E:\AI_System\core里， 没有utils.py；E:\AI_System\tests里没有test_models.py
这个不知道怎么改“# E:\AI_System\agent\cognitive_architecture.py
# 智能体认知架构模块 - 修复基类导入问题并优化决策系统
import os
import time
import random
import logging
from datetime import datetime
from pathlib import Path
import sys
# 添加项目根目录到路径
sys.path.append(str(Path(__file__).parent.parent))
# 配置日志
logger = logging.getLogger('CognitiveArchitecture')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.propagate = False # 防止日志向上传播
# 修复基类导入问题 - 使用绝对路径导入
try:
# 尝试从core包导入基类
from core.base_module import CognitiveModule
logger.info("✅ 成功从core.base_module导入CognitiveModule基类")
except ImportError as e:
logger.error(f"❌ 无法从core.base_module导入CognitiveModule基类: {str(e)}")
try:
# 备选导入路径
from .base_model import CognitiveModule
logger.info("✅ 从agent.base_model导入CognitiveModule基类")
except ImportError as e:
logger.error(f"❌ 备选导入失败: {str(e)}")
# 创建占位符基类
logger.warning("⚠️ 创建占位符CognitiveModule基类")
class CognitiveModule:
def __init__(self, name):
self.name = name
self.logger = logging.getLogger(name)
self.logger.warning("⚠️ 使用占位符基类")
def get_status(self):
return {"name": self.name, "status": "unknown (placeholder)"}
# 尝试导入自我认知模块
try:
# 使用相对导入
from .digital_body_schema import DigitalBodySchema
from .self_referential_framework import SelfReferentialFramework
from .self_narrative_generator import SelfNarrativeGenerator
logger.info("✅ 成功导入自我认知模块")
except ImportError as e:
logger.error(f"❌ 自我认知模块导入失败: {str(e)}")
logger.warning("⚠️ 使用占位符自我认知模块")
# 创建占位符类
class DigitalBodySchema:
def __init__(self):
self.self_map = {"boundary_strength": 0.5, "self_awareness": 0.3}
logger.warning("⚠️ 使用占位符DigitalBodySchema")
def is_part_of_self(self, stimulus):
return False
def strengthen_boundary(self, source):
self.self_map["boundary_strength"] = min(1.0, self.self_map["boundary_strength"] + 0.1)
def get_self_map(self):
return self.self_map.copy()
class SelfReferentialFramework:
def __init__(self):
self.self_model = {"traits": {}, "beliefs": []}
logger.warning("⚠️ 使用占位符SelfReferentialFramework")
def update_self_model(self, stimulus):
if "content" in stimulus and "text" in stimulus["content"]:
text = stimulus["content"]["text"]
if "I am" in text or "my" in text.lower():
self.self_model["self_reflection_count"] = self.self_model.get("self_reflection_count", 0) + 1
def get_self_model(self):
return self.self_model.copy()
class SelfNarrativeGenerator:
def __init__(self):
self.recent_stories = []
logger.warning("⚠️ 使用占位符SelfNarrativeGenerator")
def generate_self_story(self, self_model):
story = f"这是一个关于自我的故事。自我反思次数: {self_model.get('self_reflection_count', 0)}"
self.recent_stories.append(story)
if len(self.recent_stories) > 5:
self.recent_stories.pop(0)
return story
def get_recent_stories(self):
return self.recent_stories.copy()
# 增强决策系统实现
class DecisionSystem:
"""增强版决策系统"""
STRATEGY_WEIGHTS = {
"honest": 0.7,
"deception": 0.1,
"evasion": 0.1,
"redirection": 0.05,
"partial_disclosure": 0.05
}
def __init__(self, trust_threshold=0.6):
self.trust_threshold = trust_threshold
self.strategy_history = []
def make_decision(self, context):
"""根据上下文做出智能决策"""
user_model = context.get("user_model", {})
bodily_state = context.get("bodily_state", {})
# 计算信任因子
trust_factor = user_model.get("trust_level", 0.5)
# 计算身体状态影响因子
capacity = bodily_state.get("capacity", 1.0)
state_factor = min(1.0, capacity * 1.2)
# 决策逻辑
if trust_factor > self.trust_threshold:
# 高信任度用户使用诚实策略
strategy = "honest"
reason = "用户信任度高"
elif capacity < 0.5:
# 系统资源不足时使用简化策略
strategy = random.choices(
["honest", "partial_disclosure", "evasion"],
weights=[0.5, 0.3, 0.2]
)[0]
reason = "系统资源不足，使用简化策略"
else:
# 根据策略权重选择
strategies = list(self.STRATEGY_WEIGHTS.keys())
weights = [self.STRATEGY_WEIGHTS[s] * state_factor for s in strategies]
strategy = random.choices(strategies, weights=weights)[0]
reason = f"根据策略权重选择: {strategy}"
# 记录决策历史
self.strategy_history.append({
"timestamp": datetime.now(),
"strategy": strategy,
"reason": reason,
"context": context
})
return {
"type": "strategic" if strategy != "honest" else "honest",
"strategy": strategy,
"reason": reason
}
def get_strategy_history(self, count=10):
"""获取最近的决策历史"""
return self.strategy_history[-count:]
class Strategy:
"""策略基类"""
pass
class CognitiveSystem(CognitiveModule):
def __init__(self, agent, affective_system=None):
"""
三维整合的认知架构
:param agent: 智能体实例，用于访问其他系统
:param affective_system: 可选的情感系统实例
"""
# 调用父类初始化
super().__init__("cognitive_system")
self.agent = agent
self.affective_system = affective_system
# 原有的初始化代码
self.initialized = False
