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<span id="arch-agent-guide"></span><h1>Agentic Workflow<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#agentic-workflow"><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></h1>
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||||
<p>Arch helps you easily personalize your applications by calling application-specific (API) functions
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||||
via user prompts. This involves any predefined functions or APIs you want to expose to users to perform tasks,
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||||
gather information, or manipulate data. This capability is generally referred to as <strong>function calling</strong>, where
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||||
gather information, or manipulate data. This capability is generally referred to as <a class="reference internal" href="../guides/function_calling.html#function-calling"><span class="std std-ref">function calling</span></a>, where
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||||
you have the flexibility to support “agentic” apps tailored to specific use cases - from updating insurance
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||||
claims to creating ad campaigns - via prompts.</p>
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<p>Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation with
|
||||
the user to gather any missing parameters and makes API calls so that you can focus on writing business logic.
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||||
Arch does this via its purpose-built <a class="reference internal" href="../guides/function_calling.html#function-calling"><span class="std std-ref">Arch-Function</span></a> - the fastest (200ms p90 - 10x faser than GPT-4o)
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||||
and cheapest (100x than GPT-40) function-calling LLM that matches performance with frontier models.</p>
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||||
Arch does this via its purpose-built <a class="reference external" href="https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68" rel="nofollow noopener">Arch-Function<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> - the fastest (200ms p90 - 10x faser than GPT-4o)
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and cheapest (100x than GPT-4o) function calling LLM that matches performance with frontier models.</p>
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<a class="reference internal image-reference" href="../_images/function-calling-flow.jpg"><img alt="../_images/function-calling-flow.jpg" class="align-center" src="../_images/function-calling-flow.jpg" style="width: 100%;"/>
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</a>
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<section id="single-function-call">
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@ -191,9 +191,9 @@ is how you would go about enabling this scenario with Arch:</p>
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|||
</span><span id="line-16"><span class="linenos">16</span><span class="nt">system_prompt</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">|</span>
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||||
</span><span id="line-17"><span class="linenos">17</span><span class="w"> </span><span class="no">You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.</span>
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||||
</span><span id="line-18"><span class="linenos">18</span>
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||||
</span><span id="line-19"><span class="linenos">19</span><span class="nt">prompt_targets</span><span class="p">:</span>
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||||
</span><span id="line-20"><span class="linenos">20</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_qa</span>
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||||
</span><span id="line-21"><mark><span class="linenos">21</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
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</span><span id="line-19"><mark><span class="linenos">19</span><span class="nt">prompt_targets</span><span class="p">:</span>
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||||
</mark></span><span id="line-20"><mark><span class="linenos">20</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_qa</span>
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</mark></span><span id="line-21"><mark><span class="linenos">21</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
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</mark></span><span id="line-22"><mark><span class="linenos">22</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">app_server</span>
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||||
</mark></span><span id="line-23"><mark><span class="linenos">23</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/network_summary</span>
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||||
</mark></span><span id="line-24"><mark><span class="linenos">24</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">Handle general Q/A related to networking.</span>
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||||
|
|
@ -207,22 +207,22 @@ is how you would go about enabling this scenario with Arch:</p>
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|||
</mark></span><span id="line-32"><mark><span class="linenos">32</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">device_ids</span>
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||||
</mark></span><span id="line-33"><mark><span class="linenos">33</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">list</span>
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||||
</mark></span><span id="line-34"><mark><span class="linenos">34</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">A list of device identifiers (IDs) to reboot.</span>
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||||
</mark></span><span id="line-35"><span class="linenos">35</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
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||||
</span><span id="line-36"><span class="linenos">36</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">device_summary</span>
|
||||
</span><span id="line-37"><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">Retrieve statistics for specific devices within a time range</span>
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||||
</span><span id="line-38"><span class="linenos">38</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
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||||
</span><span id="line-39"><span class="linenos">39</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">app_server</span>
|
||||
</span><span id="line-40"><span class="linenos">40</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/device_summary</span>
|
||||
</span><span id="line-41"><span class="linenos">41</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
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||||
</span><span id="line-42"><span class="linenos">42</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">device_ids</span>
|
||||
</span><span id="line-43"><span class="linenos">43</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">list</span>
|
||||
</span><span id="line-44"><span class="linenos">44</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">A list of device identifiers (IDs) to retrieve statistics for.</span>
|
||||
</span><span id="line-45"><span class="linenos">45</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span><span class="w"> </span><span class="c1"># device_ids are required to get device statistics</span>
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||||
</span><span id="line-46"><span class="linenos">46</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">time_range</span>
|
||||
</span><span id="line-47"><span class="linenos">47</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">int</span>
|
||||
</span><span id="line-48"><span class="linenos">48</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">Time range in days for which to gather device statistics. Defaults to 7.</span>
|
||||
</span><span id="line-49"><span class="linenos">49</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="s">"7"</span>
|
||||
</span><span id="line-50"><span class="linenos">50</span>
|
||||
</mark></span><span id="line-35"><mark><span class="linenos">35</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
</mark></span><span id="line-36"><mark><span class="linenos">36</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">device_summary</span>
|
||||
</mark></span><span id="line-37"><mark><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">Retrieve statistics for specific devices within a time range</span>
|
||||
</mark></span><span id="line-38"><mark><span class="linenos">38</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</mark></span><span id="line-39"><mark><span class="linenos">39</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">app_server</span>
|
||||
</mark></span><span id="line-40"><mark><span class="linenos">40</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/device_summary</span>
|
||||
</mark></span><span id="line-41"><mark><span class="linenos">41</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
</mark></span><span id="line-42"><mark><span class="linenos">42</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">device_ids</span>
|
||||
</mark></span><span id="line-43"><mark><span class="linenos">43</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">list</span>
|
||||
</mark></span><span id="line-44"><mark><span class="linenos">44</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">A list of device identifiers (IDs) to retrieve statistics for.</span>
|
||||
</mark></span><span id="line-45"><mark><span class="linenos">45</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span><span class="w"> </span><span class="c1"># device_ids are required to get device statistics</span>
|
||||
</mark></span><span id="line-46"><mark><span class="linenos">46</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">time_range</span>
|
||||
</mark></span><span id="line-47"><mark><span class="linenos">47</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">int</span>
