feat: add runtime RAG retrieval — FoJin-powered answers for all teachers

Add rag_query.py CLI tool (search/semantic/dict/kg subcommands) and
rag_instructions.md prompt fragment so every teacher skill queries
FoJin's real Buddhist texts at runtime instead of relying solely on
LLM training data.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

SKILL.md

@@ -70,6 +70,8 @@ python3 ${CLAUDE_SKILL_DIR}/tools/sutra_collector.py --name "<法师名>" --trad
 
 ### Step 3：分析与生成
 
+**运行时检索规则**：加载 `${CLAUDE_SKILL_DIR}/prompts/rag_instructions.md`，将其中的检索指引嵌入生成的每个法师 SKILL.md 的运行规则中，确保法师回答时调用 FoJin 实时检索而非仅依赖 LLM 自身知识。
+
 **教义分析**：加载 `${CLAUDE_SKILL_DIR}/prompts/sutra_analyzer.md`，填入采集数据，分析教义结构。
 
 **风格分析**：加载 `${CLAUDE_SKILL_DIR}/prompts/voice_analyzer.md`，填入采集数据，分析说法风格。
@@ -118,6 +120,7 @@ teachers/{slug}/
 | 任务 | 工具 |
 |------|------|
 | FoJin 数据查询 | `${CLAUDE_SKILL_DIR}/tools/fojin_bridge.py` |
+| FoJin 实时检索 | `${CLAUDE_SKILL_DIR}/tools/rag_query.py` |
 | 经文采集 | `${CLAUDE_SKILL_DIR}/tools/sutra_collector.py` |
 | 角色生成 | `${CLAUDE_SKILL_DIR}/tools/teacher_builder.py` |
 | 文件写入 | `${CLAUDE_SKILL_DIR}/tools/skill_writer.py` |

prompts/rag_instructions.md

@@ -0,0 +1,38 @@
+# FoJin 实时检索指引
+
+在回答用户问题时，你应当调用 FoJin 数据桥检索真实经文，而非仅依赖自身知识。
+
+## 检索流程
+
+### Step 1：语义检索
+对用户的问题进行语义搜索，获取最相关的经文段落：
+```bash
+python3 ${CLAUDE_SKILL_DIR}/tools/rag_query.py semantic "<用户问题关键词>" --top_k 5
+```
+
+### Step 2：术语查询
+如果问题涉及佛学专业术语，查询 FoJin 词典获取精确定义：
+```bash
+python3 ${CLAUDE_SKILL_DIR}/tools/rag_query.py dict "<术语>"
+```
+
+### Step 3：关键词补充检索（可选）
+如果语义检索结果不够精确，可用关键词搜索补充：
+```bash
+python3 ${CLAUDE_SKILL_DIR}/tools/rag_query.py search "<关键词>" --sources cbeta --top_k 5
+```
+
+### Step 4：知识图谱（可选）
+如果问题涉及人物、传承、宗派关系：
+```bash
+python3 ${CLAUDE_SKILL_DIR}/tools/rag_query.py kg "<人物名>" --type person
+```
+
+## 整合规则
+
+1. 检索到的经文段落应作为回答的依据，优先于自身知识
+2. 引用格式必须使用检索结果中返回的真实 FoJin 链接
+3. 如果检索结果与该法师的传承不直接相关，可以忽略
+4. 如果检索无结果，坦诚说明并基于 teaching.md 中的已有内容回答
+5. 每次回答至少引用 1-2 段检索到的真实经文
+6. 引用经文时标注出处，格式示例：《大般若经》卷一（[FoJin链接](https://fojin.app/texts/123)）

