修复faiss_pool知识库缓存key错误 (#1507)

server/knowledge_base/kb_cache/base.py

@@ -22,7 +22,11 @@ class ThreadSafeObject:
 
     def __repr__(self) -> str:
         cls = type(self).__name__
-        return f"<{cls}: key: {self._key}, obj: {self._obj}>"
+        return f"<{cls}: key: {self.key}, obj: {self._obj}>"
+
+    @property
+    def key(self):
+        return self._key
 
     @contextmanager
     def acquire(self, owner: str = "", msg: str = ""):
@@ -30,13 +34,13 @@ class ThreadSafeObject:
         try:
             self._lock.acquire()
             if self._pool is not None:
-                self._pool._cache.move_to_end(self._key)
+                self._pool._cache.move_to_end(self.key)
             if log_verbose:
-                logger.info(f"{owner} 开始操作：{self._key}。{msg}")
+                logger.info(f"{owner} 开始操作：{self.key}。{msg}")
             yield self._obj
         finally:
             if log_verbose:
-                logger.info(f"{owner} 结束操作：{self._key}。{msg}")
+                logger.info(f"{owner} 结束操作：{self.key}。{msg}")
             self._lock.release()
 
     def start_loading(self):

server/knowledge_base/kb_cache/faiss_cache.py

@@ -7,7 +7,7 @@ import os
 class ThreadSafeFaiss(ThreadSafeObject):
     def __repr__(self) -> str:
         cls = type(self).__name__
-        return f"<{cls}: key: {self._key}, obj: {self._obj}, docs_count: {self.docs_count()}>"
+        return f"<{cls}: key: {self.key}, obj: {self._obj}, docs_count: {self.docs_count()}>"
 
     def docs_count(self) -> int:
         return len(self._obj.docstore._dict)
@@ -17,7 +17,7 @@ class ThreadSafeFaiss(ThreadSafeObject):
             if not os.path.isdir(path) and create_path:
                 os.makedirs(path)
             ret = self._obj.save_local(path)
-            logger.info(f"已将向量库 {self._key} 保存到磁盘")
+            logger.info(f"已将向量库 {self.key} 保存到磁盘")
         return ret
 
     def clear(self):
@@ -27,7 +27,7 @@ class ThreadSafeFaiss(ThreadSafeObject):
             if ids:
                 ret = self._obj.delete(ids)
                 assert len(self._obj.docstore._dict) == 0
-            logger.info(f"已将向量库 {self._key} 清空")
+            logger.info(f"已将向量库 {self.key} 清空")
         return ret
 
 
@@ -66,10 +66,10 @@ class KBFaissPool(_FaissPool):
             embed_device: str = embedding_device(),
     ) -> ThreadSafeFaiss:
         self.atomic.acquire()
-        cache = self.get(kb_name+vector_name)
+        cache = self.get((kb_name, vector_name)) # 用元组比拼接字符串好一些
         if cache is None:
-            item = ThreadSafeFaiss(kb_name, pool=self)
+            item = ThreadSafeFaiss((kb_name, vector_name), pool=self)
-            self.set(kb_name+vector_name, item)
+            self.set((kb_name, vector_name), item)
             with item.acquire(msg="初始化"):
                 self.atomic.release()
                 logger.info(f"loading vector store in '{kb_name}/{vector_name}' from disk.")
@@ -90,7 +90,7 @@ class KBFaissPool(_FaissPool):
                 item.finish_loading()
         else:
             self.atomic.release()
-        return self.get(kb_name+vector_name)
+        return self.get((kb_name, vector_name))
 
 
 class MemoFaissPool(_FaissPool):

server/knowledge_base/kb_service/faiss_kb_service.py

@@ -18,18 +18,21 @@ from server.utils import torch_gc
 class FaissKBService(KBService):
     vs_path: str
     kb_path: str
+    vector_name: str = "vector_store"
 
     def vs_type(self) -> str:
         return SupportedVSType.FAISS
 
     def get_vs_path(self):
-        return os.path.join(self.get_kb_path(), "vector_store")
+        return os.path.join(self.get_kb_path(), self.vector_name)
 
     def get_kb_path(self):
         return os.path.join(KB_ROOT_PATH, self.kb_name)
 
     def load_vector_store(self) -> ThreadSafeFaiss:
-        return kb_faiss_pool.load_vector_store(kb_name=self.kb_name, embed_model=self.embed_model)
+        return kb_faiss_pool.load_vector_store(kb_name=self.kb_name,
+                                               vector_name=self.vector_name,
+                                               embed_model=self.embed_model)
 
     def save_vector_store(self):
         self.load_vector_store().save(self.vs_path)