|
||||
</mark></span><span id="line-48"><mark><span class="linenos">48</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">Time range in days for which to gather device statistics. Defaults to 7.</span>
|
||||
</mark></span><span id="line-49"><mark><span class="linenos">49</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">7</span>
|
||||
</mark></span><span id="line-50"><span class="linenos">50</span>
|
||||
</span><span id="line-51"><span class="linenos">51</span><span class="c1"># Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.</span>
|
||||
</span><span id="line-52"><span class="linenos">52</span><span class="nt">endpoints</span><span class="p">:</span>
|
||||
</span><span id="line-53"><span class="linenos">53</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span>
|
||||
|
|
@ -324,9 +324,9 @@ the user’s intent.</p>
|
|||
</span><span id="line-16"><span class="linenos">16</span><span class="nt">system_prompt</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">|</span>
|
||||
</span><span id="line-17"><span class="linenos">17</span><span class="w"> </span><span class="no">You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.</span>
|
||||
</span><span id="line-18"><span class="linenos">18</span>
|
||||
</span><span id="line-19"><span class="linenos">19</span><span class="nt">prompt_targets</span><span class="p">:</span>
|
||||
</span><span id="line-20"><span class="linenos">20</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_qa</span>
|
||||
</span><span id="line-21"><mark><span class="linenos">21</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</span><span id="line-19"><mark><span class="linenos">19</span><span class="nt">prompt_targets</span><span class="p">:</span>
|
||||
</mark></span><span id="line-20"><mark><span class="linenos">20</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_qa</span>
|
||||
</mark></span><span id="line-21"><mark><span class="linenos">21</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</mark></span><span id="line-22"><mark><span class="linenos">22</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">app_server</span>
|
||||
</mark></span><span id="line-23"><mark><span class="linenos">23</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/network_summary</span>
|
||||
</mark></span><span id="line-24"><mark><span class="linenos">24</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">Handle general Q/A related to networking.</span>
|
||||
|
|
@ -340,22 +340,22 @@ the user’s intent.</p>
|
|||
</mark></span><span id="line-32"><mark><span class="linenos">32</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">device_ids</span>
|
||||
</mark></span><span id="line-33"><mark><span class="linenos">33</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">list</span>
|
||||
</mark></span><span id="line-34"><mark><span class="linenos">34</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">A list of device identifiers (IDs) to reboot.</span>
|
||||
</mark></span><span id="line-35"><span class="linenos">35</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
</span><span id="line-36"><span class="linenos">36</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">device_summary</span>
|
||||
</span><span id="line-37"><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">Retrieve statistics for specific devices within a time range</span>
|
||||
</span><span id="line-38"><span class="linenos">38</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</span><span id="line-39"><span class="linenos">39</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">app_server</span>
|
||||
</span><span id="line-40"><span class="linenos">40</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/device_summary</span>
|
||||
</span><span id="line-41"><span class="linenos">41</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
</span><span id="line-42"><span class="linenos">42</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">device_ids</span>
|
||||
</span><span id="line-43"><span class="linenos">43</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">list</span>
|
||||
</span><span id="line-44"><span class="linenos">44</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">A list of device identifiers (IDs) to retrieve statistics for.</span>
|
||||
</span><span id="line-45"><span class="linenos">45</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span><span class="w"> </span><span class="c1"># device_ids are required to get device statistics</span>
|
||||
</span><span id="line-46"><span class="linenos">46</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">time_range</span>
|
||||
</span><span id="line-47"><span class="linenos">47</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">int</span>
|
||||
</span><span id="line-48"><span class="linenos">48</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">Time range in days for which to gather device statistics. Defaults to 7.</span>
|
||||
</span><span id="line-49"><span class="linenos">49</span><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="s">"7"</span>
|
||||
</span><span id="line-50"><span class="linenos">50</span>
|
||||
</mark></span><span id="line-35"><mark><span class="linenos">35</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
</mark></span><span id="line-36"><mark><span class="linenos">36</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">device_summary</span>
|
||||
</mark></span><span id="line-37"><mark><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">Retrieve statistics for specific devices within a time range</span>
|
||||
</mark></span><span id="line-38"><mark><span class="linenos">38</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</mark></span><span id="line-39"><mark><span class="linenos">39</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">app_server</span>
|
||||
</mark></span><span id="line-40"><mark><span class="linenos">40</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/device_summary</span>
|
||||
</mark></span><span id="line-41"><mark><span class="linenos">41</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
</mark></span><span id="line-42"><mark><span class="linenos">42</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">device_ids</span>
|
||||
</mark></span><span id="line-43"><mark><span class="linenos">43</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">list</span>
|
||||
</mark></span><span id="line-44"><mark><span class="linenos">44</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">A list of device identifiers (IDs) to retrieve statistics for.</span>
|
||||
</mark></span><span id="line-45"><mark><span class="linenos">45</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span><span class="w"> </span><span class="c1"># device_ids are required to get device statistics</span>
|
||||
</mark></span><span id="line-46"><mark><span class="linenos">46</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">time_range</span>
|
||||
</mark></span><span id="line-47"><mark><span class="linenos">47</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">int</span>
|
||||
</mark></span><span id="line-48"><mark><span class="linenos">48</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">Time range in days for which to gather device statistics. Defaults to 7.</span>
|
||||
</mark></span><span id="line-49"><mark><span class="linenos">49</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">7</span>
|
||||
</mark></span><span id="line-50"><span class="linenos">50</span>
|
||||
</span><span id="line-51"><span class="linenos">51</span><span class="c1"># Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.</span>
|
||||
</span><span id="line-52"><span class="linenos">52</span><span class="nt">endpoints</span><span class="p">:</span>
|
||||
</span><span id="line-53"><span class="linenos">53</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span>
|
||||
|
|
|
|||
|
|
@ -157,7 +157,7 @@
|
|||
Retrieval-Augmented Generation (RAG) applications.</p>
|
||||
<section id="parameter-extraction-for-rag">
|
||||
<h2>Parameter Extraction for RAG<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#parameter-extraction-for-rag" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#parameter-extraction-for-rag'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
|
||||
<p>To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters,
|
||||
<p>To build RAG (Retrieval Augmented Generation) applications, you can configure prompt targets with parameters,
|
||||
enabling Arch to retrieve critical information in a structured way for processing. This approach improves the
|
||||
retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull
|
||||
the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can
|
||||
|
|
@ -243,11 +243,11 @@ streamline data retrieval and processing to build more efficient and precise RAG
|
|||
<h2>[Coming Soon] <a class="reference external" href="https://github.com/orgs/katanemo/projects/1/views/1?pane=issue&itemId=82697909" rel="nofollow noopener">Drift Detection via Arch Intent-Markers<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a><a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#coming-soon-drift-detection-via-arch-intent-markers" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#coming-soon-drift-detection-via-arch-intent-markers'"><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>Developers struggle to efficiently handle <code class="docutils literal notranslate"><span class="pre">follow-up</span></code> or <code class="docutils literal notranslate"><span class="pre">clarification</span></code> questions. Specifically, when users ask for
|
||||
changes or additions to previous responses their AI applications often generate entirely new responses instead of adjusting
|
||||
previous ones.Arch offers <strong>intent</strong> tracking as a feature so that developers can know when the user has shifted away from a
|
||||
previous ones. Arch offers <code class="docutils literal notranslate"><span class="pre">intent</span> <span class="pre">tracking</span></code> as a feature so that developers can know when the user has shifted away from a
|
||||
previous intent so that they can dramatically improve retrieval accuracy, lower overall token cost and improve the speed of
|
||||
their responses back to users.</p>
|
||||
<p>Arch uses its built-in lightweight NLI and embedding models to know if the user has steered away from an active intent.
