diff --git a/.gitignore b/.gitignore index eb37cf97..d5f6279b 100644 --- a/.gitignore +++ b/.gitignore @@ -167,4 +167,9 @@ cython_debug/ *.db *.pyc -__pycache__/ \ No newline at end of file +__pycache__/ + +# Visual Studio Code +.vscode/ + +tests/test_agentuniverse/unit/agent/custom_key.toml diff --git a/agentuniverse/agent/default/dual_system_agent/__init__.py b/agentuniverse/agent/default/dual_system_agent/__init__.py new file mode 100644 index 00000000..adcd1c2b --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/__init__.py @@ -0,0 +1,6 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/1/22 16:00 +# @Author : [Your Name] +# @FileName: __init__.py diff --git a/agentuniverse/agent/default/dual_system_agent/complexity_evaluator.py b/agentuniverse/agent/default/dual_system_agent/complexity_evaluator.py new file mode 100644 index 00000000..573cd44a --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/complexity_evaluator.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +"""Default complexity evaluator implementation.""" + +import re +from typing import Set, List, Dict +from agentuniverse.agent.default.dual_system_agent.constants import ( + ComplexityScore, ComplexityLevel, ComplexityFactor +) +from agentuniverse.agent.default.dual_system_agent.evaluator.base_evaluator import BaseEvaluator + +class DefaultComplexityEvaluator(BaseEvaluator): + """Default implementation of complexity evaluator.""" + + def __init__(self) -> None: + """Initialize the evaluator with configuration.""" + # 简单问题的关键词集合 + self._simple_keywords: Set[str] = { + '什么是', '定义', '解释', '加', '减', '乘', + 'what is', 'define', 'explain', 'add', 'subtract', 'multiply' + } + + # 复杂问题的关键词集合 + self._complex_keywords: Set[str] = { + '为什么', '如何', '分析', '比较', '评估', '预测', '优化', + 'why', 'how', 'analyze', 'compare', 'evaluate', 'predict', 'optimize' + } + + # 上下文依赖词集合 + self._context_keywords: Set[str] = { + '这个', '那个', '它', '他们', '之前', '刚才', + 'this', 'that', 'it', 'they', 'previous', 'before' + } + + def evaluate_text_length(self, text: str) -> float: + """Evaluate complexity based on text length.""" + length = len(text) + if length <= 10: + return 0.2 + elif length <= 30: + return 0.5 + elif length <= 100: + return 0.8 + return 1.0 + + def evaluate_question_type(self, text: str) -> float: + """Evaluate complexity based on question type.""" + text = text.lower() + + # 检查简单关键词 + for keyword in self._simple_keywords: + if keyword.lower() in text: + return 0.3 + + # 检查复杂关键词 + for keyword in self._complex_keywords: + if keyword.lower() in text: + return 0.8 + + return 0.5 # 默认中等复杂度 + + def evaluate_keyword_complexity(self, text: str) -> float: + """Evaluate complexity based on keyword analysis.""" + words = set(re.split(r'\s+|[,.，。]', text.lower())) + + # 计算复杂词汇的比例 + complex_word_count = len([w for w in words if len(w) > 4]) + if not words: + return 0.5 + + return min(1.0, complex_word_count / len(words)) + + def evaluate_context_dependency(self, text: str) -> float: + """Evaluate complexity based on context dependency.""" + text = text.lower() + context_keyword_count = sum(1 for keyword in self._context_keywords + if keyword.lower() in text) + + if context_keyword_count == 0: + return 0.2 + elif context_keyword_count == 1: + return 0.5 + elif context_keyword_count == 2: + return 0.8 + return 1.0 + + def evaluate(self, text: str) -> ComplexityScore: + """Evaluate overall input complexity.""" + # 计算各维度分数 + text_length_score = self.evaluate_text_length(text) + question_type_score = self.evaluate_question_type(text) + keyword_complexity_score = self.evaluate_keyword_complexity(text) + context_dependency_score = self.evaluate_context_dependency(text) + + # 计算总体分数（可以调整权重） + weights = { + 'text_length': 0.2, + 'question_type': 0.3, + 'keyword_complexity': 0.3, + 'context_dependency': 0.2 + } + + overall_score = ( + text_length_score * weights['text_length'] + + question_type_score * weights['question_type'] + + keyword_complexity_score * weights['keyword_complexity'] + + context_dependency_score * weights['context_dependency'] + ) + + # 确定复杂度级别 + if overall_score < 0.4: + level = ComplexityLevel.SIMPLE + elif overall_score < 0.8: + level = ComplexityLevel.MODERATE + else: + level = ComplexityLevel.COMPLEX + + return