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_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml
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@ -58,7 +58,7 @@ listeners:
agents:
- id: rag_agent
description: virtual assistant for retrieval augmented generation tasks
filter_chain:
input_filters:
- input_guards
# Model listener for direct LLM access
@ -66,6 +66,9 @@ listeners:
name: model_1
address: 0.0.0.0
port: 12000
# Optional: attach input filters for guardrails on direct LLM requests
# input_filters:
# - input_guards
# Prompt listener for function calling (for prompt_targets)
- type: prompt

2
concepts/agents.html
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@ -267,7 +267,7 @@
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 16, 2026. </p>
<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 19, 2026. </p>
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43
concepts/filter_chain.html
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@ -186,6 +186,8 @@ about and even harder to evolve.</p>
</section>
<section id="configuration-example">
<h2>Configuration example<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#configuration-example" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#configuration-example'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<section id="agent-listener-filter-chain">
<h3>Agent listener filter chain<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#agent-listener-filter-chain" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#agent-listener-filter-chain'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>The example below shows a configuration where an agent uses a filter chain with two filters: a query rewriter,
and a context builder that prepares retrieval context before the agent runs.</p>
<div class="literal-block-wrapper docutils container" id="id1">
@ -241,6 +243,39 @@ and a context builder that prepares retrieval context before the agent runs.</p>
<li><p>When a request arrives at <code class="docutils literal notranslate"><span class="pre">agent_1</span></code>, Plano executes the filters in order before handing control to <code class="docutils literal notranslate"><span class="pre">rag_agent</span></code>.</p></li>
</ul>
</section>
<section id="model-listener-filter-chain">
<h3>Model listener filter chain<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#model-listener-filter-chain" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#model-listener-filter-chain'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h3>
<p>Filter chains can also be attached directly to a <strong>model listener</strong>. This lets you run input guardrails on
direct LLM proxy requests (<code class="docutils literal notranslate"><span class="pre">/v1/chat/completions</span></code>, <code class="docutils literal notranslate"><span class="pre">/v1/responses</span></code>, etc.) without an agent layer in between.</p>
<div class="literal-block-wrapper docutils container" id="id2">
<div class="code-block-caption"><span class="caption-text">Model listener with a content-safety filter chain</span><a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#id2"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></div>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="nt">filters</span><span class="p">:</span>
</span><span id="line-2"><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">id</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">content_guard</span>
</span><span id="line-3"><span class="w"> </span><span class="nt">url</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http://content-guard:10500</span>
</span><span id="line-4"><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http</span>
</span><span id="line-5">
</span><span id="line-6"><span class="nt">model_providers</span><span class="p">:</span>
</span><span id="line-7"><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o-mini</span>
</span><span id="line-8"><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$OPENAI_API_KEY</span>
</span><span id="line-9"><span class="w"> </span><span class="nt">default</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-10">
</span><span id="line-11"><span class="nt">listeners</span><span class="p">:</span>
</span><span id="line-12"><span class="w"> </span><span class="p p-Indicator">-</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">model</span>
</span><span id="line-13"><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">llm_gateway</span>
</span><span id="line-14"><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12000</span>
</span><span id="line-15"><span class="w"> </span><span class="nt">filter_chain</span><span class="p">:</span>
</span><span id="line-16"><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">content_guard</span>
</span></code></pre></div>
</div>
</div>
<p>In this setup:</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">filter_chain</span></code> is declared at the listener level (not per-agent).</p></li>
<li><p>When a request arrives at the model listener, Plano executes the filters in order before forwarding the request to the upstream LLM provider.</p></li>
<li><p>If a filter rejects the request (HTTP 4xx), the error is returned to the caller and the LLM is never called.</p></li>
</ul>
</section>
</section>
<section id="filter-chain-programming-model-http-and-mcp">
