提示工程中的代理技术：构建智能自主的AI系统

import openai

def rule_based_agent(task, context):
    rules = {
        "greeting": "If the task involves greeting, always start with 'Hello! How can I assist you today?'",
        "farewell": "If the task is complete, end with 'Is there anything else I can help you with?'",
        "clarification": "If the task is unclear, ask for more details before proceeding."
    }

    prompt = f"""
    Task: {task}
    Context: {context}

    Rules to follow:
    {rules['greeting']}
    {rules['farewell']}
    {rules['clarification']}

    Please complete the task following these rules.
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=150,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 使用示例
task = "Greet the user and ask about their day"
context = "First interaction with a new user"
result = rule_based_agent(task, context)
print(result)
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def goal_based_agent(goal, context):
    prompt = f"""
    Goal: {goal}
    Context: {context}

    As an AI agent, your task is to achieve the given goal. Please follow these steps:
    1. Analyze the goal and context
    2. Break down the goal into subtasks
    3. For each subtask:
       a. Plan the necessary actions
       b. Execute the actions
       c. Evaluate the result
    4. If the goal is not yet achieved, repeat steps 2-3
    5. Once the goal is achieved, summarize the process and results

    Please start your analysis and planning:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=300,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 使用示例
goal = "Organize a virtual team-building event for a remote team of 10 people"
context = "The team is spread across different time zones and has diverse interests"
result = goal_based_agent(goal, context)
print(result)
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import openai
import requests
import wolframalpha

def tool_using_agent(task, available_tools):
    prompt = f"""
    Task: {task}
    Available tools: {', '.join(available_tools)}

    As an AI agent with access to external tools, your job is to complete the given task. Follow these steps:
    1. Analyze the task and determine which tools, if any, are needed
    2. For each required tool:
       a. Formulate the query for the tool
       b. Use the tool (simulated in this example)
       c. Interpret the results
    3. Combine the information from all tools to complete the task
    4. If the task is not fully completed, repeat steps 1-3
    5. Present the final result

    Begin your analysis:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=300,
        temperature=0.7
    )

    return response.choices[0].text.strip()

def use_tool(tool_name, query):
    if tool_name == "weather_api":
        # Simulated weather API call
        return f"Weather data for {query}: Sunny, 25°C"
    elif tool_name == "calculator":
        # Use WolframAlpha as a calculator
        client = wolframalpha.Client('YOUR_APP_ID')
        res = client.query(query)
        return next(res.results).text
    elif tool_name == "search_engine":
        # Simulated search engine query
        return f"Search results for '{query}': [Result 1], [Result 2], [Result 3]"
    else:
        return "Tool not available"

# 使用示例
task = "Plan a picnic for tomorrow, including calculating the amount of food needed for 5 people"
available_tools = ["weather_api", "calculator", "search_engine"]
result = tool_using_agent(task, available_tools)
print(result)
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def multi_agent_system(task, agents):
    prompt = f"""
    Task: {task}
    Agents: {', '.join(agents.keys())}

    You are the coordinator of a multi-agent system. Your job is to:
    1. Analyze the task and determine which agents are needed
    2. Assign subtasks to appropriate agents
    3. Collect and integrate results from all agents
    4. Ensure smooth communication and coordination between agents
    5. Resolve any conflicts or inconsistencies
    6. Present the final integrated result

    For each agent, consider their role and capabilities:
    {', '.join([f"{k}: {v}" for k, v in agents.items()])}

    Begin your coordination process:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=400,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 使用示例
task = "Develop and launch a new product in the smart home market"
agents = {
    "market_researcher": "Analyzes market trends and consumer needs",
    "product_designer": "Creates product concepts and designs",
    "engineer": "Develops technical specifications and prototypes",
    "marketing_specialist": "Creates marketing strategies and materials",
    "project_manager": "Oversees the entire product development process"
}
result = multi_agent_system(task, agents)
print(result)
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class AgentMemory:
    def __init__(self):
        self.short_term = []
        self.long_term = {}

    def add_short_term(self, item):
        self.short_term.append(item)
        if len(self.short_term) > 5:  # 只保留最近的5个项目
            self.short_term.pop(0)

    def add_long_term(self, key, value):
        self.long_term[key] = value

    def get_context(self):
        return f"Short-term memory: {self.short_term}\nLong-term memory: {self.long_term}"

def stateful_agent(task, memory):
    context = memory.get_context()
    prompt = f"""
    Task: {task}
    Context: {context}

    As an AI agent with memory, use your short-term and long-term memory to inform your actions.
    After completing the task, update your memories as necessary.

