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<section id="terminology">
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<span id="arch-terminology"></span><h1>Terminology<a class="headerlink" href="#terminology" title="Link to this heading">#</a></h1>
|
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<p>A few definitions before we dive into the main architecture documentation. Arch borrows from Envoy’s terminology
|
||||
to keep things consistent in logs, traces and in code.</p>
|
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<p><strong>Downstream(Ingress)</strong>: An downstream client (web application, etc.) connects to Arch, sends prompts, and receives responses.</p>
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<p><strong>Upstream(Egress)</strong>: An upstream host that receives connections and prompts from Arch, and returns context or responses for a prompt</p>
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<a class="reference internal image-reference" href="../../../_images/network-topology-ingress-egress.jpg"><img alt="../../../_images/network-topology-ingress-egress.jpg" class="align-center" src="../../../_images/network-topology-ingress-egress.jpg" style="width: 100%;" />
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<p><strong>Listener</strong>: A listener is a named network location (e.g., port, address, path etc.) that Arch listens on to process prompts
|
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before forwarding them to your application server endpoints. rch enables you to configure one listener for downstream connections
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#upstream-egress">Upstream (Egress)</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#configure-listener">Configure Listener</a></li>
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<section id="listener">
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<span id="arch-overview-listeners"></span><h1>Listener<a class="headerlink" href="#listener" title="Link to this heading">#</a></h1>
|
||||
<p>Listener is a top level primitive in Arch, which simplifies the configuration required to bind incoming
|
||||
connections from downstream clients, and for egress connections to LLMs (hosted or API)</p>
|
||||
<p>Arch builds on Envoy’s Listener subsystem to streamline connection managemet for developers. Arch minimizes
|
||||
the complexity of Envoy’s listener setup by using best-practices and exposing only essential settings,
|
||||
making it easier for developers to bind connections without deep knowledge of Envoy’s configuration model. This
|
||||
simplification ensures that connections are secure, reliable, and optimized for performance.</p>
|
||||
<section id="downstream-ingress">
|
||||
<h2>Downstream (Ingress)<a class="headerlink" href="#downstream-ingress" title="Link to this heading">#</a></h2>
|
||||
<p>Developers can configure Arch to accept connections from downstream clients. A downstream listener acts as the
|
||||
primary entry point for incoming traffic, handling initial connection setup, including network filtering, gurdrails,
|
||||
and additional network security checks. For more details on prompt security and safety,
|
||||
see <a class="reference internal" href="../prompt_processing/prompt_processing.html#arch-overview-prompt-handling"><span class="std std-ref">here</span></a></p>
|
||||
</section>
|
||||
<section id="upstream-egress">
|
||||
<h2>Upstream (Egress)<a class="headerlink" href="#upstream-egress" title="Link to this heading">#</a></h2>
|
||||
<p>Arch automatically configures a listener to route requests from your application to upstream LLM API providers (or hosts).
|
||||
When you start Arch, it creates a listener for egress traffic based on the presence of the <code class="docutils literal notranslate"><span class="pre">llm_providers</span></code> configuration
|
||||
section in the <code class="docutils literal notranslate"><span class="pre">prompt_config.yml</span></code> file. Arch binds itself to a local address such as <code class="docutils literal notranslate"><span class="pre">127.0.0.1:9000/v1</span></code> or a DNS-based
|
||||
address like <code class="docutils literal notranslate"><span class="pre">arch.local:9000/v1</span></code> for outgoing traffic. For more details on LLM providers, read <a class="reference internal" href="llm_provider.html#llm-providers"><span class="std std-ref">here</span></a></p>
|
||||
</section>
|
||||
<section id="configure-listener">
|
||||
<h2>Configure Listener<a class="headerlink" href="#configure-listener" title="Link to this heading">#</a></h2>
|
||||
<p>To configure a Downstream (Ingress) Listner, simply add the <code class="docutils literal notranslate"><span class="pre">listener</span></code> directive to your <code class="docutils literal notranslate"><span class="pre">prompt_config.yml</span></code> file:</p>
|
||||
<div class="literal-block-wrapper docutils container" id="id1">
|
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<div class="code-block-caption"><span class="caption-text"><a class="reference download internal" download="" href="../../../_downloads/a0b0970c8fa22d4fe861e8362965b77c/getting-started.yml"><code class="xref download docutils literal notranslate"><span class="pre">arch-getting-started.yml</span></code></a></span><a class="headerlink" href="#id1" title="Link to this code">#</a></div>
|
||||
