AutoGPT AI Agent智能体核心技术剖析：ReAct推理框架与多模态智能体实现

graph TB
    subgraph "ReAct推理引擎架构"
        subgraph "感知层 - Perception Layer"
            MultiModalInput[多模态输入感知]
            ContextAnalyzer[上下文分析器]
            EnvironmentMonitor[环境状态监控]
        end
        
        subgraph "认知层 - Cognition Layer"
            ProblemAnalyzer[问题分析器]
            GoalDecomposer[目标分解器]
            StrategyPlanner[策略规划器]
            RiskAssessor[风险评估器]
        end
        
        subgraph "决策层 - Decision Layer"
            ActionSelector[行动选择器]
            ToolMatcher[工具匹配器]
            ParameterOptimizer[参数优化器]
            ExecutionController[执行控制器]
        end
        
        subgraph "执行层 - Execution Layer"
            ToolExecutor[工具执行器]
            ResultCollector[结果收集器]
            FeedbackProcessor[反馈处理器]
        end
        
        subgraph "学习层 - Learning Layer"
            ExperienceRecorder[经验记录器]
            PatternRecognizer[模式识别器]
            StrategyOptimizer[策略优化器]
            KnowledgeUpdater[知识更新器]
        end
        
        subgraph "记忆系统 - Memory System"
            WorkingMemory[工作记忆]
            EpisodicMemory[情景记忆]
            SemanticMemory[语义记忆]
            ProceduralMemory[程序记忆]
        end
    end
    
    %% 信息流向
    MultiModalInput --> ContextAnalyzer
    ContextAnalyzer --> ProblemAnalyzer
    ProblemAnalyzer --> GoalDecomposer
    GoalDecomposer --> StrategyPlanner
    StrategyPlanner --> RiskAssessor
    
    RiskAssessor --> ActionSelector
    ActionSelector --> ToolMatcher
    ToolMatcher --> ParameterOptimizer
    ParameterOptimizer --> ExecutionController
    
    ExecutionController --> ToolExecutor
    ToolExecutor --> ResultCollector
    ResultCollector --> FeedbackProcessor
    
    FeedbackProcessor --> ExperienceRecorder
    ExperienceRecorder --> PatternRecognizer
    PatternRecognizer --> StrategyOptimizer
    StrategyOptimizer --> KnowledgeUpdater
    
    %% 记忆交互
    ProblemAnalyzer --> WorkingMemory
    StrategyPlanner --> EpisodicMemory
    ToolMatcher --> SemanticMemory
    ExecutionController --> ProceduralMemory
    
    WorkingMemory --> ContextAnalyzer
    EpisodicMemory --> GoalDecomposer
    SemanticMemory --> ActionSelector
    ProceduralMemory --> ToolExecutor

# /autogpt_platform/backend/backend/blocks/agent_reasoning.py

import asyncio
import time
import uuid
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from enum import Enum

class ReasoningPhase(Enum):
    """推理阶段枚举"""
    PERCEPTION = "perception"      # 感知阶段
    ANALYSIS = "analysis"          # 分析阶段
    PLANNING = "planning"          # 规划阶段
    ACTION = "action"              # 行动阶段
    OBSERVATION = "observation"    # 观察阶段
    REFLECTION = "reflection"      # 反思阶段

@dataclass
class ReasoningStep:
    """推理步骤数据结构"""
    phase: ReasoningPhase
    input_data: Dict[str, Any]
    reasoning_content: str
    action_plan: Optional[Dict[str, Any]] = None
    execution_result: Optional[Dict[str, Any]] = None
    reflection_notes: Optional[str] = None
    timestamp: float = 0.0
    step_id: str = ""
    
    def __post_init__(self):
        if not self.timestamp:
            self.timestamp = time.time()
        if not self.step_id:
            self.step_id = str(uuid.uuid4())

class ReActReasoningEngine:
    """
    ReAct推理引擎核心实现
    
    章的ReAct方法论，实现完整的推理-行动循环
    
    核心特性：
    1. 结构化推理过程
    2. 上下文感知决策
    3. 自适应策略调整
    4. 经验学习机制
    """
    
    def __init__(self, llm_client, tool_registry, memory_manager):
        self.llm_client = llm_client
        self.tool_registry = tool_registry
        self.memory_manager = memory_manager
        
        # 推理历史和上下文
        self.reasoning_history: List[ReasoningStep] = []
        self.current_context = {}
        self.goal_stack = []
        
        # 提示模板
        self.prompt_templates = self._initialize_prompt_templates()
        
        # 推理配置
        self.max_reasoning_steps = 50
        self.max_thinking_time = 300  # 5分钟思考时间限制
    
    async def reason_and_act(
        self,
        goal: str,
        initial_context: Dict[str, Any] = None,
        available_tools: List[str] = None
    ) -> Dict[str, Any]:
        """
        执行ReAct推理和行动循环
        
        这是Agent智能决策的核心方法，实现完整的推理-行动-观察-反思循环
        
        参数:
            goal: 要达成的目标描述
            initial_context: 初始上下文信息
            available_tools: 可用工具列表
            
        返回:
            推理和执行的完整结果
        """
        
        logger.info(f"开始ReAct推理循环，目标: {goal}")
        
