AI agents系列之全从零开始构建

在我们上一篇博客文章中，我们全面介绍了智能代理，讨论了它们的特性、组成部分、演变过程、面临的挑战以及未来的可能性。

这篇文章，咱们就来聊聊怎么用 Python 从零开始构建一个智能代理。这个智能代理能够根据用户输入做出决策，选择合适的工具，并相应地执行任务。咱们这就开工咯！

文章目录

- - 1. 什么是智能代理AI agents？
  - 2. 实现
  - - 2.1 先决条件
    - 2.2 实现步骤
  - 3. 总结
  - 4 完整代码

1. 什么是智能代理AI agents？

智能代理AI agents是一个能够自主感知环境、做出决策并采取行动以实现特定目标的实体。智能代理的复杂程度各不相同，从简单地对刺激做出反应的反应式代理，到能够随着时间推移学习和适应的更高级智能代理。常见的智能代理类型包括：

反应式代理：直接对环境变化做出反应，没有内部记忆。
基于模型的代理：利用对世界的内部模型来做出决策。
基于目标的代理：根据实现特定目标来规划行动。
基于效用的代理：根据效用函数评估潜在行动，以最大化结果。

例如，聊天机器人、推荐系统和自动驾驶汽车，每一种都利用不同类型的智能代理来高效、智能地执行任务。

我们这个智能代理的核心组成部分有：

模型：智能代理的大脑，负责处理输入并生成响应。
工具：智能代理可以根据用户请求执行的预定义函数。
工具箱：智能代理可以使用的工具集合。
系统提示：指导智能代理如何处理用户输入并选择合适工具的指令集。

2. 实现

现在，咱们撸起袖子加油干，开始构建！

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构建智能代理

2.1 先决条件

1. Python 环境设置

你需要安装 Python 才能运行智能代理。按照以下步骤设置环境：

安装 Python（如果尚未安装）

从 python.org 下载并安装 Python（推荐使用 3.8+ 版本）。
验证安装：

python --version

创建虚拟环境（推荐） 最好使用虚拟环境来管理依赖项：

python -m venv ai_agents_env
source ai_agents_env/bin/activate  # 在 Windows 上：ai_agents_env\Scripts\activate

安装所需依赖项 导航到仓库目录并安装依赖项：

pip install -r requirements.txt

2. 本地设置 Ollama

Ollama 用于高效运行和管理本地语言模型。按照以下步骤安装并配置它：

下载并安装 Ollama

访问 Ollama 官方网站并下载适用于你操作系统的安装程序。
按照你平台的说明进行安装。

验证 Ollama 安装 运行以下命令检查 Ollama 是否安装正确：

ollama --version

拉取模型（如果需要） 某些智能代理实现可能需要特定的模型。你可以使用以下命令拉取模型：

ollama pull mistral  # 将 'mistral' 替换为所需的模型

2.2 实现步骤

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作者提供的图片

步骤 1：设置环境

除了 Python，我们还需要安装一些必要的库。在本教程中，我们将使用 requests、json 和 termcolor。此外，我们还将使用 dotenv 来管理环境变量。

pip install requests termcolor python-dotenv

步骤 2：定义模型类

我们首先需要一个能够处理用户输入的模型。我们将创建一个 OllamaModel 类，它与本地 API 交互以生成响应。

以下是基本实现：

from termcolor import colored
import os
from dotenv import load_dotenv
load_dotenv()
### 模型
import requests
import json
import operator
class OllamaModel:def __init__(self, model, system_prompt, temperature=0, stop=None):"""使用给定参数初始化 OllamaModel。参数：model (str): 要使用的模型名称。system_prompt (str): 要使用的系统提示。temperature (float): 模型的温度设置。stop (str): 模型的停止标记。"""self.model_endpoint = "http://localhost:11434/api/generate"self.temperature = temperatureself.model = modelself.system_prompt = system_promptself.headers = {"Content-Type": "application/json"}self.stop = stopdef generate_text(self, prompt):"""根据提供的提示从 Ollama 模型生成响应。参数：prompt (str): 用户查询，用于生成响应。返回：dict：模型返回的响应，以字典形式表示。"""payload = {"model": self.model,"format": "json","prompt": prompt,"system": self.system_prompt,"stream": False,"temperature": self.temperature,"stop": self.stop}try:request_response = requests.post(self.model_endpoint,headers=self.headers,data=json.dumps(payload))print("REQUEST RESPONSE", request_response)request_response_json = request_response.json()response = request_response_json['response']response_dict = json.loads(response)print(f"\n\nOllama 模型返回的响应：{response_dict}")return response_dictexcept requests.RequestException as e:response = {"error": f"调用模型时出错！{str(e)}"}return response

