451 lines
28 KiB
Markdown
Executable File
451 lines
28 KiB
Markdown
Executable File
# Graph Report - /home/xia-lab/wutx/00test/bbav5 (2026-04-15)
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## Corpus Check
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- Large corpus: 539 files · ~689,095 words. Semantic extraction will be expensive (many Claude tokens). Consider running on a subfolder, or use --no-semantic to run AST-only.
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## Summary
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- 2119 nodes · 4216 edges · 94 communities detected
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- Extraction: 50% EXTRACTED · 50% INFERRED · 0% AMBIGUOUS · INFERRED: 2107 edges (avg confidence: 0.5)
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- Token cost: 0 input · 0 output
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## God Nodes (most connected - your core abstractions)
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1. `AgentState` - 142 edges
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2. `AgentMessage` - 142 edges
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3. `BaseAgent` - 111 edges
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4. `Orchestrator` - 69 edges
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5. `LangGraphOrchestrator` - 53 edges
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6. `KnowledgeAgentV2` - 50 edges
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7. `DiagnosisAgentV2` - 48 edges
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8. `LearningAgentV2` - 47 edges
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9. `ResultParserAgentV2` - 47 edges
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10. `ValidatorAgentV2` - 47 edges
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## Surprising Connections (you probably didn't know these)
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- `Stage-level Tool Registry for Multi-Agent System 将bbav20311的分析阶段脚本封装为Agent可调用的To` --uses--> `JobInfo` [INFERRED]
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/home/xia-lab/wutx/00test/bbav5/gfa_core/mas/tools/stage_tools.py → /home/xia-lab/wutx/00test/bbav5/apps/web/pipeline_backend.py
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- `创建新的StageToolRegistry实例` --uses--> `JobInfo` [INFERRED]
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/home/xia-lab/wutx/00test/bbav5/gfa_core/mas/tools/stage_tools.py → /home/xia-lab/wutx/00test/bbav5/apps/web/pipeline_backend.py
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- `main()` --calls--> `ExpressionSkill` [INFERRED]
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/home/xia-lab/wutx/00test/bbav5/skills/karyotype/run.py → /home/xia-lab/wutx/00test/bbav5/skills/expression/run.py
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- `main()` --calls--> `SummarySkill` [INFERRED]
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/home/xia-lab/wutx/00test/bbav5/skills/karyotype/run.py → /home/xia-lab/wutx/00test/bbav5/skills/summary/run.py
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- `main()` --calls--> `InterspeciesSkill` [INFERRED]
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/home/xia-lab/wutx/00test/bbav5/skills/karyotype/run.py → /home/xia-lab/wutx/00test/bbav5/skills/interspecies/run.py
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## Communities
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### Community 0 - "Agent system"
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Cohesion: 0.02
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Nodes (113): AnalysisAgent, AgentMessage, AgentState, BaseAgent, LLMMessage, QueryMode, 从文件加载系统提示词，如果不存在则返回默认提示词, create_diagnosis_agent() (+105 more)
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### Community 1 - "Skills"
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Cohesion: 0.02
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Nodes (41): AnnotationSkill, DomainSkill, GeneIdentifySkill, generate_string_url(), KaryotypeSkill, load_config(), main(), MotifSkill (+33 more)
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### Community 2 - "Agent system"