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="s">"0.1-beta"</span>
|
||||
<span class="hll"><span class="linenos"> 2</span><span class="nt">listener</span><span class="p">:</span>
|
||||
</span><span class="hll"><span class="linenos"> 3</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1 | 0.0.0.0</span>
|
||||
</span><span class="hll"><span class="linenos"> 4</span><span class="w"> </span><span class="nt">port_value</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8080</span><span class="w"> </span><span class="c1">#If you configure port 443, you'll need to update the listener with tls_certificates</span>
|
||||
</span><span class="hll"><span class="linenos"> 5</span><span class="w"> </span><span class="nt">messages</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">tuple | hugging-face-messages-api</span>
|
||||
</span><span class="linenos"> 6</span>
|
||||
<span class="linenos"> 7</span><span class="nt">system_prompts</span><span class="p">:</span>
|
||||
<span class="linenos"> 8</span><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">network_assistant</span>
|
||||
<span class="linenos"> 9</span><span class="w"> </span><span class="nt">content</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">You are a network assistant that just offers facts about the operational health of the network</span>
|
||||
<span class="linenos">10</span>
|
||||
<span class="linenos">11</span><span class="nt">llm_providers</span><span class="p">:</span>
|
||||
<span class="linenos">12</span><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="s">"OpenAI"</span>
|
||||
<span class="linenos">13</span><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">$OPEN_AI_KEY</span>
|
||||
<span class="linenos">14</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span>
|
||||
<span class="linenos">15</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
<span class="linenos">16</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="s">"Mistral"</span>
|
||||
<span class="linenos">17</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_KEY</span>
|
||||
<span class="linenos">18</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">mixtral8-7B</span>
|
||||
</pre></div>
|
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<h1>LLM Provider</h1>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#example-using-the-arch-python-sdk">Example: Using the Arch Python SDK</a></li>
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<section id="llm-provider">
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<span id="llm-providers"></span><h1>LLM Provider<a class="headerlink" href="#llm-provider" title="Link to this heading">#</a></h1>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">llm_provider</span></code> is a top-level primitive in Arch, helping developers centrally define, secure, observe,
|
||||
and manage the usage of of their LLMs. Arch builds on Envoy’s reliable <a class="reference external" href="https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/upstream/cluster_manager">cluster subsystem</a>
|
||||
to manage egress traffic to LLMs, which includes intelligent routing, retry and fail-over mechanisms,
|
||||
ensuring high availability and fault tolerance. This abstraction also enables developers to seamlessly switching between LLM providers or upgrade LLM versions, simplifying the integration and scaling of LLMs across
|
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applications.</p>
|
||||
<p>Below is an example of how you can configure <code class="docutils literal notranslate"><span class="pre">llm_providers</span></code> with an instance of an Arch gateway.</p>
|
||||
<div class="literal-block-wrapper docutils container" id="id1">
|
||||
<div class="code-block-caption"><span class="caption-text"><a class="reference download internal" download="" href="../../../_downloads/a0b0970c8fa22d4fe861e8362965b77c/getting-started.yml"><code class="xref download docutils literal notranslate"><span class="pre">arch-getting-started.yml</span></code></a></span><a class="headerlink" href="#id1" title="Link to this code">#</a></div>
|
||||
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="s">"0.1-beta"</span>
|
||||
<span class="linenos"> 2</span><span class="nt">listener</span><span class="p">:</span>
|
||||
<span class="linenos"> 3</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1 | 0.0.0.0</span>
|
||||
<span class="linenos"> 4</span><span class="w"> </span><span class="nt">port_value</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8080</span><span class="w"> </span><span class="c1">#If you configure port 443, you'll need to update the listener with tls_certificates</span>
|
||||
<span class="linenos"> 5</span><span class="w"> </span><span class="nt">messages</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">tuple | hugging-face-messages-api</span>
|
||||
<span class="linenos"> 6</span>
|
||||
<span class="linenos"> 7</span><span class="nt">system_prompts</span><span class="p">:</span>
|
||||
<span class="linenos"> 8</span><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">network_assistant</span>
|
||||
<span class="linenos"> 9</span><span class="w"> </span><span class="nt">content</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">You are a network assistant that just offers facts about the operational health of the network</span>
|
||||
<span class="linenos">10</span>
|
||||