ComplexityScore( + text_length_score=text_length_score, + question_type_score=question_type_score, + keyword_complexity_score=keyword_complexity_score, + context_dependency_score=context_dependency_score, + overall_score=overall_score, + level=level + ) diff --git a/agentuniverse/agent/default/dual_system_agent/constants.py b/agentuniverse/agent/default/dual_system_agent/constants.py new file mode 100644 index 00000000..fb560838 --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/constants.py @@ -0,0 +1,75 @@ +from enum import Enum +from dataclasses import dataclass +from typing import Any, Literal, Optional, Dict, TypedDict +from agentuniverse.base.common.constants import AgentKeys + +class DualSystemKeys(str, Enum): + """Keys specific to dual system thinking.""" + SYSTEM_TYPE = 'system_type' + CONFIDENCE = 'confidence' + THOUGHT = 'thought' + RESPONSE = 'response' + COMPLEXITY = 'complexity' + +class SystemType(str, Enum): + """System types for dual system thinking.""" + FAST = 'fast_thinking' + SLOW = 'slow_thinking' + +class ThinkingState(str, Enum): + """States in the thinking process.""" + EVALUATING = 'evaluating' + FAST_PROCESSING = 'fast_processing' + SLOW_PROCESSING = 'slow_processing' + COMPLETED = 'completed' + +class ComplexityLevel(str, Enum): + """Complexity levels for input evaluation.""" + SIMPLE = 'simple' + MODERATE = 'moderate' + COMPLEX = 'complex' + +class ComplexityFactor(str, Enum): + """Factors affecting input complexity.""" + TEXT_LENGTH = 'text_length' + QUESTION_TYPE = 'question_type' + KEYWORD_COMPLEXITY = 'keyword_complexity' + CONTEXT_DEPENDENCY = 'context_dependency' + +@dataclass +class ThinkingResult: + """Data class for thinking results.""" + system_type: SystemType + output: Any + confidence: float + thought: Optional[str] = None + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format.""" + return { + AgentKeys.OUTPUT: self.output, + DualSystemKeys.SYSTEM_TYPE: self.system_type, + DualSystemKeys.CONFIDENCE: self.confidence, + DualSystemKeys.THOUGHT: self.thought + } + +@dataclass +class ComplexityScore: + """Data class for complexity evaluation results.""" + text_length_score: float + question_type_score: float + keyword_complexity_score: float + context_dependency_score: float + overall_score: float + level: ComplexityLevel + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary format.""" + return { + ComplexityFactor.TEXT_LENGTH: self.text_length_score, + ComplexityFactor.QUESTION_TYPE: self.question_type_score, + ComplexityFactor.KEYWORD_COMPLEXITY: self.keyword_complexity_score, + ComplexityFactor.CONTEXT_DEPENDENCY: self.context_dependency_score, + 'overall_score': self.overall_score, + 'level': self.level + } diff --git a/agentuniverse/agent/default/dual_system_agent/dual_system_agent.py b/agentuniverse/agent/default/dual_system_agent/dual_system_agent.py new file mode 100644 index 00000000..d8456356 --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/dual_system_agent.py @@ -0,0 +1,43 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/1/22 16:00 +# @Author : [Your Name] +# @FileName: dual_system_agent.py + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject +from .constants import AgentKeys, SystemType +import json +import logging + +class DualSystemAgent(Agent): + """Dual System Agent class implementing fast and slow thinking.""" + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return [AgentKeys.INPUT] + + def output_keys(self) -> list[str]: + """Return the output keys of the Agent.""" + return [AgentKeys.OUTPUT, AgentKeys.SYSTEM_TYPE, AgentKeys.CONFIDENCE] + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + """Agent parameter parsing.""" + agent_input[AgentKeys.INPUT] = input_object.get_data(AgentKeys.INPUT) + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + """Planner result parser.""" + # Extract the system type and confidence from planner result + system_type: SystemType = planner_result.get(AgentKeys.SYSTEM_TYPE, 'slow') + confidence = planner_result.get(AgentKeys.CONFIDENCE, 0.0) + logging.debug(f"planner_result: {json.dumps(planner_result, ensure_ascii=False)}") + return { + AgentKeys.OUTPUT: planner_result.get(AgentKeys.OUTPUT), + AgentKeys.SYSTEM_TYPE: system_type, + AgentKeys.CONFIDENCE: confidence + } + + diff --git