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Cohesion: 0.04
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Nodes (71): BaseAgent, BaseModel, DiagnosisAgentV2, KnowledgeAgentV2, GraphState, LangGraphOrchestrator, LearningAgentV2, agent_chat() (+63 more)
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### Community 3 - "API clients"
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Cohesion: 0.03
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Nodes (59): bbav20311 API 客户端模块 集成多个植物数据库和转录组数据平台 API 参考 ClawBio 的 gwas-lookup 多 API 并行查询模式, APIResult, MultiAPIQuery, 多 API 并行查询模块 参考 ClawBio 的 gwas-lookup 模式 同时查询多个数据库，合并结果, 获取 API 调用配置 Args: query_type: 查询类型 para, 执行单个 API 查询（带计时） Args: api_name: API 名称, 生成统一报告 Args: results: 各 API 的查询结果 query, 多 API 并行查询管理器 参考 ClawBio gwas-lookup 的并行查询模式 同时查询多个数据库，统一返回结果 (+51 more)
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### Community 4 - "Memory system"
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Cohesion: 0.05
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Nodes (15): BaseMemory, MemoryEntry, SearchResult, BaseMemory, AnalysisCase, CaseMemory, create_knowledge_agent(), KnowledgeEntry (+7 more)
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### Community 5 - "Pipeline/Tools"
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Cohesion: 0.06
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Nodes (28): ToolAdapter, ConversationStatus, 会话状态 - 使用str继承以便JSON序列化, Enum, ExecutionResult, ExecutionStatus, ToolExecutor, ModelSelector (+20 more)
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### Community 6 - "Community 6"
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Cohesion: 0.06
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Nodes (18): ABC, BaseLLMProvider, LLMResponse, BaseLLMProvider, DeepSeekProvider, DeepSeek Provider - 使用阿里云百炼平台, DeepSeek模型Provider - 通过阿里云百炼平台调用 支持的模型: - deepseek-chat: 通用对话模型, GLMProvider (+10 more)
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### Community 7 - "Agent system"
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Cohesion: 0.05
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Nodes (25): AgentMessage, from_dict(), get_message_bus(), init_message_bus(), MessageBus, MessagePriority, MessageType, Agent消息总线系统 支持Agent间异步消息传递和协作 (+17 more)
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### Community 8 - "Community 8"
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Cohesion: 0.06
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Nodes (39): AnnotationConfig, AnnotationResult, 自动运行功能注释 优先级: 本地json > 本地har > API, read_fasta(), run_annotation(), try_api(), try_local_har(), try_local_json() (+31 more)
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### Community 9 - "Agent system"
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Cohesion: 0.07
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Nodes (19): create_intent_agent_v2(), IntentAgentV2, LangGraph Orchestrator - bbav5 新版本 基于 LangGraph 的 9-Agent 工作流编排, Router Agent 节点 - 意图识别, Knowledge Agent 节点 - 知识检索, 从 manifest 文件查询已完成的阶段, Planning Agent 节点 - 任务规划, Execution Agent 节点 - 任务执行 (+11 more)
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### Community 10 - "Community 10"
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Cohesion: 0.08
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Nodes (35): Conversation, ConversationManager, ConversationSummary, Conversation Manager - 类似ChatGPT的多会话管理 支持用户维度、会话列表、历史消息, 列出用户的所有会话（类似ChatGPT左侧边栏） Returns: List[Conversation, 会话管理器 功能： 1. 创建/删除/归档会话 2. 按用户列出所有会话 3. 获取会话消息历史 4. 自动更, add_message(), AddMessageRequest (+27 more)
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### Community 11 - "WeChat integration"
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Cohesion: 0.08
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Nodes (18): 发送文本消息 Args: user_id: 用户微信ID message: 消, 发送"正在输入..."状态 Args: user_id: 用户微信ID, 发送任务完成通知 Args: user_id: 用户微信ID task_inf, 获取新消息（长轮询） Args: timeout: 轮询超时时间（秒）, 启动消息监听循环 Args: callback: 消息处理回调函数 poll_, 调用 MCP 工具 首先尝试使用真实的 mcp-wechat-server，如果失败则回退到模拟模式, 任务通知器 - 与 bbav20311 任务系统集成 使用示例: notifier = WeChatTaskNotifier(, 微信集成类 使用方法: 1. 初始化: wechat = WeChatIntegration() 2. 登录: await w (+10 more)