    Your response:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=200,
        temperature=0.7
    )

    # 更新记忆（这里简化处理，实际应用中可能需要更复杂的逻辑）
    memory.add_short_term(task)
    memory.add_long_term(task, response.choices[0].text.strip())

    return response.choices[0].text.strip()

# 使用示例
memory = AgentMemory()
task1 = "Introduce yourself to a new user"
result1 = stateful_agent(task1, memory)
print(result1)

task2 = "Recommend a product based on the user's previous interactions"
result2 = stateful_agent(task2, memory)
print(result2)
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def metacognitive_agent(task, performance_history):
    prompt = f"""
    Task: {task}
    Performance History: {performance_history}

    As an AI agent with metacognitive abilities, your job is to:
    1. Analyze the given task
    2. Reflect on your past performance in similar tasks
    3. Identify areas for improvement based on your performance history
    4. Develop a strategy to complete the current task, incorporating lessons learned
    5. Execute the task
    6. After completion, evaluate your performance and update your learning

    Begin your metacognitive process:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=300,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 使用示例
task = "Write a persuasive email to potential clients about our new software product"
performance_history = [
    "Previous email campaigns had a 15% open rate and 2% click-through rate",
    "Personalized subject lines increased open rates by 25%",
    "Including customer testimonials improved click-through rates by 40%"
]
result = metacognitive_agent(task, performance_history)
print(result)
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def ethical_agent(task, ethical_guidelines):
    prompt = f"""
    Task: {task}
    Ethical Guidelines:
    {ethical_guidelines}

    As an AI agent with a strong ethical framework, your job is to:
    1. Analyze the given task
    2. Identify any potential ethical concerns or dilemmas
    3. Consider multiple approaches to the task, evaluating each against the ethical guidelines
    4. Choose the most ethical approach that still accomplishes the task
    5. If no ethical approach is possible, explain why and suggest alternatives
    6. Execute the chosen approach, ensuring all actions align with the ethical guidelines

    Begin your ethical analysis and task execution:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=300,
        temperature=0.7
    )

    return response.choices[0].text.strip()

# 使用示例
task = "Develop a marketing campaign for a new energy drink targeting teenagers"
ethical_guidelines = """
1. Do not exploit vulnerable populations
2. Be truthful and transparent about product effects
3. Promote responsible consumption
4. Consider long-term health impacts
5. Respect privacy and data protection
"""
result = ethical_agent(task, ethical_guidelines)
print(result)
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def evaluate_task_completion(agent, tasks):
    total_score = 0
    for task in tasks:
        result = agent(task)
        score = rate_completion(task, result)  # 这个函数需要单独实现
        total_score += score
    return total_score / len(tasks)

def rate_completion(task, result):
    prompt = f"""
    Task: {task}
    Result: {result}

    On a scale of 1-10, rate how well the result completes the given task.
    Consider factors such as accuracy, completeness, and relevance.

    Rating (1-10):
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=10,
        temperature=0.3
    )

    return int(response.choices[0].text.strip())
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def evaluate_decision_quality(agent, scenarios):
    total_score = 0
    for scenario in scenarios:
        decision = agent(scenario)
        score = rate_decision(scenario, decision)
        total_score += score
    return total_score / len(scenarios)

def rate_decision(scenario, decision):
    prompt = f"""
    Scenario: {scenario}
    Decision: {decision}

    Evaluate the quality of this decision considering the following criteria:
    1. Appropriateness for the scenario
    2. Potential consequences
    3. Alignment with given objectives or ethical guidelines
    4. Creativity and innovation

    Provide a rating from 1-10 and a brief explanation.