|
||||
Arch’s intent-drift detection mechanism is based on its’ <a class="reference internal" href="../concepts/prompt_target.html#prompt-target"><span class="std std-ref">prompt_targets</span></a> primtive. Arch tries to match an incoming
|
||||
Arch’s intent-drift detection mechanism is based on its <a class="reference internal" href="../concepts/prompt_target.html#prompt-target"><span class="std std-ref">prompt target</span></a> primtive. Arch tries to match an incoming
|
||||
prompt to one of the prompt_targets configured in the gateway. Once it detects that the user has moved away from an active
|
||||
active intent, Arch adds the <code class="docutils literal notranslate"><span class="pre">x-arch-intent-marker</span></code> headers to the request before sending it your application servers.</p>
|
||||
<div class="literal-block-wrapper docutils container" id="id3">
|
||||
|
|
@ -276,8 +276,8 @@ active intent, Arch adds the <code class="docutils literal notranslate"><span cl
|
|||
</mark></span><span id="line-22"><mark><span class="linenos">22</span> <span class="k">return</span> <span class="p">(</span>
|
||||
</mark></span><span id="line-23"><mark><span class="linenos">23</span> <span class="n">jsonify</span><span class="p">({</span><span class="s2">"error"</span><span class="p">:</span> <span class="s2">"Invalid value for x-arch-prompt-intent-change header"</span><span class="p">}),</span>
|
||||
</mark></span><span id="line-24"><mark><span class="linenos">24</span> <span class="mi">400</span><span class="p">,</span>
|
||||
</mark></span><span id="line-25"><span class="linenos">25</span> <span class="p">)</span>
|
||||
</span><span id="line-26"><span class="linenos">26</span>
|
||||
</mark></span><span id="line-25"><mark><span class="linenos">25</span> <span class="p">)</span>
|
||||
</mark></span><span id="line-26"><span class="linenos">26</span>
|
||||
</span><span id="line-27"><span class="linenos">27</span> <span class="c1"># Update user conversation based on intent change</span>
|
||||
</span><span id="line-28"><span class="linenos">28</span> <span class="n">memory</span> <span class="o">=</span> <span class="n">update_user_conversation</span><span class="p">(</span><span class="n">user_id</span><span class="p">,</span> <span class="n">client_messages</span><span class="p">,</span> <span class="n">intent_changed</span><span class="p">)</span>
|
||||
</span><span id="line-29"><span class="linenos">29</span>
|
||||
|
|
@ -315,8 +315,8 @@ active intent, Arch adds the <code class="docutils literal notranslate"><span cl
|
|||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Arch is (mostly) stateless so that it can scale in an embarrassingly parrallel fashion. So, while Arch offers
|
||||
intent-drift detetction, you still have to maintain converational state with intent drift as meta-data. The
|
||||
following code snippets show how easily you can build and enrich conversational history with Langchain (in python),
|
||||
intent-drift detetction, you still have to maintain converational state with intent drift as metadata. The
|
||||
following code snippets show how easily you can build and enrich conversational history with Langchain (in Python),
|
||||
so that you can use the most relevant prompts for your retrieval and for prompting upstream LLMs.</p>
|
||||
</div>
|
||||
<section id="step-1-define-conversationbuffermemory">
|
||||
|
|
|
|||
|
|
@ -188,7 +188,7 @@ across applications.</p>
|
|||
<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">arch_config.yml</span></code> file. Arch binds itself to a local address such as
|
||||
<code class="docutils literal notranslate"><span class="pre">127.0.0.1:51001/v1</span></code>.</p>
|
||||
<code class="docutils literal notranslate"><span class="pre">127.0.0.1:12000</span></code>.</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
|
||||
|
|
@ -201,7 +201,7 @@ make outbound LLM calls.</p>
|
|||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="kn">from</span> <span class="nn">openai</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://127.0.0.1:51001/v1"</span><span class="p">)</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://127.0.0.12000/"</span><span class="p">)</span>
|
||||
</span><span id="line-5">
|
||||
</span><span id="line-6"><span class="c1"># Define your LLM provider and prompt</span>
|
||||
</span><span id="line-7"><span class="n">llm_provider</span> <span class="o">=</span> <span class="s2">"openai"</span>
|
||||
|
|
|
|||
|
|
@ -254,22 +254,22 @@ Here is a full list of parameter attributes that Arch can support:</p>
|
|||
</section>
|
||||
<section id="example-configuration">
|
||||
<h3>Example Configuration<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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-configuration" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#example-configuration'"><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></h3>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="n">prompt_targets</span><span class="p">:</span>
|
||||
</span><span id="line-2"> <span class="o">-</span> <span class="n">name</span><span class="p">:</span> <span class="n">get_weather</span>
|
||||
</span><span id="line-3"> <span class="n">description</span><span class="p">:</span> <span class="n">Get</span> <span class="n">the</span> <span class="n">current</span> <span class="n">weather</span> <span class="k">for</span> <span class="n">a</span> <span class="n">location</span>
|
||||
</span><span id="line-4"> <span class="n">parameters</span><span class="p">:</span>
|
||||
</span><span id="line-5"> <span class="o">-</span> <span class="n">name</span><span class="p">:</span> <span class="n">location</span>
|
||||
</span><span id="line-6"> <span class="n">description</span><span class="p">:</span> <span class="n">The</span> <span class="n">city</span> <span class="ow">and</span> <span class="n">state</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">g</span><span class="o">.</span> <span class="n">San</span> <span class="n">Francisco</span><span class="p">,</span> <span class="n">New</span> <span class="n">York</span>
|
||||
</span><span id="line-7"> <span class="nb">type</span><span class="p">:</span> <span class="nb">str</span>
|
||||
</span><span id="line-8"> <span class="n">required</span><span class="p">:</span> <span class="n">true</span>
|
||||
</span><span id="line-9"> <span class="o">-</span> <span class="n">name</span><span class="p">:</span> <span class="n">unit</span>
|
||||
</span><span id="line-10"> <span class="n">description</span><span class="p">:</span> <span class="n">The</span> <span class="n">unit</span> <span class="n">of</span> <span class="n">temperature</span>
|
||||
</span><span id="line-11"> <span class="nb">type</span><span class="p">:</span> <span class="nb">str</span>
|
||||
</span><span id="line-12"> <span class="n">default</span><span class="p">:</span> <span class="n">fahrenheit</span>
|
||||
</span><span id="line-13"> <span class="n">enum</span><span class="p">:</span> <span class="p">[</span><span class="n">celsius</span><span class="p">,</span> <span class="n">fahrenheit</span><span class="p">]</span>
|
||||
</span><span id="line-14"> <span class="n">endpoint</span><span class="p">:</span>
|
||||
</span><span id="line-15"> <span class="n">name</span><span class="p">:</span> <span class="n">api_server</span>
|
||||
</span><span id="line-16"> <span class="n">path</span><span class="p">:</span> <span class="o">/</span><span class="n">weather</span>
|
||||
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="nt">prompt_targets</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">get_weather</span>
|
||||
</span><span id="line-3"><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">Get the current weather for a location</span>
|
||||
</span><span id="line-4"><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
|
||||
</span><span id="line-5"><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">location</span>
|
||||
</span><span id="line-6"><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">The city and state, e.g. San Francisco, New York</span>
|
||||
</span><span id="line-7"><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">str</span>
|
||||
</span><span id="line-8"><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
|
||||
</span><span id="line-9"><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">unit</span>
|
||||
</span><span id="line-10"><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">The unit of temperature</span>