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### Community 12 - "Memory system"
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Cohesion: 0.11
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Nodes (21): get_storage(), init_storage(), 会话存储模块 - 提供持久化存储功能 功能: 1. 用户会话历史保存到 SQLite 数据库 2. 活跃任务状态保存 3. 服务重启后能恢复会话, 关闭存储（SQLite 不需要显式关闭，但保留接口一致性）, SessionStorage, add_user_task(), call_mcp_tool(), create_task() (+13 more)
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### Community 13 - "API clients"
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Cohesion: 0.07
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Nodes (23): DomainResult, EnhancedDomainAnalyzer, EnhancedReportGenerator, GeneInfo, 增强分析模块 本地优先 + API 增强的混合模式 设计原则: 1. 本地工具优先执行核心分析 2. API 用于补充最新信息和验证 3. API 失败不影, 运行本地 HMMER hmmscan 实际实现应调用 subprocess 运行 hmmscan 这里返回模拟, 解析 HMMER domtblout 文件, 用 API 增强 Domain 结果 Args: domain: 本地分析结果 (+15 more)
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### Community 14 - "CLI runner scripts"
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Cohesion: 0.08
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Nodes (28): generate_manifest(), generate_network_plot(), generate_report(), get_tool_versions(), main(), 生成PPI网络图 Args: network_data: 网络边列表 output_file: 输出PDF文件, 运行PPI分析 Args: gene_ids: 基因ID列表 species: 物种代码 ou, run_ppi_analysis() (+20 more)
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### Community 15 - "CLI runner scripts"
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Cohesion: 0.09
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Nodes (34): combine_hmm_blast_results(), compute_file_hash(), find_pep_file(), find_reference_file(), find_user_query_hmm(), get_pfam_from_rag(), get_tool_versions(), hmmfetch_bin() (+26 more)
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### Community 16 - "CLI runner scripts"
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Cohesion: 0.1
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Nodes (31): calculate_cooccurrence(), calculate_enrichment(), calculate_promoter_complexity(), calculate_timeout(), categorize_element(), compute_file_hash(), find_gene_id_file(), find_genome_file() (+23 more)
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### Community 17 - "CLI runner scripts"
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Cohesion: 0.14
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Nodes (24): check_meme_available(), compute_file_hash(), default_label(), generate_report(), get_tool_versions(), load_manifest(), main(), parse_meme_xml_basic() (+16 more)
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### Community 18 - "CLI runner scripts"
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Cohesion: 0.14
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Nodes (23): calculate_domain_metrics(), compute_file_hash(), find_latest_manifest(), generate_report(), get_tool_versions(), main(), parse_domtblout(), prepare_pfam_database() (+15 more)
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### Community 19 - "CLI runner scripts"
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Cohesion: 0.15
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Nodes (22): build_phylogenetic_tree(), compute_file_hash(), filter_gff_files(), get_file_info(), get_tool_versions(), main(), merge_protein_sequences(), pdf_to_png() (+14 more)
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### Community 20 - "Pipeline/Tools"
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Cohesion: 0.11
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Nodes (8): Stage-level Tool Registry for Multi-Agent System 将bbav20311的分析阶段脚本封装为Agent可调用的To, 执行分析阶段 Args: stage_tool: 阶段工具定义 job_id:, StageTool, StageToolExecutor, StageToolRegistry, 测试StageToolRegistry存在, TestGeneIdentifySkill, TestSkillRegistry
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### Community 21 - "CLI runner scripts"
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Cohesion: 0.16
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Nodes (19): compute_blast_stats(), compute_collinearity_stats(), compute_file_hash(), filter_gff3(), get_tool_versions(), main(), prepare_directories(), 写入符合schema v1的manifest文件 (+11 more)
