MiniMax-M2 工具调用指南
简介
MiniMax-M2 模型支持工具调用功能，使模型能够识别何时需要调用外部工具，并以结构化格式输出工具调用参数。本文档提供了有关如何使用 MiniMax-M2 工具调用功能的详细说明。
基础示例
以下 Python 脚本基于 OpenAI SDK 实现了一个天气查询工具调用示例：
from openai import OpenAI
import json
client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
def get_weather(location: str, unit: str):
return f"Getting the weather for {location} in {unit}..."
tool_functions = {"get_weather": get_weather}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location", "unit"]
}
}
}]
response = client.chat.completions.create(
model=client.models.list().data[0].id,
messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}],
tools=tools,
tool_choice="auto"
)
print(response)
tool_call = response.choices[0].message.tool_calls[0].function
print(f"Function called: {tool_call.name}")
print(f"Arguments: {tool_call.arguments}")
print(f"Result: {get_weather(**json.loads(tool_call.arguments))}")
输出示例：
Function called: get_weather
Arguments: {"location": "San Francisco, CA", "unit": "celsius"}
Result: Getting the weather for San Francisco, CA in celsius...
手动解析模型输出
我们强烈建议使用 vLLM 或 SGLnag 来解析工具调用。 如果您无法使用支持 MiniMax-M2 的推理引擎（如 vLLM 和 SGLang）的内置解析器，或需要使用其他推理框架（如 transformers、TGI 等），您可以使用以下方法手动解析模型的原始输出。这种方法需要您自己解析模型输出的 XML 标签格式。
使用 Transformers 的示例
这是一个使用 transformers 库的完整示例：
from transformers import AutoTokenizer
def get_default_tools():
return [
{
"name": "get_current_weather",
"description": "Get the latest weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "A certain city, such as Beijing, Shanghai"
}
},
}
"required": ["location"],
"type": "object"
}
]
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "What's the weather like in Shanghai today?"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
]
# Enable function calling tools
tools = get_default_tools()
# Apply chat template and include tool definitions
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
tools=tools
)
# Send request (using any inference service)
import requests
payload = {
"model": "MiniMaxAI/MiniMax-M2",
"prompt": text,
"max_tokens": 4096
}
response = requests.post(
"http://localhost:8000/v1/completions",
headers={"Content-Type": "application/json"},
json=payload,
stream=False,
)
# Model output needs manual parsing
raw_output = response.json()["choices"][0]["text"]
print("Raw output:", raw_output)
# Use the parsing function below to process the output
tool_calls = parse_tool_calls(raw_output, tools)
🛠️ 工具调用定义
工具结构
工具调用需要在请求体中定义 tools 字段。每个工具由以下部分组成：
{
"tools": [
{
"name": "search_web",
"description": "Search function.",
"parameters": {
"properties": {
"query_list": {
"description": "Keywords for search, list should contain 1 element.",
"items": { "type": "string" },
"type": "array"
},
"query_tag": {
"description": "Category of query",
"items": { "type": "string" },
"type": "array"
}
},
"required": [ "query_list", "query_tag" ],
"type": "object"
}
}
]
}
字段说明：
name：函数名称description：函数描述parameters：函数参数定义properties：参数属性定义，其中键是参数名称，值包含详细的参数描述required：必需参数列表type：参数类型（通常为 "object"）
内部处理格式
在 MiniMax-M2 模型内部处理时，工具定义会被转换为特殊格式并连接到输入文本中。以下是一个完整示例：
]~!b[]~b]system
You are a helpful assistant.
# Tools
You may call one or more tools to assist with the user query.