<span class="hll"><span class="linenos">11</span><span class="nt">llm_providers</span><span class="p">:</span>
|
||||
</span><span class="hll"><span class="linenos">12</span><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="s">"OpenAI"</span>
|
||||
</span><span class="hll"><span class="linenos">13</span><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">$OPEN_AI_KEY</span>
|
||||
</span><span class="hll"><span class="linenos">14</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span>
|
||||
</span><span class="hll"><span class="linenos">15</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
</span><span class="hll"><span class="linenos">16</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="s">"Mistral"</span>
|
||||
</span><span class="hll"><span class="linenos">17</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_KEY</span>
|
||||
</span><span class="hll"><span class="linenos">18</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">mixtral8-7B</span>
|
||||
</span><span class="linenos">19</span>
|
||||
<span class="linenos">20</span><span class="nt">prompt_endpoints</span><span class="p">:</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>When you start Arch, it creates a listener port for egress traffic based on the presence of <code class="docutils literal notranslate"><span class="pre">llm_providers</span></code>
|
||||
configuration section in the <code class="docutils literal notranslate"><span class="pre">prompt_config.yml</span></code> file. Arch binds itself to a local address such as
|
||||
<code class="docutils literal notranslate"><span class="pre">127.0.0.1:9000/v1</span></code> or a DNS-based address like <code class="docutils literal notranslate"><span class="pre">arch.local:9000/v1</span></code> for egress traffic.</p>
|
||||
</div>
|
||||
<p>Arch also offers vendor-agnostic SDKs and libraries to make LLM calls to API-based LLM providers (like OpenAI,
|
||||
Anthropic, Mistral, Cohere, etc.) and supports calls to OSS LLMs that are hosted on your infrastructure. Arch
|
||||
abstracts the complexities of integrating with different LLM providers, providing a unified interface for making
|
||||
calls, handling retries, managing rate limits, and ensuring seamless integration with cloud-based and on-premise
|
||||
LLMs. Simply configure the details of the LLMs your application will use, and Arch offers a unified interface to
|
||||
make outbound LLM calls.</p>
|
||||
</section>
|
||||
<section id="example-using-the-arch-python-sdk">
|
||||
<h1>Example: Using the Arch Python SDK<a class="headerlink" href="#example-using-the-arch-python-sdk" title="Link to this heading">#</a></h1>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">arch_client</span> <span class="kn">import</span> <span class="n">ArchClient</span>
|
||||
|
||||
<span class="c1"># Initialize the Arch client</span>
|
||||
<span class="n">client</span> <span class="o">=</span> <span class="n">ArchClient</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.1:9000/v1"</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Define your LLM provider and prompt</span>
|
||||
<span class="n">model_id</span> <span class="o">=</span> <span class="s2">"openai"</span>
|
||||
<span class="n">prompt</span> <span class="o">=</span> <span class="s2">"What is the capital of France?"</span>
|
||||
|
||||
<span class="c1"># Send the prompt to the LLM through Arch</span>
|
||||
<span class="n">response</span> <span class="o">=</span> <span class="n">client</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">llm_provider</span><span class="o">=</span><span class="n">llm_provider</span><span class="p">,</span> <span class="n">prompt</span><span class="o">=</span><span class="n">prompt</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Print the response</span>
|
||||
<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="p">)</span>
|
||||
</pre></div>
|
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<h1>Model Serving</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#local-serving-cpu-moderate">Local Serving (CPU - Moderate)</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#local-serving-gpu-fast">Local Serving (GPU- Fast)</a></li>
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<section id="model-serving">
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<span id="arch-model-serving"></span><h1>Model Serving<a class="headerlink" href="#model-serving" title="Link to this heading">#</a></h1>
|
||||
<p>Arch is a set of <strong>two</strong> self-contained processes that are designed to run alongside your application
|
||||
servers (or on a separate host connected via a network). The first process is designated to manage low-level
|
||||
networking and HTTP related comcerns, and the other process is for <strong>model serving</strong>, which helps Arch make
|
||||
intelligent decisions about the incoming prompts. The model server is designed to call the purpose-built
|
||||
<a class="reference internal" href="../../../llms/llms.html#llms-in-arch"><span class="std std-ref">LLMs</span></a> in Arch.</p>
|
||||
<a class="reference internal image-reference" href="../../../_images/arch-system-architecture.jpg"><img alt="../../../_images/arch-system-architecture.jpg" class="align-center" src="../../../_images/arch-system-architecture.jpg" style="width: 50%;" />
|
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</a>
|
||||
<hr class="docutils" />
|