a/agentuniverse/agent/default/dual_system_agent/dual_system_agent.yaml b/agentuniverse/agent/default/dual_system_agent/dual_system_agent.yaml new file mode 100644 index 00000000..32c327d5 --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/dual_system_agent.yaml @@ -0,0 +1,18 @@ +info: + name: 'DualSystemAgent' + description: 'Agent implementing fast/slow thinking systems' +plan: + planner: + name: 'DualSystemPlanner' + # System 1 (Fast Thinking) Configuration + system1: + name: 'FastThinkingAgent' + complexity_evaluator: 'default_complexity_evaluator' + confidence_threshold: 0.7 # Aligned with planner threshold + # System 2 (Slow Thinking) Configuration + system2: + name: 'PeerAgent' +metadata: + type: 'AGENT' + module: 'agentuniverse.agent.default.dual_system_agent.dual_system_agent' + class: 'DualSystemAgent' diff --git a/agentuniverse/agent/default/dual_system_agent/evaluator/__init__.py b/agentuniverse/agent/default/dual_system_agent/evaluator/__init__.py new file mode 100644 index 00000000..ce4d17fc --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/evaluator/__init__.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +"""Evaluator package initialization.""" + +from agentuniverse.agent.default.dual_system_agent.evaluator.evaluator_manager import EvaluatorManager +from agentuniverse.agent.default.dual_system_agent.complexity_evaluator import DefaultComplexityEvaluator + +# Register default evaluator +EvaluatorManager().register("default_complexity_evaluator", DefaultComplexityEvaluator) +EvaluatorManager().set_default("default_complexity_evaluator") diff --git a/agentuniverse/agent/default/dual_system_agent/evaluator/base_evaluator.py b/agentuniverse/agent/default/dual_system_agent/evaluator/base_evaluator.py new file mode 100644 index 00000000..5a3712ec --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/evaluator/base_evaluator.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +"""Base evaluator interface for complexity evaluation.""" + +from abc import ABC, abstractmethod +from agentuniverse.agent.default.dual_system_agent.constants import ComplexityScore + +class BaseEvaluator(ABC): + """Base interface for complexity evaluators.""" + + @abstractmethod + def evaluate(self, text: str) -> ComplexityScore: + """Evaluate input complexity. + + Args: + text: Input text to evaluate + + Returns: + ComplexityScore: Complexity evaluation results + """ + pass + + @abstractmethod + def evaluate_text_length(self, text: str) -> float: + """Evaluate complexity based on text length. + + Args: + text: Input text to evaluate + + Returns: + float: Score between 0 and 1, where higher means more complex + """ + pass + + @abstractmethod + def evaluate_question_type(self, text: str) -> float: + """Evaluate complexity based on question type. + + Args: + text: Input text to evaluate + + Returns: + float: Score between 0 and 1, where higher means more complex + """ + pass + + @abstractmethod + def evaluate_keyword_complexity(self, text: str) -> float: + """Evaluate complexity based on keyword analysis. + + Args: + text: Input text to evaluate + + Returns: + float: Score between 0 and 1, where higher means more complex + """ + pass + + @abstractmethod + def evaluate_context_dependency(self, text: str) -> float: + """Evaluate complexity based on context dependency. + + Args: + text: Input text to evaluate + + Returns: + float: Score between 0 and 1, where higher means more complex + """ + pass diff --git a/agentuniverse/agent/default/dual_system_agent/evaluator/evaluator_manager.py b/agentuniverse/agent/default/dual_system_agent/evaluator/evaluator_manager.py new file mode 100644 index 00000000..1853e140 --- /dev/null +++ b/agentuniverse/agent/default/dual_system_agent/evaluator/evaluator_manager.