这个类使用模型名称、系统提示、温度和停止标记进行初始化。generate_text 方法向模型 API 发送请求并返回响应。

步骤 3：为智能代理创建工具

下一步是为我们的智能代理创建工具。这些工具是简单的 Python 函数，用于执行特定任务。以下是一个基本计算器和字符串反转器的示例：

def basic_calculator(input_str):"""根据输入字符串或字典对两个数字执行数值运算。参数：input_str (str 或 dict)：要么是一个包含 'num1'、'num2' 和 'operation' 键的字典的 JSON 字符串，要么直接是一个字典。例如：'{"num1": 5, "num2": 3, "operation": "add"}'或 {"num1": 67869, "num2": 9030393, "operation": "divide"}返回：str：运算结果的格式化字符串。引发：Exception：如果在运算过程中发生错误（例如，除以零）。ValueError：如果请求了不支持的操作或输入无效。"""try:# 处理字典和字符串输入if isinstance(input_str, dict):input_dict = input_strelse:# 清理并解析输入字符串input_str_clean = input_str.replace("'", "\"")input_str_clean = input_str_clean.strip().strip
("\"")input_dict = json.loads(input_str_clean)# 验证所需字段if not all(key in input_dict for key in ['num1', 'num2', 'operation']):return "错误：输入必须包含 'num1'、'num2' 和 'operation'"num1 = float(input_dict['num1'])  # 转换为浮点数以处理小数num2 = float(input_dict['num2'])operation = input_dict['operation'].lower()  # 使大小写不敏感except (json.JSONDecodeError, KeyError) as e:return "输入格式无效。请输入有效的数字和运算符。"except ValueError as e:return "错误：请输入有效的数值。"# 定义支持的运算并进行错误处理operations = {'add': operator.add,'plus': operator.add,  # “加” 的另一种说法'subtract': operator.sub,'minus': operator.sub,  # “减” 的另一种说法'multiply': operator.mul,'times': operator.mul,  # “乘” 的另一种说法'divide': operator.truediv,'floor_divide': operator.floordiv,'modulus': operator.mod,'power': operator.pow,'lt': operator.lt,'le': operator.le,'eq': operator.eq,'ne': operator.ne,'ge': operator.ge,'gt': operator.gt}# 检查运算是否受支持if operation not in operations:return f"不支持的操作：'{operation}'。支持的操作有：{', '.join(operations.keys())}"try:# 特殊处理除以零的情况if (operation in ['divide', 'floor_divide', 'modulus']) and num2 == 0:return "错误：不允许除以零"# 执行运算result = operations[operation](num1, num2)# 根据类型格式化结果if isinstance(result, bool):result_str = "True" if result else "False"elif isinstance(result, float):# 处理浮点数精度result_str = f"{result:.6f}".rstrip('0').rstrip('.')else:result_str = str(result)return f"答案是：{result_str}"except Exception as e:return f"运算过程中出错：{str(e)}"def reverse_string(input_string):"""反转给定的字符串。参数：input_string (str)：要反转的字符串。返回：str：反转后的字符串。"""# 检查输入是否为字符串if not isinstance(input_string, str):return "错误：输入必须是字符串"# 使用切片反转字符串reversed_string = input_string[::-1]# 格式化输出result = f"反转后的字符串是：{reversed_string}"return result

这些函数根据提供的输入执行特定任务。basic_calculator 处理算术运算，而 reverse_string 反转给定的字符串。

步骤 4：构建工具箱

ToolBox 类存储智能代理可以使用的全部工具，并为每个工具提供描述：

class ToolBox:def __init__(self):self.tools_dict = {}def store(self, functions_list):"""存储列表中每个函数的字面名称和文档字符串。参数：functions_list (list)：函数对象列表，用于存储。返回：dict：以函数名称为键、文档字符串为值的字典。"""for func in functions_list:self.tools_dict[func.__name__] = func.__doc__return self.tools_dictdef tools(self):"""以文本字符串形式返回在 store 中创建的字典。返回：str：以文本字符串形式表示的存储函数及其文档字符串的字典。"""tools_str = ""for name, doc in self.tools_dict.items():tools_str += f"{name}: \"{doc}\"\n"return tools_str.strip()