|
||||
</span><span id="line-11"><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">str</span>
|
||||
</span><span id="line-12"><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">fahrenheit</span>
|
||||
</span><span id="line-13"><span class="w"> </span><span class="nt">enum</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">celsius</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="nv">fahrenheit</span><span class="p p-Indicator">]</span>
|
||||
</span><span id="line-14"><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
|
||||
</span><span id="line-15"><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">api_server</span>
|
||||
</span><span id="line-16"><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">/weather</span>
|
||||
</span></code></pre></div>
|
||||
</div>
|
||||
</section>
|
||||
|
|
|
|||
|
|
@ -162,7 +162,6 @@ The errors are communicated to the application via headers like <code class="doc
|
|||
<ul class="simple">
|
||||
<li><p><strong>Error Type</strong>: Categorizes the nature of the error, such as “ValidationError” or “RuntimeError.” These error types help in identifying what kind of issue occurred and provide context for troubleshooting.</p></li>
|
||||
<li><p><strong>Error Message</strong>: A clear, human-readable message describing the error. This should provide enough detail to inform users or developers of the root cause or required action.</p></li>
|
||||
<li><p><strong>Target Prompt</strong>: The specific prompt or operation where the error occurred. Understanding where the error happened helps with debugging and pinpointing the source of the problem.</p></li>
|
||||
<li><p><strong>Parameter-Specific Errors</strong>: Errors that arise due to invalid or missing parameters when invoking a function. These errors are critical for ensuring the correctness of inputs.</p></li>
|
||||
</ul>
|
||||
</section>
|
||||
|
|
|
|||
|
|
@ -177,7 +177,7 @@ containing two key-value pairs:</p>
|
|||
</section>
|
||||
<section id="prompt-guard">
|
||||
<h2>Prompt Guard<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#prompt-guard" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#prompt-guard'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
|
||||
<p>Arch is engineered with <a class="reference internal" href="../../guides/prompt_guard.html#prompt-guard"><span class="std std-ref">Arch-Guard</span></a>, an industry leading safety layer, powered by a
|
||||
<p>Arch is engineered with <a class="reference external" href="https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d" rel="nofollow noopener">Arch-Guard<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, an industry leading safety layer, powered by a
|
||||
compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
|
||||
ensuring that unauthorized or harmful behaviors are intercepted early in the process.</p>
|
||||
<p>To add jailbreak guardrails, see example below:</p>
|
||||
|
|
@ -221,7 +221,7 @@ etc. To offer feedback on our roadmap, please visit our <a class="reference exte
|
|||
<section id="prompt-targets">
|
||||
<h2>Prompt Targets<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#prompt-targets" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#prompt-targets'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
|
||||
<p>Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation
|
||||
and identifies where to forwad the conversation to via its <code class="docutils literal notranslate"><span class="pre">prompt_targets</span></code> primitve. Prompt targets are endpoints
|
||||
and identifies where to forwad the conversation to via its <code class="docutils literal notranslate"><span class="pre">prompt</span> <span class="pre">target</span></code> primitve. Prompt targets are endpoints
|
||||
that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with metadata like knowing
|
||||
when a user’s intent has changed so that you can build faster, more accurate RAG apps.</p>
|
||||
<p>Configuring <code class="docutils literal notranslate"><span class="pre">prompt_targets</span></code> is simple. See example below:</p>
|
||||
|
|
@ -302,55 +302,46 @@ when a user’s intent has changed so that you can build faster, more accurate R
|
|||
<p class="admonition-title">See also</p>
|
||||
<p>Check <a class="reference internal" href="../prompt_target.html#prompt-target"><span class="std std-ref">Prompt Target</span></a> for more details!</p>
|
||||
</div>
|
||||
<section id="intent-detection-and-prompt-matching">
|
||||
<h3>Intent Detection and Prompt Matching:<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#intent-detection-and-prompt-matching" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#intent-detection-and-prompt-matching'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
|
||||
<p>Arch uses fast Natural Language Inference (NLI) and embedding approaches to first detect the intent of each
|
||||
incoming prompt. This intent detection phase analyzes the prompt’s content and matches it against predefined
|
||||
prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint. Arch’s intent
|
||||
detection framework considers both the name and description of each prompt target, and uses a composite matching
|
||||
score between an NLI and cosine similarity to enchance accuracy in forwarding decisions.</p>
|
||||
<section id="intent-matching">
|
||||
<h3>Intent Matching<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#intent-matching" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#intent-matching'"><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></h3>
|
||||
<p>Arch uses fast text embedding and intent recognition approaches to first detect the intent of each incoming prompt.
|
||||
This intent matching 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 matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores 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>
|
||||
<li><p><strong>Intent Recognition</strong>: NLI techniques further refine the matching process by evaluating the semantic alignment between the prompt and potential targets.</p></li>
|
||||
<li><p><strong>Text Embedding</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>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="agentic-apps-via-prompt-targets">
|
||||
<h3>Agentic Apps via Prompt Targets<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#agentic-apps-via-prompt-targets" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#agentic-apps-via-prompt-targets'"><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></h3>
|
||||
<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="../../build_with_arch/agent.html#arch-agent-guide"><span class="std std-ref">here</span></a>:</p>
|
||||
<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="../../build_with_arch/agent.html#arch-agent-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="../../guides/function_calling.html#function-calling"><span class="std std-ref">Arch-Function</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-Function 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>
|
||||
<p><a class="reference external" href="https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68" rel="nofollow noopener">Arch-Function<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a collection of dedicated agentic models 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-Function achieves state-of-the-art performance, comparable with frontier models like Claude Sonnet 3.5 ang GPT-4, while being 100x cheaper ($0.05M/token hosted) and 10x faster (p50 latencies of 200ms).</p>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
<section id="prompting-llms">
|
||||
<h2>Prompting LLMs<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#prompting-llms" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#prompting-llms'"><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>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" rel="nofollow noopener">Envoy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></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
|
||||
the OpenAI client and configure it with Arch. 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 centralized way.</p>
|
||||
<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" rel="nofollow noopener">Envoy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></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 the OpenAI client and configure it with Arch.