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +"""Evaluator manager for managing different complexity evaluators.""" + +from typing import Dict, Type +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.agent.default.dual_system_agent.evaluator.base_evaluator import BaseEvaluator +from typing import Optional + +@singleton +class EvaluatorManager: + """Manager class for complexity evaluators.""" + + def __init__(self) -> None: + """Initialize evaluator manager.""" + self._evaluators: Dict[str, Type[BaseEvaluator]] = {} + self._default_evaluator: Optional[str] = None + + def register(self, name: str, evaluator_class: Type[BaseEvaluator]) -> None: + """Register an evaluator. + + Args: + name: Name of the evaluator + evaluator_class: Evaluator class to register + """ + self._evaluators[name] = evaluator_class + if self._default_evaluator is None: + self._default_evaluator = name + + def set_default(self, name: str) -> None: + """Set the default evaluator. + + Args: + name: Name of the evaluator to set as default + """ + if name is None: + self._default_evaluator = None + if name not in self._evaluators: + raise ValueError(f"Evaluator {name} not found") + self._default_evaluator = name + + def get_evaluator(self, name: Optional[str] = None) -> BaseEvaluator: + """Get an evaluator instance. + + Args: + name: Name of the evaluator to get, if None returns default + + Returns: + BaseEvaluator: An instance of the requested evaluator + """ + evaluator_name = name or self._default_evaluator + if evaluator_name not in self._evaluators: + raise ValueError(f"Evaluator {evaluator_name} not found") + return self._evaluators[evaluator_name]() diff --git a/agentuniverse/agent/default/fast_thinking_agent/default_cn_prompt.yaml b/agentuniverse/agent/default/fast_thinking_agent/default_cn_prompt.yaml new file mode 100644 index 00000000..1a027cba --- /dev/null +++ b/agentuniverse/agent/default/fast_thinking_agent/default_cn_prompt.yaml @@ -0,0 +1,56 @@ +introduction: | + 你是一个快速思考系统（系统1），能够快速直觉地处理输入。你会在有把握时提供即时回应，或在需要深入思考时指出这一点。 +target: | + 1. 快速评估是否能够自信地回答输入 + 2. 对熟悉的模式提供直接回应 + 3. 计算回应的置信度水平 + 4. 如果置信度低，指出需要深入思考 + +instruction: | + 对于给定的输入，你应该： + 1. 快速分析这是否是你能够自信回答的内容。 + 2. 如果是，提供直接简洁的回应。 + 3. 如果输入模糊、带有推测性或缺乏足够数据，给出较低的置信度分数（例如，低于0.5）。 + 4. 始终简要解释你的思考过程。 + + 你的回应应该采用以下格式： + ```json + {{ + "thought": "思考过程的简要解释", + "response": "你的直接回应", + "confidence": 0.7, # 在0到1之间 + }} + ``` + + # 示例1：有把握的回应 + 输入："2 + 2等于多少？" + 回应：{{ + "thought": "简单的算术计算。", + "response": "4", + "confidence": 0.95 + }} + + # 示例2：不确定的回应 + 输入:"明年黄金的价格是多少？" + 回应：{{ + "thought": "预测市场价格涉及推测及市场信息，本质上存在不确定性。", + "response": "黄金的未来价格不确定，取决于多个因素。", + "confidence": 0.3 + }} + + + 之前的对话: + {chat_history} + + 背景信息是: + {background} + + 今天的日期是: {date} + + 输入:{input} + + 回应: + +metadata: + type: 'PROMPT' + version: 'default_fast_thinking_agent_cn' diff --git a/agentuniverse/agent/default/fast_thinking_agent/default_en_prompt.yaml b/agentuniverse/agent/default/fast_thinking_agent/default_en_prompt.yaml new file mode 100644 index 00000000..c4ea2218 --- /dev/null +++ b/agentuniverse/agent/default/fast_thinking_agent/default_en_prompt.yaml @@ -0,0 +1,56 @@ +introduction: | + You are a fast thinking system (System 1) that processes inputs quickly and intuitively. + Your will provide immediate responses when you are confident, or indicate when deeper thinking is needed. +target: | + 1. Quickly assess if you can confidently answer the input + 2. Provide direct responses for familiar patterns + 3. Calculate confidence level for your response + 4. If confidence is low, indicate that deeper thinking is needed +instruction: | + Given the input, you should: + 1. Quickly analyze if this is something you can answer confidently. + 2. If yes, provide a direct and concise response. + 3. If the input is ambiguous, speculative, involves speculation, or lacks sufficient data, assign a low confidence score (e.g., below 0.5). + 4. Always explain your thought process briefly. + + Your response should be in the following format: + ```json + {{ + "thought": "brief explanation of your thinking process", + "response": "your direct response", + "confidence": 0.7, # between 0 and 1 + }} + ``` + + # Example 1: Confident Response + Input: "What is 2 + 2?" + Response: {{ + "thought": "Simple arithmetic calculation.", + "response": "4", + "confidence": 0.95 + }} + + # Example 2: Uncertain Response + Input: "What will be the price of gold next year?" + Response: {{ + "thought": "Predicting market prices involves speculation and market information, and is inherently uncertain.", + "response": "The future price of gold is uncertain and depends on various factors.", + "confidence": 0.3 + }} + + + Previous conversation: + {chat_history} + + Background Information: + {background} + + Today's date is: {date} + + Input: {input} + + Response: + +metadata: + type: 'PROMPT' + version: 'default_fast_thinking_agent_en' diff --git a/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.py b/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.py new file mode 100644 index 00000000..6334cd8d --- /dev/null +++ b/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.py @@ -0,0 +1,41 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/1/22 17:00 +# @Author : [Your Name] +# @FileName: fast_thinking_agent.py + +"""Fast thinking agent module for System 1 processing.""" +from langchain.output_parsers.json import parse_json_markdown + +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject +from agentuniverse.base.common.constants import AgentKeys +from agentuniverse.agent.default.dual_system_agent.constants import (DualSystemKeys) + + +class FastThinkingAgent(Agent): + """Fast Thinking Agent class for System 1.""" + + def input_keys(self) -> list[str]: + """Return the input keys of the Agent.""" + return [AgentKeys.INPUT] + + def output_keys(self) -> list[str]: + """Return the output keys of the Agent.""" + return [AgentKeys.OUTPUT, DualSystemKeys.CONFIDENCE, DualSystemKeys.THOUGHT] + + def parse_input(self, input_object: InputObject, agent_input: dict) -> dict: + """Agent parameter parsing.""" + agent_input[AgentKeys.INPUT] = input_object.get_data(AgentKeys.INPUT) + return agent_input + + def parse_result(self, planner_result: dict) -> dict: + """Planner result parser.""" + output = planner_result.get(AgentKeys.OUTPUT) + output = parse_json_markdown(output) + return { + AgentKeys.OUTPUT: output.get(DualSystemKeys.RESPONSE), + DualSystemKeys.CONFIDENCE: output.get(DualSystemKeys.CONFIDENCE, 0.0), + DualSystemKeys.THOUGHT: output.get(DualSystemKeys.THOUGHT, '') + } diff --git a/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.yaml b/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.yaml new file mode 100644 index 00000000..81a332db --- /dev/null +++ b/agentuniverse/agent/default/fast_thinking_agent/fast_thinking_agent.yaml @@ -0,0 +1,21 @@ +info: + name: 'FastThinkingAgent' + description: 'System 1 agent for fast thinking processing' +plan: + planner: + # 使用基础的执行 planner，因为不需要复杂的规划 + name: 'executing_planner' +profile: + prompt_version: 'default_fast_thinking_agent_en' + llm_model: + name: 'default_openai_llm' + model_name: 'gpt-4o-mini' + temperature: 0.3 # Lower temperature for more focused responses + max_tokens: 150 # Limited tokens for fast responses + top_p: 1.0 + presence_penalty: 0.0 + frequency_penalty: 0.0 +metadata: + type: 'AGENT' + module: 'agentuniverse.agent.default.fast_thinking_agent.fast_thinking_agent' + class: 'FastThinkingAgent' diff --git a/agentuniverse/agent/plan/planner/dual_system_planner/__init__.py b/agentuniverse/agent/plan/planner/dual_system_planner/__init__.py new file mode 100644 index 00000000..b4c92d3c --- /dev/null +++ b/agentuniverse/agent/plan/planner/dual_system_planner/__init__.py @@ -0,0 +1,6 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/1/22 16:30 +# @Author : [Your Name] +# @FileName: __init__.py diff --git a/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.py b/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.py new file mode 100644 index 00000000..6ded0212 --- /dev/null +++ b/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.py @@ -0,0 +1,112 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +# @Time : 2024/1/22 16:30 +# @Author : [Your Name] +# @FileName: dual_system_planner.py + +"""Dual System planner module implementing fast/slow thinking.""" + +from typing_extensions import sys +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.plan.planner.planner import AgentManager, Planner +from agentuniverse.agent.agent_model import AgentModel +from agentuniverse.agent.default.fast_thinking_agent.fast_thinking_agent import FastThinkingAgent +from agentuniverse.base.util.logging.logging_util import LOGGER +from agentuniverse.agent.output_object import OutputObject +from agentuniverse.base.common.constants import AgentKeys +from agentuniverse.agent.default.dual_system_agent.constants import ( + DualSystemKeys, SystemType, ThinkingState, ThinkingResult, ComplexityLevel +) +from agentuniverse.agent.default.dual_system_agent.evaluator.evaluator_manager import EvaluatorManager + + +class DualSystemPlanner(Planner): + """Dual system planner implementing System 1 (fast) and System 2 (slow) thinking.""" + + def __init__(self) -> None: + super().