fix: defer faiss C library import to prevent process hang on startup (#8696)

* fix: defer faiss C library import to prevent process hang on startup

Move top-level `import faiss` from embedding_storage.py to __init__() method,
and make the EmbeddingStorage import in vec_db.py lazy. This prevents the faiss
C library from being loaded at module import time, which can cause process hang
(SIGILL or deadlock) with faiss-cpu 1.14.2 on certain CPU architectures.

Ref #8695

* chore: apply review suggestions

- Preserve original exception context with `from e` when re-raising ImportError
- Move EmbeddingStorage import back to top level in vec_db.py (safe now that
  faiss is no longer imported at module level in embedding_storage.py)

* ci: trigger re-run

---------

Co-authored-by: Blueteemo <Blueteemo@users.noreply.github.com>

astrbot/core/db/vec_db/faiss_impl/__init__.py

@@ -1,3 +1,9 @@
-from .vec_db import FaissVecDB
+def __getattr__(name: str):
+    if name == "FaissVecDB":
+        from .vec_db import FaissVecDB
+
+        return FaissVecDB
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
 
 __all__ = ["FaissVecDB"]

astrbot/core/db/vec_db/faiss_impl/embedding_storage.py

@@ -1,9 +1,3 @@
-try:
-    import faiss
-except ModuleNotFoundError:
-    raise ImportError(
-        "faiss 未安装。请使用 'pip install faiss-cpu' 或 'pip install faiss-gpu' 安装。",
-    )
 import os
 
 import numpy as np
@@ -11,6 +5,13 @@ import numpy as np
 
 class EmbeddingStorage:
     def __init__(self, dimension: int, path: str | None = None) -> None:
+        try:
+            import faiss
+        except ModuleNotFoundError as e:
+            raise ImportError(
+                "faiss 未安装。请使用 'pip install faiss-cpu' 或 'pip install faiss-gpu' 安装。",
+            ) from e
+        self._faiss = faiss
         self.dimension = dimension
         self.path = path
         self.index = None
@@ -67,7 +68,7 @@ class EmbeddingStorage:
 
         """
         assert self.index is not None, "FAISS index is not initialized."
-        faiss.normalize_L2(vector)
+        self._faiss.normalize_L2(vector)
         distances, indices = self.index.search(vector, k)
         return distances, indices
 
@@ -92,4 +93,4 @@ class EmbeddingStorage:
         """
         if self.index is None:
             return
-        faiss.write_index(self.index, self.path)
+        self._faiss.write_index(self.index, self.path)