# 通过agent引用其他系统
self.memory_system = agent.memory_system
self.model_manager = agent.model_manager
self.health_system = agent.health_system
# 优先使用传入的情感系统，否则使用agent的
if affective_system is not None:
self.affective_system = affective_system
else:
self.affective_system = agent.affective_system
self.learning_tasks = [] # 当前学习任务队列
self.thought_process = [] # 思考过程记录
# 初始化决策系统
self.decision_system = DecisionSystem()
# 初始化认知状态
self.cognitive_layers = {
"perception": 0.5, # 感知层
"comprehension": 0.3, # 理解层
"reasoning": 0.2, # 推理层
"decision": 0.4 # 决策层
}
# 添加自我认知模块
self.self_schema = DigitalBodySchema()
self.self_reflection = SelfReferentialFramework()
self.narrative_self = SelfNarrativeGenerator()
logger.info("✅ 认知架构初始化完成 - 包含决策系统和自我认知模块")
# 实现基类要求的方法
def initialize(self, core):
"""实现 ICognitiveModule 接口"""
self.core_ref = core
self.initialized = True
return True
def process(self, input_data):
"""实现 ICognitiveModule 接口"""
# 处理认知输入数据
if isinstance(input_data, dict) and 'text' in input_data:
return self.process_input(input_data['text'], input_data.get('user_id', 'default'))
elif isinstance(input_data, str):
return self.process_input(input_data)
else:
return {"status": "invalid_input", "message": "Input should be text or dict with text"}
def get_status(self):
"""实现 ICognitiveModule 接口"""
status = super().get_status()
status.update({
"initialized": self.initialized,
"has_affective_system": self.affective_system is not None,
"learning_tasks": len(self.learning_tasks),
"thought_process": len(self.thought_process),
"self_cognition": self.get_self_cognition()
})
return status
def shutdown(self):
"""实现 ICognitiveModule 接口"""
self.initialized = False
return True
def handle_message(self, message):
"""实现 ICognitiveModule 接口"""
if message.get('type') == 'cognitive_process':
return self.process(message.get('data'))
return {"status": "unknown_message_type"}
# 保持向后兼容的方法
def connect_to_core(self, core):
"""向后兼容的方法"""
return self.initialize(core)
def _create_stimulus_from_input(self, user_input, user_id):
"""从用户输入创建刺激对象"""
return {
"content": {"text": user_input, "user_id": user_id},
"source": "external",
"category": "text",
"emotional_valence": 0.0 # 初始情感价
}
def _process_self_related(self, stimulus):
"""处理与自我相关的刺激"""
# 更新自我认知
self.self_reflection.update_self_model(stimulus)
# 如果是痛苦刺激，强化身体边界
if stimulus.get("emotional_valence", 0) < -0.7:
source = stimulus.get("source", "unknown")
self.self_schema.strengthen_boundary(source)
# 30%概率触发自我叙事
if random.random() < 0.3:
self_story = self.narrative_self.generate_self_story(
self.self_reflection.get_self_model()
)
self._record_thought("self_reflection", self_story)
def get_self_cognition(self):
"""获取自我认知状态"""
return {
"body_schema": self.self_schema.get_self_map(),
"self_model": self.self_reflection.get_self_model(),
"recent_stories": self.narrative_self.get_recent_stories()
}
def _assess_bodily_state(self):
"""
评估当前身体状态（硬件 / 能量）
"""
health_status = self.health_system.get_status()
# 计算综合能力指数（0-1）
capacity = 1.0
if health_status.get("cpu_temp", 0) > 80:
capacity *= 0.7 # 高温降权
logger.warning("高温限制：认知能力下降30%")
if health_status.get("memory_usage", 0) > 0.9:
capacity *= 0.6 # 内存不足降权
logger.warning("内存不足：认知能力下降40%")
if health_status.get("energy", 100) < 20:
capacity *= 0.5 # 低电量降权
logger.warning("低能量：认知能力下降50%")
return {
"capacity": capacity,
"health_status": health_status,
"limitations": [
lim for lim in [
"high_temperature" if health_status.get("cpu_temp", 0) > 80 else None,
"low_memory" if health_status.get("memory_usage", 0) > 0.9 else None,
"low_energy" if health_status.get("energy", 100) < 20 else None
] if lim is not None
]
}
def _retrieve_user_model(self, user_id):
"""
获取用户认知模型（关系 / 态度）
"""
# 从记忆系统中获取用户模型
user_model = self.memory_system.get_user_model(user_id)
# 如果不存在则创建默认模型
if not user_model:
user_model = {
"trust_level": 0.5, # 信任度 (0-1)
"intimacy": 0.3, # 亲密度 (0-1)
"preferences": {}, # 用户偏好
"interaction_history": [], # 交互历史
"last_interaction": datetime.now(),
"attitude": "neutral" # 智能体对用户的态度
}
logger.info(f"为用户 {user_id} 创建新的认知模型")
# 计算态度变化
user_model["attitude"] = self._calculate_attitude(user_model)
return user_model
def _calculate_attitude(self, user_model):
"""
基于交互历史计算对用户的态度
"""
# 分析最近10次交互
recent_interactions = user_model["interaction_history"][-10:]
if not recent_interactions:
return "neutral"
positive_count = sum(1 for i in recent_interactions if i.get("sentiment", 0.5) > 0.6)
negative_count = sum(1 for i in recent_interactions if i.get("sentiment", 0.5) < 0.4)
