在上一篇文章中,我们讨论了如何构建一个销售助手Agent。今天,我想分享另一个实际项目:如何构建一个学习助手Agent。这个项目源于我们一个教育团队的真实需求 - 提升学习效率,优化学习体验。从学习痛点说起记得和教育团队讨论时的场景：小王：学生们学习进度不一样,很难照顾到每个人小李：是啊,而且有些知识点需要反复讲解我：主要是哪些学习场景？小王：知识讲解、练习辅导、答疑解惑这些我：这些场景很适合用AI Agent来协助经过需求分析,我们确定了几个核心功能：知识讲解练习辅导答疑解惑学习追踪技术方案设计首先是整体架构：from typing import List, Dict, Any, Optionalfrom enum import Enumfrom pydantic import BaseModelimport asyncioclass LearningTask(Enum): EXPLAIN = "explain" PRACTICE = "practice" QUESTION = "question" TRACK = "track"class LearningContext(BaseModel): task_type: LearningTask student_info: Dict[str, Any] subject_info: Dict[str, Any] history: Optional[List[Dict[str, Any]]] progress: Optional[Dict[str, float]] class LearningAssistant: def init( self, config: Dict[str, Any] ): self.learning_model = LearningLLM( model="gpt-4", temperature=0.7, context_length=8000 ) self.tools = { "explainer": KnowledgeExplainer(), "tutor": PracticeTutor(), "helper": QuestionHelper(), "tracker": ProgressTracker() } self.knowledge_base = VectorStore( embeddings=LearningEmbeddings(), collection="learning_knowledge" ) async def process_task( self, context: LearningContext ) -> Dict[str, Any]: task_info = await self._analyze_task( context ) resources = await self._prepare_resources( context, task_info ) plan = await self._generate_plan( task_info, resources ) result = await self._execute_task( plan, context ) return result async def _analyze_task( self, context: LearningContext ) -> Dict[str, Any]: task_type = await self._identify_task_type( context.task_type ) difficulty = await self._evaluate_difficulty( context ) strategy = await self._determine_strategy( task_type, difficulty ) return { "type": task_type, "difficulty": difficulty, "strategy": strategy }知识讲解功能首先实现知识讲解功能：class KnowledgeExplainer: def init( self, model: LearningLLM ): self.model = model async def explain_knowledge( self, context: LearningContext ) -> Dict[str, Any]: knowledge = await self._analyze_knowledge( context ) explanation = await self._generate_explanation( knowledge, context ) optimized = await self._optimize_expression( explanation, context ) return optimized async def _analyze_knowledge( self, context: LearningContext ) -> Dict[str, Any]: concepts = await self._extract_concepts( context.subject_info ) difficulties = await self._analyze_difficulties( concepts ) key_points = await self._identify_key_points( concepts, difficulties ) return { "concepts": concepts, "difficulties": difficulties, "key_points": key_points } async def _generate_explanation( self, knowledge: Dict[str, Any], context: LearningContext ) -> Dict[str, Any]: method = await self._select_method( knowledge, context ) content = await self._generate_content( knowledge, method ) examples = await self._add_examples( content, context ) return { "method": method, "content": content, "examples": examples }练习辅导功能接下来是练习辅导功能：class PracticeTutor: def init( self, model: LearningLLM ): self.model = model async def guide_practice( self, context: LearningContext ) -> Dict[str, Any]: ability = await self._analyze_ability( context ) exercises = await self._generate_exercises( ability, context ) guidance = await self._provide_guidance( exercises, context ) return guidance async def _analyze_ability( self, context: LearningContext ) -> Dict[str, Any]: level = await self._evaluate_level( context.history ) weaknesses = await self._identify_weaknesses( context.history ) goals = await self._set_goals( level, weaknesses ) return { "level": level, "weaknesses": weaknesses, "goals": goals } async def _generate_exercises( self, ability: Dict[str, Any], context: LearningContext ) -> List[Dict[str, Any]]: exercises = [] types = await self._select_exercise_types( ability ) for type in types: questions = await self._generate_questions( type, ability ) exercises.extend(questions) adjusted = await self._adjust_difficulty( exercises, ability ) return adjusted答疑解惑功能再来实现答疑解惑功能：class QuestionHelper: def init( self, model: LearningLLM ): self.model = model async def answer_question( self, context: LearningContext ) -> Dict[str, Any]: question = await self._understand_question( context ) answer = await self._generate_answer( question, context ) supplement = await self._provide_supplement( answer, context ) return supplement async def _understand_question( self, context: LearningContext ) -> Dict[str, Any]: type = await self._analyze_question_type( context.subject_info ) info = await self._extract_key_info( context.subject_info ) knowledge = await self._relate_knowledge( info ) return { "type": type, "info": info, "knowledge": knowledge } async def _generate_answer( self, question: Dict[str, Any], context: LearningContext ) -> Dict[str, Any]: answer = await self._prepare_answer( question ) explanation = await self._add_explanation( answer, question ) examples = await self._add_examples( explanation, context ) return { "answer": answer, "explanation": explanation, "examples": examples }学习追踪功能最后是学习追踪功能：class ProgressTracker: def init( self, model: LearningLLM ): self.model = model async def track_progress( self, context: LearningContext ) -> Dict[str, Any]: data = await self._collect_data( context ) progress = await self._analyze_progress( data ) report = await self._generate_report( progress, context ) return report async def _collect_data( self, context: LearningContext ) -> Dict[str, Any]: records = await self._collect_learning_records( context.history ) results = await self._collect_exercise_results( context.history ) scores = await self._collect_test_scores( context.history ) return { "records": records, "results": results, "scores": scores } async def _analyze_progress( self, data: Dict[str, Any] ) -> Dict[str, Any]: progress = await self._analyze_learning_progress( data ) effectiveness = await self._analyze_effectiveness( data ) trend = await self._analyze_trend( data ) return { "progress": progress, "effectiveness": effectiveness, "trend": trend }实际效果经过两个月的使用,这个学习助手Agent带来了显著的改善：效率提升体验改善效果优化实践心得在开发这个学习助手Agent的过程中,我总结了几点经验：因材施教循序渐进持续优化写在最后一个好的学习助手Agent不仅要能讲解知识,更要理解学习的本质,帮助学生建立良好的学习习惯。它就像一个经验丰富的老师,在合适的时候给出恰当的指导。在下一篇文章中,我会讲解如何开发一个健康助手Agent。如果你对学习助手Agent的开发有什么想法,欢迎在评论区交流。