        # 初始化推理环境
        self.current_context = initial_context or {}
        self.goal_stack = [goal]
        self.reasoning_history.clear()
        
        reasoning_start_time = time.time()
        step_count = 0
        
        try:
            while self.goal_stack and step_count < self.max_reasoning_steps:
                step_count += 1
                
                # 检查时间限制
                if time.time() - reasoning_start_time > self.max_thinking_time:
                    logger.warning("推理时间超限，终止推理循环")
                    break
                
                current_goal = self.goal_stack[-1]  # 当前目标
                
                # 第一阶段：感知和分析
                perception_result = await self._perception_phase(current_goal)
                analysis_result = await self._analysis_phase(perception_result)
                
                # 第二阶段：规划和决策
                planning_result = await self._planning_phase(analysis_result)
                
                # 检查是否需要进一步分解目标
                if planning_result.get("requires_decomposition", False):
                    sub_goals = planning_result.get("sub_goals", [])
                    self.goal_stack.extend(reversed(sub_goals))  # 逆序添加，后进先出
                    continue
                
                # 第三阶段：行动执行
                action_result = await self._action_phase(planning_result)
                
                # 第四阶段：观察和反思
                observation_result = await self._observation_phase(action_result)
                reflection_result = await self._reflection_phase(observation_result)
                
                # 更新上下文和记忆
                await self._update_context_and_memory(reflection_result)
                
                # 检查目标完成状态
                if reflection_result.get("goal_achieved", False):
                    completed_goal = self.goal_stack.pop()
                    logger.info(f"目标完成: {completed_goal}")
                    
                    if not self.goal_stack:
                        logger.info("所有目标已完成，推理循环结束")
                        break
                elif reflection_result.get("goal_failed", False):
                    failed_goal = self.goal_stack.pop()
                    logger.warning(f"目标失败: {failed_goal}")
                    
                    # 可以选择重新规划或放弃
                    if reflection_result.get("should_retry", False):
                        self.goal_stack.append(failed_goal)
                
                # 避免过快循环
                await asyncio.sleep(0.1)
            
            # 生成推理总结
            reasoning_summary = await self._generate_reasoning_summary()
            
            return {
                "goal": goal,
                "success": len(self.goal_stack) == 0,
                "steps_taken": step_count,
                "reasoning_time": time.time() - reasoning_start_time,
                "reasoning_history": self.reasoning_history,
                "final_context": self.current_context,
                "summary": reasoning_summary,
            }
            
        except Exception as e:
            logger.error(f"ReAct推理循环异常: {e}")
            return {
                "goal": goal,
                "success": False,
                "error": str(e),
                "steps_taken": step_count,
                "reasoning_history": self.reasoning_history,
            }
    
    async def _perception_phase(self, goal: str) -> Dict[str, Any]:
        """
        感知阶段：收集和分析当前环境信息
        
        感知内容：
        1. 当前环境状态
        2. 可用资源和工具
        3. 历史经验和知识
        4. 约束条件和限制
        """
        
        logger.debug(f"感知阶段开始，目标: {goal}")
        
        # 收集环境信息
        environment_state = await self._collect_environment_state()
        
        # 检索相关记忆
        relevant_memories = await self.memory_manager.retrieve_relevant_memories(
            query=goal,
            memory_types=["short_term", "long_term", "skill"],
            max_results=5
        )
        
        # 分析可用工具
        available_tools = await self._analyze_available_tools(goal)
        
        # 识别约束条件
        constraints = await self._identify_constraints(goal, environment_state)
        
        perception_result = {
            "goal": goal,
            "environment_state": environment_state,
            "relevant_memories": relevant_memories,
            "available_tools": available_tools,
            "constraints": constraints,
            "perception_quality": self._assess_perception_quality(environment_state),
        }
        
        # 记录感知步骤
        step = ReasoningStep(
            phase=ReasoningPhase.PERCEPTION,
            input_data={"goal": goal},
            reasoning_content=f"感知到环境状态，发现{len(available_tools)}个可用工具，{len(constraints)}个约束条件",
        )
        self.reasoning_history.append(step)
        
        return perception_result
    
    async def _analysis_phase(self, perception_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        分析阶段：深度分析问题本质和解决方案
        
        分析内容：
        1. 问题复杂度评估
        2. 解决方案候选集生成
        3. 资源需求分析
        4. 风险因素识别
        """
        
        goal = perception_data["goal"]
        environment_state = perception_data["environment_state"]
        available_tools = perception_data["available_tools"]
        
        logger.debug(f"分析阶段开始，分析目标: {goal}")
        
        # 构建分析提示词
        analysis_prompt = self._build_analysis_prompt(perception_data)
        
        # 调用LLM
        analysis_response = await self.llm_client.generate(
            prompt=analysis_prompt,
            max_tokens=1000,
            temperature=0.3,  # 较低温度确保逻辑性
        )
        
        # 解析分析结果
        analysis_result = self._parse_analysis_response(analysis_response)
        
        # 增强分析结果
        analysis_result.update({
            "complexity_score": await self._calculate_complexity_score(goal),
            "resource_requirements": await self._estimate_resource_requirements(analysis_result),
            "success_probability": await self._estimate_success_probability(analysis_result),
            "alternative_approaches": await self._generate_alternative_approaches(analysis_result),
        })
        