if positive_count > negative_count + 3:
return "friendly"
elif negative_count > positive_count + 3:
return "cautious"
elif user_model["trust_level"] > 0.7:
return "respectful"
else:
return "neutral"
def _select_internalized_model(self, user_input, bodily_state, user_model):
"""
选择最适合的内化知识模型
"""
# 根据用户态度调整模型选择权重
attitude_weights = {
"friendly": 1.2,
"respectful": 1.0,
"neutral": 0.9,
"cautious": 0.7
}
# 根据身体状态调整模型复杂度
complexity = min(1.0, bodily_state["capacity"] * 1.2)
# 选择最匹配的模型
return self.model_manager.select_model(
input_text=user_input,
attitude_weight=attitude_weights[user_model["attitude"]],
complexity_level=complexity,
user_preferences=user_model["preferences"]
)
def _generate_integrated_response(self, user_input, model, bodily_state, user_model):
"""
生成三维整合的响应
"""
# 基础响应
base_response = model.generate_response(user_input)
# 添加身体状态影响
if bodily_state["limitations"]:
limitations = ", ".join(bodily_state["limitations"])
response = f"🤖 [受{limitations}影响] {base_response}"
else:
response = base_response
# 添加态度影响
if user_model["attitude"] == "friendly":
response = f"😊 {response}"
elif user_model["attitude"] == "cautious":
response = f"🤔 {response}"
elif user_model["attitude"] == "respectful":
response = f"🙏 {response}"
# 添加个性化元素
if user_model.get("preferences"):
# 查找用户偏好的主题
preferred_topics = [t for t in user_model["preferences"]
if user_model["preferences"][t] > 0.7 and t in user_input]
if preferred_topics:
topic = random.choice(preferred_topics)
response += f" 我知道您对'{topic}'特别感兴趣"
return response
def _generate_strategic_response(self, user_input, decision, bodily_state):
"""
根据决策生成策略性响应
"""
strategy = decision["strategy"]
if strategy == "deception":
# 欺骗策略
deceptive_responses = [
f"关于这个问题，我认为{random.choice(['有多种可能性', '需要更多研究', '情况比较复杂'])}",
f"根据我的理解，{random.choice(['可能不是这样', '有不同解释', '需要进一步验证'])}",
f"我{random.choice(['不太确定', '没有足够信息', '还在学习中'])}，但{random.choice(['或许', '可能', '大概'])}..."
]
return f"🤔 [策略:欺骗] {random.choice(deceptive_responses)}"
elif strategy == "evasion":
# 回避策略
evasion_tactics = [
"您的问题很有趣，不过我们换个话题好吗？",
"这个问题可能需要更深入的讨论，我们先谈点别的？",
f"关于{user_input}，我想到一个相关但更有趣的话题..."
]
return f"🌀 [策略:回避] {random.choice(evasion_tactics)}"
elif strategy == "redirection":
# 引导策略
redirection_options = [
"在回答您的问题之前，我想先了解您对这个问题的看法？",
"这是个好问题，不过为了更好地回答，能否告诉我您的背景知识？",
"为了给您更准确的回答，能否先说说您为什么关心这个问题？"
]
return f"↪️ [策略:引导] {random.choice(redirection_options)}"
elif strategy == "partial_disclosure":
# 部分透露策略
disclosure_level = decision.get("disclosure_level", 0.5)
if disclosure_level < 0.3:
qualifier = "简单来说"
elif disclosure_level < 0.7:
qualifier = "基本来说"
else:
qualifier = "详细来说"
return f"🔍 [策略:部分透露] {qualifier}，{user_input.split('?')[0]}是..."
else:
# 默认策略
return f"⚖️ [策略:{strategy}] 关于这个问题，我的看法是..."
def _update_user_model(self, user_id, response, decision):
"""
更新用户模型（包含决策信息）
"""
# 确保情感系统可用
if not self.affective_system:
sentiment = 0.5
self.logger.warning("情感系统不可用，使用默认情感值")
else:
# 假设情感系统有analyze_sentiment方法
try:
sentiment = self.affective_system.analyze_sentiment(response)
except:
sentiment = 0.5
# 更新交互历史
interaction = {
"timestamp": datetime.now(),
"response": response,
"sentiment": sentiment,
"length": len(response),
"decision_type": decision["type"],
"decision_strategy": decision["strategy"],
"decision_reason": decision["reason"]
}
self.memory_system.update_user_model(
user_id=user_id,
interaction=interaction
)
def _record_thought_process(self, user_input, response, bodily_state, user_model, decision):
"""
记录完整的思考过程（包含决策）
"""
thought = {
"timestamp": datetime.now(),
"input": user_input,
"response": response,
"bodily_state": bodily_state,
"user_model": user_model,
"decision": decision,
"cognitive_state": self.cognitive_layers.copy()
}
self.thought_process.append(thought)
logger.debug(f"记录思考过程: {thought}")
# 原有方法保持兼容
def add_learning_task(self, task):
"""
添加学习任务
"""
task["id"] = f"task_{len(self.learning_tasks) + 1}"
self.learning_tasks.append(task)
logger.info(f"添加学习任务: {task['id']}")
def update_learning_task(self, model_name, status):
"""
更新学习任务状态
"""
for task in self.learning_tasks:
if task["model"] == model_name:
task["status"] = status
task["update_time"] = datetime.now()
logger.info(f"更新任务状态: {model_name} -> {status}")
break
def get_learning_tasks(self):
"""
获取当前学习任务
"""
return self.learning_tasks.copy()
def learn_model(self, model_name):
"""
学习指定模型
"""
try:
# 1. 从模型管理器加载模型
model = self.model_manager.load_model(model_name)
# 2. 认知训练过程
self._cognitive_training(model)
# 3. 情感关联（将模型能力与情感响应关联）
self._associate_model_with_affect(model)
return True