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<p>Arch’ is designed to be deployed in your cloud VPC, on a on-premises host, and can work on devices that don’t
|
||||
have a GPU. Note, GPU devices are need for fast and cost-efficient use, so that Arch (model server, specifically)
|
||||
can process prompts quickly and forward control back to the applicaton host. There are three modes in which Arch
|
||||
can be configured to run its <strong>model server</strong> subsystem:</p>
|
||||
<section id="local-serving-cpu-moderate">
|
||||
<h2>Local Serving (CPU - Moderate)<a class="headerlink" href="#local-serving-cpu-moderate" title="Link to this heading">#</a></h2>
|
||||
<p>The following bash commands enable you to configure the model server subsystem in Arch to run local on device
|
||||
and only use CPU devices. This will be the slowest option but can be useful in dev/test scenarios where GPUs
|
||||
might not be available.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>archgw<span class="w"> </span>up<span class="w"> </span>--local<span class="w"> </span>-cpu
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="local-serving-gpu-fast">
|
||||
<h2>Local Serving (GPU- Fast)<a class="headerlink" href="#local-serving-gpu-fast" title="Link to this heading">#</a></h2>
|
||||
<p>The following bash commands enable you to configure the model server subsystem in Arch to run locally on the
|
||||
machine and utilize the GPU available for fast inference across all model use cases, including function calling
|
||||
guardails, etc.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>archgw<span class="w"> </span>up<span class="w"> </span>--local
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="cloud-serving-gpu-blazing-fast">
|
||||
<h2>Cloud Serving (GPU - Blazing Fast)<a class="headerlink" href="#cloud-serving-gpu-blazing-fast" title="Link to this heading">#</a></h2>
|
||||
<p>The command below instructs Arch to intelligently use GPUs locally for fast intent detection, but default to
|
||||
cloud serving for function calling and guardails scenarios to dramatically improve the speed and overall performance
|
||||
of your applications.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>archgw<span class="w"> </span>up
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
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|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div id="jb-print-docs-body" class="onlyprint">
|
||||
<h1>Prompts</h1>
|
||||
<!-- Table of contents -->
|
||||
<div id="print-main-content">
|
||||
<div id="jb-print-toc">
|
||||
|
||||
<div>
|
||||
<h2> Contents </h2>
|
||||
</div>
|
||||
<nav aria-label="Page">
|
||||
<ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Prompts</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#messages">Messages</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompt-guardrails">Prompt Guardrails</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompt-targets">Prompt Targets</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#intent-detection-and-prompt-matching">Intent Detection and Prompt Matching:</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#agentic-apps-via-prompt-targets">Agentic Apps via Prompt Targets</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompting-llms">Prompting LLMs</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-using-the-arch-python-sdk">Example: Using the Arch Python SDK</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-using-openai-client-with-arch-as-an-egress-gateway">Example: Using OpenAI Client with Arch as an Egress Gateway</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
</nav>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div id="searchbox"></div>
|
||||
<article class="bd-article">
|
||||
|
||||
<section id="prompts">
|
||||
<span id="arch-overview-prompt-handling"></span><h1>Prompts<a class="headerlink" href="#prompts" title="Link to this heading">#</a></h1>
|
||||
<p>Arch’s primary design point is to securely accept, process and handle prompts. To do that effectively,
|
||||
Arch relies on Envoy’s HTTP <a class="reference external" href="https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/http/http_connection_management">connection management</a>,
|
||||
subsystem and its <strong>prompt handler</strong> subsystem engineered with purpose-built <a class="reference internal" href="../../../llms/llms.html#llms-in-arch"><span class="std std-ref">LLMs</span></a> to
|
||||
implement critical functionality on behalf of developers so that you can stay focused on business logic.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Arch’s <strong>prompt handler</strong> subsystem interacts with the <strong>model</strong> subsytem through Envoy’s cluster manager
|
||||
system to ensure robust, resilient and fault-tolerant experience in managing incoming prompts. Read more
|
||||
about the <a class="reference internal" href="../model_serving/model_serving.html#arch-model-serving"><span class="std std-ref">model subsystem</span></a> and how the LLMs are hosted in Arch.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="messages">
|
||||
<h1>Messages<a class="headerlink" href="#messages" title="Link to this heading">#</a></h1>
|
||||
<p>Arch accepts messages directly from the body of the HTTP request in a format that follows the <a class="reference external" href="https://huggingface.co/docs/text-generation-inference/en/messages_api">Hugging Face Messages API</a>.
