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<h1 id="tool_calling">Tool_Calling</h1>
<p>This document describes how to configure the server in FastDeploy to use the tool parser, and how to invoke tools from the client.</p>
<h2 id="tool-call-parser-for-ernie-series-models">Tool Call parser for Ernie series models</h2>
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Parser Name</th>
</tr>
</thead>
<tbody>
<tr>
<td>baidu/ERNIE-4.5-21B-A3B-Thinking</td>
<td>ernie-x1</td>
</tr>
<tr>
<td>baidu/ERNIE-4.5-VL-28B-A3B-Thinking</td>
<td>ernie-45-vl-thinking</td>
</tr>
</tbody>
</table>
<h2 id="quickstart">Quickstart</h2>
<h3 id="starting-fastdeploy-with-tool-calling-enabled">Starting FastDeploy with Tool Calling Enabled.</h3>
<p>Launch the server with tool-calling enabled.This example uses ERNIE-4.5-21B-A3B.Leverage the ernie-x1 reasoning parser and the ernie-x1 tool-call parser from the fastdeploy directory to extract the model’s reasoning content, response content, and the tool-calling information:</p>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server
--model /models/ERNIE-4.5-21B-A3B \
--port 8000 \
--reasoning-parser ernie-x1 \
--tool-call-parser ernie-x1
</code></pre>
<h3 id="example-of-triggering-tool-calling">Example of triggering tool calling</h3>
<p>Make a request containing the tool to trigger the model to use the available tool:</p>
<pre><code class="language-python">curl -X POST http://0.0.0.0:8000/v1/chat/completions \
-H &quot;Content-Type: application/json&quot; \
-d '{
&quot;messages&quot;: [
{
&quot;role&quot;: &quot;user&quot;,
&quot;content&quot;: &quot;What's the weather in Beijing?&quot;
}
],
&quot;tools&quot;: [
{
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;description&quot;: &quot;Get the current weather in a given location&quot;,
&quot;parameters&quot;: {
&quot;type&quot;: &quot;object&quot;,
&quot;properties&quot;: {
&quot;location&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;description&quot;: &quot;City name, for example: Beijing&quot;
},
&quot;unit&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;enum&quot;: [&quot;c&quot;, &quot;f&quot;],
&quot;description&quot;: &quot;Temperature units: c = Celsius, f = Fahrenheit&quot;
}
},
&quot;required&quot;: [&quot;location&quot;, &quot;unit&quot;],
&quot;additionalProperties&quot;: false
},
&quot;strict&quot;: true
}
}
],
&quot;stream&quot;: false
}'
</code></pre>
<p>The example output is as follows. It shows that the model's output of the thought process <code>reasoning_content</code> and tool call information <code>tool_calls</code> was successfully parsed, and the current response content <code>content</code> is empty,<code>finish_reason</code> is <code>tool_calls</code>:</p>
<pre><code class="language-bash">{
&quot;choices&quot;: [
{
&quot;index&quot;: 0,
&quot;message&quot;: {
&quot;role&quot;: &quot;assistant&quot;,
&quot;content&quot;: &quot;&quot;,
&quot;multimodal_content&quot;: null,
&quot;reasoning_content&quot;: &quot;User wants to ... &quot;,
&quot;tool_calls&quot;: [
{
&quot;id&quot;: &quot;chatcmpl-tool-bc90641c67e44dbfb981a79bc986fbe5&quot;,
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;arguments&quot;: &quot;{\&quot;location\&quot;: \&quot;北京\&quot;, \&quot;unit\&quot;: \&quot;c\&quot;}&quot;
}
}
],
&quot;finish_reason&quot;: &quot;tool_calls&quot;
}
}
]
}
</code></pre>
<h2 id="parallel-tool-calls">Parallel Tool Calls</h2>
<p>If the model can generate parallel tool calls, FastDeploy will return a list:</p>
<pre><code class="language-bash">tool_calls=[
{&quot;id&quot;: &quot;...&quot;, &quot;function&quot;: {...}},
{&quot;id&quot;: &quot;...&quot;, &quot;function&quot;: {...}}
]
</code></pre>
<h2 id="requests-containing-tools-in-the-conversation-history">Requests containing tools in the conversation history</h2>