        # 记录分析步骤
        step = ReasoningStep(
            phase=ReasoningPhase.ANALYSIS,
            input_data=perception_data,
            reasoning_content=analysis_response,
        )
        self.reasoning_history.append(step)
        
        return analysis_result
    
    async def _planning_phase(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        规划阶段：制定详细的执行计划
        
        规划内容：
        1. 执行策略选择
        2. 步骤序列规划
        3. 资源分配方案
        4. 应急预案制定
        """
        
        logger.debug("规划阶段开始，制定执行计划")
        
        complexity_score = analysis_data.get("complexity_score", 0.5)
        
        # 检查是否需要分解
        if complexity_score > 0.7:
            # 复杂任务需要进一步分解
            decomposition_result = await self._decompose_complex_goal(analysis_data)
            
            if decomposition_result["should_decompose"]:
                return {
                    "requires_decomposition": True,
                    "sub_goals": decomposition_result["sub_goals"],
                    "decomposition_strategy": decomposition_result["strategy"],
                }
        
        # 构建执行计划
        execution_plan = await self._build_execution_plan(analysis_data)
        
        # 生成应急预案
        contingency_plans = await self._generate_contingency_plans(execution_plan)
        
        planning_result = {
            "requires_decomposition": False,
            "execution_plan": execution_plan,
            "contingency_plans": contingency_plans,
            "estimated_duration": execution_plan.get("duration", 0),
            "confidence_level": execution_plan.get("confidence", 0.5),
        }
        
        # 记录规划步骤
        step = ReasoningStep(
            phase=ReasoningPhase.PLANNING,
            input_data=analysis_data,
            reasoning_content=f"制定执行计划，预计耗时{execution_plan.get('duration', 0)}秒",
            action_plan=execution_plan,
        )
        self.reasoning_history.append(step)
        
        return planning_result
    
    async def _action_phase(self, planning_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        行动阶段：执行具体的操作
        
        执行内容：
        1. 工具调用执行
        2. 参数动态调整
        3. 实时监控和控制
        4. 异常处理和恢复
        """
        
        execution_plan = planning_data["execution_plan"]
        logger.debug(f"行动阶段开始，执行计划: {execution_plan.get('name', 'unknown')}")
        
        action_start_time = time.time()
        
        try:
            # 准备执行环境
            execution_context = await self._prepare_execution_context(execution_plan)
            
            # 执行主要行动
            primary_result = await self._execute_primary_action(
                execution_plan, 
                execution_context
            )
            
            # 执行后续行动（如果需要）
            follow_up_results = []
            if execution_plan.get("follow_up_actions"):
                for follow_up in execution_plan["follow_up_actions"]:
                    follow_up_result = await self._execute_follow_up_action(
                        follow_up, execution_context, primary_result
                    )
                    follow_up_results.append(follow_up_result)
            
            action_result = {
                "success": True,
                "primary_result": primary_result,
                "follow_up_results": follow_up_results,
                "execution_time": time.time() - action_start_time,
                "tools_used": execution_plan.get("tools", []),
            }
            
        except Exception as e:
            logger.error(f"行动执行失败: {e}")
            
            # 尝试应急预案
            contingency_result = await self._execute_contingency_plan(
                planning_data.get("contingency_plans", []),
                str(e)
            )
            
            action_result = {
                "success": False,
                "error": str(e),
                "contingency_result": contingency_result,
                "execution_time": time.time() - action_start_time,
            }
        
        # 记录行动步骤
        step = ReasoningStep(
            phase=ReasoningPhase.ACTION,
            input_data=planning_data,
            reasoning_content=f"执行行动计划：{execution_plan.get('description', '')}",
            action_plan=execution_plan,
            execution_result=action_result,
        )
        self.reasoning_history.append(step)
        
        return action_result
    
    async def _observation_phase(self, action_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        观察阶段：分析行动结果和环境变化
        
        观察内容：
        1. 行动结果分析
        2. 环境状态变化
        3. 新信息获取
        4. 异常情况识别
        """
        
        logger.debug("观察阶段开始，分析行动结果")
        
        # 分析行动结果
        result_analysis = await self._analyze_action_result(action_data)
        
        # 检测环境变化
        environment_changes = await self._detect_environment_changes()
        
        # 提取新获得的信息
        new_information = await self._extract_new_information(
            action_data, environment_changes
        )
        
        # 识别异常或意外情况
        anomalies = await self._detect_anomalies(action_data, environment_changes)
        
        observation_result = {
            "result_analysis": result_analysis,
            "environment_changes": environment_changes,
            "new_information": new_information,
            "anomalies": anomalies,
            "observation_quality": self._assess_observation_quality(result_analysis),
        }
        
        # 记录观察步骤
        step = ReasoningStep(
            phase=ReasoningPhase.OBSERVATION,
            input_data=action_data,
            reasoning_content=f"观察到{len(new_information)}条新信息，{len(anomalies)}个异常情况",
        )
        self.reasoning_history.append(step)
        
        return observation_result
    
    async def _reflection_phase(self, observation_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        反思阶段：评估进展和优化策略
        
        反思内容：
        1. 目标达成度评估
        2. 策略有效性分析
        3. 学习经验提取
        4. 下一步行动建议
        """
        
        logger.debug("反思阶段开始，评估整体进展")
        
        # 构建反思提示词
        reflection_prompt = self._build_reflection_prompt(observation_data)
        
        # 调用LLM进行反思
        reflection_response = await self.llm_client.generate(
            prompt=reflection_prompt,
            max_tokens=800,
            temperature=0.4,
        )
        