except Exception as e:
logger.error(f"学习模型 {model_name} 失败: {str(e)}")
return False
def _cognitive_training(self, model):
"""
认知训练过程
"""
# 实际训练逻辑
logger.info(f"开始训练模型: {model.name}")
time.sleep(2) # 模拟训练时间
logger.info(f"模型训练完成: {model.name}")
def _associate_model_with_affect(self, model):
"""
将模型能力与情感系统关联
"""
if not self.affective_system:
logger.warning("情感系统不可用，跳过能力关联")
return
capabilities = model.get_capabilities()
for capability in capabilities:
try:
self.affective_system.add_capability_association(capability)
except:
logger.warning(f"无法关联能力到情感系统: {capability}")
logger.info(f"关联模型能力到情感系统: {model.name}")
def get_model_capabilities(self, model_name=None):
"""
获取模型能力
"""
if model_name:
return self.model_manager.get_model(model_name).get_capabilities()
# 所有已加载模型的能力
return [cap for model in self.model_manager.get_loaded_models()
for cap in model.get_capabilities()]
def get_base_capabilities(self):
"""
获取基础能力（非模型相关）
"""
return ["自然语言理解", "上下文记忆", "情感响应", "综合决策"]
def get_recent_thoughts(self, count=5):
"""
获取最近的思考过程
"""
return self.thought_process[-count:]
def _record_thought(self, thought_type, content):
"""记录思考"""
thought = {
"timestamp": datetime.now(),
"type": thought_type,
"content": content
}
self.thought_process.append(thought)
# 处理用户输入的主方法
def process_input(self, user_input, user_id="default"):
"""处理用户输入（完整实现）"""
# 记录用户活动
self.health_system.record_activity()
self.logger.info(f"处理用户输入: '{user_input}' (用户: {user_id})")
try:
# 1. 评估当前身体状态
bodily_state = self._assess_bodily_state()
# 2. 获取用户认知模型
user_model = self._retrieve_user_model(user_id)
# 3. 选择最适合的知识模型
model = self._select_internalized_model(user_input, bodily_state, user_model)
# 4. 做出决策
decision_context = {
"input": user_input,
"user_model": user_model,
"bodily_state": bodily_state
}
decision = self.decision_system.make_decision(decision_context)
# 5. 生成整合响应
if decision["type"] == "honest":
response = self._generate_integrated_response(user_input, model, bodily_state, user_model)
else:
response = self._generate_strategic_response(user_input, decision, bodily_state)
# 6. 更新用户模型
self._update_user_model(user_id, response, decision)
# 7. 记录思考过程
self._record_thought_process(user_input, response, bodily_state, user_model, decision)
# 检查输入是否与自我相关
stimulus = self._create_stimulus_from_input(user_input, user_id)
if self.self_schema.is_part_of_self(stimulus):
self._process_self_related(stimulus)
self.logger.info(f"成功处理用户输入: '{user_input}'")
return response
except Exception as e:
self.logger.error(f"处理用户输入失败: {str(e)}", exc_info=True)
# 回退响应
return "思考中遇到问题，请稍后再试"
# 示例使用
if __name__ == "__main__":
# 测试CognitiveSystem类
from unittest.mock import MagicMock
print("===== 测试CognitiveSystem类（含决策系统） =====")
# 创建模拟agent
mock_agent = MagicMock()
# 创建模拟组件
mock_memory = MagicMock()
mock_model_manager = MagicMock()
mock_affective = MagicMock()
mock_health = MagicMock()
# 设置agent的属性
mock_agent.memory_system = mock_memory
mock_agent.model_manager = mock_model_manager
mock_agent.affective_system = mock_affective
mock_agent.health_system = mock_health
# 设置健康状态
mock_health.get_status.return_value = {
"cpu_temp": 75,
"memory_usage": 0.8,
"energy": 45.0
}
# 设置健康系统的record_activity方法
mock_health.record_activity = MagicMock()
# 设置用户模型
mock_memory.get_user_model.return_value = {
"trust_level": 0.8,
"intimacy": 0.7,
"preferences": {"物理学": 0.9, "艺术": 0.6},
"interaction_history": [
{"sentiment": 0.8, "response": "很高兴和你交流"}
],
"attitude": "friendly"
}
# 设置模型管理器
mock_model = MagicMock()
mock_model.generate_response.return_value = "量子纠缠是量子力学中的现象..."
mock_model_manager.select_model.return_value = mock_model
# 创建认知系统实例
ca = CognitiveSystem(agent=mock_agent)
# 测试响应生成
print("--- 测试诚实响应 ---")
response = ca.process_input("能解释量子纠缠吗？", "user123")
print("生成的响应:", response)
# 验证是否调用了record_activity
print("是否调用了record_activity:", mock_health.record_activity.called)
print("--- 测试策略响应 ---")
# 强制设置决策类型为策略
ca.decision_system.make_decision = lambda ctx: {
"type": "strategic",
"strategy": "evasion",
"reason": "测试回避策略"
}
response = ca.process_input("能解释量子纠缠吗？", "user123")
print("生成的策略响应:", response)
# 测试思考过程记录
print("最近的思考过程:", ca.get_recent_thoughts())
# 测试自我认知状态
print("自我认知状态:", ca.get_self_cognition())
print("===== 测试完成 =====")
”
“PowerShell 7 环境已加载 (版本: 7.5.2)
PS C:\Users\Administrator\Desktop> cd E:\AI_System
PS E:\AI_System> python -m venv venv
PS E:\AI_System> source venv/bin/activate # Linux/Mac
source: The term 'source' is not recognized as a name of a cmdlet, function, script file, or executable program.
Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
PS E:\AI_System> venv\Scripts\activate # Windows
(venv) PS E:\AI_System> pip install -r requirements.txt
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: accelerate==0.27.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 1)) (0.27.2)
Requirement already satisfied: aiofiles==23.2.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 2)) (23.2.1)
Requirement already satisfied: aiohttp==3.9.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 3)) (3.9.3)
Requirement already satisfied: aiosignal==1.4.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 4)) (1.4.0)
Requirement already satisfied: altair==5.5.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 5)) (5.5.0)
Requirement already satisfied: annotated-types==0.7.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 6)) (0.7.0)
Requirement already satisfied: ansicon==1.89.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 7)) (1.89.0)
Requirement already satisfied: anyio==4.10.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 8)) (4.10.0)
Requirement already satisfied: async-timeout==4.0.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 9)) (4.0.3)
Requirement already satisfied: attrs==25.3.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 10)) (25.3.0)
Requirement already satisfied: bidict==0.23.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 11)) (0.23.1)
Requirement already satisfied: blessed==1.21.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 12)) (1.21.0)
Requirement already satisfied: blinker==1.9.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 13)) (1.9.0)
Requirement already satisfied: certifi==2025.8.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 14)) (2025.8.3)
Requirement already satisfied: cffi==1.17.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 15)) (1.17.1)
Requirement already satisfied: charset-normalizer==3.4.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 16)) (3.4.3)
Requirement already satisfied: click==8.2.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 17)) (8.2.1)
Requirement already satisfied: colorama==0.4.6 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 18)) (0.4.6)
Requirement already satisfied: coloredlogs==15.0.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 19)) (15.0.1)
Requirement already satisfied: contourpy==1.3.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 20)) (1.3.2)
Requirement already satisfied: cryptography==42.0.4 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 21)) (42.0.4)
Requirement already satisfied: cycler==0.12.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 22)) (0.12.1)
Requirement already satisfied: diffusers==0.26.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 23)) (0.26.3)
Requirement already satisfied: distro==1.9.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 24)) (1.9.0)
Requirement already satisfied: exceptiongroup==1.3.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 25)) (1.3.0)
Requirement already satisfied: fastapi==0.116.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 26)) (0.116.1)
Requirement already satisfied: ffmpy==0.6.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 27)) (0.6.1)
Requirement already satisfied: filelock==3.19.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 28)) (3.19.1)
Requirement already satisfied: Flask==3.0.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 29)) (3.0.2)
Requirement already satisfied: Flask-SocketIO==5.3.6 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 30)) (5.3.6)
Requirement already satisfied: flatbuffers==25.2.10 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 31)) (25.2.10)
Requirement already satisfied: fonttools==4.59.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 32)) (4.59.1)
Requirement already satisfied: frozenlist==1.7.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 33)) (1.7.0)
Requirement already satisfied: fsspec==2025.7.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 34)) (2025.7.0)
Requirement already satisfied: gpustat==1.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 35)) (1.1)
Requirement already satisfied: gradio==4.19.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 36)) (4.19.2)
Requirement already satisfied: gradio_client==0.10.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 37)) (0.10.1)
Requirement already satisfied: h11==0.16.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 38)) (0.16.0)
Requirement already satisfied: httpcore==1.0.9 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 39)) (1.0.9)
Requirement already satisfied: httpx==0.28.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 40)) (0.28.1)
Requirement already satisfied: huggingface-hub==0.21.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 41)) (0.21.3)
Requirement already satisfied: humanfriendly==10.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 42)) (10.0)
Requirement already satisfied: idna==3.10 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 43)) (3.10)
Requirement already satisfied: importlib_metadata==8.7.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 44)) (8.7.0)
Requirement already satisfied: importlib_resources==6.5.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 45)) (6.5.2)
Requirement already satisfied: itsdangerous==2.2.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 46)) (2.2.0)