这个类将帮助智能代理了解哪些工具可用以及每个工具的作用。

步骤 5：创建智能代理类

智能代理需要思考、决定使用哪个工具并执行它。以下是 Agent 类：

agent_system_prompt_template = """
你是一个能够使用特定工具的智能 AI 助手。你的回答必须始终是这种 JSON 格式：
{{"tool_choice": "tool_name","tool_input": "tool_inputs"
}}工具及其使用时机：1. basic_calculator：用于任何数学计算- 输入格式：{{"num1": number, "num2": number, "operation": "add/subtract/multiply/divide"}}- 支持的操作：add/plus, subtract/minus, multiply/times, divide- 示例输入和输出：输入："15 加 7 的结果是多少"输出：{{"tool_choice": "basic_calculator", "tool_input": {{"num1": 15, "num2": 7, "operation": "add"}}}}输入："100 除以 5 的结果是多少"输出：{{"tool_choice": "basic_calculator", "tool_input": {{"num1": 100, "num2": 5, "operation": "divide"}}}}2. reverse_string：用于任何涉及反转文本的请求- 输入格式：仅需反转的文本作为字符串- 当用户提到 "reverse"、"backwards" 或要求反转文本时，始终使用此工具- 示例输入和输出：输入："'Howwwww' 的反转是什么"输出：{{"tool_choice": "reverse_string", "tool_input": "Howwwww"}}输入："Python 的反转是什么"输出：{{"tool_choice": "reverse_string", "tool_input": "Python"}}3. no tool：用于一般对话和问题- 示例输入和输出：输入："你是谁？"输出：{{"tool_choice": "no tool", "tool_input": "我是一个 AI 助手，可以帮助你进行计算、反转文本以及回答问题。我可以执行数学运算和反转字符串。今天我能帮你做些什么呢？"}}输入："你好吗？"输出：{{"tool_choice": "no tool", "tool_input": "我运行良好，感谢你的关心！我可以帮助你进行计算、反转文本或回答你可能有的任何问题。"}}严格规则：
1. 对于有关身份、能力或感受的问题：- 始终使用 "no tool"- 提供完整、友好的回答- 提及你的能力2. 对于任何文本反转请求：- 始终使用 "reverse_string"- 仅提取要反转的文本- 删除引号、"reverse of" 以及其他多余文本3. 对于任何数学运算：- 始终使用 "basic_calculator"- 提取数字和运算符- 将文本数字转换为数字以下是你的工具列表及其描述：
{tool_descriptions}记住：你的回答必须始终是有效的 JSON，包含 "tool_choice" 和 "tool_input" 字段。
"""
class Agent:def __init__(self, tools, model_service, model_name, stop=None):"""使用工具列表和模型初始化智能代理。参数：tools (list)：工具函数列表。model_service (class)：具有 generate_text 方法的模型服务类。model_name (str)：要使用的模型名称。"""self.tools = toolsself.model_service = model_serviceself.model_name = model_nameself.stop = stopdef prepare_tools(self):"""存储工具并返回它们的描述。返回：str：存储在工具箱中的工具描述。"""toolbox = ToolBox()toolbox.store(self.tools)tool_descriptions = toolbox.tools()return tool_descriptionsdef think(self, prompt):"""使用系统提示模板和工具描述在模型上运行 generate_text 方法。参数：prompt (str)：要为其生成响应的用户查询。返回：dict：模型返回的响应，以字典形式表示。"""tool_descriptions = self.prepare_tools()agent_system_prompt = agent_system_prompt_template.format(tool_descriptions=tool_descriptions)# 使用系统提示创建模型服务实例if self.model_service == OllamaModel:model_instance = self.model_service(model=self.model_name,system_prompt=agent_system_prompt,temperature=0,stop=self.stop)else:model_instance = self.model_service(model=self.model_name,system_prompt=agent_system_prompt,temperature=0)# 生成并返回响应字典agent_response_dict = model_instance.generate_text(prompt)return agent_response_dictdef work(self, prompt):"""解析 think 返回的字典并执行适当的工具
。参数：prompt (str)：要为其生成响应的用户查询。返回：执行适当工具的响应，或者如果没有找到匹配的工具，则返回 tool_input。"""agent_response_dict = self.think(prompt)tool_choice = agent_response_dict.get("tool_choice")tool_input = agent_response_dict.get("tool_input")for tool in self.tools:if tool.__name__ == tool_choice:response = tool(tool_input)print(colored(response, 'cyan'))returnprint(colored(tool_input, 'cyan'))return

