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thisthat committed Oct 17, 2024
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<title>3. Standard vs. RAG Version - Dynatrace Observability Lab: LLM Observability</title>
<title>3. How it works (RAG) - Dynatrace Observability Lab: LLM Observability</title>



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<h1>3. Standard vs. RAG Version</h1>
<h1>3. How it works (RAG)</h1>

<p>This demo is available in two flavors: <em>standard</em> and <em>RAG</em>.</p>
<p>The "standard" demo uses OpenAI's ChatGPT (coupled with an on-cluster Weaviate cache) to look up destination advice for any destination.</p>
<p>The "Retrieval-Augmented Generation" (RAG) version (available <a href="https://github.com/dynatrace-perfclinics/obslab-llm-observability/tree/ollama-pinecone" target="_blank">on the ollama-pinecone branch</a>) mimicks training an LLM on an internal knowledgebase.
It will produce custom destination advice for places the system has explicitly been trained on (the files in the <a href="https://github.com/dynatrace-perfclinics/obslab-llm-observability/tree/ollama-pinecone/destinations" target="_blank">destinations folder</a>).
Namely, <code>Bali</code> and <code>Sydney</code>. For other locations, the model will provide an answer based on its own knowledge.
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<title>2. How it works - Dynatrace Observability Lab: LLM Observability</title>
<title>2. How it works (Standard) - Dynatrace Observability Lab: LLM Observability</title>



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<h1>2. How it works</h1>
<h1>2. How it works (Standard)</h1>

<p>The user interacts with the demo app (travel advisor) on port <code>30100</code>. The app is monitored either via native OpenTelemetry.</p>
<p>The user enters a destination (eg. <code>Sydney</code>):</p>
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<li>The response is returned and cached so that subsequent calls for the same destination (eg. <code>Sydney</code>) are served from the cache. This saves roundtrips to ChatGPT and thus <code>$</code>.</li>
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<h2 id="click-here-to-continue-with-the-exercise"><a href="../standard-rag-differences/">&gt;&gt; Click here to continue with the exercise</a><a class="headerlink" href="#click-here-to-continue-with-the-exercise" title="Permanent link">#</a></h2>
<h2 id="click-here-to-continue-with-the-exercise"><a href="../how-it-works-rag/">&gt;&gt; Click here to continue with the exercise</a><a class="headerlink" href="#click-here-to-continue-with-the-exercise" title="Permanent link">#</a></h2>



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<h1 id="large-language-model-observability-with-dynatrace">Large Language Model Observability with Dynatrace<a class="headerlink" href="#large-language-model-observability-with-dynatrace" title="Permanent link">#</a></h1>
<p>Demo application for giving travel advice written in Python. Observability signals by <a href="https://opentelemetry.io" target="_blank">OpenTelemetry</a> and <a href="https://www.traceloop.com/docs/openllmetry/introduction" target="_blank">OpenLLMetry</a>.</p>
<p>Uses OpenAI ChatGPT to generate advice for a given destination.</p>
<p>The demo application has two different versions:</p>
<ul>
<li>Standard</li>
<li>Retrieval-augmented generation (RAG)</li>
</ul>
<p>The standard version uses OpenAI ChatGPT to generate advice for a given destination and use Weaviate as a cache layer.
The RAG version uses Ollama to generate advice for a given destination and PineCone
to fetch additional context to refine the advice provided by Ollama.</p>
<p><img alt="title" src="images/screenshot.png" /></p>
<p>This hands-on is also available as an <a href="https://info.dynatrace.com/apac-all-wb-ensure-ai-project-success-with-ai-observability-24973-registration.html" target="_blank">on-demand webinar</a>.</p>
<h2 id="architecture">Architecture<a class="headerlink" href="#architecture" title="Permanent link">#</a></h2>
<h2 id="standard-version-architecture">Standard Version Architecture<a class="headerlink" href="#standard-version-architecture" title="Permanent link">#</a></h2>
<p><img alt="architecture" src="images/architecture.jpg" /></p>
<h2 id="rag-version-architecture">RAG Version Architecture<a class="headerlink" href="#rag-version-architecture" title="Permanent link">#</a></h2>
<p><img alt="architecture" src="images/rag-architecture.jpg" /></p>
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