">#</a></h1>
<p><strong>TL;DR: Industrialize projects powered by LLMs easily.</strong></p>
<p>LLMs have the capability to address various tasks when provided with specific instructions in the form of input prompts. They can function as a βreasoning engineβ for constructing applications. However, deploying and industrializing these applications poses significant challenges for two main reasons.</p>
<p>Firstly, prompts typically encapsulate application logic in their definition, yet they are treated merely as input arguments. This means that a poorly formulated prompt input has the potential to disrupt the application.</p>
<p>Secondly, crafting specific prompts for each task is not only a laborious task but also a complex one. Minor alterations in the input prompt can result in different outcomes, rendering them highly error-prone. Additionally, when composing prompts, considerations extend beyond the task itself to include factors such as the specific LLM being used, the modelβs capacity, and other relevant aspects.</p>
<section id="how-do-we-do-it">
<h2>π How do we do it?<a class="headerlink" href="#how-do-we-do-it" title="Permalink to this heading">#</a></h2>
<p><strong>TL;DR: Treating prompts and code equally!!!</strong></p>
<p>Promptmeteo aims to address the aforementioned issues by dividing the prompt definition into two distinct parts: the task logic, coded within prompt templates, and the concrete problem, included as argument variables. Promptmeteo incorporates high-level objects for various tasks, implemented through <cite>.py</cite> and <cite>.prompt</cite> files.</p>
<section id="prebuilt-tasks">
<h3>π Prebuilt tasks<a class="headerlink" href="#prebuilt-tasks" title="Permalink to this heading">#</a></h3>
<p>The project incorporates high-level objects designed to address various NLP tasks, including text classification, named entity recognition, and code generation. These objects only require configuration parameters for execution, eliminating the need to parse the output from the LLMs.</p>
</section>
<section id="ease-of-deployment">
<h3>βοΈ Ease of Deployment<a class="headerlink" href="#ease-of-deployment" title="Permalink to this heading">#</a></h3>
<p>Promptmeteo modules adhere to a model interface similar to Scikit-Learn. By defining an interface with independent methods for training, predicting, saving, and loading the model, Promptmeteo enables training in a separate pipeline from prediction. This facilitates the reuse of conventional ML pipelines for LLM projects.</p>
</section>
<section id="model-artifacts">
<h3>π¦ Model Artifacts<a class="headerlink" href="#model-artifacts" title="Permalink to this heading">#</a></h3>
<p>To enhance results, LLMs can incorporate examples in their prompts. Promptmeteo supports training with examples, and for reproducibility, the training process can be stored as a binary model artifact. This allows storing and reusing training results multiple times with new data. The training process stores embeddings from the input text in a vector database, such as FAISS.</p>
</section>
<section id="llms-integration">
<h3>βοΈ LLMs Integration<a class="headerlink" href="#llms-integration" title="Permalink to this heading">#</a></h3>
<p>Promptmeteo integrates different LLMs through LangChain. This includes models that can be executed locally and remote API calls from providers like OpenAI and HuggingFace.</p>
</section>
<section id="prompt-templating">
<h3>π Prompt Templating<a class="headerlink" href="#prompt-templating" title="Permalink to this heading">#</a></h3>
<p>Establishing a concrete format for creating prompts in Promptmeteo (<cite>.prompt</cite>) not only facilitates programmatic use but also enables versioning of prompts. This approach aids in understanding changes when they occur and allows for the definition of code tests oriented toward prompt testing. This testing encompasses aspects such as validating language use and ensuring the prompt size is appropriate for the model.</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">TEMPLATE</span><span class="p">:</span>