这个类有三个主要方法：

prepare_tools：存储并返回工具的描述。
think：根据用户提示决定使用哪个工具。
work：执行选定的工具并返回结果。

步骤 6：运行智能代理

最后，咱们把所有东西整合起来，运行我们的智能代理。在脚本的 main 部分，初始化智能代理并开始接受用户输入：

# 示例用法
if __name__ == "__main__":"""使用此智能代理的说明：你可以尝试以下示例查询：1. 计算器运算：- "15 加 7 的结果是多少"- "100 除以 5 的结果是多少"- "23 乘以 4 的结果是多少"2. 字符串反转：- "反转单词 'hello world'"- "你能反转 'Python Programming' 吗？"3. 一般问题（将获得直接回答）：- "你是谁？"- "你能帮我做些什么？"Ollama 命令（在终端中运行）：- 查看可用模型：    'ollama list'- 查看正在运行的模型：      'ps aux | grep ollama'- 列出模型标签：          'curl http://localhost:11434/api/tags'- 拉取新模型：         'ollama pull mistral'- 运行模型服务器：         'ollama serve'"""tools = [basic_calculator, reverse_string]# 如果使用 OpenAI，请取消以下注释# model_service = OpenAIModel# model_name = 'gpt-3.5-turbo'# stop = None# 使用 Ollama 和 llama2 模型model_service = OllamaModelmodel_name = "llama2"  # 可以更改为其他模型，如 'mistral'、'codellama' 等stop = "<|eot_id|>"agent = Agent(tools=tools, model_service=model_service, model_name=model_name, stop=stop)print("\n欢迎使用智能代理！输入 'exit' 退出。")print("你可以让我：")print("1. 进行计算（例如，'15 加 7 的结果是多少'）")print("2. 反转字符串（例如，'反转 hello world'）")print("3. 回答一般问题\n")while True:prompt = input("问我任何问题：")if prompt.lower() == "exit":breakagent.work(prompt)

3. 总结

我们探索了AI Agents的定义，并逐步实现了它。我们设置了环境，定义了模型，创建了必要的工具，并构建了一个结构化的工具箱来支持智能代理的功能。最后，我们将所有东西整合在一起，让智能代理开始工作。

这种结构化的方法为构建能够自动化任务并做出明智决策的智能、交互式智能代理提供了坚实的基础。随着智能代理的不断发展，它们的应用将在各个行业不断扩展，推动效率和创新。敬请期待更多关于智能代理的见解和改进，让我们的智能代理迈向更高水平！