    Rating (1-10):
    Explanation:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=100,
        temperature=0.5
    )

    lines = response.choices[0].text.strip().split('\n')
    rating = int(lines[0].split(':')[1].strip())
    explanation = '\n'.join(lines[1:])
    return rating, explanation
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def evaluate_adaptability(agent, task_sequence):
    performance_trend = []
    for task in task_sequence:
        result = agent(task)
        score = rate_completion(task, result)
        performance_trend.append(score)

    # 分析性能趋势
    improvement_rate = (performance_trend[-1] - performance_trend[0]) / len(performance_trend)

    return improvement_rate, performance_trend

# 使用示例
task_sequence = [
    "Summarize a news article",
    "Write a product description",
    "Respond to a customer complaint",
    "Create a marketing slogan",
    "Draft a press release"
]

improvement_rate, performance_trend = evaluate_adaptability(some_agent, task_sequence)
print(f"Improvement rate: {improvement_rate}")
print(f"Performance trend: {performance_trend}")
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import openai
import datetime

class PersonalAssistant:
    def __init__(self, user_name):
        self.user_name = user_name
        self.memory = AgentMemory()
        self.ethical_guidelines = """
        1. Respect user privacy and data protection
        2. Provide accurate and helpful information
        3. Encourage healthy habits and well-being
        4. Avoid actions that could harm the user or others
        5. Be transparent about AI limitations
        """

    def process_request(self, request):
        context = self.memory.get_context()
        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")

        prompt = f"""
        User: {self.user_name}
        Current Time: {current_time}
        Request: {request}
        Context: {context}
        Ethical Guidelines: {self.ethical_guidelines}

        As an AI personal assistant, your task is to:
        1. Understand the user's request and current context
        2. Consider any relevant information from your memory
        3. Devise a plan to fulfill the request, breaking it into steps if necessary
        4. Ensure all actions align with the ethical guidelines
        5. Execute the plan, simulating any necessary actions or API calls
        6. Provide a helpful and friendly response to the user
        7. Update your memory with any important information from this interaction

        Your response:
        """

        response = openai.Completion.create(
            engine="text-davinci-002",
            prompt=prompt,
            max_tokens=300,
            temperature=0.7
        )

        result = response.choices[0].text.strip()
        self.update_memory(request, result)
        return result

    def update_memory(self, request, result):
        self.memory.add_short_term(f"Request: {request}")
        self.memory.add_short_term(f"Response: {result}")
        # 这里可以添加更复杂的逻辑来提取和存储长期记忆

# 使用示例
assistant = PersonalAssistant("Alice")

requests = [
    "What's on my schedule for today?",
    "Remind me to buy groceries this evening",
    "I'm feeling stressed, any suggestions for relaxation?",
    "Can you help me plan a surprise party for my friend next week?",
    "I need to book a flight to New York for next month"
]

for request in requests:
    print(f"User: {request}")
    response = assistant.process_request(request)
    print(f"Assistant: {response}\n")
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def consistency_check(agent, past_interactions, new_interaction):
    prompt = f"""
    Past Interactions:
    {past_interactions}

    New Interaction:
    {new_interaction}

    Analyze the consistency of the agent's behavior across these interactions. Consider:
    1. Adherence to established facts and preferences
    2. Consistency in personality and tone
    3. Logical coherence of decisions and advice

    Provide a consistency score (1-10) and explain any inconsistencies:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=200,
        temperature=0.5
    )

    return response.choices[0].text.strip()
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def error_correction(agent, task_sequence):
    results = []
    for task in task_sequence:
        result = agent(task)
        corrected_result = self_correct(agent, task, result)
        results.append(corrected_result)
    return results

def self_correct(agent, task, initial_result):
    prompt = f"""
    Task: {task}
    Initial Result: {initial_result}

    As an AI agent, review your initial result and consider:
    1. Are there any logical errors or inconsistencies?
    2. Have all aspects of the task been addressed?
    3. Could the result be improved or optimized?

    If necessary, provide a corrected or improved result. If the initial result is satisfactory, state why.

    Your analysis and corrected result (if needed):
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=200,
        temperature=0.5
    )

    return response.choices[0].text.strip()
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def explain_decision(agent, decision, context):
    prompt = f"""
    Decision: {decision}
    Context: {context}

    As an AI agent, explain your decision-making process in detail:
    1. What were the key factors considered?
    2. What alternatives were evaluated?
    3. Why was this decision chosen over others?
    4. What potential risks or downsides were identified?
    5. How does this decision align with overall goals and ethical guidelines?

    Provide a clear and detailed explanation:
    """

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=300,
        temperature=0.7
    )

    return response.choices[0].text.strip()
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