|
||||
This design allows developers to pass a list of messages, where each message is represented as a dictionary
|
||||
containing two key-value pairs:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p><strong>Role</strong>: Defines the role of the message sender, such as “user” or “assistant”.</p></li>
|
||||
<li><p><strong>Content</strong>: Contains the actual text of the message.</p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
<section id="prompt-guardrails">
|
||||
<h1>Prompt Guardrails<a class="headerlink" href="#prompt-guardrails" title="Link to this heading">#</a></h1>
|
||||
<p>Arch is engineered with <a class="reference internal" href="../../../llms/llms.html#llms-in-arch"><span class="std std-ref">Arch-Guard</span></a>, an industry leading safety layer, powered by a
|
||||
compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
|
||||
ensuring that unauthorized or harmful behaviors are intercepted early in the process.</p>
|
||||
<p>To add jailbreak guardrails, see example below:</p>
|
||||
<div class="literal-block-wrapper docutils container" id="id1">
|
||||
<div class="code-block-caption"><span class="caption-text"><a class="reference download internal" download="" href="../../../_downloads/a0b0970c8fa22d4fe861e8362965b77c/getting-started.yml"><code class="xref download docutils literal notranslate"><span class="pre">arch-getting-started.yml</span></code></a></span><a class="headerlink" href="#id1" title="Link to this code">#</a></div>
|
||||
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="s">"0.1-beta"</span>
|
||||
<span class="linenos"> 2</span><span class="nt">listener</span><span class="p">:</span>
|
||||
<span class="linenos"> 3</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1 | 0.0.0.0</span>
|
||||
<span class="linenos"> 4</span><span class="w"> </span><span class="nt">port_value</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8080</span><span class="w"> </span><span class="c1">#If you configure port 443, you'll need to update the listener with tls_certificates</span>
|
||||
<span class="linenos"> 5</span><span class="w"> </span><span class="nt">messages</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">tuple | hugging-face-messages-api</span>
|
||||
<span class="linenos"> 6</span>
|
||||
<span class="linenos"> 7</span><span class="nt">system_prompts</span><span class="p">:</span>
|
||||
<span class="linenos"> 8</span><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">network_assistant</span>
|
||||
<span class="linenos"> 9</span><span class="w"> </span><span class="nt">content</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">You are a network assistant that just offers facts about the operational health of the network</span>
|
||||
<span class="linenos">10</span>
|
||||
<span class="linenos">11</span><span class="nt">llm_providers</span><span class="p">:</span>
|
||||
<span class="linenos">12</span><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="s">"OpenAI"</span>
|
||||
<span class="linenos">13</span><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">$OPEN_AI_KEY</span>
|
||||
<span class="linenos">14</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span>
|
||||
<span class="linenos">15</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
<span class="linenos">16</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="s">"Mistral"</span>
|
||||
<span class="linenos">17</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_KEY</span>
|
||||
<span class="linenos">18</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">mixtral8-7B</span>
|
||||
<span class="linenos">19</span>
|
||||
<span class="linenos">20</span><span class="nt">prompt_endpoints</span><span class="p">:</span>
|
||||
<span class="linenos">21</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">"http://127.0.0.2"</span>
|
||||
<span class="linenos">22</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">"http://127.0.0.1"</span>
|
||||
<span class="linenos">23</span>
|
||||
<span class="hll"><span class="linenos">24</span><span class="nt">prompt_guards</span><span class="p">:</span>
|
||||
</span><span class="hll"><span class="linenos">25</span><span class="w"> </span><span class="nt">input-guard</span><span class="p">:</span>
|
||||
</span><span class="hll"><span class="linenos">26</span><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="c1">#jailbreak</span>
|
||||
</span><span class="hll"><span class="linenos">27</span><span class="w"> </span><span class="nt">on-exception-message</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Looks like you are curious about my abilities. But I can only</span>
|
||||
</span><span class="linenos">28</span>
|
||||
<span class="linenos">29</span><span class="nt">prompt_targets</span><span class="p">:</span>
|
||||
<span class="linenos">30</span><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">information_extraction</span>
|
||||
<span class="linenos">31</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">RAG</span>
|
||||
<span class="linenos">32</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">this prompt handles all information extractions scenarios</span>
|
||||
<span class="linenos">33</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/agent/summary</span>
|
||||
<span class="linenos">34</span>
|
||||
<span class="linenos">35</span><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">reboot_network_device</span>