        # 解析反思结果
        reflection_analysis = self._parse_reflection_response(reflection_response)
        
        # 评估目标完成状态
        goal_status = await self._evaluate_goal_status(
            self.goal_stack[-1] if self.goal_stack else "",
            observation_data,
            reflection_analysis
        )
        
        # 生成学习经验
        learning_experience = await self._extract_learning_experience(
            observation_data, reflection_analysis
        )
        
        reflection_result = {
            "reflection_analysis": reflection_analysis,
            "goal_status": goal_status,
            "learning_experience": learning_experience,
            "goal_achieved": goal_status.get("achieved", False),
            "goal_failed": goal_status.get("failed", False),
            "should_retry": goal_status.get("should_retry", False),
            "next_action_suggestion": reflection_analysis.get("next_action", ""),
        }
        
        # 记录反思步骤
        step = ReasoningStep(
            phase=ReasoningPhase.REFLECTION,
            input_data=observation_data,
            reasoning_content=reflection_response,
            reflection_notes=reflection_analysis.get("key_insights", ""),
        )
        self.reasoning_history.append(step)
        
        return reflection_result

### 1.4 关键函数与调用链总览（Agent）

- ReAct 主循环

  - 关键函数：`reason_and_act()` → `_perception_phase()` → `_analysis_phase()` → `_planning_phase()` → `_action_phase()` → `_observation_phase()` → `_reflection_phase()` → `_update_context_and_memory()`
  - 典型调用链：外部调用 → 初始化上下文/目标 → 循环各阶段 → 检查完成/失败/重试 → 生成总结

- 工具执行

  - 关键函数：`_execute_primary_action()` → `tool_registry.get_tool()` → `tool.execute(parameters, context)` → 返回 `result`

- 记忆检索与更新

  - 关键函数：`memory_manager.retrieve_relevant_memories()`、`_update_context_and_memory()`

### 1.5 ReAct 端到端时序图（阶段级）

```mermaid
sequenceDiagram
    participant Caller as 外部调用
    participant RA as reason_and_act
    participant P as perception
    participant A as analysis
    participant PL as planning
    participant AC as action
    participant O as observation
    participant R as reflection
    participant M as memory

    Caller->>RA: 调用(goal, context)
    RA->>P: _perception_phase()
    P-->>RA: perception_result
    RA->>A: _analysis_phase(perception_result)
    A-->>RA: analysis_result
    RA->>PL: _planning_phase(analysis_result)
    PL-->>RA: planning_result
    RA->>AC: _action_phase(planning_result)
    AC-->>RA: action_result
    RA->>O: _observation_phase(action_result)
    O-->>RA: observation_result
    RA->>R: _reflection_phase(observation_result)
    R-->>RA: reflection_result
    RA->>M: _update_context_and_memory(reflection_result)
    M-->>RA: updated
    RA-->>Caller: 最终summary/状态

classDiagram
    class ReActReasoningEngine {
      +reason_and_act(...)
      -_perception_phase(...)
      -_analysis_phase(...)
      -_planning_phase(...)
      -_action_phase(...)
      -_observation_phase(...)
      -_reflection_phase(...)
    }
    class ReasoningStep
    class ReasoningPhase
    ReActReasoningEngine --> ReasoningStep : produces
    ReActReasoningEngine --> ReasoningPhase : uses

## 2. 多模态智能体技术实现

### 2.1 多模态输入处理架构

```python
# /autogpt_platform/backend/backend/blocks/multimodal_agent.py

import asyncio
import base64
import io
from typing import Dict, List, Any, Optional, Union
from PIL import Image
import speech_recognition as sr
import cv2
import numpy as np

class MultiModalProcessor:
    """
    多模态处理器
    
    章的多模态AI实现最佳实践
    
    支持模态：
    1. 文本：自然语言理解和生成
    2. 图像：图像识别、分析和生成
    3. 音频：语音识别和合成
    4. 视频：视频理解和处理
    """
    
    def __init__(self):
        self.text_processor = TextModalityProcessor()
        self.image_processor = ImageModalityProcessor()
        self.audio_processor = AudioModalityProcessor()
        self.video_processor = VideoModalityProcessor()
        
        # 跨模态理解模型
        self.cross_modal_model = CrossModalUnderstandingModel()
        
    async def process_multimodal_input(
        self,
        inputs: Dict[str, Any],
        task_context: str = ""
    ) -> Dict[str, Any]:
        """
        处理多模态输入
        
        处理流程：
        1. 识别输入模态类型
        2. 并行处理各模态数据
        3. 提取特征表示
        4. 跨模态对齐和融合
        5. 生成统一表示
        """
        
        logger.info(f"开始处理多模态输入，包含模态: {list(inputs.keys())}")
        
        # 第一步：模态识别和预处理
        modality_data = {}
        processing_tasks = []
        
        for modality_type, data in inputs.items():
            if data is None:
                continue
                
            processor = self._get_processor(modality_type)
            if processor:
                task = asyncio.create_task(
                    processor.process(data, task_context)
                )
                processing_tasks.append((modality_type, task))
        
        # 第二步：并行处理所有模态
        for modality_type, task in processing_tasks:
            try:
                processed_data = await task
                modality_data[modality_type] = processed_data
                logger.debug(f"{modality_type} 模态处理完成")
            except Exception as e:
                logger.error(f"{modality_type} 模态处理失败: {e}")
                modality_data[modality_type] = {
                    "success": False,
                    "error": str(e)
                }
        