<h2>Filter Chain Programming Model (HTTP and MCP)<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#filter-chain-programming-model-http-and-mcp" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#filter-chain-programming-model-http-and-mcp'"><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>Filters are implemented as simple RESTful endpoints reachable via HTTP. If you want to use the <a class="reference external" href="https://modelcontextprotocol.io/" rel="nofollow noopener">Model Context Protocol (MCP)<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>, you can configure that as well, which makes it easy to write filters in any language. However, you can also write a filter as a plain HTTP service.</p>
@ -284,7 +319,11 @@ powerful abstraction for evolving your agent workflows over time.</p>
<div class="sticky top-16 -mt-10 max-h-[calc(100vh-5rem)] h-full overflow-y-auto pt-6 space-y-2"><p class="font-medium">On this page</p>
<ul>
<li><a :data-current="activeSection === '#typical-use-cases'" class="reference internal" href="#typical-use-cases">Typical Use Cases</a></li>
<li><a :data-current="activeSection === '#configuration-example'" class="reference internal" href="#configuration-example">Configuration example</a></li>
<li><a :data-current="activeSection === '#configuration-example'" class="reference internal" href="#configuration-example">Configuration example</a><ul>
<li><a :data-current="activeSection === '#agent-listener-filter-chain'" class="reference internal" href="#agent-listener-filter-chain">Agent listener filter chain</a></li>
<li><a :data-current="activeSection === '#model-listener-filter-chain'" class="reference internal" href="#model-listener-filter-chain">Model listener filter chain</a></li>
</ul>
</li>
<li><a :data-current="activeSection === '#filter-chain-programming-model-http-and-mcp'" class="reference internal" href="#filter-chain-programming-model-http-and-mcp">Filter Chain Programming Model (HTTP and MCP)</a></li>
</ul>
</div>
@ -294,7 +333,7 @@ powerful abstraction for evolving your agent workflows over time.</p>
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 16, 2026. </p>
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5
concepts/listeners.html
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@ -205,6 +205,9 @@ the scenes.</p>
<p>Under the hood, Plano opens outbound HTTP(S) connections to upstream LLM providers using its unified API surface and
smart model routing. For more details on how Plano talks to models and how providers are configured, see
<a class="reference internal" href="llm_providers/llm_providers.html#llm-providers"><span class="std std-ref">LLM providers</span></a>.</p>
<p>Model listeners also support <a class="reference internal" href="filter_chain.html#filter-chain"><span class="std std-ref">Filter Chains</span></a>. By adding a <code class="docutils literal notranslate"><span class="pre">filter_chain</span></code> to a model listener
you can run input guardrails, content-safety checks, or other preprocessing on direct LLM requests before they reach
the upstream provider — without requiring an agent layer.</p>
</section>
<section id="configure-listeners">
<h2>Configure Listeners<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#configure-listeners" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#configure-listeners'"><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>
@ -267,7 +270,7 @@ application to LLMs (API-based or hosted) via prompt targets.</p>
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concepts/llm_providers/client_libraries.html
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@ -660,7 +660,7 @@ Implement fallback logic for better reliability:</p>
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@ -304,7 +304,7 @@ Use your preferred client library without changing existing code (see <a class="
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@ -1140,7 +1140,7 @@ Any provider that implements the OpenAI API interface can be configured using cu
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@ -473,7 +473,7 @@ that you can test and modify locally for multi-turn RAG scenarios.</p>
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@ -226,7 +226,7 @@ This gives Plano several advantages:</p>
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@ -372,7 +372,7 @@ on the stuff that matters most.</p>
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@ -647,7 +647,7 @@ instead of a file.</p></li>
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@ -248,7 +248,7 @@ Access logs can be exported to centralized logging systems (e.g., ELK stack or F
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@ -260,7 +260,7 @@ are some sample configuration files for both, respectively.</p>
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@ -216,7 +216,7 @@
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@ -792,7 +792,7 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste
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@ -1003,7 +1003,7 @@ Plano makes it easy to build and scale these systems by managing the orchestrati
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@ -298,7 +298,7 @@ the agent. If validation fails (<code class="docutils literal notranslate"><span
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@ -453,7 +453,7 @@
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45
includes/llms.txt
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@ -1,6 +1,6 @@
Plano Docs v0.4.12
llms.txt (auto-generated)
Generated (UTC): 2026-03-16T20:28:12.638297+00:00
Generated (UTC): 2026-03-19T00:59:16.211749+00:00
Table of contents
- Agents (concepts/agents)
@ -163,6 +163,8 @@ Because these behaviors live in the dataplane rather than inside individual agen
Configuration example
Agent listener filter chain
The example below shows a configuration where an agent uses a filter chain with two filters: a query rewriter,
and a context builder that prepares retrieval context before the agent runs.
