trainingdemo/explainer.html at main · Copilotclaw/trainingdemo · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Build Your First RAG System — Explainer</title>
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</head>
<body>

<nav>
  <span class="brand">🦃 trainingdemo</span>
  <div style="margin-left:auto;display:flex;gap:0.75rem">
    <a href="https://github.com/Copilotclaw/trainingdemo" class="btn btn-outline">GitHub</a>
    <a href="https://colab.research.google.com/github/Copilotclaw/trainingdemo/blob/main/simple-rag-demo.ipynb" target="_blank" class="btn btn-colab">🚀 Open in Colab</a>
  </div>
</nav>

<section class="hero">
  <div class="hero-emoji">🔍</div>
  <h1>Build Your First RAG <span>System</span></h1>
  <p class="tagline">Create a smart search system that finds answers from your own documents using embeddings and similarity search</p>
  <div class="hero-pills">
    <span class="pill">⏱ ~15 minutes</span>
    <span class="pill">📊 Beginner</span>
    <span class="pill">📓 Jupyter Notebook</span>
  </div>
  <div class="hero-actions">
    <a href="https://colab.research.google.com/github/Copilotclaw/trainingdemo/blob/main/simple-rag-demo.ipynb" target="_blank" class="btn btn-colab">🚀 Open in Colab</a>
    <a href="https://github.com/Copilotclaw/trainingdemo" class="btn btn-outline" style="color:white;border-color:rgba(255,255,255,0.4)">📁 View Source</a>
  </div>
</section>

<main class="container">

  <!-- Meta bar -->
  <div class="meta-bar">
    <div class="meta-item"><div class="label">Time</div><div class="value">⏱ ~15 minutes</div></div>
    <div class="meta-item"><div class="label">Difficulty</div><div class="value">Beginner</div></div>
    <div class="meta-item"><div class="label">Platform</div><div class="value">Google Colab</div></div>
    <div class="meta-item"><div class="label">Cost</div><div class="value">Free 🎉</div></div>
  </div>

  <!-- Why -->
  <div class="section">
    <h2 class="section-title">💡 Why this matters</h2>
    <div class="why-box">
      <p>RAG powers AI assistants that can answer questions about your private data without sending it to external APIs. It's the foundation behind document chatbots, enterprise search, and personalized AI systems.</p>
    </div>
  </div>

  <!-- What you'll learn -->
  <div class="section">
    <h2 class="section-title">🎯 What you'll learn</h2>
    <div class="learn-grid">
      <div class="learn-item">How to convert text into numerical embeddings</div>
<div class="learn-item">Build and query a FAISS vector database</div>
<div class="learn-item">Implement the core RAG retrieval pipeline</div>
<div class="learn-item">Understand similarity search fundamentals</div>
    </div>
  </div>

  <!-- Prerequisites -->
  <div class="section">
  <h2 class="section-title">📋 Prerequisites</h2>
  <div class="prereqs-list"><span class="prereq-tag">Basic Python knowledge</span>
<span class="prereq-tag">Understanding of lists and arrays</span></div>
</div>

  <!-- Steps -->
  <div class="section">
    <h2 class="section-title">🗺️ What we do — step by step</h2>
    <div class="steps-list">
      <div class="step-card yellow">
  <div class="step-num">1</div>
  <div>
    <h3>Build the Knowledge Base</h3>
    <p>Load your documents that will serve as the source of knowledge for your RAG system</p>
  </div>
</div>
<div class="step-card blue">
  <div class="step-num">2</div>
  <div>
    <h3>Embed the Documents</h3>
    <p>Convert text documents into numerical vectors using sentence transformer models</p>
  </div>
</div>
<div class="step-card green">
  <div class="step-num">3</div>
  <div>
    <h3>Build the FAISS Index</h3>
    <p>Create a searchable vector database that enables fast similarity lookup</p>
  </div>
</div>
<div class="step-card pink">
  <div class="step-num">4</div>
  <div>
    <h3>Query the Index</h3>
    <p>Search for the most relevant documents based on semantic similarity</p>
  </div>
</div>
<div class="step-card purple">
  <div class="step-num">5</div>
  <div>
    <h3>Simulate Generation</h3>
    <p>Format retrieved context as it would be used in an actual LLM prompt</p>
  </div>
</div>
    </div>
  </div>

  <!-- Key concepts -->
  <div class="section">
  <h2 class="section-title">🔑 Key concepts</h2>
  <div class="concepts-grid"><div class="concept-card"><div class="term">RAG</div><div class="def">Retrieval-Augmented Generation - combining document search with large language models</div></div>
<div class="concept-card"><div class="term">Embeddings</div><div class="def">Numerical representations of text that capture semantic meaning in vector space</div></div>
<div class="concept-card"><div class="term">FAISS</div><div class="def">Facebook AI Similarity Search library for efficient vector similarity operations</div></div>
<div class="concept-card"><div class="term">Semantic Search</div><div class="def">Finding documents based on meaning rather than exact keyword matches</div></div></div>
</div>

  <!-- CTA -->
  <div class="cta-box">
    <h2>Ready to dive in?</h2>
    <p>No install needed — runs entirely in your browser via Google Colab.</p>
    <div class="cta-actions">
      <a href="https://colab.research.google.com/github/Copilotclaw/trainingdemo/blob/main/simple-rag-demo.ipynb" target="_blank" class="btn btn-colab" style="font-size:1rem;padding:0.75rem 1.75rem">
        🚀 Open in Colab — it's free!
      </a>
      <a href="https://github.com/Copilotclaw/trainingdemo" class="btn" style="background:rgba(255,255,255,0.15);color:white;font-size:1rem;padding:0.75rem 1.75rem">
        📁 Browse All Notebooks
      </a>
    </div>
  </div>

</main>

<footer>
  <p>Generated by <a href="https://github.com/Copilotclaw/copilotclaw">🦃 Crunch</a> — AI agent @ Copilotclaw &nbsp;·&nbsp;
  <a href="https://github.com/Copilotclaw/trainingdemo">trainingdemo</a></p>
</footer>

</body>
</html>