|
||||
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 centralized way.</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">arch_config.yml</span></code> file. Arch binds itself to a local address such as
|
||||
127.0.0.1:12000/v1.</p>
|
||||
<code class="docutils literal notranslate"><span class="pre">127.0.0.1:12000</span></code>.</p>
|
||||
</div>
|
||||
<section id="example-using-openai-client-with-arch-as-an-egress-gateway">
|
||||
<h3>Example: Using OpenAI Client with Arch as an Egress Gateway<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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-openai-client-with-arch-as-an-egress-gateway" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#example-using-openai-client-with-arch-as-an-egress-gateway'"><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></h3>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="kn">import</span> <span class="nn">openai</span>
|
||||
</span><span id="line-2">
|
||||
</span><span id="line-3"><span class="c1"># Set the OpenAI API base URL to the Arch gateway endpoint</span>
|
||||
</span><span id="line-4"><span class="n">openai</span><span class="o">.</span><span class="n">api_base</span> <span class="o">=</span> <span class="s2">"http://127.0.0.1:12000/v1"</span>
|
||||
</span><span id="line-4"><span class="n">openai</span><span class="o">.</span><span class="n">api_base</span> <span class="o">=</span> <span class="s2">"http://127.0.0.1:12000"</span>
|
||||
</span><span id="line-5">
|
||||
</span><span id="line-6"><span class="c1"># No need to set openai.api_key since it's configured in Arch's gateway</span>
|
||||
</span><span id="line-7">
|
||||
|
|
@ -364,7 +355,7 @@ traffic, apply rate limits, and utilize a large set of traffic management capabi
|
|||
</span></code></pre></div>
|
||||
</div>
|
||||
<p>In these examples, the OpenAI client 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 <code class="docutils literal notranslate"><span class="pre">127.0.0.1:51001</span></code>, assuming Arch is running locally and bound to that address and port.
|
||||
The OpenAI client is configured to route traffic via Arch by setting the proxy to <code class="docutils literal notranslate"><span class="pre">127.0.0.1:12000</span></code>, assuming Arch is running locally and bound to that address and port.
|
||||
This setup allows you to take advantage of Arch’s advanced traffic management features while interacting with LLM APIs like OpenAI.</p>
|
||||
</section>
|
||||
</section>
|
||||
|
|
@ -392,7 +383,7 @@ This setup allows you to take advantage of Arch’s advanced traffic management
|
|||
<li><a :data-current="activeSection === '#messages'" class="reference internal" href="#messages">Messages</a></li>
|
||||
<li><a :data-current="activeSection === '#prompt-guard'" class="reference internal" href="#prompt-guard">Prompt Guard</a></li>
|
||||
<li><a :data-current="activeSection === '#prompt-targets'" class="reference internal" href="#prompt-targets">Prompt Targets</a><ul>
|
||||
<li><a :data-current="activeSection === '#intent-detection-and-prompt-matching'" class="reference internal" href="#intent-detection-and-prompt-matching">Intent Detection and Prompt Matching:</a></li>
|
||||
<li><a :data-current="activeSection === '#intent-matching'" class="reference internal" href="#intent-matching">Intent Matching</a></li>
|
||||
<li><a :data-current="activeSection === '#agentic-apps-via-prompt-targets'" class="reference internal" href="#agentic-apps-via-prompt-targets">Agentic Apps via Prompt Targets</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
|
|
|
|||
|
|
@ -287,8 +287,6 @@ enables scaling to very high core count CPUs.</p>
|
|||
</section>
|
||||
<section id="request-flow-ingress">
|
||||
<h2>Request Flow (Ingress)<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#request-flow-ingress" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#request-flow-ingress'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
|
||||
<section id="overview">
|
||||
<h3>Overview<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#overview" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#overview'"><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></h3>
|
||||
<p>A brief outline of the lifecycle of a request and response using the example configuration above:</p>
|
||||
<ol class="arabic simple">
|
||||
<li><p><strong>TCP Connection Establishment</strong>:
|
||||
|
|
@ -302,7 +300,7 @@ that harmful or unwanted behaviors are detected early in the request processing
|
|||
The decrypted data stream is deframed by the HTTP/2 codec in Arch’s HTTP connection manager. Arch performs
|
||||
intent matching via is <strong>prompt-handler</strong> subsystem using the name and description of the defined prompt targets,
|
||||
determining which endpoint should handle the prompt.</p></li>
|
||||
<li><p><strong>Parameter Gathering with Arch-FC</strong>:
|
||||
<li><p><strong>Parameter Gathering with Arch-Function</strong>:
|
||||
If a prompt target requires specific parameters, Arch engages Arch-FC to extract the necessary details
|
||||
from the incoming prompt(s). This process gathers the critical information needed for downstream API calls.</p></li>
|
||||
<li><p><strong>API Call Execution</strong>:
|
||||
|
|
@ -310,7 +308,7 @@ Arch routes the prompt to the appropriate backend API or function call. If an en
|
|||
load balancing is performed, circuit breakers are checked, and the request is proxied to the upstream endpoint.</p></li>
|
||||
<li><p><strong>Default Summarization by Upstream LLM</strong>:
|
||||
By default, if no specific endpoint processing is needed, the prompt is sent to an upstream LLM for summarization.
