[WIP] Guide document for function calling

MiniMax-Text-01_Function_Call_Guide.md

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+# MiniMax-Text-01 Function Call Guide
+
+## 📖 Introduction
+
+MiniMax-Text-01 model supports function calling capability, allowing the model to identify when an external function needs to be called and output function call parameters in a structured format. This document provides detailed instructions on how to use the function calling feature of MiniMax-Text-01.
+
+## 🛠️ Defining Function Calls
+
+### Function Structure
+
+Function calls need to be defined in the `tools` field of the request body. Each function consists of:
+
+```json
+{
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "function_name",           // Function name, required
+        "description": "function_description", // Brief description of the function's purpose
+        "parameters": {                    // Parameter definition in JSON Schema format
+          "type": "object",                // Overall type, fixed as "object"
+          "properties": {                  // Parameter property object
+            "param_name": {                // Parameter name
+              "description": "Parameter description",    // Description
+              "type": "string|number|boolean|array|object" // Type
+            }
+          },
+          "required": ["param1", "param2"]  // List of required parameters
+        }
+      }
+    }
+  ]
+}
+```
+
+### Example
+
+Below is a simple example of a weather query function definition:
+
+```json
+"tools": [
+  {
+    "type": "function",
+    "function": {
+      "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"]
+      }
+    }
+  }
+]
+```
+
+### Complete Request Example
+
+Below is a complete Python code example that includes function definitions:
+
+```python
+payload = json.dumps({
+    "model": "MiniMax-Text-01",
+    "messages": [
+        {
+            "role": "system",
+            "content": "MM Intelligent Assistant is a large-scale language model developed by MiniMax and has no interfaces to call other products. MiniMax is a China technology company that has been committed to conducting research related to large models."
+        },
+        {
+            "role": "user",
+            "content": "What's the weather like in Shanghai today?"
+        }
+    ],
+    "tools": [
+        {
+            "type": "function",
+            "function": {
+                "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"]
+                }
+            }
+        }
+    ],
+    "tool_choice": "auto",
+    "stream": True,
+    "max_tokens": 10000,
+    "temperature": 0.9,
+    "top_p": 1
+})
+```
+
+## 🔄 Function Call Input Format
+
+When processed internally by the model, function definitions are converted to a special format and concatenated to the input text:
+
+```
+<beginning_of_sentence>system function_setting=functions
+{"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"]}}<end_of_sentence>
+```
+
+Important notes:
+1. Function definitions are placed after the system settings and before the conversation data
+2. Function definitions are marked with `function_setting=functions`
+3. Each function is defined as a JSON string
+4. The area ends with `<end_of_sentence>`
+
+## 📤 Model Function Call Output
+
+When the model decides to call a function, it outputs the function call information in a special format:
+
+````
+<function_call>```typescript
+functions.get_current_weather({"location": "Shanghai"})
+```
+````
+
+"<function_call>" is a special token, followed by "functions.function_name(parameter json structure)". The parameters need to be string-matched and executed externally.
+
+## 📥 Handling Function Results
+
+After a function is successfully executed, the model will return output in the following format:
+
+````typescript
+```typescript
+functions.get_current_weather({"location": "Shanghai"})
+```
+````
+
+You can use the following regular expression method to extract the function name and parameters for subsequent processing:
+
+````python
+def parse_function_calls(content: str):
+    """
+    Parse the function call content returned by the model, extract function name and parameters
+    
+    Parameters:
+        content: The original content string returned by the model
+        
+    Returns:
+        A dictionary of parsed function call information, including function name and parameters
+    """
+    # Match typescript code block
+    pattern = r"```typescript\n(.+?)?\n```"
+    matches = re.finditer(pattern, content, re.DOTALL)
+    
+    for match in matches:
+        function_code = match.group(1)
+        # Extract function name and parameters
+        function_match = re.search(r'functions\.(\w+)\((.+)\)', function_code)
+        
+        if not function_match:
+            continue
+            
+        function_name = function_match.group(1)
+        arguments_str = function_match.group(2)
+        
+        try:
+            # Parse parameter JSON
+            arguments = json.loads(arguments_str)
+            print(f"Function call: {function_name}, Parameters: {arguments}")
+            
+            # Example: Handle weather query function
+            if function_name == "get_current_weather":
+                location = arguments.get("location", "Unknown location")
+                # Build function execution result
+                return {
+                    "role": "function", 
+                    "name": function_name, 
+                    "text": json.dumps({
+                        "location": location, 
+                        "temperature": "25", 
+                        "unit": "celsius", 
+                        "weather": "Sunny"
+                    }, ensure_ascii=False)
+                }
+        except json.JSONDecodeError as e:
+            print(f"Parameter parsing failed: {arguments_str}, Error: {e}")
+    
+    return {}
+````
+
+After successfully parsing the function call, you should add the function execution result to the conversation history so that the model can access and utilize this information in subsequent interactions.