@ -219,6 +221,38 @@ The listeners section wires the rag_agent behind an agent listener and attaches
When a request arrives at agent_1, Plano executes the filters in order before handing control to rag_agent.
Model listener filter chain
Filter chains can also be attached directly to a model listener. This lets you run input guardrails on
direct LLM proxy requests (/v1/chat/completions, /v1/responses, etc.) without an agent layer in between.
Model listener with a content-safety filter chain
filters:
- id: content_guard
url: http://content-guard:10500
type: http
model_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
listeners:
- type: model
name: llm_gateway
port: 12000
filter_chain:
- content_guard
In this setup:
The filter_chain is declared at the listener level (not per-agent).
When a request arrives at the model listener, Plano executes the filters in order before forwarding the request to the upstream LLM provider.
If a filter rejects the request (HTTP 4xx), the error is returned to the caller and the LLM is never called.
Filter Chain Programming Model (HTTP and MCP)
Filters are implemented as simple RESTful endpoints reachable via HTTP. If you want to use the Model Context Protocol (MCP), you can configure that as well, which makes it easy to write filters in any language. However, you can also write a filter as a plain HTTP service.
@ -305,6 +339,10 @@ Under the hood, Plano opens outbound HTTP(S) connections to upstream LLM provide
smart model routing. For more details on how Plano talks to models and how providers are configured, see
LLM providers.
Model listeners also support Filter Chains. By adding a filter_chain to a model listener
you can run input guardrails, content-safety checks, or other preprocessing on direct LLM requests before they reach
the upstream provider — without requiring an agent layer.
Configure Listeners
Listeners are configured via the listeners block in your Plano configuration. You can define one or more inbound
@ -6316,7 +6354,7 @@ listeners:
agents:
- id: rag_agent
description: virtual assistant for retrieval augmented generation tasks
filter_chain:
input_filters:
- input_guards
# Model listener for direct LLM access
@ -6324,6 +6362,9 @@ listeners:
name: model_1
address: 0.0.0.0
port: 12000
# Optional: attach input filters for guardrails on direct LLM requests
# input_filters:
# - input_guards
# Prompt listener for function calling (for prompt_targets)
- type: prompt

2
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@ -247,7 +247,7 @@ Resources</label><div class="sd-tab-content docutils">
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@ -437,7 +437,7 @@ Use this page as the canonical source for command syntax, options, and recommend
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85
resources/configuration_reference.html
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@ -227,7 +227,7 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
</span><span id="line-58"><span class="linenos"> 58</span><span class="w"> </span><span class="nt">agents</span><span class="p">:</span>
</span><span id="line-59"><span class="linenos"> 59</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">id</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">rag_agent</span>
</span><span id="line-60"><span class="linenos"> 60</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">virtual assistant for retrieval augmented generation tasks</span>
</span><span id="line-61"><span class="linenos"> 61</span><span class="w"> </span><span class="nt">filter_chain</span><span class="p">:</span>
</span><span id="line-61"><span class="linenos"> 61</span><span class="w"> </span><span class="nt">input_filters</span><span class="p">:</span>
</span><span id="line-62"><span class="linenos"> 62</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">input_guards</span>
</span><span id="line-63"><span class="linenos"> 63</span>
</span><span id="line-64"><span class="linenos"> 64</span><span class="w"> </span><span class="c1"># Model listener for direct LLM access</span>
@ -235,46 +235,49 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
</span><span id="line-66"><span class="linenos"> 66</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">model_1</span>
</span><span id="line-67"><span class="linenos"> 67</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-68"><span class="linenos"> 68</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12000</span>
</span><span id="line-69"><span class="linenos"> 69</span>
</span><span id="line-70"><span class="linenos"> 70</span><span class="w"> </span><span class="c1"># Prompt listener for function calling (for prompt_targets)</span>
</span><span id="line-71"><span class="linenos"> 71</span><span class="w"> </span><span class="p p-Indicator">-</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">prompt</span>