|
||||
<span class="linenos">36</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/agent/action</span>
|
||||
<span class="linenos">37</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">used to help network operators with perform device operations like rebooting a device.</span>
|
||||
<span class="linenos">38</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
<span class="linenos">39</span><span class="nt">error_target</span><span class="p">:</span><span class="w"> </span><span class="c1">#handle errors from Bolt or upstream LLMs</span>
|
||||
<span class="linenos">40</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">“error_handler”</span>
|
||||
<span class="linenos">41</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/errors</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>As a roadmap item, Arch will expose the ability for developers to define custom guardrails via Arch-Guard-v2,
|
||||
and add support for additional safety checks defined by developers and hazardous categories like, violent crimes, privacy, hate,
|
||||
etc. To offer feedback on our roadmap, please visit our <a class="reference external" href="https://github.com/orgs/katanemo/projects/1">github page</a></p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="prompt-targets">
|
||||
<h1>Prompt Targets<a class="headerlink" href="#prompt-targets" title="Link to this heading">#</a></h1>
|
||||
<p>Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation
|
||||
and identifies where to forwad the conversation to via its essential <code class="docutils literal notranslate"><span class="pre">prompt_targets</span></code> primitve. Prompt targets
|
||||
are endpoints that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with
|
||||
metadata like knowing when a user’s intent has changed so that you can build faster, more accurate RAG apps.</p>
|
||||
<p>Configuring <code class="docutils literal notranslate"><span class="pre">prompt_targets</span></code> is simple. See example below:</p>
|
||||
<div class="literal-block-wrapper docutils container" id="id2">
|
||||
<div class="code-block-caption"><span class="caption-text"><a class="reference download internal" download="" href="../../../_downloads/a0b0970c8fa22d4fe861e8362965b77c/getting-started.yml"><code class="xref download docutils literal notranslate"><span class="pre">arch-getting-started.yml</span></code></a></span><a class="headerlink" href="#id2" title="Link to this code">#</a></div>
|
||||
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="nt">version</span><span class="p">:</span><span class="w"> </span><span class="s">"0.1-beta"</span>
|
||||
<span class="linenos"> 2</span><span class="nt">listener</span><span class="p">:</span>
|
||||
<span class="linenos"> 3</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1 | 0.0.0.0</span>
|
||||
<span class="linenos"> 4</span><span class="w"> </span><span class="nt">port_value</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8080</span><span class="w"> </span><span class="c1">#If you configure port 443, you'll need to update the listener with tls_certificates</span>
|
||||
<span class="linenos"> 5</span><span class="w"> </span><span class="nt">messages</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">tuple | hugging-face-messages-api</span>
|
||||
<span class="linenos"> 6</span>
|
||||
<span class="linenos"> 7</span><span class="nt">system_prompts</span><span class="p">:</span>
|
||||
<span class="linenos"> 8</span><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">network_assistant</span>
|
||||
<span class="linenos"> 9</span><span class="w"> </span><span class="nt">content</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">You are a network assistant that just offers facts about the operational health of the network</span>
|
||||
<span class="linenos">10</span>
|
||||
<span class="linenos">11</span><span class="nt">llm_providers</span><span class="p">:</span>
|
||||
<span class="linenos">12</span><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="s">"OpenAI"</span>
|
||||
<span class="linenos">13</span><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">$OPEN_AI_KEY</span>
|
||||
<span class="linenos">14</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span>
|
||||
<span class="linenos">15</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
<span class="linenos">16</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="s">"Mistral"</span>
|
||||
<span class="linenos">17</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_KEY</span>
|
||||
<span class="linenos">18</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">mixtral8-7B</span>
|
||||
<span class="linenos">19</span>
|
||||
<span class="linenos">20</span><span class="nt">prompt_endpoints</span><span class="p">:</span>
|
||||
<span class="linenos">21</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">"http://127.0.0.2"</span>
|
||||
<span class="linenos">22</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">"http://127.0.0.1"</span>
|
||||
<span class="linenos">23</span>
|
||||
<span class="linenos">24</span><span class="nt">prompt_guards</span><span class="p">:</span>
|
||||
<span class="linenos">25</span><span class="w"> </span><span class="nt">input-guard</span><span class="p">:</span>
|
||||
<span class="linenos">26</span><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="c1">#jailbreak</span>
|
||||