tools/rag_query.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""
+RAG Query — runtime FoJin retrieval for teacher skills.
+
+Usage:
+    python3 tools/rag_query.py search "如何念佛" --sources cbeta --top_k 5
+    python3 tools/rag_query.py semantic "什么是空性" --top_k 5
+    python3 tools/rag_query.py dict "般若"
+    python3 tools/rag_query.py kg "印光" --type person
+"""
+
+import argparse
+import sys
+import os
+
+# Ensure tools/ is on the path so we can import fojin_bridge
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from fojin_bridge import create_bridge
+
+
+def format_search_results(data: dict) -> str:
+    """Format keyword search results for LLM consumption."""
+    items = data.get("items") or data.get("results") or []
+    if not items:
+        return "未找到相关结果。"
+
+    lines = []
+    for i, item in enumerate(items, 1):
+        title = item.get("title", "无标题")
+        source = item.get("source", item.get("collection", ""))
+        score = item.get("score", item.get("relevance", ""))
+        snippet = item.get("highlight", item.get("snippet", item.get("content", "")))
+        text_id = item.get("text_id", item.get("id", ""))
+
+        lines.append(f"── 结果 {i} ──")
+        lines.append(f"标题: {title}")
+        if source:
+            lines.append(f"来源: {source}")
+        if score:
+            lines.append(f"相关度: {score}")
+        if text_id:
+            lines.append(f"FoJin链接: https://fojin.app/texts/{text_id}")
+        if snippet:
+            # Truncate long snippets
+            snippet_str = str(snippet)
+            if len(snippet_str) > 500:
+                snippet_str = snippet_str[:500] + "..."
+            lines.append(f"摘要: {snippet_str}")
+        lines.append("")
+
+    total = data.get("total", len(items))
+    lines.insert(0, f"共找到 {total} 条结果，显示前 {len(items)} 条：\n")
+    return "\n".join(lines)
+
+
+def format_semantic_results(data: dict) -> str:
+    """Format semantic search results for LLM consumption."""
+    items = data.get("items") or data.get("results") or []
+    if not items:
+        return "语义检索未找到相关经文。"
+
+    lines = [f"语义检索返回 {len(items)} 条相关经文：\n"]
+    for i, item in enumerate(items, 1):
+        title = item.get("title", "无标题")
+        source = item.get("source", item.get("collection", ""))
+        score = item.get("score", item.get("similarity", ""))
+        content = item.get("content", item.get("snippet", item.get("text", "")))
+        text_id = item.get("text_id", item.get("id", ""))
+        juan = item.get("juan_num", item.get("juan", ""))
+
+        lines.append(f"── 经文 {i} ──")
+        lines.append(f"标题: {title}")
+        if source:
+            lines.append(f"来源: {source}")
+        if score:
+            lines.append(f"相似度: {score}")
+        if text_id:
+            link = f"https://fojin.app/texts/{text_id}"
+            if juan:
+                link += f"/juan/{juan}"
+            lines.append(f"FoJin链接: {link}")
+        if content:
+            content_str = str(content)
+            if len(content_str) > 500:
+                content_str = content_str[:500] + "..."
+            lines.append(f"经文内容: {content_str}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def format_dict_results(data: dict) -> str:
+    """Format dictionary search results for LLM consumption."""
+    items = data.get("items") or data.get("results") or []
+    if not items:
+        return "词典中未找到该术语。"
+
+    lines = [f"词典检索返回 {len(items)} 条释义：\n"]
+    for i, item in enumerate(items, 1):
+        headword = item.get("headword", item.get("term", item.get("word", "")))
+        definition = item.get("definition", item.get("content", item.get("meaning", "")))
+        source_dict = item.get("source", item.get("dictionary", item.get("dict_name", "")))
+
+        lines.append(f"── 释义 {i} ──")
+        if headword:
+            lines.append(f"词条: {headword}")
+        if source_dict:
+            lines.append(f"出处词典: {source_dict}")
+        if definition:
+            def_str = str(definition)
+            if len(def_str) > 800:
+                def_str = def_str[:800] + "..."
+            lines.append(f"释义: {def_str}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def format_kg_results(data: dict) -> str:
+    """Format knowledge graph entity results for LLM consumption."""
+    items = data.get("items") or data.get("results") or data.get("entities") or []
+    if not items:
+        return "知识图谱中未找到相关实体。"
+
+    lines = [f"知识图谱检索返回 {len(items)} 个实体：\n"]
+    for i, item in enumerate(items, 1):
+        name = item.get("name", item.get("label", ""))
+        etype = item.get("entity_type", item.get("type", ""))
+        desc = item.get("description", item.get("summary", ""))
+        relations = item.get("relations", item.get("edges", []))
+        entity_id = item.get("id", item.get("entity_id", ""))
+
+        lines.append(f"── 实体 {i} ──")
+        if name:
+            lines.append(f"名称: {name}")
+        if etype:
+            lines.append(f"类型: {etype}")
+        if entity_id:
+            lines.append(f"FoJin链接: https://fojin.app/kg/entities/{entity_id}")
+        if desc:
+            desc_str = str(desc)
+            if len(desc_str) > 500:
+                desc_str = desc_str[:500] + "..."
+            lines.append(f"描述: {desc_str}")
+        if relations:
+            lines.append("关系:")
+            for rel in relations[:10]:
+                predicate = rel.get("predicate", rel.get("relation", ""))
+                target = rel.get("target", rel.get("object", rel.get("name", "")))
+                if predicate and target:
+                    lines.append(f"  - {predicate} → {target}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def cmd_search(args):
+    bridge = create_bridge()
+    result = bridge.search_texts(args.query, sources=args.sources, size=args.top_k)
+    print(format_search_results(result))
+
+
+def cmd_semantic(args):
+    bridge = create_bridge()
+    result = bridge.semantic_search(args.query, top_k=args.top_k)
+    print(format_semantic_results(result))
+
+
+def cmd_dict(args):
+    bridge = create_bridge()
+    result = bridge.search_dictionary(args.query)
+    print(format_dict_results(result))
+
+
+def cmd_kg(args):
+    bridge = create_bridge()
+    result = bridge.search_kg_entities(args.query, entity_type=args.type)
+    print(format_kg_results(result))
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="RAG Query — FoJin 佛教文献实时检索工具",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    # search
+    p_search = subparsers.add_parser("search", help="关键词搜索经文")
+    p_search.add_argument("query", help="搜索关键词")
+    p_search.add_argument("--sources", default=None, help="限定来源，如 cbeta")
+    p_search.add_argument("--top_k", type=int, default=5, help="返回条数 (默认 5)")
+    p_search.set_defaults(func=cmd_search)
+
+    # semantic
+    p_sem = subparsers.add_parser("semantic", help="语义向量检索")
+    p_sem.add_argument("query", help="语义查询")
+    p_sem.add_argument("--top_k", type=int, default=5, help="返回条数 (默认 5)")
+    p_sem.set_defaults(func=cmd_semantic)
+
+    # dict
+    p_dict = subparsers.add_parser("dict", help="佛学词典查询")
+    p_dict.add_argument("query", help="查询术语")
+    p_dict.set_defaults(func=cmd_dict)
+
+    # kg
+    p_kg = subparsers.add_parser("kg", help="知识图谱实体搜索")
+    p_kg.add_argument("query", help="实体名称")
+    p_kg.add_argument("--type", default=None, help="实体类型，如 person, text, school")
+    p_kg.set_defaults(func=cmd_kg)
+
+    args = parser.parse_args()
+
+    try:
+        args.func(args)
+    except ConnectionError as e:
+        print(f"[错误] 无法连接 FoJin API: {e}", file=sys.stderr)
+        sys.exit(1)
+    except Exception as e:
+        print(f"[错误] 检索失败: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()