</span><span id="line-72"><span class="linenos"> 72</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">prompt_function_listener</span>
</span><span id="line-73"><span class="linenos"> 73</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-74"><span class="linenos"> 74</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10000</span>
</span><span id="line-75"><span class="linenos"> 75</span><span class="w"> </span><span class="c1"># This listener is used for prompt_targets and function calling</span>
</span><span id="line-76"><span class="linenos"> 76</span>
</span><span id="line-77"><span class="linenos"> 77</span><span class="c1"># Reusable service endpoints</span>
</span><span id="line-78"><span class="linenos"> 78</span><span class="nt">endpoints</span><span class="p">:</span>
</span><span id="line-79"><span class="linenos"> 79</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span>
</span><span id="line-80"><span class="linenos"> 80</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:80</span>
</span><span id="line-81"><span class="linenos"> 81</span><span class="w"> </span><span class="nt">connect_timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.005s</span>
</span><span id="line-82"><span class="linenos"> 82</span>
</span><span id="line-83"><span class="linenos"> 83</span><span class="w"> </span><span class="nt">mistral_local</span><span class="p">:</span>
</span><span id="line-84"><span class="linenos"> 84</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:8001</span>
</span><span id="line-69"><span class="linenos"> 69</span><span class="w"> </span><span class="c1"># Optional: attach input filters for guardrails on direct LLM requests</span>
</span><span id="line-70"><span class="linenos"> 70</span><span class="w"> </span><span class="c1"># input_filters:</span>
</span><span id="line-71"><span class="linenos"> 71</span><span class="w"> </span><span class="c1"># - input_guards</span>
</span><span id="line-72"><span class="linenos"> 72</span>
</span><span id="line-73"><span class="linenos"> 73</span><span class="w"> </span><span class="c1"># Prompt listener for function calling (for prompt_targets)</span>
</span><span id="line-74"><span class="linenos"> 74</span><span class="w"> </span><span class="p p-Indicator">-</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">prompt</span>
</span><span id="line-75"><span class="linenos"> 75</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">prompt_function_listener</span>
</span><span id="line-76"><span class="linenos"> 76</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-77"><span class="linenos"> 77</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10000</span>
</span><span id="line-78"><span class="linenos"> 78</span><span class="w"> </span><span class="c1"># This listener is used for prompt_targets and function calling</span>
</span><span id="line-79"><span class="linenos"> 79</span>
</span><span id="line-80"><span class="linenos"> 80</span><span class="c1"># Reusable service endpoints</span>
</span><span id="line-81"><span class="linenos"> 81</span><span class="nt">endpoints</span><span class="p">:</span>
</span><span id="line-82"><span class="linenos"> 82</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span>
</span><span id="line-83"><span class="linenos"> 83</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:80</span>
</span><span id="line-84"><span class="linenos"> 84</span><span class="w"> </span><span class="nt">connect_timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.005s</span>
</span><span id="line-85"><span class="linenos"> 85</span>
</span><span id="line-86"><span class="linenos"> 86</span><span class="c1"># Prompt targets for function calling and API orchestration</span>
</span><span id="line-87"><span class="linenos"> 87</span><span class="nt">prompt_targets</span><span class="p">:</span>
</span><span id="line-88"><span class="linenos"> 88</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">get_current_weather</span>
</span><span id="line-89"><span class="linenos"> 89</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">Get current weather at a location.</span>
</span><span id="line-90"><span class="linenos"> 90</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
</span><span id="line-91"><span class="linenos"> 91</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">location</span>
</span><span id="line-92"><span class="linenos"> 92</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">The location to get the weather for</span>
</span><span id="line-93"><span class="linenos"> 93</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-94"><span class="linenos"> 94</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">string</span>
</span><span id="line-95"><span class="linenos"> 95</span><span class="w"> </span><span class="nt">format</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">City, State</span>