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### Community 22 - "CLI runner scripts"
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Cohesion: 0.16
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Nodes (19): compute_file_hash(), compute_msa_metrics(), extract_best_model(), get_tool_versions(), main(), prepare_directories(), 写入符合schema v1的manifest文件 注意：artifacts必须是字典格式，不是列表, 验证Identify阶段的输出文件并获取必要信息 Args: workspace: 工作空间目录（workspace/<job (+11 more)
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### Community 23 - "CLI runner scripts"
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Cohesion: 0.17
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Nodes (11): detect_db_type(), detect_species(), get_tool_versions(), main(), MultiSpeciesDetector, MultiSpeciesGenomePreparer, 如果文件是.gz格式，解压并返回解压后的路径, scan_input_dir() (+3 more)
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### Community 24 - "Community 24"
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Cohesion: 0.12
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Nodes (10): BaseSettings, ConfigLoader, LLMConfig, ModelConfig, ProviderConfig, Settings, ToolConfig, ToolInputConfig (+2 more)
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### Community 25 - "CLI runner scripts"
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Cohesion: 0.19
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Nodes (18): calculate_structure_metrics(), compute_file_hash(), filter_gff3(), generate_report(), get_tool_versions(), gff3_to_tsv(), main(), 解析亚家族文件路径 命名规则: {prefix}.geneID.subfamily (如 Ft.SPL.geneID.subfamily) (+10 more)
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### Community 26 - "Skills"
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Cohesion: 0.24
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Nodes (2): ExpressionSkill, 解析分组，返回 {group_name: [sample1, sample2, ...]}
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### Community 27 - "CLI runner scripts"
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Cohesion: 0.18
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Nodes (17): add_artifact_to_manifest(), compute_file_hash(), convert_gff3_to_gtf(), generate_count_matrix(), generate_manifest(), generate_report(), get_tool_versions(), main() (+9 more)
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### Community 28 - "Agent system"
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Cohesion: 0.2
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Nodes (9): AgentCostStats, CostEntry, CostTracker, get_cost_tracker(), print_cost_report(), 成本追踪系统 - 监控Token消耗和API调用成本 参考Claude Code的cost-tracker.ts实现, 记录一次LLM调用成本 Args: agent_name: Agent名称 p, Token成本追踪器 功能: 1. 记录每次LLM调用的token消耗 2. 按Agent统计成本 3. 按时间段统计 (+1 more)
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### Community 29 - "Pipeline/Tools"
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Cohesion: 0.15
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Nodes (12): check_tool(), create_yn00_ctl(), extract_sequences(), parse_cds_fasta(), parse_ortholog_pairs(), 运行pal2nal将蛋白比对转换为密码子比对, 解析CDS FASTA文件，返回基因ID到序列的字典, 解析同源基因对文件，格式: gene1\tgene2 (+4 more)
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### Community 30 - "CLI runner scripts"
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Cohesion: 0.18
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Nodes (8): 带可复现性支持的 pipeline 包装器 使用方法: from gfa_core.pipeline_with_repro import run_stage_, 带可复现性支持的 run_stage 包装器 在原始 run_stage 基础上，自动记录命令和生成可复现性文件, run_stage_with_repro(), create_reproducibility_bundle(), 可复现性支持模块 自动生成 commands.sh, environment.yml, checksums.sha256, 复制到项目级 reproducibility 目录, 创建完整的可复现性包 Args: job_id: 任务ID prefix: 前缀 worksp, ReproducibilityBundle
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### Community 31 - "CLI runner scripts"
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Cohesion: 0.23
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Nodes (13): main(), 运行基因家族鉴定任务，并发送微信通知 使用示例: await run_identify_with_notification(", 运行多阶段 pipeline，每个阶段都发送通知 使用示例: await run_pipeline_with_notifica, run_identify_with_notification(), run_pipeline_with_notifications(), call_mcp_tool(), check_login_status(), notify_task_complete() (+5 more)
