docs(rag): add MemoryBear RAG implementation docs v1.0

Submit the formed RAG documentation set produced across Sprint-1/2/3
(WS-12 through WS-26) under docs/rag/. Includes:

- README.md / INDEX.md: landing + total index (responsibility matrix,
  review verdicts, dual-link to source issues)
- overview/: full-pipeline architecture (4 .mmd diagrams),
  11-stage boundary contracts, doc map, source-code inventory
- pipeline/: 5 deep-dives (Loader/Parser/Chunking, Embedding,
  VDB & retrieval, GraphRAG, Rerank/Prompt/LLM)
- graphrag/, end-to-end/: v1.0 formal versions with full source
  retained as reference
- evolution/: 11 architecture-refactor proposals,
  6-direction roadmap, capability map
- review/: S3-T1 / S3-T2 final reviews, S2-T7 final summary
- _indexes/: glossary (81 terms), source->doc reverse index, chart index
- _release/: v1.0-RC1 release manifest, versioning convention,
  ops & freshness plan
- _meta/README.md: placeholder noting WS-12 governance assets gap

Aggregate review score 92.6/100 (8/8 PASS, 31/31 source-code spot
checks hit). The legacy docs/ ignore in .gitignore is narrowed to
docs/* with an explicit allowlist for docs/rag/.

Refs: WS-26
Co-authored-by: multica-agent <github@multica.ai>

docs/rag/overview/02-indexing-pipeline.mmd

@@ -0,0 +1,87 @@
+%% MemoryBear 文档入库时序图（Indexing Pipeline）
+%% 起点：用户上传 / API 调用；终点：向量入库 + GraphRAG 索引完成
+
+sequenceDiagram
+    autonumber
+    participant User as 用户/API
+    participant API as document_controller.py:275<br/>parse_documents()
+    participant Celery as Celery Worker<br/>tasks.py
+    participant DB as PostgreSQL<br/>(Document / Knowledge)
+    participant Chunker as app/naive.py:508<br/>chunk()
+    participant Parser as deepdoc/parser/<br/>(PDF/DOCX/HTML/...)
+    tokenizer as nlp/__init__.py<br/>tokenize / naive_merge
+    participant Embed as llm/embedding_model.py<br/>Base.encode()
+    participant VDB as ESVectorFactory<br/>elasticsearch_vector.py
+    participant Graph as graphrag/general/index.py<br/>run_graphrag_for_kb()
+
+    Note over User,VDB: === 阶段 1：文件上传与触发 ===
+    User->>API: POST /documents (file / URL)
+    API->>DB: INSERT Document (status=pending)
+    API->>Celery: delay parse_document(file_path, document_id)
+
+    Note over Celery,VDB: === 阶段 2：文档解析与分块 ===
+    Celery->>DB: SELECT Document, Knowledge
+    Celery->>Celery: _build_vision_model()
+    Celery->>Chunker: chunk(filename, binary, vision_model)
+
+    alt PDF 格式
+        Chunker->>Parser: RAGPdfParser.__call__()
+        Parser->>Parser: __images__() → OCR → _layouts_rec()
+        Parser->>Parser: _table_transformer_job()
+        Parser->>Parser: _text_merge() + _concat_downward()
+        Parser-->>Chunker: sections: List[(text, tag)]<br/>tables: List[(image, html)]
+    else DOCX 格式
+        Chunker->>Parser: RAGDocxParser.parse()
+        Parser-->>Chunker: sections, tables
+    else HTML/MD/TXT/Excel
+        Chunker->>Parser: 对应 Parser
+        Parser-->>Chunker: sections
+    end
+
+    alt 按文档类型路由
+        Chunker->>Chunker: book.py / paper.py / laws.py / ...
+        Chunker->>tokenizer: hierarchical_merge() / tree_merge()
+    else 默认 naive
+        Chunker->>tokenizer: naive_merge(sections, chunk_token_num)
+    end
+
+    tokenizer->>tokenizer: tokenize(d) → content_ltks / content_sm_ltks
+    tokenizer->>tokenizer: tokenize_chunks() → 附 page_num / position / image
+    tokenizer-->>Celery: res: List[Dict] (chunk dicts)
+
+    Note over Celery,VDB: === 阶段 3：向量化与存储 ===
+    Celery->>DB: progress=0.8
+    Celery->>VDB: delete_by_metadata_field(document_id)
+
+    alt auto_questions 开启
+        Celery->>Celery: ThreadPool 并发生成问题
+        Celery->>Embed: question_proposal(chat_mdl, content)
+    end
+
+    Celery->>Embed: encode(chunk_texts) → np.array
+    Embed-->>Celery: vectors + token_count
+
+    loop 每 batch
+        Celery->>Celery: 组装 DocumentChunk(page_content, vector, metadata)
+        Celery->>VDB: insert_documents(chunks)
+        VDB->>VDB: cosineSimilarity 索引 + BM25
+        VDB-->>Celery: ack
+    end
+
+    Celery->>DB: UPDATE Document (progress=1.0, chunk_num=N)
+
+    Note over Celery,Graph: === 阶段 4：GraphRAG 异步构建 ===
+    Celery->>Celery: build_graphrag_for_document.delay()
+    Celery->>Graph: run_graphrag_for_kb(document_ids)
+    Graph->>Graph: generate_subgraph() per chunk
+    Graph->>Graph: LLM 抽取 entities + relations
+    Graph->>Graph: merge_subgraph() → nx.pagerank
+    opt entity_resolution
+        Graph->>Graph: resolve_entities() (LLM 匹配)
+    end
+    opt community_reports (general only)
+        Graph->>Graph: leiden.run() 层次聚类
+        Graph->>Graph: CommunityReportsExtractor → LLM 报告
+    end
+    Graph->>VDB: store graph entities / relations / reports
+    Graph-->>Celery: done