</span><span id="line-96"><span class="linenos"> 96</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">days</span>
</span><span id="line-97"><span class="linenos"> 97</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">the number of days for the request</span>
</span><span id="line-98"><span class="linenos"> 98</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-99"><span class="linenos"> 99</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-100"><span class="linenos">100</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
</span><span id="line-101"><span class="linenos">101</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-102"><span class="linenos">102</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">/weather</span>
</span><span id="line-103"><span class="linenos">103</span><span class="w"> </span><span class="nt">http_method</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">POST</span>
</span><span id="line-104"><span class="linenos">104</span>
</span><span id="line-105"><span class="linenos">105</span><span class="c1"># OpenTelemetry tracing configuration</span>
</span><span id="line-106"><span class="linenos">106</span><span class="nt">tracing</span><span class="p">:</span>
</span><span id="line-107"><span class="linenos">107</span><span class="w"> </span><span class="c1"># Random sampling percentage (1-100)</span>
</span><span id="line-108"><span class="linenos">108</span><span class="w"> </span><span class="nt">random_sampling</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100</span>
</span><span id="line-86"><span class="linenos"> 86</span><span class="w"> </span><span class="nt">mistral_local</span><span class="p">:</span>
</span><span id="line-87"><span class="linenos"> 87</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:8001</span>
</span><span id="line-88"><span class="linenos"> 88</span>
</span><span id="line-89"><span class="linenos"> 89</span><span class="c1"># Prompt targets for function calling and API orchestration</span>
</span><span id="line-90"><span class="linenos"> 90</span><span class="nt">prompt_targets</span><span class="p">:</span>
</span><span id="line-91"><span class="linenos"> 91</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">get_current_weather</span>
</span><span id="line-92"><span class="linenos"> 92</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">Get current weather at a location.</span>
</span><span id="line-93"><span class="linenos"> 93</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
</span><span id="line-94"><span class="linenos"> 94</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">location</span>
</span><span id="line-95"><span class="linenos"> 95</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">The location to get the weather for</span>
</span><span id="line-96"><span class="linenos"> 96</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-97"><span class="linenos"> 97</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">string</span>
</span><span id="line-98"><span class="linenos"> 98</span><span class="w"> </span><span class="nt">format</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">City, State</span>
</span><span id="line-99"><span class="linenos"> 99</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">days</span>
</span><span id="line-100"><span class="linenos">100</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">the number of days for the request</span>
</span><span id="line-101"><span class="linenos">101</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-102"><span class="linenos">102</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-103"><span class="linenos">103</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
</span><span id="line-104"><span class="linenos">104</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-105"><span class="linenos">105</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">/weather</span>
</span><span id="line-106"><span class="linenos">106</span><span class="w"> </span><span class="nt">http_method</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">POST</span>
</span><span id="line-107"><span class="linenos">107</span>
</span><span id="line-108"><span class="linenos">108</span><span class="c1"># OpenTelemetry tracing configuration</span>
</span><span id="line-109"><span class="linenos">109</span><span class="nt">tracing</span><span class="p">:</span>
</span><span id="line-110"><span class="linenos">110</span><span class="w"> </span><span class="c1"># Random sampling percentage (1-100)</span>
</span><span id="line-111"><span class="linenos">111</span><span class="w"> </span><span class="nt">random_sampling</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100</span>
</span></code></pre></div>
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