【收藏必备】Python构建AI Agent完整指南：从零开始到实战应用，新手友好

本文详细介绍了如何使用Python从零开始构建一个AI Agent。首先解释了Agent的概念和类型，然后指导读者设置开发环境、定义Model类、创建工具和工具箱，以及实现Agent类。文章提供了完整的代码示例，包括计算器和字符串反转器作为工具，最后展示了如何初始化和运行这个Agent，使其能够根据用户输入选择适当的工具并执行任务。这是一个结构化的指南，为构建能够自动执行任务和做出明智决策的智能交互Agent奠定了基础。

1、什么是Agent？

Agent是指一种能够感知环境、做出决策并采取行动以实现特定目标的自主实体。AI Agent的复杂程度各不相同，既有仅对刺激做出反应的简单反应式智能体，也有能够随时间推移不断学习和适应的高级智能体。常见的智能体类型包括：

• Reactive Agents: 直接响应环境变化，不具备内部记忆。
• Model-Based Agents: 利用内部世界模型进行决策的智能体。
• Goal-Based Agents: 以实现特定目标为基础规划行动。
• Utility-Based Agents: 基于效用函数评估潜在行动，以实现结果的最大化。

示例包括聊天机器人、推荐系统和自动驾驶汽车，它们各自利用不同类型的Agent，以高效且智能的方式执行任务。

智能体的核心组成部分如下：

• Models: 智能体的大脑，负责处理输入信息并做出反应。
• Tools: 智能体可根据用户请求执行的预定义功能。
• Toolbox: 智能体可使用的工具集合。
• System Prompt: 指导智能体处理用户输入并选择正确工具的指令集。

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2、实 现‍

现在，让我们卷起袖子开始实现吧！

• 前提条件

本教程的完整代码可在 AI Agents GitHub 代码库中找到。

Github: https://github.com/vsingh9076/AI-Agents/tree/main/build-agent-from-scratch

运行代码前，请确保您的系统满足以下前提条件：

• Python 环境设置

运行AI Agent需要安装 Python虚拟环境。请按照以下步骤设置虚拟环境：

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

安装所需的依赖项，导航至本仓库代码页面，按照requirements.txt安装所需依赖，指令如下：

pip install -r requirements.txt 

• 在本地设置 Ollama

Ollama 用于高效运行和管理本地大语言模型。请按照以下步骤进行安装和配置：大家可以访问 Ollama 官方网站，下载适用于你操作系统的安装程序。

Ollama官网：https://ollama.com/

按照官网说明进行安装后，大家运行以下命令检查 Ollama 是否已正确安装：

ollama --version 

拉取模型，某些Agent的实现可能需要特定的模型。您可以使用以下命令拉取模型：

ollama pull mistral # Replace 'mistral' with the model needed 

3、定义Model类

本文实现的具体流程如下：

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 

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

下面是一个简单的代码实现：

from termcolor import colored import os from dotenv import load_dotenv load_dotenv() ### Models import requests import json import operator 

class 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 = temperature self.model = model self.system_prompt = system_prompt self.headers = {"Content-Type": "application/json"} self.stop = stop def 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_dict except requests.RequestException as e: response = {"error": f"Error in invoking model! {str(e)}"} return response 

该类使用参数model、system_prompt、temperature和stop token进行初始化。其中generate_text 函数向模型 API 发送请求并返回响应。

4、创建Agent所需工具

下一步是创建智能体Agent可以使用的工具。这些工具是执行特定任务的简单 Python 函数。下面是一个基本计算器和一个字符串反转器的示例：

def 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 inputs if isinstance(input_str, dict): input_dict = input_str else: # Clean and parse the input string input_str_clean = input_str.replace("'", "\"") input_str_clean = input_str_clean.strip().strip("\"") input_dict = json.loads(input_str_clean) # Validate required fields if 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 numbers num2 = float(input_dict['num2']) operation = input_dict['operation'].lower() # Make case-insensitive except (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 handling operations = { '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 supported if operation not in operations: return f"Unsupported operation: '{operation}'. Supported operations are: {', '.join(operations.keys())}" try: # Special handling for division by zero if (operation in ['divide', 'floor_divide', 'modulus']) and num2 == 0: return "Error: Division by zero is not allowed" # Perform the operation result = operations[operation](num1, num2) # Format result based on type if isinstance(result, bool): result_str = "True" if result else "False" elif isinstance(result, float): # Handle floating point precision result_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 string if not isinstance(input_string, str): return "Error: Input must be a string" # Reverse the string using slicing reversed_string = input_string[::-1] # Format the output result = f"The reversed string is: {reversed_string}" return result 

这些函数旨在根据所提供的输入执行特定任务。basic_calculator 处理算术运算，而 reverse_string 则反转给定的字符串。

5、创建工具箱

工具箱ToolBox类存储了智能体可以使用的所有工具，并提供了每种工具的说明：

class 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_dict def 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. """ for name, doc in self.tools_dict.items(): tools_str += f"{name}: \"{doc}\"\n" return tools_str.strip() 

这个类将帮助智能体了解哪些工具可用以及每种工具的具体用途。

6、创建Agent类

Agent需要思考、决定使用哪种工具并执行它。下面是Agent类的代码实现：

系统提示词如下：

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 capabilities 2. For ANY text reversal request: - ALWAYS use "reverse_string" - Extract ONLY the text to be reversed - Remove quotes, "reverse of", and other extra text 3. For ANY math operations: - ALWAYS use "basic_calculator" - Extract the numbers and operation - Convert text numbers to digits Here 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. """ 

Agent类的代码实现如下：

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 = tools self.model_service = model_service self.model_name = model_name self.stop = stop def 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_descriptions def 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 prompt 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 ) # Generate and return the response dictionary agent_response_dict = model_instance.generate_text(prompt) return agent_response_dict def 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')) return print(colored(tool_input, 'cyan')) return 

该类有三个主要方法：

• prepare_tools: 存储并返回工具说明。
• think: 根据用户提示决定使用哪种工具。
• work: 执行所选工具并返回结果。

7、运行Agent

最后，让我们将所有内容整合在一起，运行我们的Agent智能体。在脚本的main入口函数内，初始化Agent并开始接受用户输入：

# 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 model model_service = OllamaModel model_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": break agent.work(prompt) 

结论

在这篇文章中，我们一步一步地探索了对Agent是什么的理解。我们建立了虚拟环境，定义了模型，创建了基本工具，并构建了一个结构化工具箱来支持我们的代理功能。最后，我们通过运行Agent，将一切整合在一起。

这种结构化方法为构建能够自动执行任务和做出明智决策的智能交互Agent奠定了坚实的基础。随着AI Agent的不断发展，其应用范围将扩展到各个行业，从而推动效率和创新。

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