AI Agent 核心原理与实现机制

class AIAgent:
    def __init__(self, llm_model):
        self.llm = llm_model
        self.memory = MemorySystem()
        self.planner = PlanningSystem()
        
    def perceive(self, observation):
        """感知环境状态"""
        processed_obs = self.process_observation(observation)
        self.memory.update(processed_obs)
        return processed_obs
    
    def reason(self, goal):
        """基于当前状态和目标进行推理"""
        context = self.memory.get_context()
        plan = self.planner.generate_plan(context, goal)
        return plan
    
    def act(self, action_plan):
        """执行行动计划"""
        for action in action_plan:
            result = self.execute_action(action)
            self.memory.update(result)
        return result

class MemorySystem:
    def __init__(self):
        self.short_term_memory = []
        self.long_term_memory = VectorDB()
        self.episodic_memory = []
        
    def store_short_term(self, info, ttl=60):
        """存储短期记忆"""
        entry = {
            'content': info,
            'timestamp': time.time(),
            'ttl': ttl
        }
        self.short_term_memory.append(entry)
        self.cleanup_expired()
        
    def store_long_term(self, info, metadata=None):
        """存储长期记忆"""
        embedding = self.embed(info['content'])
        self.long_term_memory.add(embedding, info, metadata)
        
    def retrieve_relevant(self, query, k=5):
        """检索相关信息"""
        query_embedding = self.embed(query)
        results = self.long_term_memory.search(query_embedding, k=k)
        return results

class PlanningSystem:
    def __init__(self):
        self.task_decomposer = TaskDecomposer()
        self.action_planner = ActionPlanner()
        
    def generate_plan(self, context, goal):
        """生成执行计划"""
        # 1. 目标分析
        subgoals = self.analyze_goal(goal)
        
        # 2. 任务分解
        tasks = self.decompose_tasks(subgoals, context)
        
        # 3. 行动序列规划
        action_sequence = self.plan_actions(tasks, context)
        
        # 4. 风险评估
        risks = self.assess_risks(action_sequence)
        
        return {
            'tasks': tasks,
            'actions': action_sequence,
            'risks': risks,
            'confidence': self.calculate_confidence(action_sequence)
        }

import asyncio
from typing import Dict, List, Any
from dataclasses import dataclass
import openai
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

@dataclass
class AgentState:
    current_task: str
    memory_buffer: List[str]
    confidence_score: float
    execution_history: List[Dict]

class ResearchAssistant:
    def __init__(self, api_key: str):
        self.client = openai.OpenAI(api_key=api_key)
        self.state = AgentState(
            current_task="",
            memory_buffer=[],
            confidence_score=0.0,
            execution_history=[]
        )
        self.vector_store = {}
        
    async def process_query(self, query: str) -> Dict[str, Any]:
        """处理用户查询"""
        # 1. 理解查询意图
        intent = await self.analyze_intent(query)
        
        # 2. 检索相关信息
        relevant_info = await self.retrieve_information(query)
        
        # 3. 生成响应
        response = await self.generate_response(query, relevant_info)
        
        # 4. 更新状态
        self.update_state(query, response)
        
        return {
            'response': response,
            'confidence': self.state.confidence_score,
            'sources': relevant_info.get('sources', [])
        }
    
    async def analyze_intent(self, query: str) -> str:
        """分析查询意图"""
        prompt = f"""
        分析以下查询的意图类型：
        Query: {query}
        
        可能的意图类型：
        - research: 研究分析
        - summary: 内容总结
        - comparison: 对比分析
        - generation: 内容生成
        - question: 问题解答
        
        请返回最合适的意图类型。
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=20
        )
        
        return response.choices[0].message.content.strip().lower()
    
    async def retrieve_information(self, query: str) -> Dict[str, Any]:
        """检索相关信息"""
        # 使用向量搜索查找相关文档
        query_embedding = await self.get_embedding(query)
        similar_docs = self.search_similar_documents(query_embedding)
        
        return {
            'documents': similar_docs[:5],  # 返回前5个相关文档
            'sources': [doc.get('source', '') for doc in similar_docs[:5]]
        }
    
    async def generate_response(self, query: str, context: Dict[str, Any]) -> str:
        """生成响应"""
        prompt = f"""
        基于以下上下文信息回答用户查询：
        
        上下文信息：
        {context.get('documents', [])}
        
        用户查询：{query}
        
        请提供详细、准确且有条理的回答。
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1000,
            temperature=0.3
        )
        
        return response.choices[0].message.content
    
    def update_state(self, query: str, response: str):
        """更新Agent状态"""
        self.state.memory_buffer.append(f"Q: {query}")
        self.state.memory_buffer.append(f"A: {response}")
        
        # 限制记忆缓冲区大小
        if len(self.state.memory_buffer) > 20:
            self.state.memory_buffer = self.state.memory_buffer[-20:]
        
        # 记录执行历史
        self.state.execution_history.append({
            'query': query,
            'response': response,
            'timestamp': time.time()
        })

# 使用示例
async def main():
    assistant = ResearchAssistant(api_key="your-api-key")
    
    # 处理用户查询
    result = await assistant.process_query("请分析AI Agent的发展趋势")
    
    print(f"Response: {result['response']}")
    print(f"Confidence: {result['confidence']}")
    print(f"Sources: {result['sources']}")

if __name__ == "__main__":
    asyncio.run(main())