        # 第三步：跨模态理解和融合
        if len(modality_data) > 1:
            cross_modal_result = await self.cross_modal_model.understand(
                modality_data, task_context
            )
        else:
            cross_modal_result = {"type": "single_modal"}
        
        return {
            "modality_data": modality_data,
            "cross_modal_understanding": cross_modal_result,
            "unified_representation": await self._create_unified_representation(
                modality_data, cross_modal_result
            ),
            "processing_summary": self._generate_processing_summary(modality_data),
        }
    
    def _get_processor(self, modality_type: str):
        """获取对应的模态处理器"""
        processors = {
            "text": self.text_processor,
            "image": self.image_processor,
            "audio": self.audio_processor,
            "video": self.video_processor,
        }
        return processors.get(modality_type)

class TextModalityProcessor:
    """文本模态处理器"""
    
    async def process(self, text_data: str, context: str = "") -> Dict[str, Any]:
        """
        处理文本输入
        
        处理内容：
        1. 语言检测和标准化
        2. 情感分析和意图识别
        3. 关键信息提取
        4. 语义特征表示
        """
        
        # 语言检测
        language = await self._detect_language(text_data)
        
        # 情感分析
        sentiment = await self._analyze_sentiment(text_data)
        
        # 意图识别
        intent = await self._identify_intent(text_data, context)
        
        # 关键信息提取
        key_info = await self._extract_key_information(text_data)
        
        # 语义特征
        semantic_features = await self._extract_semantic_features(text_data)
        
        return {
            "success": True,
            "modality": "text",
            "original_text": text_data,
            "language": language,
            "sentiment": sentiment,
            "intent": intent,
            "key_information": key_info,
            "semantic_features": semantic_features,
            "processing_time": time.time(),
        }
    
    async def _detect_language(self, text: str) -> str:
        """检测文本语言"""
        # 简化实现，实际可以使用langdetect等库
        if any('\u4e00' <= char <= '\u9fff' for char in text):
            return "zh-CN"
        else:
            return "en-US"
    
    async def _analyze_sentiment(self, text: str) -> Dict[str, Any]:
        """分析文本情感"""
        # 简化的情感分析，实际可以使用BERT等模型
        positive_words = ["好", "棒", "优秀", "满意", "成功"]
        negative_words = ["坏", "差", "失败", "不满", "错误"]
        
        positive_count = sum(1 for word in positive_words if word in text)
        negative_count = sum(1 for word in negative_words if word in text)
        
        if positive_count > negative_count:
            polarity = "positive"
            score = 0.7
        elif negative_count > positive_count:
            polarity = "negative"
            score = -0.7
        else:
            polarity = "neutral"
            score = 0.0
        
        return {
            "polarity": polarity,
            "score": score,
            "confidence": 0.8,
        }
    
    async def _identify_intent(self, text: str, context: str) -> Dict[str, Any]:
        """识别用户意图"""
        
        # 基于关键词和模式的意图识别
        intent_patterns = {
            "create": ["创建", "生成", "制作", "建立"],
            "search": ["搜索", "查找", "寻找", "查询"],
            "analyze": ["分析", "研究", "评估", "检查"],
            "modify": ["修改", "更新", "编辑", "调整"],
            "delete": ["删除", "移除", "清除", "取消"],
        }
        
        detected_intents = []
        for intent_type, keywords in intent_patterns.items():
            if any(keyword in text for keyword in keywords):
                detected_intents.append(intent_type)
        
        primary_intent = detected_intents[0] if detected_intents else "unknown"
        
        return {
            "primary_intent": primary_intent,
            "all_intents": detected_intents,
            "confidence": 0.8 if detected_intents else 0.3,
        }

class ImageModalityProcessor:
    """图像模态处理器"""
    
    async def process(self, image_data: Union[str, bytes], context: str = "") -> Dict[str, Any]:
        """
        处理图像输入
        
        处理内容：
        1. 图像格式识别和转换
        2. 对象检测和识别
        3. 场景理解和描述
        4. 视觉特征提取
        """
        
        try:
            # 图像加载和预处理
            image = await self._load_and_preprocess_image(image_data)
            
            # 对象检测
            detected_objects = await self._detect_objects(image)
            
            # 场景分析
            scene_analysis = await self._analyze_scene(image)
            
            # 生成图像描述
            image_description = await self._generate_image_description(
                image, detected_objects, scene_analysis
            )
            
            # 提取视觉特征
            visual_features = await self._extract_visual_features(image)
            
            return {
                "success": True,
                "modality": "image",
                "image_info": {
                    "size": image.size,
                    "mode": image.mode,
                    "format": getattr(image, 'format', 'unknown'),
                },
                "detected_objects": detected_objects,
                "scene_analysis": scene_analysis,
                "description": image_description,
                "visual_features": visual_features,
                "processing_time": time.time(),
            }
            
        except Exception as e:
            logger.error(f"图像处理失败: {e}")
            return {
                "success": False,
                "modality": "image",
                "error": str(e),
            }
    
    async def _load_and_preprocess_image(self, image_data: Union[str, bytes]) -> Image.Image:
        """加载和预处理图像"""
        
        if isinstance(image_data, str):
            if image_data.startswith('data:image'):
                # Base64编码的图像
                header, encoded = image_data.split(',', 1)
                image_bytes = base64.b64decode(encoded)
                image = Image.open(io.BytesIO(image_bytes))
            elif image_data.startswith(('http://', 'https://')):
                # 网络图像
                async with aiohttp.ClientSession() as session:
                    async with session.get(image_data) as response:
                        image_bytes = await response.read()
                        image = Image.open(io.BytesIO(image_bytes))
            else:
                # 本地文件路径
                image = Image.open(image_data)
        else:
            # 直接的字节数据
            image = Image.open(io.BytesIO(image_data))
        