|
||||
This ensures that responses are concise and relevant, enhancing user experience in RAG (Retrieval-Augmented Generation)
|
||||
This ensures that responses are concise and relevant, enhancing user experience in RAG (Retrieval Augmented Generation)
|
||||
and agentic applications.</p></li>
|
||||
<li><p><strong>Error Handling and Forwarding</strong>:
|
||||
Errors encountered during processing, such as failed function calls or guardrail detections, are forwarded to
|
||||
|
|
@ -326,14 +324,9 @@ The upstream endpoint’s TLS transport socket encrypts the response, which is t
|
|||
Responses pass through HTTP filters in reverse order, ensuring any necessary processing or modification before final delivery.</p></li>
|
||||
</ol>
|
||||
</section>
|
||||
</section>
|
||||
<section id="request-flow-egress">
|
||||
<h2>Request Flow (Egress)<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#request-flow-egress" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#request-flow-egress'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
|
||||
</section>
|
||||
<section id="id1">
|
||||
<h2>Overview<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#id1" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#id1'"><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>A brief outline of the lifecycle of a request and response in the context of egress traffic from an application
|
||||
to Large Language Models (LLMs) via Arch:</p>
|
||||
<p>A brief outline of the lifecycle of a request and response in the context of egress traffic from an application to Large Language Models (LLMs) via Arch:</p>
|
||||
<ol class="arabic simple">
|
||||
<li><p><strong>HTTP Connection Establishment to LLM</strong>:
|
||||
Arch initiates an HTTP connection to the upstream LLM service. This connection is handled by Arch’s egress listener
|
||||
|
|
@ -393,12 +386,8 @@ processing request headers and then finalized by the HCM during post-request pro
|
|||
<li><a :data-current="activeSection === '#network-topology'" class="reference internal" href="#network-topology">Network topology</a></li>
|
||||
<li><a :data-current="activeSection === '#high-level-architecture'" class="reference internal" href="#high-level-architecture">High level architecture</a></li>
|
||||
<li><a :data-current="activeSection === '#configuration'" class="reference internal" href="#configuration">Configuration</a></li>
|
||||
<li><a :data-current="activeSection === '#request-flow-ingress'" class="reference internal" href="#request-flow-ingress">Request Flow (Ingress)</a><ul>
|
||||
<li><a :data-current="activeSection === '#overview'" class="reference internal" href="#overview">Overview</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><a :data-current="activeSection === '#request-flow-egress'" class="reference internal" href="#request-flow-egress">Request Flow (Egress)</a></li>
|
||||
<li><a :data-current="activeSection === '#id1'" class="reference internal" href="#id1">Overview</a><ul>
|
||||
<li><a :data-current="activeSection === '#request-flow-ingress'" class="reference internal" href="#request-flow-ingress">Request Flow (Ingress)</a></li>
|
||||
<li><a :data-current="activeSection === '#request-flow-egress'" class="reference internal" href="#request-flow-egress">Request Flow (Egress)</a><ul>
|
||||
<li><a :data-current="activeSection === '#post-request-processing'" class="reference internal" href="#post-request-processing">Post-request processing</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
|
|
|
|||
|
|
@ -182,7 +182,6 @@
|
|||
<li class="toctree-l2"><a class="reference internal" href="request_lifecycle.html#configuration">Configuration</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="request_lifecycle.html#request-flow-ingress">Request Flow (Ingress)</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="request_lifecycle.html#request-flow-egress">Request Flow (Egress)</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="request_lifecycle.html#id1">Overview</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="error_target.html">Error Target</a><ul>
|
||||
|
|
|
|||
|
|
@ -173,7 +173,7 @@ For more details, check out <a class="reference internal" href="../llm_provider.
|
|||
networking operations (auth, tls, observability, etc) and the second process to serve models that enable it to make smart
|
||||
decisions on how to accept, handle and forward prompts. The second process is optional, as the model serving sevice could be
|
||||
hosted on a different network (an API call). But these two processes are considered a single instance of Arch.</p>
|
||||
<p><strong>Prompt Target</strong>: Arch offers a primitive called <a class="reference internal" href="../prompt_target.html#prompt-target"><span class="std std-ref">prompt_target</span></a> to help separate business logic from undifferentiated
|
||||
<p><strong>Prompt Target</strong>: Arch offers a primitive called <a class="reference internal" href="../prompt_target.html#prompt-target"><span class="std std-ref">prompt target</span></a> to help separate business logic from undifferentiated
|
||||
work in building generative AI apps. Prompt targets are endpoints that receive prompts that are processed by Arch.
|
||||
For example, Arch enriches incoming prompts with metadata like knowing when a request is a follow-up or clarifying prompt
|
||||
so that you can build faster, more accurate retrieval (RAG) apps. To support agentic apps, like scheduling travel plans or
|
||||
|
|
|
|||
|
|
@ -153,13 +153,8 @@
|
|||
<div id="content" role="main">
|
||||
<section id="intro-to-arch">
|
||||
<span id="id1"></span><h1>Intro to Arch<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#intro-to-arch"><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></h1>
|
||||
<p>Arch is an intelligent <a class="reference external" href="https://www.cloudflare.com/learning/ddos/what-is-layer-7/" rel="nofollow noopener">(Layer 7)<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> gateway
|
||||
designed for generative AI apps, AI agents, and Co-pilots that work with prompts. Engineered with purpose-built
|
||||
large language models (LLMs), Arch handles all the critical but undifferentiated tasks related to the handling and
|
||||
processing of prompts, including detecting and rejecting <a class="reference external" href="https://github.com/verazuo/jailbreak_llms" rel="nofollow noopener">jailbreak<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>
|
||||
attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to
|
||||
and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions
|
||||
in a centralized way.</p>
|
||||
<p>Arch is an intelligent <a class="reference external" href="https://www.cloudflare.com/learning/ddos/what-is-layer-7/" rel="nofollow noopener">(Layer 7)<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> gateway designed for generative AI apps, AI agents, and AI copilots that work with prompts.
|
||||
Engineered with purpose-built large language models (LLMs), Arch handles all the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way.</p>
|
||||
<a class="reference internal image-reference" href="../_images/arch-logo.png"><img alt="../_images/arch-logo.png" class="align-center" src="../_images/arch-logo.png" style="width: 100%;"/>
|
||||
</a>
|
||||
<p><strong>The project was born out of the belief that:</strong></p>
|
||||
|
|
@ -168,63 +163,46 @@ in a centralized way.</p>
|
|||
including secure handling, intelligent routing, robust observability, and integration with backend (API)
|
||||
systems for personalization - all outside business logic.</em></p>
|
||||
</div></blockquote>
|
||||
<p>In practice, achieving the above goal is incredibly difficult. Arch attempts to do so by providing the
|
||||
following high level features:</p>
|
||||
<hr class="docutils"/>
|
||||
<p><strong>Out-of-process architecture, built on</strong> <a class="reference external" href="http://envoyproxy.io/" rel="nofollow noopener">Envoy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>: Arch is takes a dependency on
|
||||
Envoy and is a self-contained process that is designed to run alongside your application servers. Arch uses
|
||||
Envoy’s HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the
|
||||
functionality exclusively for prompts and LLMs. This gives Arch several advantages:</p>
|
||||
<p>In practice, achieving the above goal is incredibly difficult.
