feat: integrate RAG rag_jrxml submodule and fix Anthropic API key

Add rag submodule for semantic JRXML chunk retrieval, refactor
retrieve node to use RAGSearcher, and fix missing api_key in
Anthropic SDK client initialization.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/rag_adapter.py

@@ -0,0 +1,155 @@
+"""RAG 适配层 — 查询已由 rag_jrxml 子项目构建好的 ChromaDB 向量知识库。
+
+rag_jrxml 独立运行产出向量库后，主项目通过此模块进行语义搜索。
+
+用法:
+    from backend.rag_adapter import search_chunks
+    context = search_chunks("如何添加饼图", k=5)
+"""
+
+import os
+import logging
+from pathlib import Path
+from typing import Optional
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logger = logging.getLogger(__name__)
+
+_PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+
+def _resolve(path: str) -> Path:
+    p = Path(path)
+    if not p.is_absolute():
+        p = _PROJECT_ROOT / p
+    return p
+
+
+class RAGSearcher:
+    """连接预构建的 ChromaDB，提供语义搜索。"""
+
+    def __init__(
+        self,
+        chroma_path: Optional[str] = None,
+        collection_name: Optional[str] = None,
+        model_name: Optional[str] = None,
+        use_gpu: Optional[bool] = None,
+        use_fp16: Optional[bool] = None,
+    ):
+        self.chroma_path = _resolve(chroma_path or os.getenv("RAG_CHROMA_PATH", "./db/chroma"))
+        self.collection_name = collection_name or os.getenv("RAG_COLLECTION_NAME", "jrxml_chunks")
+        model_path = model_name or os.getenv("RAG_EMBED_MODEL", "./rag/models/paraphrase-multilingual-MiniLM-L12-v2")
+        # 如果本地路径存在则使用本地，否则当 Hub 模型名使用
+        resolved = _resolve(model_path)
+        self.model_name = str(resolved) if resolved.exists() else model_path
+        self.use_gpu = use_gpu if use_gpu is not None else os.getenv("RAG_USE_GPU", "true").lower() in ("true", "1")
+        self.use_fp16 = use_fp16 if use_fp16 is not None else os.getenv("RAG_USE_FP16", "true").lower() in ("true", "1")
+
+        self._model = None
+        self._client = None
+        self._collection = None
+
+    # ---- 模型懒加载 ----
+    @property
+    def model(self):
+        if self._model is None:
+            import torch
+            from sentence_transformers import SentenceTransformer
+
+            device = "cuda" if (self.use_gpu and torch.cuda.is_available()) else "cpu"
+            logger.info("加载嵌入模型: %s (device=%s)", self.model_name, device)
+            model = SentenceTransformer(self.model_name, device=device)
+            if device == "cuda" and self.use_fp16:
+                model = model.half()
+            self._model = model
+        return self._model
+
+    # ---- ChromaDB 懒连接 ----
+    @property
+    def client(self):
+        if self._client is None:
+            import chromadb
+            self._client = chromadb.PersistentClient(path=str(self.chroma_path))
+        return self._client
+
+    @property
+    def collection(self):
+        if self._collection is None:
+            self._collection = self.client.get_collection(self.collection_name)
+        return self._collection
+
+    def is_ready(self) -> bool:
+        try:
+            self.client.get_collection(self.collection_name)
+            return True
+        except Exception:
+            return False
+
+    # ---- 语义搜索 ----
+    def search(self, query: str, k: int = 5, threshold: Optional[float] = None) -> list[dict]:
+        """搜索相关 JRXML chunks，返回 [{id, content, metadata, distance}, ...]."""
+        if not self.is_ready():
+            logger.warning("ChromaDB 集合 '%s' 不存在，请先在 rag/ 子项目中运行管线", self.collection_name)
+            return []
+
+        query_embedding = self.model.encode(
+            query, normalize_embeddings=True, show_progress_bar=False
+        )
+
+        results = self.collection.query(
+            query_embeddings=[query_embedding.tolist()],
+            n_results=k,
+            include=["documents", "metadatas", "distances"],
+        )
+
+        output = []
+        if not results["ids"] or not results["ids"][0]:
+            return output
+
+        for i, doc_id in enumerate(results["ids"][0]):
+            dist = results["distances"][0][i]
+            if threshold is not None and dist > threshold:
+                continue
+            output.append({
+                "id": doc_id,
+                "content": results["documents"][0][i],
+                "metadata": results["metadatas"][0][i],
+                "distance": dist,
+            })
+        return output
+
+    def search_as_context(self, query: str, k: int = 5) -> str:
+        """搜索并返回拼接好的上下文字符串，可直接注入 LLM prompt。"""
+        results = self.search(query, k=k)
+        if not results:
+            return ""
+
+        parts = []
+        for r in results:
+            meta = r["metadata"]
+            header = f"[类型:{meta.get('chunk_type', 'N/A')}]"
+            if meta.get("report_name"):
+                header += f" [报表:{meta['report_name']}]"
+            if meta.get("band_name"):
+                header += f" [区域:{meta['band_name']}]"
+            parts.append(f"{header}\n{r['content']}")
+        return "\n\n---\n\n".join(parts)
+
+
+# 全局单例，避免重复加载模型
+_searcher: Optional[RAGSearcher] = None
+
+
+def _get_searcher() -> RAGSearcher:
+    global _searcher
+    if _searcher is None:
+        _searcher = RAGSearcher()
+    return _searcher
+
+
+def search_chunks(query: str, k: int = 5) -> str:
+    """搜索 JRXML 知识库并返回拼接后的上下文文本（便捷函数）。"""
+    return _get_searcher().search_as_context(query, k=k)