<span class="w"> </span><span class="s">"I</span><span class="nv"> </span><span class="s">need</span><span class="nv"> </span><span class="s">you</span><span class="nv"> </span><span class="s">to</span><span class="nv"> </span><span class="s">help</span><span class="nv"> </span><span class="s">me</span><span class="nv"> </span><span class="s">with</span><span class="nv"> </span><span class="s">a</span><span class="nv"> </span><span class="s">text</span><span class="nv"> </span><span class="s">classification</span><span class="nv"> </span><span class="s">task.</span>
<span class="w"> </span><span class="s">{__PROMPT_DOMAIN__}</span>
<span class="w"> </span><span class="s">{__PROMPT_LABELS__}</span>
<span class="w"> </span><span class="s">{__CHAIN_THOUGHT__}</span>
<span class="w"> </span><span class="s">{__ANSWER_FORMAT__}"</span>
<span class="nt">PROMPT_DOMAIN</span><span class="p">:</span>
<span class="w"> </span><span class="s">"The</span><span class="nv"> </span><span class="s">texts</span><span class="nv"> </span><span class="s">you</span><span class="nv"> </span><span class="s">will</span><span class="nv"> </span><span class="s">be</span><span class="nv"> </span><span class="s">processing</span><span class="nv"> </span><span class="s">are</span><span class="nv"> </span><span class="s">from</span><span class="nv"> </span><span class="s">the</span><span class="nv"> </span><span class="s">{__DOMAIN__}</span><span class="nv"> </span><span class="s">domain."</span>
<span class="nt">PROMPT_LABELS</span><span class="p">:</span>
<span class="w"> </span><span class="s">"I</span><span class="nv"> </span><span class="s">want</span><span class="nv"> </span><span class="s">you</span><span class="nv"> </span><span class="s">to</span><span class="nv"> </span><span class="s">classify</span><span class="nv"> </span><span class="s">the</span><span class="nv"> </span><span class="s">texts</span><span class="nv"> </span><span class="s">into</span><span class="nv"> </span><span class="s">one</span><span class="nv"> </span><span class="s">of</span><span class="nv"> </span><span class="s">the</span><span class="nv"> </span><span class="s">following</span><span class="nv"> </span><span class="s">categories:</span>
<span class="w"> </span><span class="s">{__LABELS__}."</span>
<span class="nt">PROMPT_DETAIL</span><span class="p">:</span>
<span class="w"> </span><span class="s">""</span>
<span class="nt">CHAIN_THOUGHT</span><span class="p">:</span>
<span class="w"> </span><span class="s">"Please</span><span class="nv"> </span><span class="s">provide</span><span class="nv"> </span><span class="s">a</span><span class="nv"> </span><span class="s">step-by-step</span><span class="nv"> </span><span class="s">argument</span><span class="nv"> </span><span class="s">for</span><span class="nv"> </span><span class="s">your</span><span class="nv"> </span><span class="s">answer,</span><span class="nv"> </span><span class="s">explain</span><span class="nv"> </span><span class="s">why</span><span class="nv"> </span><span class="s">you</span>
<span class="w"> </span><span class="s">believe</span><span class="nv"> </span><span class="s">your</span><span class="nv"> </span><span class="s">final</span><span class="nv"> </span><span class="s">choice</span><span class="nv"> </span><span class="s">is</span><span class="nv"> </span><span class="s">justified."</span>
<span class="nt">ANSWER_FORMAT</span><span class="p">:</span>
<span class="w"> </span><span class="s">"In</span><span class="nv"> </span><span class="s">your</span><span class="nv"> </span><span class="s">response,</span><span class="nv"> </span><span class="s">include</span><span class="nv"> </span><span class="s">only</span><span class="nv"> </span><span class="s">the</span><span class="nv"> </span><span class="s">name</span><span class="nv"> </span><span class="s">of</span><span class="nv"> </span><span class="s">the</span><span class="nv"> </span><span class="s">class</span><span class="nv"> </span><span class="s">as</span><span class="nv"> </span><span class="s">a</span><span class="nv"> </span><span class="s">single</span><span class="nv"> </span><span class="s">word,</span><span class="nv"> </span><span class="s">in</span>
<span class="w"> </span><span class="s">lowercase,</span><span class="nv"> </span><span class="s">without</span><span class="nv"> </span><span class="s">punctuation,</span><span class="nv"> </span><span class="s">and</span><span class="nv"> </span><span class="s">without</span><span class="nv"> </span><span class="s">adding</span><span class="nv"> </span><span class="s">any</span><span class="nv"> </span><span class="s">other</span><span class="nv"> </span><span class="s">statements</span><span class="nv"> </span><span class="s">or</span>