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### Community 32 - "CLI runner scripts"
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Cohesion: 0.27
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Nodes (3): AnnotationAPISkill, main(), 提交序列到eggNOG，返回nogname
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### Community 33 - "CLI runner scripts"
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Cohesion: 0.28
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Nodes (13): calculate_physicochemical_properties(), get_tool_versions(), main(), parse_fasta(), plot_properties_r(), plot_subcellular_results(), plot_tmhmm_results(), prepare_directories() (+5 more)
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### Community 34 - "API clients"
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Cohesion: 0.2
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Nodes (7): APIConfig, ConfigManager, get_api_config(), get_config_manager(), API 配置管理模块 管理各数据库 API 的密钥和配置 支持从 YAML 文件和环境变量读取, API 配置管理器 管理所有 API 的配置，支持： 1. YAML 配置文件 2. 环境变量 3. 默认值, 获取指定服务的配置 Args: service: 服务名称（ncbi, pfam, phytozome
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### Community 35 - "Skills"
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Cohesion: 0.2
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Nodes (2): SummarySkill, TestKaryotype
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### Community 36 - "Skills"
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Cohesion: 0.21
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Nodes (2): InterspeciesSkill, TestKaryotype
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### Community 37 - "CLI runner scripts"
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Cohesion: 0.26
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Nodes (13): compute_file_hash(), convert_pdf_to_png(), create_empty_tandem_file(), generate_genome_len(), get_tool_versions(), main(), prepare_directories(), prepare_gene_id_file() (+5 more)
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### Community 38 - "CLI runner scripts"
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Cohesion: 0.28
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Nodes (12): compute_file_hash(), filter_pairs_by_gene_list(), find_matching_id(), get_tool_versions(), load_gene_list(), main(), parse_fasta(), parse_pairs() (+4 more)
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### Community 39 - "Pipeline/Tools"
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Cohesion: 0.21
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Nodes (12): find_input_files(), get_identify_params_help(), get_pfam_from_knowledge(), IdentifyResult, parse_prefix(), Identify Tool - 封装 run_identify.py 为 LLM 可调用的 Tool, 在 input/<job_id>/ 中查找输入文件 返回: {"pep": Path, "gff": Path, "hmm": Path, ", 执行基因家族鉴定 参数: job_id: 任务ID (对应input/<job_id>/目录) prefix: (+4 more)
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### Community 40 - "CLI runner scripts"
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Cohesion: 0.27
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Nodes (11): compute_file_hash(), get_tool_versions(), main(), prepare_directories(), 验证Identify阶段的输出文件并获取必要信息 Args: workspace: 工作空间目录（workspace/<job, 创建Expression阶段的标准目录结构, 写入符合schema v1的manifest文件 注意：artifacts必须是字典格式，不是列表, run_expression_analysis() (+3 more)
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### Community 41 - "CLI runner scripts"
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Cohesion: 0.31
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Nodes (9): generate_family_plots(), generate_manifest(), generate_report(), get_tool_versions(), main(), parse_de_results(), 为基因家族生成特异性绘图（MA图和火山图）, run_deseq2() (+1 more)
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### Community 42 - "CLI runner scripts"
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Cohesion: 0.33
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Nodes (10): compute_file_hash(), generate_manifest(), generate_report(), get_sample_stats(), get_tool_versions(), load_samples_config(), main(), 加载样本配置文件（支持合并格式：samples + groups + compare） (+2 more)
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### Community 43 - "CLI runner scripts"
