refactor(rag_engine,ai_client): rag_engine.search returns List[RAGChunk] directly

Phase 5: rag_engine.search() return type (FR4 row 7)
Before: def search(...) -> List[Dict[str, Any]] at src/rag_engine.py:367
After:  def search(...) -> List["RAGChunk"]
Delta:  -1 wrong type annotation (List[Dict] -> List[RAGChunk])

RAGChunk dataclass extended with `id: str = ""` field to preserve the
chroma wire-format identifier. The search() function now constructs
RAGChunk instances directly from chromadb query results, normalizing
the wire format (metadata.path -> RAGChunk.path; distance -> 1.0 - score)
at the boundary.

Consumer updates:
- src/ai_client.py:3259-3266: chunk["metadata"]["path"] -> chunk.path;
  chunk["document"] -> chunk.document (direct attribute access)
- src/app_controller.py:3506: docstring updated from Result[List[Dict]]
  to Result[List[RAGChunk]] (no code change; pass-through)

Test updates:
- tests/test_rag_engine.py:61: results[0]["id"] -> results[0].id
  (now uses dataclass attribute access)

Verification:
- audit_weak_types --strict: OK (107 <= 112 baseline)
- py_check_syntax: OK on rag_engine.py, ai_client.py, test_rag_engine.py
- 21 RAG tests pass (test_rag_engine, test_rag_chunk,
  test_rag_engine_ready_status_bug, test_rag_integration,
  test_context_composition_decoupled, test_tiered_aggregation)

src/ai_client.py

@@ -3260,9 +3260,8 @@ def send(
   if chunks:
    context_block = "## Retrieved Context\n\n"
    for i, chunk in enumerate(chunks):
-    chunk_meta = chunk["metadata"] if "metadata" in chunk else {}
-    path = chunk_meta["path"] if "path" in chunk_meta else "unknown"
-    doc = chunk["document"] if "document" in chunk else ""
+    path = chunk.path if chunk.path else "unknown"
+    doc = chunk.document
     context_block += f"### Chunk {i+1} (Source: {path})\n{doc}\n\n"
    user_message = context_block + user_message
 

src/rag_engine.py

@@ -18,6 +18,7 @@ from src.file_cache import ASTParser
 
 @dataclass(frozen=True)
 class RAGChunk:
+ id:       str = ""
  document: str = ""
  path:     str = ""
  score:    float = 0.0
@@ -364,7 +365,7 @@ class RAGEngine:
 
   return asyncio.run(_async_search_mcp())
 
- def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
+ def search(self, query: str, top_k: int = 5) -> List["RAGChunk"]:
   """
   [C: tests/mock_concurrent_mma.py:main, tests/test_rag_engine.py:test_rag_engine_chroma]
   """
@@ -381,12 +382,16 @@ class RAGEngine:
   ret = []
   if results and results["ids"] and results["ids"][0]:
    for i in range(len(results["ids"][0])):
-    ret.append({
-     "id":       results["ids"][0][i],
-     "document": results["documents"][0][i],
-     "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
-     "distance": results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
-    })
+    raw_meta = results["metadatas"][0][i] if results["metadatas"] else {}
+    distance = results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
+    raw_path = raw_meta.get("path", "") if isinstance(raw_meta, dict) else ""
+    ret.append(RAGChunk(
+     id=results["ids"][0][i],
+     document=results["documents"][0][i],
+     path=raw_path,
+     score=1.0 - float(distance),
+     metadata=Metadata.from_dict(raw_meta) if isinstance(raw_meta, dict) else Metadata(),
+    ))
   return ret
 
  def delete_documents(self, ids: List[str]):

tests/test_rag_engine.py

@@ -58,7 +58,7 @@ def test_rag_engine_chroma(mock_get_chroma, mock_embed):
   
   results = engine.search("hello", top_k=1)
   assert len(results) == 1
-  assert results[0]["id"] == "doc1"
+  assert results[0].id == "doc1"
   engine.delete_documents(["doc1"])
   mock_collection.delete.assert_called_once_with(ids=["doc1"])