Here are the tools available in JSONSchema format:
<tools>
<tool>{"name": "search_web", "description": "Search function.", "parameters": {"type": "object", "properties": {"query_list": {"type": "array", "items": {"type": "string"}, "description": "Keywords for search, list should contain 1 element."}, "query_tag": {"type": "array", "items": {"type": "string"}, "description": "Category of query"}}, "required": ["query_list", "query_tag"]}}</tool>
</tools>
When making tool calls, use XML format to invoke tools and pass parameters:
<minimax:tool_call>
<invoke name="tool-name-1">
<parameter name="param-key-1">param-value-1</parameter>
<parameter name="param-key-2">param-value-2</parameter>
...
</invoke>
[e~[
]~b]user
When were the latest announcements from OpenAI and Gemini?[e~[
]~b]ai
<think>
格式说明：
]~!b[]~b]system：系统消息开始标记[e~[：消息结束标记]~b]user：用户消息开始标记]~b]ai：助手消息开始标记]~b]tool：工具结果消息开始标记<tools>...</tools>：工具定义区域，每个工具都用<tool>标签包装，内容为 JSON Schema<minimax:tool_call>...</minimax:tool_call>：工具调用区域<think>...</think>：生成过程中的思考过程标记
模型输出格式
MiniMax-M2 使用结构化的 XML 标签格式：
<minimax:tool_call>
<invoke name="search_web">
<parameter name="query_tag">["technology", "events"]</parameter>
<parameter name="query_list">["\"OpenAI\" \"latest\" \"release\""]</parameter>
</invoke>
<invoke name="search_web">
<parameter name="query_tag">["technology", "events"]</parameter>
<parameter name="query_list">["\"Gemini\" \"latest\" \"release\""]</parameter>
</invoke>
</minimax:tool_call>
每个工具调用使用 <invoke name="function_name"> 标签，参数使用 <parameter name="parameter_name"> 标签包装。
手动解析工具调用结果
解析工具调用
MiniMax-M2 使用结构化的 XML 标签，这需要一种不同的解析方法。核心函数如下：
import re
import json
from typing import Any, Optional, List, Dict
def extract_name(name_str: str) -> str:
"""Extract name from quoted string"""
name_str = name_str.strip()
if name_str.startswith('"') and name_str.endswith('"'):
return name_str[1:-1]
elif name_str.startswith("'") and name_str.endswith("'"):
return name_str[1:-1]
return name_str
def convert_param_value(value: str, param_type: str) -> Any:
"""Convert parameter value based on parameter type"""
if value.lower() == "null":
return None
param_type = param_type.lower()
if param_type in ["string", "str", "text"]:
return value
elif param_type in ["integer", "int"]:
try:
return int(value)
except (ValueError, TypeError):
return value
elif param_type in ["number", "float"]:
try:
val = float(value)
return val if val != int(val) else int(val)
except (ValueError, TypeError):
return value
elif param_type in ["boolean", "bool"]:
return value.lower() in ["true", "1"]
elif param_type in ["object", "array"]:
try:
return json.loads(value)
except json.JSONDecodeError:
return value
else:
# Try JSON parsing, return string if failed
try:
return json.loads(value)
except json.JSONDecodeError:
return value
def parse_tool_calls(model_output: str, tools: Optional[List[Dict]] = None) -> List[Dict]:
"""
Extract all tool calls from model output
Args:
model_output: Complete output text from the model
tools: Tool definition list for getting parameter type information, format can be:
- [{"name": "...", "parameters": {...}}]
- [{"type": "function", "function": {"name": "...", "parameters": {...}}}]
Returns:
Parsed tool call list, each element contains name and arguments fields
Example:
>>> tools = [{
... "name": "get_weather",
... "parameters": {
... "type": "object",
... "properties": {
... "location": {"type": "string"},
... "unit": {"type": "string"}
... }
... }
... }]
>>> output = '''<minimax:tool_call>
... <invoke name="get_weather">
... <parameter name="location">San Francisco</parameter>
... <parameter name="unit">celsius</parameter>
... </invoke>
... </minimax:tool_call>'''
>>> result = parse_tool_calls(output, tools)
