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>

agent/nodes.py

@@ -10,7 +10,6 @@ from typing import Dict
 from dotenv import load_dotenv
 
 from agent.state import AgentState
-from backend.embeddings import get_embeddings
 from backend.llm import get_llm
 from backend.validation import validate_jrxml
 
@@ -422,26 +421,13 @@ def _now_iso() -> str:
 
 
 def retrieve(state: AgentState) -> Dict:
-    """在 Chroma 中搜索相关的 JRXML 模板和组件。"""
+    """在 Chroma 中搜索相关的 JRXML 模板和组件（使用 rag_jrxml 语义分块管线）。"""
     try:
-        embeddings = get_embeddings()
-        from langchain_chroma import Chroma
+        from backend.rag_adapter import search_chunks
 
-        persist_dir = os.getenv("CHROMA_PERSIST_DIR", "./db/chroma")
-        if not os.path.exists(persist_dir) or not os.listdir(persist_dir):
-            state["retrieved_context"] = ""
-            return state
-
-        vectorstore = Chroma(
-            embedding_function=embeddings,
-            persist_directory=persist_dir,
-        )
         user_input = state.get("user_input", "")
-        docs = vectorstore.similarity_search(user_input, k=5)
-        context_parts = []
-        for d in docs:
-            context_parts.append(d.page_content)
-        state["retrieved_context"] = "\n\n---\n\n".join(context_parts)
+        context = search_chunks(user_input, k=5)
+        state["retrieved_context"] = context
     except Exception:
         state["retrieved_context"] = ""
     return state