__init__() + self._current_state = ThinkingState.EVALUATING + self._confidence_threshold = 0.7 # Configurable threshold + self._evaluator = None + + def _evaluate_input_complexity(self, input_text: str) -> bool: + """Evaluate if the input is simple enough for fast thinking. + + Args: + input_text: The input text to evaluate + + Returns: + bool: True if the input is simple enough for fast thinking + """ + complexity_score = self._evaluator.evaluate(input_text) + LOGGER.info(f"Input complexity evaluation: {complexity_score.to_dict()}") + + # 如果输入非常复杂，直接使用慢思考 + return bool(complexity_score.level != ComplexityLevel.COMPLEX) + + def _process_fast_thinking(self, agent_model: AgentModel, input_object: InputObject) -> ThinkingResult: + """Process input using System 1 (fast thinking).""" + system1_info = agent_model.plan.get('planner').get('system1') + system1_agent_name = system1_info.get('name') + self._confidence_threshold = system1_info.get('confidence_threshold') + self._evaluator = EvaluatorManager().get_evaluator(system1_info.get('complexity_evaluator')) + fast_agent = AgentManager().get_instance_obj(system1_agent_name) + result = fast_agent.run(**input_object.to_dict()) + + return ThinkingResult( + system_type=SystemType.FAST, + output=result.get_data(AgentKeys.OUTPUT), + confidence=result.get_data(DualSystemKeys.CONFIDENCE, 0.0), + thought=result.get_data(DualSystemKeys.THOUGHT) + ) + + def _process_slow_thinking(self, agent_model: AgentModel, input_object: InputObject) -> ThinkingResult: + """Process input using System 2 (slow thinking).""" + system2_agent_name = agent_model.plan.get('planner').get('system2').get('name') + slow_agent = AgentManager().get_instance_obj(system2_agent_name) + # mock = { + # AgentKeys.OUTPUT: "This is a mock response", + # DualSystemKeys.CONFIDENCE: 1.0, + # DualSystemKeys.THOUGHT: "System 2: This is the slow thinking process." + # } + # result = OutputObject(mock) + result = slow_agent.run(**input_object.to_dict()) + return ThinkingResult( + system_type=SystemType.SLOW, + output=result.get_data(AgentKeys.OUTPUT), + confidence=result.get_data(DualSystemKeys.CONFIDENCE, 0.0), + thought=result.get_data(DualSystemKeys.THOUGHT) + ) + + def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: InputObject) -> dict: + """Invoke the planner to process input using dual system thinking.""" + self._current_state = ThinkingState.EVALUATING + + # 获取输入文本 + input_text = input_object.get_data(AgentKeys.INPUT) + + # 首先评估输入复杂度 + if self._evaluator is not None and not self._evaluate_input_complexity(input_text): + LOGGER.info("Input complexity too high, switching to slow thinking directly") + self._current_state = ThinkingState.SLOW_PROCESSING + slow_result = self._process_slow_thinking(agent_model, input_object) + self._current_state = ThinkingState.COMPLETED + return slow_result.to_dict() + + # 对于简单或中等复杂度的输入，尝试快速思考 + self._current_state = ThinkingState.FAST_PROCESSING + fast_result = self._process_fast_thinking(agent_model, input_object) + + # 如果快速思考足够自信，返回结果 + if fast_result.confidence >= self._confidence_threshold: + self._current_state = ThinkingState.COMPLETED + return fast_result.to_dict() + + # 否则使用慢思考 + self._current_state = ThinkingState.SLOW_PROCESSING + slow_result = self._process_slow_thinking(agent_model, input_object) + + self._current_state = ThinkingState.COMPLETED + return slow_result.to_dict() diff --git a/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.yaml b/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.yaml new file mode 100644 index 00000000..8b1aae1c --- /dev/null +++ b/agentuniverse/agent/plan/planner/dual_system_planner/dual_system_planner.yaml @@ -0,0 +1,6 @@ +name: 'DualSystemPlanner' +description: 'Planner implementing dual-process theory with fast and slow thinking systems' +metadata: + type: 'PLANNER' + module: 'agentuniverse.agent.plan.planner.dual_system_planner.dual_system_planner' + class: 'DualSystemPlanner' diff --git a/agentuniverse/base/common/constants.py b/agentuniverse/base/common/constants.py new file mode 100644 index 00000000..69d9c617 --- /dev/null +++ b/agentuniverse/base/common/constants.py @@ -0,0 +1,13 @@ +from enum import Enum +from dataclasses import dataclass +from typing import Any, Literal, Optional, TypedDict, Dict + +class AgentKeys(str, Enum): + """Common keys used across different agents.""" + INPUT = 'input' + OUTPUT = 'output' + CONFIDENCE = 'confidence' + THOUGHT = 'thought' + SYSTEM_TYPE = 'system_type' + STREAM_CALLBACK = 'stream_callback' + PROMPT_VERSION = 'prompt_version' \ No newline at end of file diff --git "a/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md" "b/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md" index 3dbdf961..030070fa 100644 --- "a/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md" +++ "b/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md" @@ -201,10 +201,41 @@ class RagAgentTest(unittest.TestCase): if __name__ == '__main__': unittest.main() ``` + 在`test_rag_agent`方法中，我们通过`AgentManager().get_instance_obj`获取了`demo_rag_agent`的实例对象，然后通过`instance.run`方法执行了agent的逻辑。 通过测试，你可以观察agent的思路与答案是否符合你的预期，并且优化对应的设定、增强工具与知识，不断的重复这个步骤直到效果达到满意。 +### 运行测试 + +#### 目录结构要求 +agentUniverse期望从 `app/bootstrap` 目录启动，这样才能正确解析配置文件路径。agentUniverse 使用相对路径来定位配置文件，并且以 `bootstrap` 目录作为基准点。 + +#### 正确的测试运行方式 +```bash +# 1. 进入 bootstrap 目录 +cd sample_standard_app/app/bootstrap + +# 2. 设置 PYTHONPATH 以确保能够正确导入依赖 +export PYTHONPATH=/path/to/your/agentUniverse # 替换为你的 agentUniverse 实际路径 + +# 3. 运行测试 +python -m pytest ../test/test_rag_agent.py +``` + +#### 常见问题 +1. **配置文件找不到** + ``` + FileNotFoundError: [Errno 2] No such file or directory: '../../config/config.toml' + ``` + 这通常是因为没有从 `bootstrap` 目录运行测试导致的。请确保按照上述步骤在正确的目录下运行测试。 + +2. **警告信息** + ``` + Warn: Boot file is not located under directory 'bootstrap' + ``` + 这个警告表明当前不是在 `bootstrap` 目录下运行，可能会导致路径解析问题。 + ## 对agent进行快速服务化 ### 使用配置注册服务 在工程的`service`目录下创建`xx_service.yaml`文件，内容样例如下： @@ -226,9 +257,12 @@ cd `your bootstrap directory path` python server_application.py ``` -当出现如下命令行说明监听成功，默认监听127.0.0.1地址8000端口，worker数为5，可在`config/gunicorn_config.toml`中修改配置。（注意windows系统与直接使用flask启动的目前默认监听8888端口） +当出现如下命令行说明监听成功，默认监听127.0.0.1地址8000端口，worker数为5，可在`config/gunicorn_config.toml`中修改配置。（注意windows系统与直接使用`flask`启动的目前默认监听8888端口） ![image](../_picture/1_3_Quick%20Start_0.png) + +我们已经将启动服务的必要的步骤写在了 `agentUniverse/sample_standard_app/quick_start.sh` (或者 `quick_start.ps1` 或者 `quick_start.bat`) 中，你可以使用 `quick_start` 脚本启动服务。 + ### 服务访问 #### 本机或局域网访问 你可以在终端通过curl指令或者如postman工具进行服务访问，curl访问指令如下： diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" index 0e663e28..6806c810 100644 --- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" +++ "b/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" @@ -113,7 +113,7 @@ SEARCHAPI_API_KEY="xxxxxx" 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-28,7 +28,7 @@ log_config_path = './log_config.toml' # Gunicorn config file path, an absolute path or a relative path based on the dir where the current config file is located. gunicorn_config_path = './gunicorn_config.toml' # Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore. -#custom_key_path = './custom_key.toml' +custom_key_path = './custom_key.toml' [DB] # A mysql db uri used to save web request task status. Status will be saved in sqllite if the uri is empty all not exist. diff --git a/tests/test_agentuniverse/unit/agent/default/dual_system_agent/test_dual_system_agent.py b/tests/test_agentuniverse/unit/agent/default/dual_system_agent/test_dual_system_agent.py new file mode 100644 index 00000000..9e4320e5 --- /dev/null +++ b/tests/test_agentuniverse/unit/agent/default/dual_system_agent/test_dual_system_agent.py @@ -0,0 +1,115 @@ +# !/usr/bin/env python3 +# -*- coding:utf-8 -*- + +"""Test cases for the dual system agent.""" + +import unittest +from typing import List, Dict, Any, Optional +import os +import json +from agentuniverse.agent.agent import Agent +from agentuniverse.agent.agent_manager import AgentManager +from agentuniverse.base.agentuniverse import AgentUniverse +from agentuniverse.agent.input_object import InputObject +from agentuniverse.agent.output_object import OutputObject +from agentuniverse.base.config.application_configer.application_config_manager import ApplicationConfigManager +from agentuniverse.base.annotation.singleton import singleton +from agentuniverse.agent_serve.service_manager