<p>If tool-call information exists in previous turns, you can construct the request as follows:</p>
<pre><code class="language-python">curl -X POST &quot;http://0.0.0.0:8000/v1/chat/completions&quot; \
-H &quot;Content-Type: application/json&quot; \
-d '{
&quot;messages&quot;: [
{
&quot;role&quot;: &quot;user&quot;,
&quot;content&quot;: &quot;Hello,What's the weather in Beijing?&quot;
},
{
&quot;role&quot;: &quot;assistant&quot;,
&quot;tool_calls&quot;: [
{
&quot;id&quot;: &quot;call_1&quot;,
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;arguments&quot;: {
&quot;location&quot;: &quot;Beijing&quot;,
&quot;unit&quot;: &quot;c&quot;
}
}
}
],
&quot;thoughts&quot;: &quot;Users need to check today's weather in Beijing.&quot;
},
{
&quot;role&quot;: &quot;tool&quot;,
&quot;tool_call_id&quot;: &quot;call_1&quot;,
&quot;content&quot;: {
&quot;type&quot;: &quot;text&quot;,
&quot;text&quot;: &quot;{\&quot;location\&quot;: \&quot;北京\&quot;,\&quot;temperature\&quot;: \&quot;23\&quot;,\&quot;weather\&quot;: \&quot;晴\&quot;,\&quot;unit\&quot;: \&quot;c\&quot;}&quot;
}
}
],
&quot;tools&quot;: [
{
&quot;type&quot;: &quot;function&quot;,
&quot;function&quot;: {
&quot;name&quot;: &quot;get_weather&quot;,
&quot;description&quot;: &quot;Determine weather in my location&quot;,
&quot;parameters&quot;: {
&quot;type&quot;: &quot;object&quot;,
&quot;properties&quot;: {
&quot;location&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;description&quot;: &quot;The city and state e.g. San Francisco, CA&quot;
},
&quot;unit&quot;: {
&quot;type&quot;: &quot;string&quot;,
&quot;enum&quot;: [
&quot;c&quot;,
&quot;f&quot;
]
}
},
&quot;additionalProperties&quot;: false,
&quot;required&quot;: [
&quot;location&quot;,
&quot;unit&quot;
]
},
&quot;strict&quot;: true
}
}
],
&quot;stream&quot;: false
}'
</code></pre>
<p>The parsed model output is as follows, containing the thought content <code>reasoning_content</code> and the response content <code>content</code>, with <code>finish_reason</code> set to stop:</p>
<pre><code class="language-bash">{
&quot;choices&quot;: [
{
&quot;index&quot;: 0,
&quot;message&quot;: {
&quot;role&quot;: &quot;assistant&quot;,
&quot;content&quot;: &quot;Today's weather in Beijing is sunny with a temperature of 23 degrees Celsius.&quot;,
&quot;reasoning_content&quot;: &quot;User wants to ...&quot;,
&quot;tool_calls&quot;: null
},
&quot;finish_reason&quot;: &quot;stop&quot;
}
]
}
</code></pre>
<h2 id="writing-a-custom-tool-parser">Writing a Custom Tool Parser</h2>
<p>FastDeploy supports custom tool parser plugins. You can refer to the following address to create a <code>tool parser</code>: <code>fastdeploy/entrypoints/openai/tool_parser</code></p>
<p>A custom parser should implement:</p>
<pre><code class="language-python"># import the required packages
# register the tool parser to ToolParserManager
@ToolParserManager.register_module(&quot;my-parser&quot;)
class ToolParser:
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
# implement the tool parse for non-stream call
def extract_tool_calls(self, model_output: str, request: ChatCompletionRequest) -&gt; ExtractToolCallInformation:
return ExtractedToolCallInformation(tools_called=False,tool_calls=[],content=text)
# implement the tool call parse for stream call
def extract_tool_calls_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
request: ChatCompletionRequest,
) -&gt; DeltaMessage | None:
return delta
</code></pre>
<p>Enable via:</p>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server
--model &lt;model path&gt;
--tool-parser-plugin &lt;absolute path of the plugin file&gt;
--tool-call-parser my-parser
</code></pre>
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