<span class="linenos">27</span><span class="w"> </span><span class="nt">on-exception-message</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Looks like you are curious about my abilities. But I can only</span>
|
||||
<span class="linenos">28</span>
|
||||
<span class="hll"><span class="linenos">29</span><span class="nt">prompt_targets</span><span class="p">:</span>
|
||||
</span><span class="hll"><span class="linenos">30</span><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">information_extraction</span>
|
||||
</span><span class="hll"><span class="linenos">31</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">RAG</span>
|
||||
</span><span class="hll"><span class="linenos">32</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">this prompt handles all information extractions scenarios</span>
|
||||
</span><span class="hll"><span class="linenos">33</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/agent/summary</span>
|
||||
</span><span class="hll"><span class="linenos">34</span>
|
||||
</span><span class="hll"><span class="linenos">35</span><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">reboot_network_device</span>
|
||||
</span><span class="hll"><span class="linenos">36</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/agent/action</span>
|
||||
</span><span class="hll"><span class="linenos">37</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">used to help network operators with perform device operations like rebooting a device.</span>
|
||||
</span><span class="hll"><span class="linenos">38</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
</span><span class="linenos">39</span><span class="nt">error_target</span><span class="p">:</span><span class="w"> </span><span class="c1">#handle errors from Bolt or upstream LLMs</span>
|
||||
<span class="linenos">40</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">“error_handler”</span>
|
||||
<span class="linenos">41</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/errors</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<section id="intent-detection-and-prompt-matching">
|
||||
<h2>Intent Detection and Prompt Matching:<a class="headerlink" href="#intent-detection-and-prompt-matching" title="Link to this heading">#</a></h2>
|
||||
<p>Arch uses fast Natural Language Inference (NLI) and embedding approaches to first detect the intent of each
|
||||
incoming prompt. This intent detection phase analyzes the prompt’s content and matches it against predefined
|
||||
prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint. Arch’s intent
|
||||
detection framework considers both the name and description of each prompt target, and uses a composite matching
|
||||
score between an NLI and cosine similarity to enchance accuracy in forwarding decisions.</p>
|
||||
<ul class="simple">
|
||||
<li><p><strong>Embeddings</strong>: By embedding the prompt and comparing it to known target vectors, Arch effectively identifies
|
||||
the closest match, ensuring that the prompt is handled by the correct downstream service.</p></li>
|
||||
<li><p><strong>NLI</strong>: NLI techniques further refine the matching process by evaluating the semantic alignment between the
|
||||
prompt and potential targets.</p></li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="agentic-apps-via-prompt-targets">
|
||||
<h2>Agentic Apps via Prompt Targets<a class="headerlink" href="#agentic-apps-via-prompt-targets" title="Link to this heading">#</a></h2>
|
||||
<p>To support agentic apps, like scheduling travel plans or sharing comments on a document - via prompts, Arch uses
|
||||
its function calling abilities to extract critical information from the incoming prompt (or a set of prompts)
|
||||
needed by a downstream backend API or function call before calling it directly. For more details on how you can
|
||||
build agentic applications using Arch, see our full guide <a class="reference internal" href="../../../getting_started/use_cases/function_calling.html#arch-function-calling-agentic-guide"><span class="std std-ref">here</span></a>:</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Arch <a class="reference internal" href="../../../llms/llms.html#llms-in-arch"><span class="std std-ref">Arch-FC</span></a> is the dedicated agentic model engineered in Arch to extract information from
|
||||
a (set of) prompts and executes necessary backend API calls. This allows for efficient handling of agentic tasks,
|
||||
such as scheduling data retrieval, by dynamically interacting with backend services. Arch-FC is a flagship 1.3
|
||||
billion parameter model that matches performance with frontier models like Claude Sonnet 3.5 ang GPT-4, while
|
||||
being 100x cheaper ($0.05M/token hosted) and 10x faster (p50 latencies of 200ms).</p>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
<section id="prompting-llms">
|
||||
<h1>Prompting LLMs<a class="headerlink" href="#prompting-llms" title="Link to this heading">#</a></h1>
|
||||
<p>Arch is a single piece of software that is designed to manage both ingress and egress prompt traffic, drawing its
|
||||
distributed proxy nature from the robust <a class="reference external" href="https://envoyproxy.io">Envoy</a>. This makes it extremely efficient and capable
|
||||
of handling upstream connections to LLMs. If your application is originating code to an API-based LLM, simply use
|
||||
Arch’s Python or JavaScript client SDK to send traffic to the desired LLM of choice. By sending traffic through Arch,
