本文详细介绍了如何使用Python从零开始构建一个AI Agent涵盖Agent的概念、类型、核心组成部分并通过实际代码演示了如何实现一个能与用户交互的智能体。文章还提供了完整的代码库和运行环境设置指南适合想要学习大模型和小白程序员参考学习。1、什么是AgentAgent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。‍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 OllamaOllama 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 needed3、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 operatorclass OllamaModel: def __init__(self, model, system_prompt, temperature0, stopNone): 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, headersself.headers, datajson.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: fError 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 fUnsupported 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 fThe answer is: {result_str} except Exception as e: return fError 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 fThe 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. 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: Im functioning well, thank you for asking! Im 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, stopNone): 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_descriptionstool_descriptions) # Create an instance of the model service with the system prompt if self.model_service OllamaModel: model_instance self.model_service( modelself.model_name, system_promptagent_system_prompt, temperature0, stopself.stop ) else: model_instance self.model_service( modelself.model_name, system_promptagent_system_prompt, temperature0 ) # 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(toolstools, model_servicemodel_service, model_namemodel_name, stopstop) 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. 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