>>> print(result)
[{'name': 'get_weather', 'arguments': {'location': 'San Francisco', 'unit': 'celsius'}}]
"""
# Quick check if tool call marker is present
if "<minimax:tool_call>" not in model_output:
return []
tool_calls = []
try:
# Match all <minimax:tool_call> blocks
tool_call_regex = re.compile(r"<minimax:tool_call>(.*?)</minimax:tool_call>", re.DOTALL)
invoke_regex = re.compile(r"<invoke name=(.*?)</invoke>", re.DOTALL)
parameter_regex = re.compile(r"<parameter name=(.*?)</parameter>", re.DOTALL)
# Iterate through all tool_call blocks
for tool_call_match in tool_call_regex.findall(model_output):
# Iterate through all invokes in this block
for invoke_match in invoke_regex.findall(tool_call_match):
# Extract function name
name_match = re.search(r'^([^>]+)', invoke_match)
if not name_match:
continue
function_name = extract_name(name_match.group(1))
# Get parameter configuration
param_config = {}
if tools:
for tool in tools:
tool_name = tool.get("name") or tool.get("function", {}).get("name")
if tool_name == function_name:
params = tool.get("parameters") or tool.get("function", {}).get("parameters")
if isinstance(params, dict) and "properties" in params:
param_config = params["properties"]
break
# Extract parameters
param_dict = {}
for match in parameter_regex.findall(invoke_match):
param_match = re.search(r'^([^>]+)>(.*)', match, re.DOTALL)
if param_match:
param_name = extract_name(param_match.group(1))
param_value = param_match.group(2).strip()
# Remove leading and trailing newlines
if param_value.startswith('\n'):
param_value = param_value[1:]
if param_value.endswith('\n'):
param_value = param_value[:-1]
# Get parameter type and convert
param_type = "string"
if param_name in param_config:
if isinstance(param_config[param_name], dict) and "type" in param_config[param_name]:
param_type = param_config[param_name]["type"]
param_dict[param_name] = convert_param_value(param_value, param_type)
tool_calls.append({
"name": function_name,
"arguments": param_dict
})
except Exception as e:
print(f"Failed to parse tool calls: {e}")
return []
return tool_calls
使用示例：
# Define tools
tools = [
{
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string"}
},
"required": ["location", "unit"]
}
}
]
# Model output
model_output = """Let me help you query the weather.
<minimax:tool_call>
<invoke name="get_weather">
<parameter name="location">San Francisco</parameter>
<parameter name="unit">celsius</parameter>
</invoke>
</minimax:tool_call>"""
# Parse tool calls
tool_calls = parse_tool_calls(model_output, tools)
# Output results
for call in tool_calls:
print(f"Function called: {call['name']}")
print(f"Arguments: {call['arguments']}")
# Output: Function called: get_weather
# Arguments: {'location': 'San Francisco', 'unit': 'celsius'}
执行工具调用
完成解析后，您可以执行相应的工具并构造返回结果：
def execute_function_call(function_name: str, arguments: dict):
"""Execute function call and return result"""
if function_name == "get_weather":
location = arguments.get("location", "Unknown location")
unit = arguments.get("unit", "celsius")
# Build function execution result
return {
"role": "tool",
"content": [
{
"name": function_name,
"type": "text",
"text": json.dumps({
"location": location,
"temperature": "25",
"unit": unit,
"weather": "Sunny"
}, ensure_ascii=False)
}
]
}
elif function_name == "search_web":
query_list = arguments.get("query_list", [])
query_tag = arguments.get("query_tag", [])
# Simulate search results
return {
"role": "tool",
"content": [
{
"name": function_name,
"type": "text",
"text": f"Search keywords: {query_list}, Category: {query_tag}\nSearch results: Relevant information found"
}
]
}
return None
将工具执行结果返回给模型
在成功解析工具调用后，您应该将工具执行结果添加到对话历史中，以便模型在后续交互中可以访问和利用这些信息。请参考 chat_template.jinja 了解连接格式。