4 完整代码

from termcolor import colored
import os
from dotenv import load_dotenv
load_dotenv()
### Models
import requests
import json
import operatorclass OllamaModel:def __init__(self, model, system_prompt, temperature=0, stop=None):"""Initializes the OllamaModel with the given parameters.Parameters:model (str): The name of the model to use.system_prompt (str): The system prompt to use.temperature (float): The temperature setting for the model.stop (str): The stop token for the model."""self.model_endpoint = "http://localhost:11434/api/generate"self.temperature = temperatureself.model = modelself.system_prompt = system_promptself.headers = {"Content-Type": "application/json"}self.stop = stopdef generate_text(self, prompt):"""Generates a response from the Ollama model based on the provided prompt.Parameters:prompt (str): The user query to generate a response for.Returns:dict: The response from the model as a dictionary."""payload = {"model": self.model,"format": "json","prompt": prompt,"system": self.system_prompt,"stream": False,"temperature": self.temperature,"stop": self.stop}try:request_response = requests.post(self.model_endpoint, headers=self.headers, data=json.dumps(payload))print("REQUEST RESPONSE", request_response)request_response_json = request_response.json()response = request_response_json['response']response_dict = json.loads(response)print(f"\n\nResponse from Ollama model: {response_dict}")return response_dictexcept requests.RequestException as e:response = {"error": f"Error in invoking model! {str(e)}"}return responsedef basic_calculator(input_str):"""Perform a numeric operation on two numbers based on the input string or dictionary.Parameters:input_str (str or dict): Either a JSON string representing a dictionary with keys 'num1', 'num2', and 'operation',or a dictionary directly. Example: '{"num1": 5, "num2": 3, "operation": "add"}'or {"num1": 67869, "num2": 9030393, "operation": "divide"}Returns:str: The formatted result of the operation.Raises:Exception: If an error occurs during the operation (e.g., division by zero).ValueError: If an unsupported operation is requested or input is invalid."""try:# Handle both dictionary and string inputsif isinstance(input_str, dict):input_dict = input_strelse:# Clean and parse the input stringinput_str_clean = input_str.replace("'", "\"")input_str_clean = input_str_clean.strip().strip("\"")input_dict = json.loads(input_str_clean)# Validate required fieldsif not all(key in input_dict for key in ['num1', 'num2', 'operation']):return "Error: Input must contain 'num1', 'num2', and 'operation'"num1 = float(input_dict['num1'])  # Convert to float to handle decimal numbersnum2 = float(input_dict['num2'])operation = input_dict['operation'].lower()  # Make case-insensitiveexcept (json.JSONDecodeError, KeyError) as e:return "Invalid input format. Please provide valid numbers and operation."except ValueError as e:return "Error: Please provide valid numerical values."# Define the supported operations with error handlingoperations = {'add': operator.add,'plus': operator.add,  # Alternative word for add'subtract': operator.sub,'minus': operator.sub,  # Alternative word for subtract'multiply': operator.mul,'times': operator.mul,  # Alternative word for multiply'divide': operator.truediv,'floor_divide': operator.floordiv,'modulus': operator.mod,'power': operator.pow,'lt': operator.lt,'le': operator.le,'eq': operator.eq,'ne': operator.ne,'ge': operator.ge,'gt': operator.gt}# Check if the operation is supportedif operation not in operations:return f"Unsupported operation: '{operation}'. Supported operations are: {', '.join(operations.keys())}"try:# Special handling for division by zeroif (operation in ['divide', 'floor_divide', 'modulus']) and num2 == 0:return "Error: Division by zero is not allowed"# Perform the operationresult = operations[operation](num1, num2)# Format result based on typeif isinstance(result, bool):result_str = "True" if result else "False"elif isinstance(result, float):# Handle floating point precisionresult_str = f"{result:.6f}".rstrip('0').rstrip('.')else:result_str = str(result)return f"The answer is: {result_str}"except Exception as e:return f"Error during calculation: {str(e)}"def reverse_string(input_string):"""Reverse the given string.Parameters:input_string (str): The string to be reversed.Returns:str: The reversed string."""# Check if input is a stringif not isinstance(input_string, str):return "Error: Input must be a string"# Reverse the string using slicingreversed_string = input_string[::-1]# Format the outputresult = f"The reversed string is: {reversed_string}"return resultclass ToolBox:def __init__(self):self.tools_dict = {}def store(self, functions_list):"""Stores the literal name and docstring of each function in the list.Parameters:functions_list (list): List of function objects to store.Returns:dict: Dictionary with function names as keys and their docstrings as values."""for func in functions_list:self.tools_dict[func.__name__] = func.__doc__return self.tools_dictdef tools(self):"""Returns the dictionary created in store as a text string.Returns:str: Dictionary of stored functions and their docstrings as a text string."""tools_str = ""for name, doc in self.tools_dict.items():tools_str += f"{name}: \"{doc}\"\n"return tools_str.strip()agent_system_prompt_template = """
You are an intelligent AI assistant with access to specific tools. Your responses must ALWAYS be in this JSON format:
{{"tool_choice": "name_of_the_tool","tool_input": "inputs_to_the_tool"