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Cohesion: 0.33
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Nodes (9): generate_manifest(), generate_report(), get_tool_versions(), index_bam(), main(), parse_hisat2_log(), run_hisat2(), sam_to_bam() (+1 more)
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### Community 44 - "Community 44"
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Cohesion: 0.29
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Nodes (3): NaturalLanguageExecutor, 自然语言执行接口 - 第一阶段：Identify Only 通过对话框输入命令执行分析, 执行自然语言查询 返回: {"type": "result"|"confirmation"|"error", "message
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### Community 45 - "Community 45"
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Cohesion: 0.31
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Nodes (10): _determine_task_type(), _extract_gene_family(), _extract_species(), _extract_stages(), parse_gene_family(), parse_intent_from_query(), parse_species(), parse_stages() (+2 more)
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### Community 46 - "Community 46"
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Cohesion: 0.33
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Nodes (9): choose_best(), detect_roles(), gene_key(), is_gz(), main(), pick_longest_isoform(), read_fasta(), strip_gz() (+1 more)
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### Community 47 - "Community 47"
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Cohesion: 0.2
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Nodes (2): MockModule, TestExpressionConfig
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### Community 48 - "Community 48"
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Cohesion: 0.33
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Nodes (9): get_cog_category(), get_go_terms(), get_kegg_terms(), main(), 保存.emapper.annotations格式文件, 提交序列到eggNOG，返回nogname (ENOG ID), read_fasta(), save_emapper_annotations() (+1 more)
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### Community 49 - "CLI runner scripts"
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Cohesion: 0.38
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Nodes (9): best_window(), conservation_profile(), extract_region_short_fasta(), glob_outputs(), main(), read_fasta(), run_cmd(), which() (+1 more)
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### Community 50 - "Memory system"
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Cohesion: 0.22
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Nodes (0):
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### Community 51 - "Agent system"
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Cohesion: 0.22
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Nodes (1): TestMemoryIntegration
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### Community 52 - "API clients"
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Cohesion: 0.22
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Nodes (2): TestAPIEndpoints, TestServiceHealth
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### Community 53 - "Pipeline/Tools"
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Cohesion: 0.33
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Nodes (8): _ensure_exe(), filter_domtblout(), HmmsearchOutputs, _parse_domtblout_line(), 通用封装：hmmsearch -> domtblout -> filter -> 提取 unique ID list, domtblout 标准列（HMMER 3）： 0 target_name 1 target_accession 2 target_le, 读取 domtblout，输出过滤后的“无#注释行”到 out_filter_path， 同时返回：过滤后命中行数、unique target_name, run_hmmsearch_pipeline()
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### Community 54 - "Pipeline/Tools"
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Cohesion: 0.33
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Nodes (8): EggNOGOutput, get_go_terms(), 读取FASTA文件，返回[(gene_id, sequence), ...], 提交序列到eggNOG，返回nogname, 运行eggNOG GO注释流程 Args: pep_file: 蛋白FASTA文件路径 output_file, read_fasta(), run_eggnog_pipeline(), submit_to_eggnog()
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### Community 55 - "Agent system"
|
||
Cohesion: 0.5
|
||
Nodes (7): get_agent_cost(), get_cost_report(), get_cost_summary(), get_suggestions(), main(), print_report(), print_summary()