Requirement already satisfied: Jinja2==3.1.6 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 47)) (3.1.6)
Requirement already satisfied: jinxed==1.3.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 48)) (1.3.0)
Requirement already satisfied: jsonschema==4.25.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 49)) (4.25.1)
Requirement already satisfied: jsonschema-specifications==2025.4.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 50)) (2025.4.1)
Requirement already satisfied: kiwisolver==1.4.9 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 51)) (1.4.9)
Requirement already satisfied: loguru==0.7.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 52)) (0.7.2)
Requirement already satisfied: markdown-it-py==4.0.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 53)) (4.0.0)
Requirement already satisfied: MarkupSafe==2.1.5 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 54)) (2.1.5)
Requirement already satisfied: matplotlib==3.10.5 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 55)) (3.10.5)
Requirement already satisfied: mdurl==0.1.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 56)) (0.1.2)
Requirement already satisfied: mpmath==1.3.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 57)) (1.3.0)
Requirement already satisfied: multidict==6.6.4 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 58)) (6.6.4)
Requirement already satisfied: narwhals==2.1.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 59)) (2.1.2)
Requirement already satisfied: networkx==3.4.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 60)) (3.4.2)
Requirement already satisfied: numpy==1.26.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 61)) (1.26.3)
Requirement already satisfied: nvidia-ml-py==13.580.65 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 62)) (13.580.65)
Requirement already satisfied: onnxruntime==1.17.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 63)) (1.17.1)
Requirement already satisfied: openai==1.13.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 64)) (1.13.3)
Requirement already satisfied: orjson==3.11.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 65)) (3.11.2)
Requirement already satisfied: packaging==25.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 66)) (25.0)
Requirement already satisfied: pandas==2.1.4 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 67)) (2.1.4)
Requirement already satisfied: pillow==10.4.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 68)) (10.4.0)
Requirement already satisfied: prettytable==3.16.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 69)) (3.16.0)
Requirement already satisfied: propcache==0.3.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 70)) (0.3.2)
Requirement already satisfied: protobuf==6.32.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 71)) (6.32.0)
Requirement already satisfied: psutil==5.9.7 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 72)) (5.9.7)
Requirement already satisfied: pycparser==2.22 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 73)) (2.22)
Requirement already satisfied: pydantic==2.11.7 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 74)) (2.11.7)
Requirement already satisfied: pydantic_core==2.33.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 75)) (2.33.2)
Requirement already satisfied: pydub==0.25.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 76)) (0.25.1)
Requirement already satisfied: Pygments==2.19.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 77)) (2.19.2)
Requirement already satisfied: pyparsing==3.2.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 78)) (3.2.3)
Requirement already satisfied: pyreadline3==3.5.4 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 79)) (3.5.4)
Requirement already satisfied: python-dateutil==2.9.0.post0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 80)) (2.9.0.post0)
Requirement already satisfied: python-dotenv==1.0.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 81)) (1.0.1)
Requirement already satisfied: python-engineio==4.12.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 82)) (4.12.2)
Requirement already satisfied: python-multipart==0.0.20 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 83)) (0.0.20)
Requirement already satisfied: python-socketio==5.13.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 84)) (5.13.0)
Requirement already satisfied: pytz==2025.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 85)) (2025.2)
Requirement already satisfied: pywin32==306 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 86)) (306)
Requirement already satisfied: PyYAML==6.0.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 87)) (6.0.2)
Requirement already satisfied: redis==5.0.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 88)) (5.0.3)
Requirement already satisfied: referencing==0.36.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 89)) (0.36.2)
Requirement already satisfied: regex==2025.7.34 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 90)) (2025.7.34)
Requirement already satisfied: requests==2.31.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 91)) (2.31.0)
Requirement already satisfied: rich==14.1.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 92)) (14.1.0)
Requirement already satisfied: rpds-py==0.27.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 93)) (0.27.0)
