importasynciofromsrc.agents.solveimportMainSolverasyncdefmain():solver=MainSolver(kb_name="ai_textbook")result=awaitsolver.solve(question="Calculate the linear convolution of x=[1,2,3] and h=[4,5]",mode="auto"    )print(result['formatted_solution'])asyncio.run(main())

data/user/solve/solve_YYYYMMDD_HHMMSS/├── investigate_memory.json    # Analysis Loop memory├── solve_chain.json           # Solve Loop steps & tool records├── citation_memory.json       # Citation management├── final_answer.md            # Final solution (Markdown)├── performance_report.json    # Performance monitoring└── artifacts/                 # Code execution outputs

importasynciofromsrc.agents.questionimportAgentCoordinatorasyncdefmain():coordinator=AgentCoordinator(kb_name="ai_textbook",output_dir="data/user/question"    )# Generate multiple questions from text requirementresult=awaitcoordinator.generate_questions_custom(requirement_text="Generate 3 medium-difficulty questions about deep learning basics",difficulty="medium",question_type="choice",count=3    )print(f"✅ Generated{result['completed']}/{result['requested']} questions")forqinresult['results']:print(f"- Relevance:{q['validation']['relevance']}")asyncio.run(main())

fromsrc.agents.question.tools.exam_mimicimportmimic_exam_questionsresult=awaitmimic_exam_questions(pdf_path="exams/midterm.pdf",kb_name="calculus",output_dir="data/user/question/mimic_papers",max_questions=5)print(f"✅ Generated{result['successful_generations']} questions")print(f"Output:{result['output_file']}")

data/user/question/custom_YYYYMMDD_HHMMSS/├── background_knowledge.json      # RAG retrieval results├── question_plan.json              # Question planning├── question_1_result.json          # Individual question results├── question_2_result.json└── ...

data/user/question/mimic_papers/└── mimic_YYYYMMDD_HHMMSS_{pdf_name}/    ├── {pdf_name}.pdf                              # Original PDF    ├── auto/{pdf_name}.md                          # MinerU parsed markdown    ├── {pdf_name}_YYYYMMDD_HHMMSS_questions.json  # Extracted questions    └── {pdf_name}_YYYYMMDD_HHMMSS_generated_questions.json  # Generated questions

data/user/guide/└── session_{session_id}.json    # Complete session state, knowledge points, chat history

data/user/co-writer/├── audio/                    # TTS audio files│   └── {operation_id}.mp3├── tool_calls/               # Tool call history│   └── {operation_id}_{tool_type}.json└── history.json              # Edit history

# Quick mode (fast research)python src/agents/research/main.py --topic"Deep Learning Basics" --preset quick# Medium mode (balanced)python src/agents/research/main.py --topic"Transformer Architecture" --preset medium# Deep mode (thorough research)python src/agents/research/main.py --topic"Graph Neural Networks" --preset deep# Auto mode (agent decides depth)python src/agents/research/main.py --topic"Reinforcement Learning" --preset auto

importasynciofromsrc.agents.researchimportResearchPipelinefromsrc.core.coreimportget_llm_config,load_config_with_mainasyncdefmain():# Load configuration (main.yaml merged with any module-specific overrides)config=load_config_with_main("research_config.yaml")llm_config=get_llm_config()# Create pipeline (agent parameters loaded from agents.yaml automatically)pipeline=ResearchPipeline(config=config,api_key=llm_config["api_key"],base_url=llm_config["base_url"],kb_name="ai_textbook"# Optional: override knowledge base    )# Run researchresult=awaitpipeline.run(topic="Attention Mechanisms in Deep Learning")print(f"Report saved to:{result['final_report_path']}")asyncio.run(main())

data/user/research/├── reports/                          # Final research reports│   ├── research_YYYYMMDD_HHMMSS.md   # Markdown report with clickable citations [[N]](#ref-N)│   └── research_*_metadata.json      # Research metadata and statistics└── cache/                            # Research process cache    └── research_YYYYMMDD_HHMMSS/        ├── queue.json                # DynamicTopicQueue state (TopicBlocks + ToolTraces)        ├── citations.json            # Citation registry with ID counters and ref_number mapping        │                             #   - citations: {citation_id: citation_info}        │                             #   - counters: {plan_counter, block_counters}        ├── step1_planning.json       # Planning phase results (subtopics + PLAN-XX citations)        ├── planning_progress.json    # Planning progress events        ├── researching_progress.json # Researching progress events        ├── reporting_progress.json   # Reporting progress events        ├── outline.json              # Three-level report outline structure        └── token_cost_summary.json   # Token usage statistics

{"research_id":"research_20241209_120000","citations": {"PLAN-01": {"citation_id":"PLAN-01","tool_type":"rag_hybrid","query":"...","summary":"..."},"CIT-1-01": {"citation_id":"CIT-1-01","tool_type":"paper_search","papers": [...],...}  },"counters": {"plan_counter":2,"block_counters": {"1":3,"2":2}  }}

# config/agents.yaml - Agent LLM parametersresearch:temperature:0.5max_tokens:12000# config/main.yaml - Research settingsresearch:# Execution Moderesearching:execution_mode:"parallel"# "series" or "parallel"max_parallel_topics:5# Max concurrent topicsmax_iterations:5# Max iterations per topic# Tool Switchesenable_rag_hybrid:true# Hybrid RAG retrievalenable_rag_naive:true# Basic RAG retrievalenable_paper_search:true# Academic paper searchenable_web_search:true# Web search (also controlled by tools.web_search.enabled)enable_run_code:true# Code execution# Queue Limitsqueue:max_length:5# Maximum topics in queue# Reportingreporting:enable_inline_citations:true# Enable clickable [N] citations in report# Presets: quick, medium, deep, auto# Global tool switches in tools sectiontools:web_search:enabled:true# Global web search switch (higher priority)

importasynciofromsrc.agents.ideagenimportIdeaGenerationWorkflow,MaterialOrganizerAgentfromsrc.core.coreimportget_llm_configasyncdefmain():llm_config=get_llm_config()# Step 1: Extract knowledge points from materialsorganizer=MaterialOrganizerAgent(api_key=llm_config["api_key"],base_url=llm_config["base_url"]    )knowledge_points=awaitorganizer.extract_knowledge_points("Your learning materials or notebook content here"    )# Step 2: Generate research ideasworkflow=IdeaGenerationWorkflow(api_key=llm_config["api_key"],base_url=llm_config["base_url"]    )result=awaitworkflow.process(knowledge_points)print(result)# Markdown formatted research ideasasyncio.run(main())