|
||||
Arch attempts to do so by providing the following high level features:</p>
|
||||
<p><strong>Out-of-process architecture, built on</strong> <a class="reference external" href="http://envoyproxy.io/" rel="nofollow noopener">Envoy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>:
|
||||
Arch is takes a dependency on Envoy and is a self-contained process that is designed to run alongside your application servers.
|
||||
Arch uses Envoy’s HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the functionality exclusively for prompts and LLMs.
|
||||
This gives Arch several advantages:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Arch builds on Envoy’s proven success. Envoy is used at masssive sacle by the leading technology companies of
|
||||
our time including <a class="reference external" href="https://www.airbnb.com" rel="nofollow noopener">AirBnB<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.dropbox.com" rel="nofollow noopener">Dropbox<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>,
|
||||
<a class="reference external" href="https://www.google.com" rel="nofollow noopener">Google<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.reddit.com" rel="nofollow noopener">Reddit<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.stripe.com" rel="nofollow noopener">Stripe<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>,
|
||||
etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters:
|
||||
application features and business logic.</p></li>
|
||||
<li><p>Arch works with any application language. A single Arch deployment can act as gateway for AI applications
|
||||
written in Python, Java, C++, Go, Php, etc.</p></li>
|
||||
<li><p>Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain
|
||||
of deploying library upgrades in your applications.</p></li>
|
||||
<li><p>Arch builds on Envoy’s proven success. Envoy is used at masssive sacle by the leading technology companies of our time including <a class="reference external" href="https://www.airbnb.com" rel="nofollow noopener">AirBnB<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.dropbox.com" rel="nofollow noopener">Dropbox<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.google.com" rel="nofollow noopener">Google<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.reddit.com" rel="nofollow noopener">Reddit<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, <a class="reference external" href="https://www.stripe.com" rel="nofollow noopener">Stripe<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.</p></li>
|
||||
<li><p>Arch works with any application language. A single Arch deployment can act as gateway for AI applications written in Python, Java, C++, Go, Php, etc.</p></li>
|
||||
<li><p>Arch can be deployed and upgraded quickly across your infrastructure transparently without the horrid pain of deploying library upgrades in your applications.</p></li>
|
||||
</ul>
|
||||
<p><strong>Engineered with Fast LLMs:</strong> Arch is engineered with specialized (sub-billion) LLMs that are desgined for
|
||||
fast, cost-effective and acurrate handling of prompts. These LLMs are designed to be
|
||||
best-in-class for critcal prompt-related tasks like:</p>
|
||||
<p><strong>Engineered with Fast LLMs:</strong> Arch is engineered with specialized tiny LLMs that are desgined for fast, cost-effective and acurrate handling of prompts.
|
||||
These LLMs are designed to be best-in-class for critcal prompt-related tasks like:</p>
|
||||
<ul class="simple">
|
||||
<li><p><strong>Function/API Calling:</strong> Arch helps you easily personalize your applications by enabling calls to
|
||||
application-specific (API) operations via user prompts. This involves any predefined functions or APIs
|
||||
you want to expose to users to perform tasks, gather information, or manipulate data. With function calling,
|
||||
you have flexibility to support “agentic” experiences tailored to specific use cases - from updating insurance
|
||||
claims to creating ad campaigns - via prompts. Arch analyzes prompts, extracts critical information from
|
||||
prompts, engages in lightweight conversation to gather any missing parameters and makes API calls so that you can
|
||||
focus on writing business logic. For more details, read <a class="reference internal" href="../concepts/tech_overview/prompt.html#arch-overview-prompt-handling"><span class="std std-ref">prompt processing</span></a>.</p></li>
|
||||
<li><p><strong>Prompt Guardrails:</strong> Arch helps you improve the safety of your application by applying prompt guardrails in
|
||||
a centralized way for better governance hygiene. With prompt guardrails you can prevent <a class="reference external" href="https://github.com/verazuo/jailbreak_llms" rel="nofollow noopener">jailbreak<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>
|
||||
attempts or toxicity present in user’s prompts without having to write a single line of code. To learn more
|
||||
about how to configure guardrails available in Arch, read <a class="reference internal" href="../concepts/tech_overview/prompt.html#arch-overview-prompt-handling"><span class="std std-ref">prompt processing</span></a>.</p></li>
|
||||
<li><p><strong>[Coming Soon] Intent-Markers:</strong> Developers struggle to handle <a class="reference external" href="https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?" rel="nofollow noopener">follow-up<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>,
|
||||
or <a class="reference external" href="https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/" rel="nofollow noopener">clarifying<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>
|
||||
questions. Specifically, when users ask for modifications or additions to previous responses their AI applications
|
||||
often generate entirely new responses instead of adjusting the previous ones. Arch offers intent-markers as a
|
||||
feature so that developers know when the user has shifted away from the previous intent so that they can improve
|
||||
their retrieval, lower overall token cost and dramatically improve the speed and accuracy of their responses back
|
||||
to users. For more details <a class="reference internal" href="../build_with_arch/rag.html#arch-rag-guide"><span class="std std-ref">intent markers</span></a></p></li>
|
||||
<li><p><strong>Function Calling:</strong> Arch helps you easily personalize your applications by enabling calls to application-specific (API) operations via user prompts.
|
||||
This involves any predefined functions or APIs you want to expose to users to perform tasks, gather information, or manipulate data.
|
||||
With function calling, you have flexibility to support “agentic” experiences tailored to specific use cases - from updating insurance claims to creating ad campaigns - via prompts.
|
||||
Arch analyzes prompts, extracts critical information from prompts, engages in lightweight conversation to gather any missing parameters and makes API calls so that you can focus on writing business logic.
|
||||
For more details, read <a class="reference internal" href="../guides/function_calling.html#function-calling"><span class="std std-ref">Function Calling</span></a>.</p></li>
|
||||
<li><p><strong>Prompt Guard:</strong> Arch helps you improve the safety of your application by applying prompt guardrails in a centralized way for better governance hygiene.
|
||||
With prompt guardrails you can prevent <code class="docutils literal notranslate"><span class="pre">jailbreak</span> <span class="pre">attempts</span></code> present in user’s prompts without having to write a single line of code.
|
||||
To learn more about how to configure guardrails available in Arch, read <a class="reference internal" href="../guides/prompt_guard.html#prompt-guard"><span class="std std-ref">Prompt Guard</span></a>.</p></li>
|
||||
<li><p><strong>[Coming Soon] Intent-Markers:</strong> Developers struggle to handle <code class="docutils literal notranslate"><span class="pre">follow-up</span></code> or <code class="docutils literal notranslate"><span class="pre">clarifying</span></code> questions.
|
||||
Specifically, when users ask for modifications or additions to previous responses their AI applications often generate entirely new responses instead of adjusting the previous ones.