+
+## 💻 Function Call Example with Transformers Library
+
+The official MiniMax-Text-01 repository provides a complete example of function calling using the Transformers library. You can view the source code in the [MiniMaxAI/MiniMax-Text-01 huggingface repository](https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/main.py).
+
+The following is the key part of implementing function calls using the Transformers library:
+
+```python
+def get_default_tools():
+    return [
+        {
+            "type": "function",
+            "function": {
+                "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"]
+                }
+            }
+        }
+    ]
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+prompt = "What's the weather like in Shanghai today?"
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant created by Minimax based on MiniMax-Text-01 model."}]},
+    {"role": "user", "content": [{"type": "text", "text": prompt}]},
+]
+
+# Enable function call tools
+tools = get_default_tools()
+
+# Apply chat template and add tool definitions
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    tools=tools
+)
+
+# Generate response
+model_inputs = tokenizer(text, return_tensors="pt").to("cuda")
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map=device_map,
+    quantization_config=quantization_config,
+    trust_remote_code=True,
+    offload_buffers=True,
+)
+generation_config = GenerationConfig(
+    max_new_tokens=20,
+    eos_token_id=200020,
+    use_cache=True,
+)
+
+# Execute generation
+generated_ids = quantized_model.generate(**model_inputs, generation_config=generation_config)
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+### Running the Example
+
+You can run the example code using the following command:
+
+```bash
+export SAFETENSORS_FAST_GPU=1
+python main.py --quant_type int8 --world_size 8 --model_id <model_path> --enable_tools
+```
+
+Parameter description:
+- `--quant_type`: Quantization type, options are "default" or "int8"
+- `--world_size`: Number of GPUs, int8 quantization requires at least 8 GPUs
+- `--model_id`: Model path
+- `--enable_tools`: Enable function call feature
+
+### Result Processing
+As expected, you will get the following output:
+
+````base
+```typescript
+functions.get_current_weather({"location": "Shanghai"})
+```
+````
+
+You can use regular expressions to extract the function to call and its corresponding parameters:
+
+````python
+def try_parse_tool_calls(content: str):
+    pattern = r"```typescript\n(.+?)?\n```"
+    matches = re.finditer(pattern, content, re.DOTALL)
+    
+    for match in matches:
+        function_code = match.group(1)
+        function_match = re.search(r'functions\.(\w+)\((.+)\)', function_code)
+        
+        if not function_match:
+            continue
+            
+        function_name = function_match.group(1)
+        arguments_str = function_match.group(2)
+        
+        try:
+            arguments = json.loads(arguments_str)
+            print(f"tool_calls: [{{'type': 'function', 'function': {{'name': '{function_name}', 'arguments': {arguments}}}}}]")
+            
+            if function_name == "get_current_weather":
+                location = arguments.get("location", "Unknown")
+                return {"role": "function", "name": function_name, "text": f'{{"location": "{location}", "temperature": "25", "unit": "celsius", "weather": "Sun"}}'}
+        except json.JSONDecodeError as e:
+            print(f"Failed parse tools: {arguments_str}, Error: {e}")
+    
+    return {}
+````
+
+### Chat Template
+
+MiniMax-Text-01 uses a specific chat template format to process function calls. The chat template is defined in `tokenizer_config.json`:
+
+```json