|
||
|
||
### Community 56 - "CLI runner scripts"
|
||
Cohesion: 0.25
|
||
Nodes (4): prepare_directories(), 创建RNA-seq Enrich阶段的标准目录结构, 运行eggNOG注释生成gene2go文件, run_eggnog_annotation()
|
||
|
||
### Community 57 - "Pipeline/Tools"
|
||
Cohesion: 0.46
|
||
Nodes (7): add_functional_annotation(), ExpressionConfig, ExpressionResult, find_differential_genes(), generate_summary_report(), run_expression_pipeline(), run_r_expression()
|
||
|
||
### Community 58 - "Agent system"
|
||
Cohesion: 0.29
|
||
Nodes (0):
|
||
|
||
### Community 59 - "Pipeline/Tools"
|
||
Cohesion: 0.52
|
||
Nodes (6): call_mcp_tool(), get_messages(), handle_message(), main(), # TODO: 实际调用 bbav20311 分析任务, send_message()
|
||
|
||
### Community 60 - "CLI runner scripts"
|
||
Cohesion: 0.52
|
||
Nodes (6): find_file_by_glob(), find_latest_manifest(), find_meme_xml(), load_manifest(), main(), run_combo_plot()
|
||
|
||
### Community 61 - "Community 61"
|
||
Cohesion: 0.33
|
||
Nodes (4): extract_fasta_by_ids(), 依据 fasta header 的第一个 token(>ID 后空格前) 提取序列 返回写出的序列条数, merge_hmm_blast_ids(), MergeOutputs
|
||
|
||
### Community 62 - "Community 62"
|
||
Cohesion: 0.47
|
||
Nodes (5): main(), Validate manifest against JSON Schema., Check that all files referenced in manifest.artifacts exist., validate_file_existence(), validate_schema()
|
||
|
||
### Community 63 - "Pipeline/Tools"
|
||
Cohesion: 0.53
|
||
Nodes (5): BlastOutputs, _ensure_exe(), _parse_blast_line(), makeblastdb + blastp + 过滤 + 输出 id_list 输出文件： {prefix}.blastout {, run_blastp_pipeline()
|
||
|
||
### Community 64 - "Community 64"
|
||
Cohesion: 0.4
|
||
Nodes (4): _complete_dependencies(), fast_intent_recognition(), 快速意图识别 - 基于关键词匹配，无需LLM调用, 快速意图识别 - 基于关键词匹配 Returns: dict: {"stage": str, "stages": List[s
|
||
|
||
### Community 65 - "CLI runner scripts"
|
||
Cohesion: 0.6
|
||
Nodes (5): main(), _prompt_bool(), _prompt_float(), _prompt_int(), _prompt_str()
|
||
|
||
### Community 66 - "CLI runner scripts"
|
||
Cohesion: 0.5
|
||
Nodes (4): integrate_with_pipeline(), 调用 annotation skill 进行功能注释 Args: protein_fasta: 蛋白序列FASTA文件路径, 集成到基因家族分析 pipeline 的示例 在基因鉴定后，对鉴定出的基因家族成员进行功能注释, run_annotation_skill()
|
||
|
||
### Community 67 - "CLI runner scripts"
|
||
Cohesion: 0.7
|
||
Nodes (4): _generate_manifest(), _generate_report(), main(), _save_error_manifest()
|
||
|
||
### Community 68 - "CLI runner scripts"
|
||
Cohesion: 0.6
|
||
Nodes (4): main(), parse_blast_results(), 运行BLASTP比对，优先使用conda环境中的blastp, run_blast()
|
||
|
||
### Community 69 - "CLI runner scripts"
|
||
Cohesion: 0.5
|
||
Nodes (4): download_geo_expression(), get_geo_metadata(), main(), 通过NCBI E-utilities获取GEO元数据
|
||
|
||
### Community 70 - "Workflows"
|
||
Cohesion: 0.5
|
||
Nodes (2): IdentifyWorkflow, _to_dict()
|
||
|
||
### Community 71 - "CLI runner scripts"
|
||
Cohesion: 0.7
|
||
Nodes (4): DomainScanOutputs, _ensure_exe(), _parse_domtblout(), run_hmmscan_pfam_pipeline()
|
||
|
||
### Community 72 - "Agent system"
|
||
Cohesion: 0.4
|
||
Nodes (0):
|
||
|
||
### Community 73 - "Community 73"
|
||
Cohesion: 0.6
|
||
Nodes (4): main(), parse_description_from_html(), 简单查询单个元件，使用requests库而不是Selenium, simple_query()
|
||
|
||
### Community 74 - "Agent system"
|
||
Cohesion: 0.67
|
||
Nodes (2): execute_agent_task(), get_orchestrator()
|
||
|
||
### Community 75 - "Community 75"
|
||
Cohesion: 0.67
|
||
Nodes (3): get_WoLF_PSORT_out_fixed(), main(), 修复版 - 使用正确的元素定位从WoLF PSORT获取结果
|
||
|
||
### Community 76 - "Community 76"
|
||
Cohesion: 0.67
|
||
Nodes (3): get_Plant_mPLoc_out(), main(), 使用Selenium自动从Plant-mPLoc网站获取亚细胞定位预测结果
|
||
|
||
### Community 77 - "Community 77"
|
||
Cohesion: 0.5
|
||
Nodes (2): list_sentence_transformers_models(), 查看sentence-transformers的内置模型
|
||
|
||
### Community 78 - "Pipeline/Tools"
|
||
Cohesion: 1.0
|
||
Nodes (2): call_mcp_tool(), main()
|
||
|
||
### Community 79 - "Community 79"
|
||
Cohesion: 1.0
|
||
Nodes (2): main(), update_file()
|
||
|
||
### Community 80 - "Pipeline/Tools"
|
||
Cohesion: 0.67
|
||
Nodes (2): apply_patch_to_pipeline_backend(), 自动应用补丁到 pipeline_backend.py
|
||
|
||
### Community 81 - "Memory system"
|
||
Cohesion: 1.0
|
||
Nodes (2): create_persistent_memory_manager(), test_persistent_memory()
|
||
|
||
### Community 82 - "CLI runner scripts"
|