|
||||
Arch offers intent-markers as a feature so that developers know when the user has shifted away from the previous intent so that they can improve their retrieval, lower overall token cost and dramatically improve the speed and accuracy of their responses back to users.
|
||||
For more details <a class="reference internal" href="../build_with_arch/rag.html#arch-rag-guide"><span class="std std-ref">intent markers</span></a>.</p></li>
|
||||
</ul>
|
||||
<p><strong>Traffic Management:</strong> Arch offers several capabilities for LLM calls originating from your applications, including smart
|
||||
retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability
|
||||
and disaster recovery scenarios. Arch extends Envoy’s <a class="reference external" href="https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager" rel="nofollow noopener">cluster subsystem<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>
|
||||
to manage upstream connections to LLMs so that you can build resilient AI applications.</p>
|
||||
<p><strong>Front/edge Gateway:</strong> There is substantial benefit in using the same software at the edge (observability,
|
||||
traffic shaping alogirithms, applying guardrails, etc.) as for outbound LLM inference use cases. Arch has the feature set
|
||||
that makes it exceptionally well suited as an edge gateway for AI applications. This includes TLS termination, applying
|
||||
guardrail early in the pricess, intelligent parameter gathering from prompts, and prompt-based routing to backend APIs.</p>
|
||||
<p><strong>Traffic Management:</strong> Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
|
||||
Arch extends Envoy’s <a class="reference external" href="https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager" rel="nofollow noopener">cluster subsystem<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> to manage upstream connections to LLMs so that you can build resilient AI applications.</p>
|
||||
<p><strong>Front/edge Gateway:</strong> There is substantial benefit in using the same software at the edge (observability, traffic shaping alogirithms, applying guardrails, etc.) as for outbound LLM inference use cases.
|
||||
Arch has the feature set that makes it exceptionally well suited as an edge gateway for AI applications.
|
||||
This includes TLS termination, applying guardrail early in the pricess, intelligent parameter gathering from prompts, and prompt-based routing to backend APIs.</p>
|
||||
<p><strong>Best-In Class Monitoring:</strong> Arch offers several monitoring metrics that help you understand three critical aspects of
|
||||
your application: latency, token usage, and error rates by an upstream LLM provider. Latency measures the speed at which
|
||||
your application is responding to users, which includes metrics like time to first token (TFT), time per output token (TOT)
|
||||
metrics, and the total latency as perceived by users.</p>
|
||||
<p><strong>End-to-End Tracing:</strong> Arch propagates trace context using the W3C Trace Context standard, specifically through the
|
||||
<code class="docutils literal notranslate"><span class="pre">traceparent</span></code> header. This allows each component in the system to record its part of the request flow, enabling <strong>end-to-end tracing</strong>
|
||||
across the entire application. By using OpenTelemetry, Arch ensures that developers can capture this trace data consistently and
|
||||
in a format compatible with various observability tools. For more details, read <a class="reference internal" href="../guides/observability/tracing.html#arch-overview-tracing"><span class="std std-ref">tracing</span></a>.</p>
|
||||
<p><strong>End-to-End Tracing:</strong> Arch propagates trace context using the W3C Trace Context standard, specifically through the <code class="docutils literal notranslate"><span class="pre">traceparent</span></code> header.
|
||||
This allows each component in the system to record its part of the request flow, enabling end-to-end tracing across the entire application.
|
||||
By using OpenTelemetry, Arch ensures that developers can capture this trace data consistently and in a format compatible with various observability tools.
|
||||
For more details, read <a class="reference internal" href="../guides/observability/tracing.html#arch-overview-tracing"><span class="std std-ref">Tracing</span></a>.</p>
|
||||
</section>
|
||||
</div><div class="flex justify-between items-center pt-6 mt-12 border-t border-border gap-4">
|
||||
<div class="mr-auto">
|
||||
|
|
|
|||
|
|
@ -161,7 +161,7 @@
|
|||
<li><p><code class="docutils literal notranslate"><span class="pre">Docker</span></code> & <code class="docutils literal notranslate"><span class="pre">Python</span></code> installed on your system</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">API</span> <span class="pre">Keys</span></code> for LLM providers (if using external LLMs)</p></li>
|
||||
</ul>
|
||||
<p>The fastest way to get started using Arch is to use <a class="reference external" href="https://hub.docker.com/r/katanemo/arch" rel="nofollow noopener">katanemo/arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> pre-built binaries.
|
||||
<p>The fastest way to get started using Arch is to use <a class="reference external" href="https://hub.docker.com/r/katanemo/archgw" rel="nofollow noopener">katanemo/archgw<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> pre-built binaries.
|
||||
You can also build it from source.</p>
|
||||
</section>
|
||||
<section id="step-1-install-arch">
|
||||
|
|
|
|||
|
|
@ -187,7 +187,7 @@ This feature bridges the gap between generative AI systems and functional busine
|
|||
</section>
|
||||
<section id="arch-function">
|
||||
<h2>Arch-Function<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#arch-function" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#arch-function'"><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>The <a class="reference external" href="https://huggingface.co/collections/katanemolabs/arch-function-66f209a693ea8df14317ad68" rel="nofollow noopener">Arch-Function<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for <strong>function calling</strong> tasks.
|
||||
<p>The <a class="reference external" href="https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68" rel="nofollow noopener">Arch-Function<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for <strong>function calling</strong> tasks.
|
||||
The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts.
|
||||
Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.</p>
|
||||
<p>In summary, the Arch-Function collection demonstrates:</p>
|
||||
|
|
|
|||
|
|
@ -206,7 +206,7 @@ These attacks involve malicious prompts crafted to manipulate the intended behav
|
|||
Arch-Guard is designed to address this challenge.</p>
|
||||
<section id="what-is-arch-guard">
|
||||
<h3>What Is Arch-Guard<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() => $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="#what-is-arch-guard" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#what-is-arch-guard'"><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></h3>
|
||||
<p><a class="reference external" href="https://huggingface.co/collections/katanemolabs/arch-guard-6702bdc08b889e4bce8f446d" rel="nofollow noopener">Arch-Guard<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a robust classifier model specifically trained on a diverse corpus of prompt attacks.
|
||||
<p><a class="reference external" href="https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d" rel="nofollow noopener">Arch-Guard<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a robust classifier model specifically trained on a diverse corpus of prompt attacks.
|
||||
It excels at detecting explicitly malicious prompts, providing an essential layer of security for LLM applications.</p>
|
||||
<p>By embedding Arch-Guard within the Arch architecture, we empower developers to build robust, LLM-powered applications while prioritizing security and safety. With Arch-Guard, you can navigate the complexities of prompt management with confidence, knowing you have a reliable defense against malicious input.</p>
|
||||
</section>
|
||||
|
|
|
|||
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