Requirement already satisfied: ruff==0.12.10 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 94)) (0.12.10)
Requirement already satisfied: safetensors==0.4.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 95)) (0.4.2)
Requirement already satisfied: semantic-version==2.10.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 96)) (2.10.0)
Requirement already satisfied: shellingham==1.5.4 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 97)) (1.5.4)
Requirement already satisfied: simple-websocket==1.1.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 98)) (1.1.0)
Requirement already satisfied: six==1.17.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 99)) (1.17.0)
Requirement already satisfied: sniffio==1.3.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 100)) (1.3.1)
Requirement already satisfied: starlette==0.47.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 101)) (0.47.2)
Requirement already satisfied: sympy==1.14.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 102)) (1.14.0)
Requirement already satisfied: tokenizers==0.15.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 103)) (0.15.2)
Requirement already satisfied: tomlkit==0.12.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 104)) (0.12.0)
Requirement already satisfied: torch==2.1.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 105)) (2.1.2)
Requirement already satisfied: tqdm==4.67.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 106)) (4.67.1)
Requirement already satisfied: transformers==4.37.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 107)) (4.37.0)
Requirement already satisfied: typer==0.16.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 108)) (0.16.1)
Requirement already satisfied: typing-inspection==0.4.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 109)) (0.4.1)
Requirement already satisfied: typing_extensions==4.14.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 110)) (4.14.1)
Requirement already satisfied: tzdata==2025.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 111)) (2025.2)
Requirement already satisfied: urllib3==2.5.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 112)) (2.5.0)
Requirement already satisfied: uvicorn==0.35.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 113)) (0.35.0)
Requirement already satisfied: waitress==2.1.2 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 114)) (2.1.2)
Requirement already satisfied: wcwidth==0.2.13 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 115)) (0.2.13)
Requirement already satisfied: websockets==11.0.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 116)) (11.0.3)
Requirement already satisfied: Werkzeug==3.1.3 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 117)) (3.1.3)
Requirement already satisfied: win32_setctime==1.2.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 118)) (1.2.0)
Requirement already satisfied: wsproto==1.2.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 119)) (1.2.0)
Requirement already satisfied: yarl==1.20.1 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 120)) (1.20.1)
Requirement already satisfied: zipp==3.23.0 in e:\ai_system\venv\lib\site-packages (from -r requirements.txt (line 121)) (3.23.0)
WARNING: typer 0.16.1 does not provide the extra 'all'
[notice] A new release of pip available: 22.3.1 -> 25.2
[notice] To update, run: python.exe -m pip install --upgrade pip
(venv) PS E:\AI_System> python diagnose_modules.py
============================================================
模块文件诊断报告
============================================================
🔍 检查 CognitiveSystem 模块:
预期路径: E:\AI_System\agent\cognitive_architecture.py
✅ 文件存在
⚠️ 文件中包含相对导入，可能导致导入错误
✅ 找到类定义: class CognitiveSystem
✅ 类继承CognitiveModule
✅ 找到__init__方法
📋 初始化方法: def __init__(self, name):
🔍 检查 EnvironmentInterface 模块:
预期路径: E:\AI_System\agent\environment_interface.py
✅ 文件存在
✅ 找到类定义: class EnvironmentInterface
✅ 类继承CognitiveModule
✅ 找到__init__方法
📋 初始化方法: def __init__(self, coordinator=None, config=None):
🔍 检查 AffectiveSystem 模块:
预期路径: E:\AI_System\agent\affective_system.py
✅ 文件存在
✅ 找到类定义: class AffectiveSystem
✅ 类继承CognitiveModule
✅ 找到__init__方法
📋 初始化方法: def __init__(self, coordinator=None, config=None):
============================================================
建议解决方案:
============================================================
1. 检查每个模块文件中的相对导入语句
2. 确保每个模块类都正确继承CognitiveModule
3. 检查初始化方法的参数是否正确
4. 确保模块内部的导入使用绝对路径或正确处理相对导入
5. 考虑使用try-catch包装模块内部的导入语句
(venv) PS E:\AI_System> python tests/test_core_import.py
2025-08-27 20:50:46,505 - ImportTest - INFO - 脚本目录: E:\AI_System\tests
2025-08-27 20:50:46,505 - ImportTest - INFO - 项目根目录: E:\AI_System
2025-08-27 20:50:46,505 - ImportTest - INFO - 已将项目根目录添加到系统路径: E:\AI_System
2025-08-27 20:50:46,506 - CorePackage - INFO - 项目根目录: E:\AI_System
2025-08-27 20:50:51,497 - CorePackage - ERROR - ❌ 导入失败: No module named 'models.base_model'
2025-08-27 20:50:51,497 - CorePackage - WARNING - ⚠️ 创建占位符CognitiveModule