||
Cohesion: 1.0
|
||
Nodes (2): main(), _now_tag()
|
||
|
||
### Community 83 - "Community 83"
|
||
Cohesion: 1.0
|
||
Nodes (2): fix_syntax_errors(), main()
|
||
|
||
### Community 84 - "Community 84"
|
||
Cohesion: 0.67
|
||
Nodes (2): 重构 workspace/{job_id}/final/ 目录结构 新结构: final/ ├── figures/, reorganize_workspace()
|
||
|
||
### Community 85 - "Community 85"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 86 - "CLI runner scripts"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 87 - "CLI runner scripts"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 88 - "Community 88"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 89 - "CLI runner scripts"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 90 - "Community 90"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
### Community 91 - "Community 91"
|
||
Cohesion: 1.0
|
||
Nodes (1): 根据任务类型选择最优模型 Args: task_type: 任务类型 pref
|
||
|
||
### Community 92 - "Community 92"
|
||
Cohesion: 1.0
|
||
Nodes (1): 根据查询内容自动选择模型 Args: query: 用户查询
|
||
|
||
### Community 93 - "Community 93"
|
||
Cohesion: 1.0
|
||
Nodes (0):
|
||
|
||
## Knowledge Gaps
|
||
- **245 isolated node(s):** `会话存储模块 - 提供持久化存储功能 功能: 1. 用户会话历史保存到 SQLite 数据库 2. 活跃任务状态保存 3. 服务重启后能恢复会话`, `关闭存储（SQLite 不需要显式关闭，但保留接口一致性）`, `微信集成类 使用方法: 1. 初始化: wechat = WeChatIntegration() 2. 登录: await w`, `登录微信 流程: 1. 调用 login_qrcode 获取二维码 2. 显示二维码供用户扫描`, `发送文本消息 Args: user_id: 用户微信ID message: 消` (+240 more)
|
||
These have ≤1 connection - possible missing edges or undocumented components.
|
||
- **Thin community `Community 85`** (2 nodes): `validate_js.py`, `validate_js_in_html()`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `CLI runner scripts`** (2 nodes): `run_summary.py`, `main()`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `CLI runner scripts`** (2 nodes): `run_hmm_pipeline.py`, `main()`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `Community 88`** (2 nodes): `plot_tree.py`, `plot_tree()`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `CLI runner scripts`** (2 nodes): `run_blast_pipeline.py`, `main()`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `Community 90`** (1 nodes): `conversation-sidebar.js`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `Community 91`** (1 nodes): `根据任务类型选择最优模型 Args: task_type: 任务类型 pref`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `Community 92`** (1 nodes): `根据查询内容自动选择模型 Args: query: 用户查询`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
- **Thin community `Community 93`** (1 nodes): `downBge-m3.py`
|
||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||
|
||
## Suggested Questions
|
||
_Questions this graph is uniquely positioned to answer:_
|
||
|
||
- **Why does `AgentState` connect `Agent system` to `Agent system`, `Agent system`, `Agent system`, `Agent system`?**
|
||
_High betweenness centrality (0.043) - this node is a cross-community bridge._
|
||
- **Why does `AgentMessage` connect `Agent system` to `Agent system`, `Agent system`, `Agent system`, `Agent system`?**
|
||
_High betweenness centrality (0.043) - this node is a cross-community bridge._
|
||
- **Why does `JobInfo` connect `Agent system` to `Community 8`, `CLI runner scripts`, `Agent system`?**
|
||
_High betweenness centrality (0.036) - this node is a cross-community bridge._
|
||
- **Are the 141 inferred relationships involving `AgentState` (e.g. with `TestRouterAgent` and `TestAgentFlow`) actually correct?**
|
||
_`AgentState` has 141 INFERRED edges - model-reasoned connections that need verification._
|
||
- **Are the 141 inferred relationships involving `AgentMessage` (e.g. with `TestRouterAgent` and `TestAgentFlow`) actually correct?**
|
||
_`AgentMessage` has 141 INFERRED edges - model-reasoned connections that need verification._
|
||
- **Are the 104 inferred relationships involving `BaseAgent` (e.g. with `DiagnosisAgent` and `DiagnosisAgent V2 - 错误诊断与修复 自动诊断分析失败原因并提供修复建议`) actually correct?**
|
||
_`BaseAgent` has 104 INFERRED edges - model-reasoned connections that need verification._
|
||
- **Are the 48 inferred relationships involving `Orchestrator` (e.g. with `ExecuteRequest` and `ChatRequest`) actually correct?**
|
||
_`Orchestrator` has 48 INFERRED edges - model-reasoned connections that need verification._ |