deploy: d215724864

build_with_arch/agent.html

@@ -412,7 +412,7 @@ the user’s intent.</p>
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build_with_arch/multi_turn.html

@@ -348,7 +348,7 @@ that you can test and modify locally for multi-turn RAG scenarios.</p>
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build_with_arch/rag.html

@@ -296,7 +296,7 @@ minutes. For additional details on how to build multi-turn RAG applications plea
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concepts/llm_provider.html

@@ -205,25 +205,36 @@ make outbound LLM calls.</p>
 <h2>Adding custom LLM Provider<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#adding-custom-llm-provider" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#adding-custom-llm-provider'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
 <p>We support any OpenAI compliant LLM for example mistral, openai, ollama etc. We also offer first class support for OpenAI, Anthropic, DeepSeek, Mistral, Groq, and Ollama based models.
 You can easily configure an LLM that communicates over the OpenAI API interface, by following the below guide.</p>
-<p>For example following code block shows you how to add an ollama-supported LLM in the <cite>arch_config.yaml</cite> file.</p>
-<p>And in the following code block shows you how to add mistral llm provider in the <cite>arch_config.yaml</cite> file.</p>
+<p>For example following code block shows you how to add an ollama-supported LLM in the <code class="docutils literal notranslate"><span class="pre">arch_config.yaml</span></code> file.</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="nt">llm_providers</span><span class="p">:</span>
+</span><span id="line-2"><span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">some_custom_llm_provider/llama3.2</span>
+</span><span id="line-3"><span class="w">    </span><span class="nt">provider_interface</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai</span>
+</span><span id="line-4"><span class="w">    </span><span class="nt">base_url</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http://host.docker.internal:11434</span>
+</span></code></pre></div>
+</div>
+<p>And in the following code block shows you how to add mistral llm provider in the <code class="docutils literal notranslate"><span class="pre">arch_config.yaml</span></code> file.</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="nt">llm_providers</span><span class="p">:</span>
+</span><span id="line-2"><span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mistral/ministral-3b-latest</span>
+</span><span id="line-3"><span class="w">    </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_API_KEY</span>
+</span></code></pre></div>
+</div>
 </section>
 <section id="example-using-the-openai-python-sdk">
 <h2>Example: Using the OpenAI Python SDK<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#example-using-the-openai-python-sdk" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#example-using-the-openai-python-sdk'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="kn">from</span><span class="w"> </span><span class="nn">openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">OpenAI</span>
 </span><span id="line-2">
-</span><span id="line-3"> <span class="c1"># Initialize the Arch client</span>
-</span><span id="line-4"> <span class="n">client</span> <span class="o">=</span> <span class="n">OpenAI</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.1:2000/"</span><span class="p">)</span>
+</span><span id="line-3"><span class="c1"># Initialize the Arch client</span>
+</span><span id="line-4"><span class="n">client</span> <span class="o">=</span> <span class="n">OpenAI</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.1:2000/"</span><span class="p">)</span>
 </span><span id="line-5">
-</span><span id="line-6"> <span class="c1"># Define your model and messages</span>
-</span><span id="line-7"> <span class="n">model</span> <span class="o">=</span> <span class="s2">"llama3.2"</span>
-</span><span id="line-8"> <span class="n">messages</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"role"</span><span class="p">:</span> <span class="s2">"user"</span><span class="p">,</span> <span class="s2">"content"</span><span class="p">:</span> <span class="s2">"What is the capital of France?"</span><span class="p">}]</span>
+</span><span id="line-6"><span class="c1"># Define your model and messages</span>
+</span><span id="line-7"><span class="n">model</span> <span class="o">=</span> <span class="s2">"llama3.2"</span>
+</span><span id="line-8"><span class="n">messages</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"role"</span><span class="p">:</span> <span class="s2">"user"</span><span class="p">,</span> <span class="s2">"content"</span><span class="p">:</span> <span class="s2">"What is the capital of France?"</span><span class="p">}]</span>
 </span><span id="line-9">
-</span><span id="line-10"> <span class="c1"># Send the messages to the LLM through Arch</span>
-</span><span id="line-11"> <span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">chat</span><span class="o">.</span><span class="n">completions</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">messages</span><span class="o">=</span><span class="n">messages</span><span class="p">)</span>
+</span><span id="line-10"><span class="c1"># Send the messages to the LLM through Arch</span>
+</span><span id="line-11"><span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">chat</span><span class="o">.</span><span class="n">completions</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">messages</span><span class="o">=</span><span class="n">messages</span><span class="p">)</span>
 </span><span id="line-12">
-</span><span id="line-13"> <span class="c1"># Print the response</span>
-</span><span id="line-14"> <span class="nb">print</span><span class="p">(</span><span class="s2">"LLM Response:"</span><span class="p">,</span> <span class="n">response</span><span class="o">.</span><span class="n">choices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">message</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
+</span><span id="line-13"><span class="c1"># Print the response</span>
+</span><span id="line-14"><span class="nb">print</span><span class="p">(</span><span class="s2">"LLM Response:"</span><span class="p">,</span> <span class="n">response</span><span class="o">.</span><span class="n">choices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">message</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
 </span></code></pre></div>
 </div>
 </section>
@@ -258,7 +269,7 @@ You can easily configure an LLM that communicates over the OpenAI API interface,
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concepts/prompt_target.html