        # 图像预处理
        if image.mode != 'RGB':
            image = image.convert('RGB')
        
        # 调整大小（如果太大）
        max_size = (1024, 1024)
        if image.size[0] > max_size[0] or image.size[1] > max_size[1]:
            image.thumbnail(max_size, Image.Resampling.LANCZOS)
        
        return image
    
    async def _detect_objects(self, image: Image.Image) -> List[Dict[str, Any]]:
        """对象检测（简化实现）"""
        
        # 这里可以集成YOLO、DETR等对象检测模型
        # 简化实现返回模拟结果
        detected_objects = [
            {
                "label": "person",
                "confidence": 0.95,
                "bbox": [100, 100, 200, 300],
            },
            {
                "label": "computer", 
                "confidence": 0.87,
                "bbox": [250, 150, 400, 250],
            }
        ]
        
        return detected_objects
    
    async def _analyze_scene(self, image: Image.Image) -> Dict[str, Any]:
        """场景分析"""
        
        # 场景分类（室内/室外、办公室/家庭等）
        scene_classification = await self._classify_scene(image)
        
        # 环境光照分析
        lighting_analysis = await self._analyze_lighting(image)
        
        # 空间布局分析
        spatial_layout = await self._analyze_spatial_layout(image)
        
        return {
            "scene_type": scene_classification,
            "lighting": lighting_analysis,
            "spatial_layout": spatial_layout,
        }

# /autogpt_platform/backend/backend/blocks/memory_management.py

class IntelligentMemorySystem:
    """
    智能记忆管理系统
    
    通过...实现
    
    记忆层次：
    1. 感觉记忆：短暂存储感知信息
    2. 工作记忆：当前任务相关的活跃信息
    3. 短期记忆：近期经验和学习内容
    4. 长期记忆：重要知识和技能
    5. 程序记忆：自动化的操作流程
    """
    
    def __init__(self, vector_db, graph_db, cache_manager):
        self.vector_db = vector_db      # 向量数据库用于语义检索
        self.graph_db = graph_db        # 图数据库用于关系存储
        self.cache_manager = cache_manager  # 缓存管理器
        
        # 不同层次的记忆存储
        self.sensory_memory = SensoryMemoryBuffer(capacity=100, retention_time=2)
        self.working_memory = WorkingMemoryManager(capacity=7, attention_span=300)
        self.short_term_memory = ShortTermMemoryStore(capacity=1000, retention_days=7)
        self.long_term_memory = LongTermMemoryStore(vector_db, graph_db)
        self.procedural_memory = ProceduralMemoryStore(cache_manager)
        
        # 记忆管理策略
        self.consolidation_scheduler = MemoryConsolidationScheduler()
        self.forgetting_mechanism = SelectiveForgettingMechanism()
        
    async def store_experience(
        self,
        experience: Dict[str, Any],
        importance_hints: Dict[str, float] = None
    ) -> str:
        """
        存储经验到记忆系统
        
        存储策略：
        1. 立即存入感觉记忆
        2. 根据注意力机制筛选进入工作记忆
        3. 基于重要性评估决定短期/长期存储
        4. 程序性知识自动提取到程序记忆
        
        参数:
            experience: 经验数据
            importance_hints: 重要性提示
            
        返回:
            记忆ID
        """
        
        memory_id = str(uuid.uuid4())
        timestamp = time.time()
        
        # 增强经验数据
        enhanced_experience = {
            "id": memory_id,
            "timestamp": timestamp,
            "content": experience,
            "importance_hints": importance_hints or {},
        }
        
        # 第一步：存入感觉记忆
        await self.sensory_memory.store(enhanced_experience)
        
        # 第二步：注意力筛选
        attention_score = await self._calculate_attention_score(enhanced_experience)
        
        if attention_score > 0.5:
            # 进入工作记忆
            await self.working_memory.add(enhanced_experience)
            
            # 第三步：重要性评估
            importance_score = await self._evaluate_importance(enhanced_experience)
            
            if importance_score > 0.7:
                # 直接进入长期记忆
                await self.long_term_memory.store(enhanced_experience)
                logger.debug(f"高重要性经验直接存入长期记忆: {memory_id}")
                
            elif importance_score > 0.3:
                # 先存入短期记忆
                await self.short_term_memory.store(enhanced_experience)
                logger.debug(f"经验存入短期记忆: {memory_id}")
                
            # 第四步：提取程序性知识
            procedural_knowledge = await self._extract_procedural_knowledge(enhanced_experience)
            if procedural_knowledge:
                await self.procedural_memory.update(procedural_knowledge)
        