+ "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<beginning_of_sentence>system ai_setting=assistant\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'user' %}{{ '<beginning_of_sentence>user name=user\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'assistant' %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{{ '<end_of_sentence>\\n' }}{% elif message['role'] == 'function' %}{{ '<beginning_of_sentence>system function_response=functions\\n' + '{\"name\": \"' + message['name'] + '\", \"response\": ' + message['content'][0]['text'] + '}' + '<end_of_sentence>\\n'}}{% endif %}{% endfor %}{% if tools %}{% for function in tools %}{{ '<beginning_of_sentence>system function_setting=functions\\n' + function | tojson + '<end_of_sentence>\\n'}}{% endfor %}{% endif %}{% if add_generation_prompt %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% endif %}"
+```
+
+## 📝 Important Notes
+
+1. Function names should follow programming language naming conventions and avoid special characters
+2. Parameter descriptions should be concise and help the model understand the parameter's purpose and constraints
+3. The model does not guarantee that it will call a function; this depends on the user's input and the model's judgment
+4. Function results should be returned in a structured format for easy processing by the model
+5. The model might not call a function even if one is provided, depending on whether it determines a function call is appropriate for the given user query 

MiniMax-Text-01_Function_Call_Guide_CN.md

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+# MiniMax-Text-01 函数调用（Function Call）功能指南
+
+## 📖 简介
+
+MiniMax-Text-01 模型支持函数调用功能，使模型能够识别何时需要调用外部函数，并以结构化格式输出函数调用参数。本文档详细介绍了如何使用 MiniMax-Text-01 的函数调用功能。
+
+## 🛠️ 函数调用的定义
+
+### 函数结构体
+
+函数调用需要在请求体中定义 `tools` 字段，每个函数由以下部分组成：
+
+```json
+{
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "function_name",           // 函数名称，必填
+        "description": "function_description", // 函数描述，应简明扼要说明函数功能
+        "parameters": {                    // 函数参数定义，符合 JSON Schema 格式
+          "type": "object",                // 参数整体类型，固定为object
+          "properties": {                  // 参数属性对象
+            "param_name": {                // 参数名称
+              "description": "参数描述",    // 参数说明
+              "type": "string|number|boolean|array|object" // 参数类型
+            }
+          },
+          "required": ["param1", "param2"]  // 必填参数列表
+        }
+      }
+    }
+  ]
+}
+```
+
+### 示例
+
+以下是一个简单的天气查询函数定义示例：
+
+```json
+"tools": [
+  {
+    "type": "function",
+    "function": {
+      "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"]
+      }
+    }
+  }
+]
+```
+
+### 完整请求示例
+
+下面是一个包含函数定义的完整Python代码示例：
+
+```python
+payload = json.dumps({
+    "model": "MiniMax-Text-01",
+    "messages": [
+        {
+            "role": "system",
+            "content": "MM Intelligent Assistant is a large-scale language model developed by MiniMax and has no interfaces to call other products. MiniMax is a China technology company that has been committed to conducting research related to large models."
+        },
+        {
+            "role": "user",
+            "content": "上海今天天气怎么样？"
+        }
+    ],
+    "tools": [
+        {
+            "type": "function",
+            "function": {
+                "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"]
+                }
+            }
+        }
+    ],
+    "tool_choice": "auto",
+    "stream": True,
+    "max_tokens": 10000,
+    "temperature": 0.9,
+    "top_p": 1
+})
+```
+
+## 🔄 函数调用的输入格式
+
+在模型内部处理时，函数定义会被转换为特殊格式并拼接到输入文本中：
+
+```
+<beginning_of_sentence>system function_setting=functions
+{"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"]}}<end_of_sentence>
+```
+
+注意事项：
+1. 函数定义位于系统设置之后、对话数据之前
+2. 使用 `function_setting=functions` 标记函数定义区域
+3. 每个函数定义使用JSON字符串表示
+4. 区域以 `<end_of_sentence>` 结束
+
+## 📤 模型的函数调用输出
+
+当模型决定调用函数时，它会在响应中使用特殊格式输出函数调用信息：
+
+````
+<function_call>```typescript
+functions.get_current_weather({"location": "上海"})
+```
+````
+
+"<function_call>" 是 special token, 后面的 "functions.函数名(参数 json 结构体)", 需要字符串匹配出参数, 交外部执行.