<span class="w"> </span><span class="s">words."</span>
</pre></div>
</div>
</section>
</section>
<section id="current-capacilities">
<h2>π Current capacilities<a class="headerlink" href="#current-capacilities" title="Permalink to this heading">#</a></h2>
<section id="available-tasks">
<h3>β
Available tasks<a class="headerlink" href="#available-tasks" title="Permalink to this heading">#</a></h3>
<p>The current available tasks in Promptmeteo are:</p>
<div class="table-wrapper docutils container" id="id1">
<table class="docutils align-default" id="id1">
<caption><span class="caption-text">Tasks</span><a class="headerlink" href="#id1" title="Permalink to this table">#</a></caption>
<thead>
<tr class="row-odd"><th class="head"><p>task type</p></th>
<th class="head"><p>description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><cite>DocumentQA</cite></p></td>
<td><p>Document-level question answering</p></td>
</tr>
<tr class="row-odd"><td><p><cite>DocumentClassifier</cite></p></td>
<td><p>Document-level classification</p></td>
</tr>
<tr class="row-even"><td><p><cite>CodeGenerator</cite></p></td>
<td><p>Code generation</p></td>
</tr>
<tr class="row-odd"><td><p><cite>ApiGenerator</cite></p></td>
<td><p>API REST generation</p></td>
</tr>
<tr class="row-even"><td><p><cite>ApiFormatter</cite></p></td>
<td><p>API REST correction</p></td>
</tr>
<tr class="row-odd"><td><p><cite>Summarizer</cite></p></td>
<td><p>Text summarization</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="available-models">
<h3>β
Available Models<a class="headerlink" href="#available-models" title="Permalink to this heading">#</a></h3>
<p>The current available <cite>model_name</cite> and <cite>language</cite> values are:</p>
<div class="table-wrapper docutils container" id="id2">
<table class="docutils align-default" id="id2">
<caption><span class="caption-text">Models</span><a class="headerlink" href="#id2" title="Permalink to this table">#</a></caption>
<thead>
<tr class="row-odd"><th class="head"><p>provider</p></th>
<th class="head"><p>name</p></th>
<th class="head"><p>languages</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>openai</p></td>
<td><p>gpt-3.5-turbo-16k</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-odd"><td><p>azure</p></td>
<td><p>gpt-3.5-turbo-16k</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-even"><td><p>hf_hub_api</p></td>
<td><p>google/flan-t5-xxl</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-odd"><td><p>hf_pipeline</p></td>
<td><p>google/flan-t5-small</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-even"><td><p>google</p></td>
<td><p>text-bison</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-odd"><td><p>google</p></td>
<td><p><a class="reference external" href="mailto:text-bison%40001">text-bison<span>@</span>001</a></p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-even"><td><p>google</p></td>
<td><p>text-bison-32k</p></td>
<td><p>es, en</p></td>
</tr>
<tr class="row-odd"><td><p>bedrock</p></td>
<td><p>anthropic.claude-v2</p></td>
<td><p>es, en</p></td>
</tr>
</tbody>
</table>
</div>
</section>
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<li><a class="reference internal" href="#">π€ What is this library for?</a><ul>
<li><a class="reference internal" href="#how-do-we-do-it">π How do we do it?</a><ul>
<li><a class="reference internal" href="#prebuilt-tasks">π Prebuilt tasks</a></li>
<li><a class="reference internal" href="#ease-of-deployment">βοΈ Ease of Deployment</a></li>
<li><a class="reference internal" href="#model-artifacts">π¦ Model Artifacts</a></li>
<li><a class="reference internal" href="#llms-integration">βοΈ LLMs Integration</a></li>
<li><a class="reference internal" href="#prompt-templating">π Prompt Templating</a></li>
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<li><a class="reference internal" href="#current-capacilities">π Current capacilities</a><ul>
<li><a class="reference internal" href="#available-tasks">β
Available tasks</a></li>
<li><a class="reference internal" href="#available-models">β
Available Models</a></li>
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