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/03-query-pipeline.mmd

@@ -0,0 +1,102 @@
+%% MemoryBear 在线检索时序图（Query Pipeline）
+%% 起点：用户 Query；终点：LLM 生成的回答
+
+sequenceDiagram
+    autonumber
+    participant User as 用户/API
+    participant WF as Workflow Engine<br/>(workflow/nodes/knowledge/node.py)
+    participant Config as config.py<br/>KnowledgeRetrievalNodeConfig
+    participant Retriever as nlp/search.py<br/>knowledge_retrieval()
+    participant Dealer as nlp/search.py:349<br/>Dealer.search()
+    participant Qryr as nlp/query.py<br/>Query理解
+    participant ESVec as ESVector<br/>elasticsearch_vector.py
+    participant Graph as graphrag/search.py<br/>KGSearch.retrieval()
+    participant Rerank as models/rerank.py<br/>RedBearRerank
+    participant Prompt as prompts/generator.py
+    participant LLM as llm/chat_model.py<br/>Base.chat()
+    participant Cache as utils/redis_conn.py
+
+    Note over User,Cache: === 阶段 1：Query 准备 ===
+    User->>WF: 用户输入 Query
+    WF->>WF: _render_template(query, variable_pool)
+    WF->>Config: 读取 knowledge_bases[]<br/>reranker_id / retrieve_type
+
+    Note over Retriever,ESVec: === 阶段 2：多知识库检索 ===
+    loop 每个 Knowledge Base
+        WF->>Retriever: knowledge_retrieval(query, config)
+        Retriever->>DB: 验证 KB 状态 (chunk_num>0, status=1)
+
+        alt RetrieveType == PARTICIPLE
+            Retriever->>ESVec: search_by_full_text(query, top_k)
+            ESVec->>ESVec: match on page_content (ik_max_word)
+            ESVec-->>Retriever: List[DocumentChunk]
+
+        else RetrieveType == SEMANTIC
+            Retriever->>ESVec: search_by_vector(query, top_k)
+            ESVec->>ESVec: script_score cosineSimilarity
+            ESVec-->>Retriever: List[DocumentChunk]
+
+        else RetrieveType == HYBRID
+            par
+                Retriever->>ESVec: search_by_vector()
+                ESVec-->>Retriever: rs1
+            and
+                Retriever->>ESVec: search_by_full_text()
+                ESVec-->>Retriever: rs2
+            end
+            Retriever->>Retriever: _deduplicate_docs(rs1, rs2)
+            Retriever->>Rerank: rerank(query, docs, top_k)
+            Rerank->>Rerank: similarity() 交叉编码评分
+            Rerank-->>Retriever: sorted docs
+
+        else RetrieveType == GRAPH
+            par
+                Retriever->>ESVec: search_by_vector()
+                ESVec-->>Retriever: rs1
+            and
+                Retriever->>ESVec: search_by_full_text()
+                ESVec-->>Retriever: rs2
+            end
+            Retriever->>Retriever: dedup + rerank
+
+            Retriever->>Graph: kg_retriever.retrieval(question)
+            Graph->>Graph: query_rewrite() → keywords + entities
+            Graph->>ESVec: get_relevant_ents_by_keywords()
+            Graph->>ESVec: get_relevant_relations_by_txt()
+            Graph->>Graph: n_hop_with_weight 路径扩展
+            Graph->>Graph: Score = pagerank * sim
+            Graph->>Graph: _community_retrieval_()
+            Graph-->>Retriever: Entity+Relation+CommunityReport chunk
+            Retriever->>Retriever: insert(0, graph_result)
+        end
+
+        Retriever-->>WF: List[DocumentChunk]
+    end
+
+    WF->>WF: _deduplicate_docs(all_results)
+
+    alt reranker_id 配置
+        WF->>Rerank: rerank(query, all_results, reranker_top_k)
+        Rerank-->>WF: reranked chunks
+    end
+
+    Note over Prompt,Cache: === 阶段 3：Prompt 组装 + LLM 生成 ===
+    WF->>WF: 返回 {"chunks": [...], "citations": [...]}
+    WF->>Prompt: citation_prompt(chunks)
+    Prompt->>Prompt: 组装 System Prompt + 检索上下文
+
+    Prompt->>Cache: get_llm_cache(model, prompt)
+    alt cache miss
+        Prompt->>LLM: chat(system, history, gen_conf)
+        LLM-->>Prompt: answer, tokens
+        Prompt->>Cache: set_llm_cache(model, prompt, answer)
+    else cache hit
+        Cache-->>Prompt: cached answer
+    end
+
+    Note over User,Cache: === 阶段 4：后处理 ===
+    Prompt->>Dealer: insert_citations(answer, chunks, chunk_v)
+    Dealer->>Dealer: pagerank*sim 定位引用位置
+    Dealer-->>Prompt: answer_with_citations, cited_ids
+
+    Prompt-->>User: 最终回答（含引用标注）