|
||||
you can propagate traces, manage and monitor traffic, apply rate limits, and utilize a large set of traffic management
|
||||
capabilities in a central place.</p>
|
||||
<div class="admonition attention">
|
||||
<p class="admonition-title">Attention</p>
|
||||
<p>When you start Arch, it automatically creates a listener port for egress calls to upstream LLMs. This is based on the
|
||||
<code class="docutils literal notranslate"><span class="pre">llm_providers</span></code> configuration section in the <code class="docutils literal notranslate"><span class="pre">prompt_config.yml</span></code> file. Arch binds itself to a local address such as
|
||||
127.0.0.1:9000/v1 or a DNS-based address like arch.local:9000/v1 for outgoing traffic.</p>
|
||||
</div>
|
||||
<section id="example-using-the-arch-python-sdk">
|
||||
<h2>Example: Using the Arch Python SDK<a class="headerlink" href="#example-using-the-arch-python-sdk" title="Link to this heading">#</a></h2>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">arch_client</span> <span class="kn">import</span> <span class="n">ArchClient</span>
|
||||
|
||||
<span class="c1"># Initialize the Arch client</span>
|
||||
<span class="n">client</span> <span class="o">=</span> <span class="n">ArchClient</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.1:9000/v1"</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Define your LLM provider and prompt</span>
|
||||
<span class="n">model_id</span> <span class="o">=</span> <span class="s2">"openai"</span>
|
||||
<span class="n">prompt</span> <span class="o">=</span> <span class="s2">"What is the capital of France?"</span>
|
||||
|
||||
<span class="c1"># Send the prompt to the LLM through Arch</span>
|
||||
<span class="n">response</span> <span class="o">=</span> <span class="n">client</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">llm_provider</span><span class="o">=</span><span class="n">llm_provider</span><span class="p">,</span> <span class="n">prompt</span><span class="o">=</span><span class="n">prompt</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Print the response</span>
|
||||
<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="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="example-using-openai-client-with-arch-as-an-egress-gateway">
|
||||
<h2>Example: Using OpenAI Client with Arch as an Egress Gateway<a class="headerlink" href="#example-using-openai-client-with-arch-as-an-egress-gateway" title="Link to this heading">#</a></h2>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">openai</span>
|
||||
|
||||
<span class="c1"># Set the OpenAI API base URL to the Arch gateway endpoint</span>
|
||||
<span class="n">openai</span><span class="o">.</span><span class="n">api_base</span> <span class="o">=</span> <span class="s2">"http://127.0.0.1:9000/v1"</span>
|
||||
|
||||
<span class="c1"># No need to set openai.api_key since it's configured in Arch's gateway</span>
|
||||
|
||||
<span class="c1"># Use the OpenAI client as usual</span>
|
||||
<span class="n">response</span> <span class="o">=</span> <span class="n">openai</span><span class="o">.</span><span class="n">Completion</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="s2">"text-davinci-003"</span><span class="p">,</span>
|
||||
<span class="n">prompt</span><span class="o">=</span><span class="s2">"What is the capital of France?"</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"OpenAI 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">text</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In these examples:</p>
|
||||
<blockquote>
|
||||
<div><p>The ArchClient is used to send traffic directly through the Arch egress proxy to the LLM of your choice, such as OpenAI.
|
||||
The OpenAI client is configured to route traffic via Arch by setting the proxy to 127.0.0.1:9000, assuming Arch is
|
||||
running locally and bound to that address and port.</p>
|
||||
</div></blockquote>
|
||||
<p>This setup allows you to take advantage of Arch’s advanced traffic management features while interacting with LLM APIs like OpenAI.</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
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<i class="fa-solid fa-list"></i> Contents
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<nav class="bd-toc-nav page-toc">
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Prompts</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#messages">Messages</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompt-guardrails">Prompt Guardrails</a></li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompt-targets">Prompt Targets</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#intent-detection-and-prompt-matching">Intent Detection and Prompt Matching:</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#agentic-apps-via-prompt-targets">Agentic Apps via Prompt Targets</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#prompting-llms">Prompting LLMs</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-using-the-arch-python-sdk">Example: Using the Arch Python SDK</a></li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-using-openai-client-with-arch-as-an-egress-gateway">Example: Using OpenAI Client with Arch as an Egress Gateway</a></li>
|
||||
</ul>
|
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</li>
|
||||
</ul>
|
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