+
+## 📥 函数执行结果的处理
+
+当函数调用成功执行后，模型将返回以下格式的输出：
+
+````typescript
+```typescript
+functions.get_current_weather({"location": "Shanghai"})
+```
+````
+
+您可以使用以下正则表达式方法提取函数名称和参数，便于后续处理：
+
+````python
+def parse_function_calls(content: str):
+    """
+    解析模型返回的函数调用内容，提取函数名和参数
+    
+    参数:
+        content: 模型返回的原始内容字符串
+        
+    返回:
+        解析后的函数调用信息字典，包含函数名和参数
+    """
+    # 匹配 typescript 代码块
+    pattern = r"```typescript\n(.+?)?\n```"
+    matches = re.finditer(pattern, content, re.DOTALL)
+    
+    for match in matches:
+        function_code = match.group(1)
+        # 提取函数名和参数
+        function_match = re.search(r'functions\.(\w+)\((.+)\)', function_code)
+        
+        if not function_match:
+            continue
+            
+        function_name = function_match.group(1)
+        arguments_str = function_match.group(2)
+        
+        try:
+            # 解析参数JSON
+            arguments = json.loads(arguments_str)
+            print(f"调用函数: {function_name}, 参数: {arguments}")
+            
+            # 示例: 处理天气查询函数
+            if function_name == "get_current_weather":
+                location = arguments.get("location", "未知位置")
+                # 构建函数执行结果
+                return {
+                    "role": "function", 
+                    "name": function_name, 
+                    "text": json.dumps({
+                        "location": location, 
+                        "temperature": "25", 
+                        "unit": "celsius", 
+                        "weather": "晴朗"
+                    }, ensure_ascii=False)
+                }
+        except json.JSONDecodeError as e:
+            print(f"参数解析失败: {arguments_str}, 错误: {e}")
+    
+    return {}
+````
+
+成功解析函数调用后，您应将函数执行结果添加到对话历史中，以便模型在后续交互中能够访问和利用这些信息。
+
+## 💻 使用 Transformers 库的函数调用示例
+
+MiniMax-Text-01 官方仓库提供了使用 Transformers 库进行函数调用的完整示例。您可以在 [MiniMaxAI/MiniMax-Text-01 huggingface 仓库](https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/main.py) 中查看源代码。
+
+以下是使用 Transformers 库实现函数调用的关键部分：
+
+```python
+def get_default_tools():
+    return [
+        {
+            "type": "function",
+            "function": {
+                "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"]
+                }
+            }
+        }
+    ]
+
+# 加载模型和分词器
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+prompt = "What's the weather like in Shanghai today?"