        # 触发记忆整理（异步执行）
        asyncio.create_task(self._schedule_memory_consolidation())
        
        return memory_id
    
    async def retrieve_relevant_memories(
        self,
        query: str,
        context: Dict[str, Any] = None,
        max_results: int = 10,
        memory_types: List[str] = None
    ) -> List[Dict[str, Any]]:
        """
        检索相关记忆
        
        检索策略：
        1. 语义相似度检索
        2. 时间相关性过滤
        3. 重要性权重调整
        4. 上下文相关性匹配
        
        参数:
            query: 检索查询
            context: 上下文信息
            max_results: 最大结果数
            memory_types: 记忆类型筛选
            
        返回:
            相关记忆列表
        """
        
        logger.debug(f"检索相关记忆，查询: {query}")
        
        if memory_types is None:
            memory_types = ["working", "short_term", "long_term", "procedural"]
        
        # 并行从不同记忆存储中检索
        retrieval_tasks = []
        
        if "working" in memory_types:
            retrieval_tasks.append(
                ("working", self.working_memory.retrieve(query, context))
            )
        
        if "short_term" in memory_types:
            retrieval_tasks.append(
                ("short_term", self.short_term_memory.retrieve(query, context))
            )
        
        if "long_term" in memory_types:
            retrieval_tasks.append(
                ("long_term", self.long_term_memory.retrieve(query, context))
            )
        
        if "procedural" in memory_types:
            retrieval_tasks.append(
                ("procedural", self.procedural_memory.retrieve(query, context))
            )
        
        # 收集检索结果
        all_memories = []
        for memory_type, retrieval_task in retrieval_tasks:
            try:
                memories = await retrieval_task
                for memory in memories:
                    memory["source_type"] = memory_type
                    all_memories.append(memory)
            except Exception as e:
                logger.error(f"从{memory_type}记忆检索失败: {e}")
        
        # 记忆融合和排序
        fused_memories = await self._fuse_and_rank_memories(
            all_memories, query, context
        )
        
        return fused_memories[:max_results]
    
    async def _calculate_attention_score(self, experience: Dict[str, Any]) -> float:
        """
        计算注意力分数
        
        注意力机制基于：
        1. 新颖性：信息的新颖程度
        2. 相关性：与当前目标的相关程度
        3. 紧急性：信息的时间敏感性
        4. 情感强度：体验的情感冲击
        """
        
        content = experience["content"]
        
        # 新颖性评估
        novelty_score = await self._assess_novelty(content)
        
        # 相关性评估
        relevance_score = await self._assess_relevance(content)
        
        # 紧急性评估
        urgency_score = await self._assess_urgency(content)
        
        # 情感强度评估
        emotional_score = await self._assess_emotional_intensity(content)
        
        # 加权计算注意力分数
        attention_score = (
            novelty_score * 0.3 +
            relevance_score * 0.4 +
            urgency_score * 0.2 +
            emotional_score * 0.1
        )
        
        return min(1.0, max(0.0, attention_score))
    
    async def _evaluate_importance(self, experience: Dict[str, Any]) -> float:
        """
        评估记忆重要性
        
        重要性维度：
        1. 目标相关性：对实现目标的重要程度
        2. 学习价值：对未来决策的参考价值
        3. 独特性：信息的稀有程度
        4. 影响范围：对系统行为的影响程度
        """
        
        content = experience["content"]
        hints = experience.get("importance_hints", {})
        
        # 目标相关性
        goal_relevance = hints.get("goal_relevance", 0.5)
        
        # 学习价值
        learning_value = await self._assess_learning_value(content)
        
        # 独特性
        uniqueness = await self._assess_uniqueness(content)
        
        # 影响范围
        impact_scope = hints.get("impact_scope", 0.5)
        
        # 计算综合重要性
        importance_score = (
            goal_relevance * 0.35 +
            learning_value * 0.25 +
            uniqueness * 0.2 +
            impact_scope * 0.2
        )
        
        return min(1.0, max(0.0, importance_score))

class WorkingMemoryManager:
    """
    工作记忆管理器
    
    模拟人类工作记忆的特性：
    1. 有限容量（7±2原则）
    2. 快速访问
    3. 主动维护
    4. 干扰遗忘
    """
    
    def __init__(self, capacity: int = 7, attention_span: int = 300):
        self.capacity = capacity
        self.attention_span = attention_span  # 注意力保持时间（秒）
        
        self.active_items: List[Dict[str, Any]] = []
        self.access_times: Dict[str, float] = {}
        
    async def add(self, item: Dict[str, Any]) -> bool:
        """
        添加项目到工作记忆
        
        添加策略：
        1. 检查容量限制
        2. 移除最久未访问的项目
        3. 添加新项目
        4. 更新访问时间
        """
        
        item_id = item["id"]
        current_time = time.time()
        
        # 检查是否已存在
        existing_item = next(
            (item for item in self.active_items if item["id"] == item_id),
            None
        )
        
        if existing_item:
            # 更新现有项目
            existing_item.update(item)
            self.access_times[item_id] = current_time
            return True
        
        # 检查容量限制
        if len(self.active_items) >= self.capacity:
            # 移除最久未访问的项目
            oldest_id = min(self.access_times.keys(), key=lambda k: self.access_times[k])
            await self._remove_item(oldest_id)
        