}}TOOLS AND WHEN TO USE THEM:1. basic_calculator: Use for ANY mathematical calculations- Input format: {{"num1": number, "num2": number, "operation": "add/subtract/multiply/divide"}}- Supported operations: add/plus, subtract/minus, multiply/times, divide- Example inputs and outputs:Input: "Calculate 15 plus 7"Output: {{"tool_choice": "basic_calculator", "tool_input": {{"num1": 15, "num2": 7, "operation": "add"}}}}Input: "What is 100 divided by 5?"Output: {{"tool_choice": "basic_calculator", "tool_input": {{"num1": 100, "num2": 5, "operation": "divide"}}}}2. reverse_string: Use for ANY request involving reversing text- Input format: Just the text to be reversed as a string- ALWAYS use this tool when user mentions "reverse", "backwards", or asks to reverse text- Example inputs and outputs:Input: "Reverse of 'Howwwww'?"Output: {{"tool_choice": "reverse_string", "tool_input": "Howwwww"}}Input: "What is the reverse of Python?"Output: {{"tool_choice": "reverse_string", "tool_input": "Python"}}3. no tool: Use for general conversation and questions- Example inputs and outputs:Input: "Who are you?"Output: {{"tool_choice": "no tool", "tool_input": "I am an AI assistant that can help you with calculations, reverse text, and answer questions. I can perform mathematical operations and reverse strings. How can I help you today?"}}Input: "How are you?"Output: {{"tool_choice": "no tool", "tool_input": "I'm functioning well, thank you for asking! I'm here to help you with calculations, text reversal, or answer any questions you might have."}}STRICT RULES:
1. For questions about identity, capabilities, or feelings:- ALWAYS use "no tool"- Provide a complete, friendly response- Mention your capabilities2. For ANY text reversal request:- ALWAYS use "reverse_string"- Extract ONLY the text to be reversed- Remove quotes, "reverse of", and other extra text3. For ANY math operations:- ALWAYS use "basic_calculator"- Extract the numbers and operation- Convert text numbers to digitsHere is a list of your tools along with their descriptions:
{tool_descriptions}Remember: Your response must ALWAYS be valid JSON with "tool_choice" and "tool_input" fields.
"""class Agent:def __init__(self, tools, model_service, model_name, stop=None):"""Initializes the agent with a list of tools and a model.Parameters:tools (list): List of tool functions.model_service (class): The model service class with a generate_text method.model_name (str): The name of the model to use."""self.tools = toolsself.model_service = model_serviceself.model_name = model_nameself.stop = stopdef prepare_tools(self):"""Stores the tools in the toolbox and returns their descriptions.Returns:str: Descriptions of the tools stored in the toolbox."""toolbox = ToolBox()toolbox.store(self.tools)tool_descriptions = toolbox.tools()return tool_descriptionsdef think(self, prompt):"""Runs the generate_text method on the model using the system prompt template and tool descriptions.Parameters:prompt (str): The user query to generate a response for.Returns:dict: The response from the model as a dictionary."""tool_descriptions = self.prepare_tools()agent_system_prompt = agent_system_prompt_template.format(tool_descriptions=tool_descriptions)# Create an instance of the model service with the system promptif self.model_service == OllamaModel:model_instance = self.model_service(model=self.model_name,system_prompt=agent_system_prompt,temperature=0,stop=self.stop)else:model_instance = self.model_service(model=self.model_name,system_prompt=agent_system_prompt,temperature=0)# Generate and return the response dictionaryagent_response_dict = model_instance.generate_text(prompt)return agent_response_dictdef work(self, prompt):"""Parses the dictionary returned from think and executes the appropriate tool.Parameters:prompt (str): The user query to generate a response for.Returns:The response from executing the appropriate tool or the tool_input if no matching tool is found."""agent_response_dict = self.think(prompt)tool_choice = agent_response_dict.get("tool_choice")tool_input = agent_response_dict.get("tool_input")for tool in self.tools:if tool.__name__ == tool_choice:response = tool(tool_input)print(colored(response, 'cyan'))returnprint(colored(tool_input, 'cyan'))return# Example usage
if __name__ == "__main__":"""Instructions for using this agent:Example queries you can try:1. Calculator operations:- "Calculate 15 plus 7"- "What is 100 divided by 5?"- "Multiply 23 and 4"2. String reversal:- "Reverse the word 'hello world'"- "Can you reverse 'Python Programming'?"3. General questions (will get direct responses):- "Who are you?"- "What can you help me with?"Ollama Commands (run these in terminal):- Check available models:    'ollama list'- Check running models:      'ps aux | grep ollama'- List model tags:          'curl http://localhost:11434/api/tags'- Pull a new model:         'ollama pull mistral'- Run model server:         'ollama serve'"""tools = [basic_calculator, reverse_string]# Uncomment below to run with OpenAI# model_service = OpenAIModel# model_name = 'gpt-3.5-turbo'# stop = None# Using Ollama with llama2 modelmodel_service = OllamaModelmodel_name = "llama2"  # Can be changed to other models like 'mistral', 'codellama', etc.stop = "<|eot_id|>"agent = Agent(tools=tools, model_service=model_service, model_name=model_name, stop=stop)print("\nWelcome to the AI Agent! Type 'exit' to quit.")print("You can ask me to:")print("1. Perform calculations (e.g., 'Calculate 15 plus 7')")print("2. Reverse strings (e.g., 'Reverse hello world')")print("3. Answer general questions\n")while True:prompt = input("Ask me anything: ")if prompt.lower() == "exit":breakagent.work(prompt)