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant created by Minimax based on MiniMax-Text-01 model."}]},
+    {"role": "user", "content": [{"type": "text", "text": prompt}]},
+]
+
+# 启用函数调用工具
+tools = get_default_tools()
+
+# 应用聊天模板，并加入工具定义
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    tools=tools
+)
+
+# 生成回复
+model_inputs = tokenizer(text, return_tensors="pt").to("cuda")
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map=device_map,
+    quantization_config=quantization_config,
+    trust_remote_code=True,
+    offload_buffers=True,
+)
+generation_config = GenerationConfig(
+    max_new_tokens=20,
+    eos_token_id=200020,
+    use_cache=True,
+)
+
+# 执行生成
+generated_ids = quantized_model.generate(**model_inputs, generation_config=generation_config)
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+### 运行方式
+
+您可以通过以下命令运行示例代码：
+
+```bash
+export SAFETENSORS_FAST_GPU=1
+python main.py --quant_type int8 --world_size 8 --model_id <model_path> --enable_tools
+```
+
+参数说明：
+- `--quant_type`: 量化类型，可选 "default" 或 "int8"
+- `--world_size`: GPU 数量，int8 量化至少需要 8 个 GPU
+- `--model_id`: 模型路径
+- `--enable_tools`: 启用函数调用功能
+
+### 结果处理
+符合预期的情况下，你将得到以下输出
+
+````base
+```typescript
+functions.get_current_weather({"location": "Shanghai"})
+```
+````
+
+你可以使用正则表达式提取出需要调用的 function 和 对应的参数
+
+````python
+def try_parse_tool_calls(content: str):
+    pattern = r"```typescript\n(.+?)?\n```"
+    matches = re.finditer(pattern, content, re.DOTALL)
+    
+    for match in matches:
+        function_code = match.group(1)
+        function_match = re.search(r'functions\.(\w+)\((.+)\)', function_code)
+        
+        if not function_match:
+            continue
+            
+        function_name = function_match.group(1)
+        arguments_str = function_match.group(2)
+        
+        try:
+            arguments = json.loads(arguments_str)
+            print(f"tool_calls: [{{'type': 'function', 'function': {{'name': '{function_name}', 'arguments': {arguments}}}}}]")
+            
+            if function_name == "get_current_weather":
+                location = arguments.get("location", "Unknown")
+                return {"role": "function", "name": function_name, "text": f'{{"location": "{location}", "temperature": "25", "unit": "celsius", "weather": "Sun"}}'}
+        except json.JSONDecodeError as e:
+            print(f"Failed parse tools: {arguments_str}, Error: {e}")
+    
+    return {}
+````
+
+### 聊天模板
+
+MiniMax-Text-01 使用特定的聊天模板格式处理函数调用。聊天模板定义在 `tokenizer_config.json` 中：
+
+```json
+ "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<beginning_of_sentence>system ai_setting=assistant\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'user' %}{{ '<beginning_of_sentence>user name=user\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'assistant' %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{{ '<end_of_sentence>\\n' }}{% elif message['role'] == 'function' %}{{ '<beginning_of_sentence>system function_response=functions\\n' + '{\"name\": \"' + message['name'] + '\", \"response\": ' + message['content'][0]['text'] + '}' + '<end_of_sentence>\\n'}}{% endif %}{% endfor %}{% if tools %}{% for function in tools %}{{ '<beginning_of_sentence>system function_setting=functions\\n' + function | tojson + '<end_of_sentence>\\n'}}{% endfor %}{% endif %}{% if add_generation_prompt %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% endif %}"
+
+```
+
+## 📝 注意事项
+
+1. 函数名称应当遵循编程语言的命名规范，避免使用特殊字符
+2. 参数描述应当简洁明了，帮助模型理解参数的用途和约束
+3. 模型并不保证每次都会调用函数，这取决于用户的输入和模型的判断
+4. 函数调用结果应当以结构化方式返回，便于模型理解和处理

tokenizer_config.json

@@ -6,5 +6,5 @@
   "model_max_length": 40960000,
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": "<end_of_document>",
-  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<beginning_of_sentence>system ai_setting=assistant\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'user' %}{{ '<beginning_of_sentence>user name=user\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'assistant' %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{{ '<end_of_sentence>\\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% endif %}"
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<beginning_of_sentence>system ai_setting=assistant\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'user' %}{{ '<beginning_of_sentence>user name=user\\n' + message['content'][0]['text'] + '<end_of_sentence>\\n'}}{% elif message['role'] == 'assistant' %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{{ '<end_of_sentence>\\n' }}{% elif message['role'] == 'function' %}{{ '<beginning_of_sentence>system function_response=functions\\n' + '{\"name\": \"' + message['name'] + '\", \"response\": ' + message['content'][0]['text'] + '}' + '<end_of_sentence>\\n'}}{% endif %}{% endfor %}{% if tools %}{% for function in tools %}{{ '<beginning_of_sentence>system function_setting=functions\\n' + function | tojson + '<end_of_sentence>\\n'}}{% endfor %}{% endif %}{% if add_generation_prompt %}{{ '<beginning_of_sentence>ai name=assistant\\n' }}{% endif %}"
 }