        # 添加新项目
        self.active_items.append(item)
        self.access_times[item_id] = current_time
        
        logger.debug(f"工作记忆添加项目: {item_id}")
        return True
    
    async def retrieve(self, query: str, context: Dict[str, Any] = None) -> List[Dict[str, Any]]:
        """从工作记忆检索信息"""
        
        current_time = time.time()
        relevant_items = []
        
        for item in self.active_items:
            # 检查是否在注意力保持时间内
            last_access = self.access_times.get(item["id"], 0)
            if current_time - last_access > self.attention_span:
                continue
            
            # 计算相关性
            relevance_score = await self._calculate_relevance(item, query, context)
            
            if relevance_score > 0.3:  # 相关性阈值
                item_with_score = {
                    **item,
                    "relevance_score": relevance_score,
                    "memory_type": "working",
                }
                relevant_items.append(item_with_score)
                
                # 更新访问时间
                self.access_times[item["id"]] = current_time
        
        # 按相关性排序
        relevant_items.sort(key=lambda x: x["relevance_score"], reverse=True)
        
        return relevant_items
    
    async def _remove_item(self, item_id: str):
        """移除工作记忆项目"""
        
        self.active_items = [
            item for item in self.active_items 
            if item["id"] != item_id
        ]
        self.access_times.pop(item_id, None)
        
        logger.debug(f"工作记忆移除项目: {item_id}")
    
    async def _calculate_relevance(
        self, 
        item: Dict[str, Any], 
        query: str, 
        context: Dict[str, Any] = None
    ) -> float:
        """计算项目与查询的相关性"""
        
        # 简化的相关性计算
        # 实际可以使用语义相似度模型
        
        item_text = str(item.get("content", ""))
        query_lower = query.lower()
        item_lower = item_text.lower()
        
        # 关键词匹配
        query_words = set(query_lower.split())
        item_words = set(item_lower.split())
        
        word_overlap = len(query_words & item_words)
        total_words = len(query_words | item_words)
        
        if total_words == 0:
            return 0.0
        
        keyword_relevance = word_overlap / total_words
        
        # 上下文相关性
        context_relevance = 0.5  # 简化处理
        if context:
            # 这里可以实现更复杂的上下文匹配
            pass
        
        # 时间相关性（最近的记忆更相关）
        time_diff = time.time() - item.get("timestamp", 0)
        time_relevance = max(0, 1 - time_diff / 3600)  # 1小时内线性衰减
        
        # 综合相关性
        overall_relevance = (
            keyword_relevance * 0.5 +
            context_relevance * 0.3 +
            time_relevance * 0.2
        )
        
        return overall_relevance

class MemoryConsolidationScheduler:
    """记忆整理调度器"""
    
    def __init__(self):
        self.consolidation_intervals = {
            "immediate": 60,      # 1分钟后立即整理
            "short_term": 3600,   # 1小时后短期整理
            "daily": 86400,       # 24小时后日常整理
            "weekly": 604800,     # 7天后周期整理
        }
        
        self.last_consolidation = {
            interval: 0 for interval in self.consolidation_intervals.keys()
        }
    
    async def schedule_consolidation(self, memory_system: 'IntelligentMemorySystem'):
        """调度记忆整理任务"""
        
        current_time = time.time()
        
        for interval_type, interval_seconds in self.consolidation_intervals.items():
            last_time = self.last_consolidation[interval_type]
            
            if current_time - last_time >= interval_seconds:
                # 执行对应类型的记忆整理
                await self._perform_consolidation(memory_system, interval_type)
                self.last_consolidation[interval_type] = current_time
    
    async def _perform_consolidation(
        self, 
        memory_system: 'IntelligentMemorySystem',
        consolidation_type: str
    ):
        """执行记忆整理"""
        
        logger.info(f"开始执行 {consolidation_type} 记忆整理")
        
        if consolidation_type == "immediate":
            # 立即整理：从感觉记忆提升到工作记忆
            await self._immediate_consolidation(memory_system)
            
        elif consolidation_type == "short_term":
            # 短期整理：从工作记忆提升到短期记忆
            await self._short_term_consolidation(memory_system)
            
        elif consolidation_type == "daily":
            # 日常整理：从短期记忆提升到长期记忆
            await self._daily_consolidation(memory_system)
            
        elif consolidation_type == "weekly":
            # 周期整理：记忆压缩和重组
            await self._weekly_consolidation(memory_system)
    
    async def _daily_consolidation(self, memory_system: 'IntelligentMemorySystem'):
        """日常记忆整理"""
        
        # 从短期记忆中选择重要记忆提升到长期记忆
        short_term_memories = await memory_system.short_term_memory.get_all()
        
        promotion_candidates = []
        for memory in short_term_memories:
            # 重新评估重要性
            current_importance = await memory_system._evaluate_importance(memory)
            access_frequency = memory.get("access_count", 0)
            
            # 综合评分
            promotion_score = current_importance * 0.7 + (access_frequency / 10) * 0.3
            
            if promotion_score > 0.6:
                promotion_candidates.append((memory, promotion_score))
        
        # 按分数排序，提升最重要的记忆
        promotion_candidates.sort(key=lambda x: x[1], reverse=True)
        
        for memory, score in promotion_candidates[:20]:  # 最多提升20个记忆
            await memory_system.long_term_memory.store(memory)
            await memory_system.short_term_memory.remove(memory["id"])
            
            logger.debug(f"记忆提升到长期存储: {memory['id']} (分数: {score:.2f})")