%% MemoryBear RAG 全链路架构图（Mermaid Flowchart）
%% 约定：浅蓝色 = 数据来源层；浅绿色 = 解析与分块；浅黄色 = 向量化与存储；浅紫色 = 检索；浅橙色 = 生成；浅灰色 = 支撑组件

flowchart TB
    subgraph DATA_SOURCES["数据来源层 (Loader)"]
        CRAWLER["Web Crawler\ncrawler/web_crawler.py\n-> 输出: CrawledDocument"]
        FEISHU["飞书 API\nintegrations/feishu/client.py\n-> 输出: 本地文件 (.docx/.pdf)"]
        YUQUE["语雀 API\nintegrations/yuque/client.py\n-> 输出: 本地文件 (.md/.html/.xlsx)"]
        UPLOAD["用户上传\ncontrollers/document_controller.py:275\n-> 输出: 文件路径"]
    end

    subgraph PARSER["文档解析与分块 (Parser + Chunking)"]
        NAIVE["app/naive.py:chunk()\n统一分块入口\nDispatch by filename extension"]
        PDFP["deepdoc/parser/pdf_parser.py\nOCR + Layout + Table"]
        DOCXP["deepdoc/parser/docx_parser.py"]
        HTMLP["deepdoc/parser/html_parser.py"]
        MDPP["deepdoc/parser/markdown_parser.py"]
        EXCELP["deepdoc/parser/excel_parser.py"]
        TXTPIP["deepdoc/parser/txt_parser.py"]
        VISION["deepdoc/vision/\nocr.py + layout_recognizer.py\n+ table_structure_recognizer.py"]
        NLP["nlp/__init__.py\ntokenize / naive_merge / hierarchical_merge"]
    end

    subgraph CHUNK_TYPES["文档类型适配 (Task Types)"]
        BOOK["app/book.py\n长文档分级分块"]
        PAPER["app/paper.py\n论文结构保持"]
        MANUAL["app/manual.py\n手册按节分块"]
        LAWS["app/laws.py\n法规层级树分块"]
        QA["app/qa.py\n问答对独立分块"]
        ONE["app/one.py\n整文件单块"]
        PIC["app/picture.py\nOCR + VLM描述"]
        AUD["app/audio.py\n语音转文本"]
    end

    subgraph EMBED["向量化 (Embedding)"]
        EMB_BASE["llm/embedding_model.py\nBase.encode(texts: list)\n→ (np.array, token_count)"]
        EMB_PROV["Provider 工厂\nOpenAI / LocalAI / Azure / Tongyi /\nHuggingFace / Xinference / VolcEngine /\nGPUStack / NVIDIA / BaiChuan"]
    end

    subgraph VDB["向量数据库 (VDB)"]
        ES_VECT["vdb/elasticsearch/elasticsearch_vector.py\nDense + Sparse 混合索引\ncosineSimilarity + BM25"]
        ES_CONN["utils/es_conn.py\nES 连接管理"]
        ES_SCHEMA["vdb/field.py\npage_content / metadata / vector / text\n+ doc_id / knowledge_id / sort_id"]
    end

    subgraph GRAPHRAG["知识图谱 (GraphRAG)"]
        G_LIGHT["graphrag/light/\ngraph_extractor.py\n实体+关系抽取\n→ nx.Graph"]
        G_GEN["graphrag/general/\ngraph_extractor.py\n→ community_reports_extractor.py\n+ mind_map_extractor.py"]
        G_LEIDEN["general/leiden.py\n层次聚类"]
        G_RESOLVE["entity_resolution.py\n实体消歧 LLM 匹配"]
        G_SEARCH["graphrag/search.py\nKGSearch.retrieval()\nQuery分析→实体检索→N-hop→社区报告"]
    end

    subgraph RETRIEVAL["检索 (Retrieval)"]
        DEALER["nlp/search.py\nDealer.search()\nHybrid: BM25 0.05 + Vector 0.95"]
        QRYR["nlp/query.py\nQuery理解 / 关键词扩展"]
        KNOWLEDGE["nlp/search.py:36\nknowledge_retrieval()\n→ 多知识库合并"]
    end

    subgraph RERANK["重排序 (Reranking)"]
        RERANK_M["models/rerank.py\nRedBearRerank\ncompress_documents() / rerank()"]
        RERANK_P["Provider: JinaRerank /\nDashScopeRerank /\nXINFERENCE / GPUSTACK"]
    end

    subgraph PROMPT["Prompt 组装"]
        PGEN["prompts/generator.py\ncitation_prompt / keyword_extraction /\nfull_question / content_tagging /\ntoc_relevance / structured_output"]
        PTEMPLATE["prompts/template.py\n加载 .md 模板文件"]
    end

    subgraph LLM["LLM 生成"]
        CHAT["llm/chat_model.py\nBase.chat() / chat_streamly()\n→ (str, tokens)"]
        CHAT_PROV["Provider 工厂\nOpenAI / Azure / LocalAI /\nXinference / Tongyi /\nHuggingFace / GPUStack / VolcEngine"]
    end

    subgraph ORCH["编排层 (Orchestration)"]
        CELERY["tasks.py\nparse_document() /\nbuild_graphrag_for_kb() /\nbuild_graphrag_for_document()"]
        WORKFLOW["workflow/nodes/knowledge/node.py\nKnowledgeRetrievalNode.execute()\n→ 检索→去重→重排→返回 chunks"]
    end

    subgraph POST["后处理"]
        CITE["插入引用标注\nDealer.insert_citations()\npagerank*sim 评分"]
        CACHE["缓存层\nutils/redis_conn.py\nLLM 结果缓存"]
    end

    %% === 数据流 ===
    DATA_SOURCES --> NAIVE
    NAIVE --> |PDF| PDFP
    NAIVE --> |DOCX| DOCXP
    NAIVE --> |HTML| HTMLP
    NAIVE --> |MD| MDPP
    NAIVE --> |XLSX| EXCELP
    NAIVE --> |TXT| TXTPIP

    PDFP --> VISION
    VISION --> NLP
    DOCXP --> NLP
    HTMLP --> NLP
    MDPP --> NLP
    EXCELP --> NLP
    TXTPIP --> NLP

    NAIVE --> |按文档类型| CHUNK_TYPES
    CHUNK_TYPES --> NLP

    NLP --> EMB_BASE
    EMB_BASE --> EMB_PROV
    EMB_PROV --> ES_VECT
    ES_SCHEMA --> ES_VECT
    ES_CONN --> ES_VECT

    NLP -.-> |"并行 (async)"| GRAPHRAG
    G_LIGHT --> G_SEARCH
    G_GEN --> G_LEIDEN
    G_GEN --> G_RESOLVE
    G_LEIDEN --> G_SEARCH
    G_RESOLVE --> G_SEARCH

    CELERY --> NAIVE
    CELERY -.-> |"触发"| GRAPHRAG

    WORKFLOW --> QRYR
    QRYR --> DEALER
    DEALER --> KNOWLEDGE
    KNOWLEDGE --> RERANK_M
    G_SEARCH --> |"GRAPH模式"| KNOWLEDGE
    RERANK_M --> RERANK_P
    RERANK_P --> PGEN
    PGEN --> PTEMPLATE
    PTEMPLATE --> CHAT
    CHAT --> CHAT_PROV
    CHAT --> CITE
    CITE --> CACHE