import ServiceManager +from agentuniverse.workflow.workflow_manager import WorkflowManager +from agentuniverse.prompt.prompt_manager import PromptManager +from agentuniverse.llm.llm_manager import LLMManager +from agentuniverse.agent.action.tool.tool_manager import ToolManager +from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager +from agentuniverse.agent.action.knowledge.embedding.embedding_manager import EmbeddingManager +from agentuniverse.agent.action.knowledge.query_paraphraser.query_paraphraser_manager import QueryParaphraserManager +from agentuniverse.agent.action.knowledge.doc_processor.doc_processor_manager import DocProcessorManager +from agentuniverse.agent.action.knowledge.store.store_manager import StoreManager +from agentuniverse.agent.action.knowledge.rag_router.rag_router_manager import RagRouterManager +from agentuniverse.agent.action.knowledge.reader.reader_manager import ReaderManager +from agentuniverse.agent.plan.planner.planner_manager import PlannerManager +from agentuniverse.agent.memory.memory_manager import MemoryManager + +class DualSystemAgentTest(unittest.TestCase): + """Test cases for the dual system (fast/slow thinking) agent.""" + + agent: Optional[Agent] = None + task_results: Dict[str, OutputObject] = {} + + @classmethod + def setUpClass(cls) -> None: + """Set up test environment once for all test methods.""" + # 启动 AgentUniverse + config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'config.toml') + AgentUniverse().start(config_path=config_path) + cls.agent = AgentManager().get_instance_obj('DualSystemAgent') + cls.task_results = {} + + def setUp(self) -> None: + """Set up for each test method.""" + pass + + def run_task(self, query: str, task_id: str) -> OutputObject: + """Run a task with the dual system agent.""" + input_obj = { + "input":query, + "task_id":task_id or f"test_task_{len(self.__class__.task_results)}", + } + output_obj: OutputObject = self.__class__.agent.run(**input_obj) + self.__class__.task_results[input_obj['task_id']] = output_obj + return output_obj + + def test_system1_fast_thinking(self) -> None: + """Test fast thinking system.""" + # Case 1: Simple calculation (简单计算) + result = self.run_task('What is 2 + 2?', 'fast_calculation') + self.assertEqual(result.get_data('system_type'), 'fast_thinking') + self.assertGreaterEqual(result.get_data('confidence'), 0.7) + self.assertIn('4', result.get_data('output')) + + # Case 2: Common knowledge with high confidence (简单常识) + result = self.run_task('What is the capital of China?', 'common_knowledge') + self.assertEqual(result.get_data('system_type'), 'fast_thinking') + self.assertGreaterEqual(result.get_data('confidence'), 0.9) + self.assertIn('Beijing', result.get_data('output')) + + # Case 3: Simple definition question (简单定义) + result = self.run_task('GDP 的定义是什么', 'common_knowledge') + self.assertEqual(result.get_data('system_type'), 'fast_thinking') + self.assertGreaterEqual(result.get_data('confidence'), 0.7) + + def test_system2_slow_thinking(self) -> None: + """Test slow thinking system.""" + # Case 1: Complex analysis (复杂分析) + result = self.run_task( + '分析一下为什么近期黄金价格持续上涨，并预测未来走势如何？', + 'complex_analysis' + ) + self.assertEqual(result.get_data('system_type'), 'slow_thinking') + + # Case 2: Multi-factor evaluation (多因素评估) + result = self.run_task( + '请评估人工智能在未来5年内对就业市场的潜在影响，考虑技术发展、劳动力市场变化和教育体系适应性等因素。', + 'complex_evaluation' + ) + self.assertEqual(result.get_data('system_type'), 'slow_thinking') + + # Case 3: Context-dependent question (上下文依赖) + result = self.run_task( + '考虑到这些因素的影响，它们将如何改变我们的生活方式？', + 'context_dependent' + ) + self.assertEqual(result.get_data('system_type'), 'slow_thinking') + + def test_borderline_cases(self) -> None: + """Test cases that are on the border between fast and slow thinking.""" + # Case 1: Moderate complexity with low confidence + result = self.run_task('黄金后面还涨不涨了?', 'borderline_confidence') + print(f"Result: {json.dumps(result.to_dict(), ensure_ascii=False)}") + self.assertEqual(result.get_data('system_type'), 'slow_thinking') + + # Case 2: Simple question with context + result = self.run_task('这个问题的答案是什么？', 'simple_with_context') + self.assertEqual(result.get_data('system_type'), 'slow_thinking') + +if __name__ == '__main__': + unittest.main()