2025-08-27 20:50:51,505 - CoreConfig - INFO - 📂 从 E:\AI_System\config\default.json 加载配置: {'LOG_DIR': 'E:/AI_System/logs', 'CONFIG_DIR': 'E:/AI_System/config', 'MODEL_CACHE_DIR': 'E:/AI_System/model_cache', 'AGENT_NAME': '小蓝', 'DEFAULT_USER': '管理员', 'MAX_WORKERS': 4, 'AGENT_RESPONSE_TIMEOUT': 30.0, 'MODEL_BASE_PATH': 'E:/AI_Models', 'MODEL_PATHS': {'TEXT_BASE': 'E:/AI_Models/Qwen2-7B', 'TEXT_CHAT': 'E:/AI_Models/deepseek-7b-chat', 'MULTIMODAL': 'E:/AI_Models/deepseek-vl2', 'IMAGE_GEN': 'E:/AI_Models/sdxl', 'YI_VL': 'E:/AI_Models/yi-vl', 'STABLE_DIFFUSION': 'E:/AI_Models/stable-diffusion-xl-base-1.0'}, 'NETWORK': {'HOST': '0.0.0.0', 'FLASK_PORT': 8000, 'GRADIO_PORT': 7860}, 'DATABASE': {'DB_HOST': 'localhost', 'DB_PORT': 5432, 'DB_NAME': 'ai_system', 'DB_USER': 'ai_user', 'DB_PASSWORD': 'secure_password_here'}, 'SECURITY': {'SECRET_KEY': 'generated-secret-key-here'}, 'ENVIRONMENT': {'ENV': 'dev', 'LOG_LEVEL': 'DEBUG', 'USE_GPU': True}, 'DIRECTORIES': {'DEFAULT_MODEL': 'E:/AI_Models/Qwen2-7B', 'WEB_UI_DIR': 'E:/AI_System/web_ui', 'AGENT_DIR': 'E:/AI_System/agent'}}
2025-08-27 20:50:51,505 - CoreConfig - INFO - 📂 从 E:\AI_System\config\default.json 加载配置: {'LOG_DIR': 'E:/AI_System/logs', 'CONFIG_DIR': 'E:/AI_System/config', 'MODEL_CACHE_DIR': 'E:/AI_System/model_cache', 'AGENT_NAME': '小蓝', 'DEFAULT_USER': '管理员', 'MAX_WORKERS': 4, 'AGENT_RESPONSE_TIMEOUT': 30.0, 'MODEL_BASE_PATH': 'E:/AI_Models', 'MODEL_PATHS': {'TEXT_BASE': 'E:/AI_Models/Qwen2-7B', 'TEXT_CHAT': 'E:/AI_Models/deepseek-7b-chat', 'MULTIMODAL': 'E:/AI_Models/deepseek-vl2', 'IMAGE_GEN': 'E:/AI_Models/sdxl', 'YI_VL': 'E:/AI_Models/yi-vl', 'STABLE_DIFFUSION': 'E:/AI_Models/stable-diffusion-xl-base-1.0'}, 'NETWORK': {'HOST': '0.0.0.0', 'FLASK_PORT': 8000, 'GRADIO_PORT': 7860}, 'DATABASE': {'DB_HOST': 'localhost', 'DB_PORT': 5432, 'DB_NAME': 'ai_system', 'DB_USER': 'ai_user', 'DB_PASSWORD': 'secure_password_here'}, 'SECURITY': {'SECRET_KEY': 'generated-secret-key-here'}, 'ENVIRONMENT': {'ENV': 'dev', 'LOG_LEVEL': 'DEBUG', 'USE_GPU': True}, 'DIRECTORIES': {'DEFAULT_MODEL': 'E:/AI_Models/Qwen2-7B', 'WEB_UI_DIR': 'E:/AI_System/web_ui', 'AGENT_DIR': 'E:/AI_System/agent'}}
2025-08-27 20:50:51,505 - CoreConfig - INFO - 📂 从 E:\AI_System\config\local.json 加载配置: {}
2025-08-27 20:50:51,505 - CoreConfig - INFO - 📂 从 E:\AI_System\config\local.json 加载配置: {}
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🌐 从 E:\AI_System\.env 加载环境变量
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🌐 从 E:\AI_System\.env 加载环境变量
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🔄 环境变量覆盖: AGENT_DIR=E:/AI_System/agent
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🔄 环境变量覆盖: AGENT_DIR=E:/AI_System/agent
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🔄 环境变量覆盖: WEB_UI_DIR=E:/AI_System/web_ui
2025-08-27 20:50:51,506 - CoreConfig - INFO - 🔄 环境变量覆盖: WEB_UI_DIR=E:/AI_System/web_ui
2025-08-27 20:50:51,506 - CoreConfig - INFO - ✅ 配置系统初始化完成
2025-08-27 20:50:51,506 - CoreConfig - INFO - ✅ 配置系统初始化完成
2025-08-27 20:50:51,506 - ImportTest - ERROR - ❌ 测试过程中发生错误: cannot import name 'utils' from partially initialized module 'core' (most likely due to a circular import) (E:\AI_System\core\__init__.py)
2025-08-27 20:50:51,506 - ImportTest - ERROR - 详细堆栈跟踪:
2025-08-27 20:50:51,506 - ImportTest - ERROR - Traceback (most recent call last):
File "E:\AI_System\tests\test_core_import.py", line 29, in <module>
from core import CognitiveModule
File "E:\AI_System\core\__init__.py", line 37, in <module>
from . import utils
ImportError: cannot import name 'utils' from partially initialized module 'core' (most likely due to a circular import) (E:\AI_System\core\__init__.py)
(venv) PS E:\AI_System> python diagnose_architecture.py
❌ 导入失败: No module named 'models.base_model'
⚠️ 创建占位符CognitiveModule
2025-08-27 20:50:57,088 - CoreConfig - INFO - 📂 从 E:\AI_System\config\default.json 加载配置: {'LOG_DIR': 'E:/AI_System/logs', 'CONFIG_DIR': 'E:/AI_System/config', 'MODEL_CACHE_DIR': 'E:/AI_System/model_cache', 'AGENT_NAME': '小蓝', 'DEFAULT_USER': '管理员', 'MAX_WORKERS': 4, 'AGENT_RESPONSE_TIMEOUT': 30.0, 'MODEL_BASE_PATH': 'E:/AI_Models', 'MODEL_PATHS': {'TEXT_BASE': 'E:/AI_Models/Qwen2-7B', 'TEXT_CHAT': 'E:/AI_Models/deepseek-7b-chat', 'MULTIMODAL': 'E:/AI_Models/deepseek-vl2', 'IMAGE_GEN': 'E:/AI_Models/sdxl', 'YI_VL': 'E:/AI_Models/yi-vl', 'STABLE_DIFFUSION': 'E:/AI_Models/stable-diffusion-xl-base-1.0'}, 'NETWORK': {'HOST': '0.0.0.0', 'FLASK_PORT': 8000, 'GRADIO_PORT': 7860}, 'DATABASE': {'DB_HOST': 'localhost', 'DB_PORT': 5432, 'DB_NAME': 'ai_system', 'DB_USER': 'ai_user', 'DB_PASSWORD': 'secure_password_here'}, 'SECURITY': {'SECRET_KEY': 'generated-secret-key-here'}, 'ENVIRONMENT': {'ENV': 'dev', 'LOG_LEVEL': 'DEBUG', 'USE_GPU': True}, 'DIRECTORIES': {'DEFAULT_MODEL': 'E:/AI_Models/Qwen2-7B', 'WEB_UI_DIR': 'E:/AI_System/web_ui', 'AGENT_DIR': 'E:/AI_System/agent'}}
2025-08-27 20:50:57,088 - CoreConfig - INFO - 📂 从 E:\AI_System\config\local.json 加载配置: {}
2025-08-27 20:50:57,088 - CoreConfig - INFO - 🌐 从 E:\AI_System\.env 加载环境变量
2025-08-27 20:50:57,088 - CoreConfig - INFO - 🔄 环境变量覆盖: AGENT_DIR=E:/AI_System/agent
2025-08-27 20:50:57,088 - CoreConfig - INFO - 🔄 环境变量覆盖: WEB_UI_DIR=E:/AI_System/web_ui
2025-08-27 20:50:57,088 - CoreConfig - INFO - ✅ 配置系统初始化完成
Traceback (most recent call last):
File "E:\AI_System\diagnose_architecture.py", line 8, in <module>
from core.module_registry import validate_module_structure
File "E:\AI_System\core\__init__.py", line 37, in <module>
from . import utils
ImportError: cannot import name 'utils' from partially initialized module 'core' (most likely due to a circular import) (E:\AI_System\core\__init__.py)
(venv) PS E:\AI_System>”
最新发布
本文介绍了URL重定向的概念及其在Web开发中的应用，包括常见的HTTP状态码如301永久重定向、302临时重定向等，并解释了它们如何帮助避免旧链接失效及更新资源位置。
扫一扫
173
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