@@ -366,7 +366,7 @@ Each parameter can be marked as required or optional. Here is a full list of par
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concepts/tech_overview/error_target.html

@@ -233,7 +233,7 @@ The errors are communicated to the application via headers like <code class="doc
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concepts/tech_overview/listener.html

@@ -236,7 +236,7 @@ address like <code class="docutils literal notranslate"><span class="pre">arch.l
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concepts/tech_overview/model_serving.html

@@ -224,7 +224,7 @@ how to generate API keys for model serving</p>
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concepts/tech_overview/prompt.html

@@ -396,7 +396,7 @@ This setup allows you to take advantage of Arch’s advanced traffic management
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concepts/tech_overview/request_lifecycle.html

@@ -396,7 +396,7 @@ processing request headers and then finalized by the HCM during post-request pro
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concepts/tech_overview/tech_overview.html

@@ -220,7 +220,7 @@
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concepts/tech_overview/terminology.html

@@ -217,7 +217,7 @@ and take appropriate actions.</p>
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concepts/tech_overview/threading_model.html

@@ -195,7 +195,7 @@ hardware threads on the machine.</p>
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get_started/intro_to_arch.html

@@ -239,7 +239,7 @@ For more details, read <a class="reference internal" href="../guides/observabili
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get_started/overview.html

@@ -332,7 +332,7 @@ simplify the interaction with upstream LLMs, and improve observability all while
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get_started/quickstart.html

@@ -436,7 +436,7 @@
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guides/agent_routing.html

@@ -291,7 +291,7 @@ please see our <a class="reference external" href="https://github.com/katanemo/a
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guides/function_calling.html

@@ -367,7 +367,7 @@ on the stuff that matters most.</p>
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guides/llm_router.html

@@ -321,7 +321,7 @@ Our preference-aligned approach matches practical definitions of performance in
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guides/observability/access_logging.html

@@ -243,7 +243,7 @@ Access logs can be exported to centralized logging systems (e.g., ELK stack or F
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+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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guides/observability/monitoring.html

@@ -255,7 +255,7 @@ are some sample configuration files for both, respectively.</p>
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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guides/observability/observability.html

@@ -207,7 +207,7 @@
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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guides/observability/tracing.html

@@ -577,7 +577,7 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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guides/prompt_guard.html

@@ -268,7 +268,7 @@ By implementing Prompt Guard, developers can provide a robust layer of input val
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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index.html

@@ -248,7 +248,7 @@ Resources</label><div class="sd-tab-content docutils">
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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resources/configuration_reference.html

@@ -270,7 +270,7 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
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-<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 26, 2025. </p>
+<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Jul 27, 2025. </p>
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search.html

@@ -215,7 +215,7 @@
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-        <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc&nbsp;Last updated: Jul 26, 2025.&nbsp;</p>
+        <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc&nbsp;Last updated: Jul 27, 2025.&nbsp;</p>
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