fix: optimize import modules

graphgen/bases/base_filter.py

@@ -1,7 +1,8 @@
 from abc import ABC, abstractmethod
-from typing import Any, Union
+from typing import TYPE_CHECKING, Any, Union
 
-import numpy as np
+if TYPE_CHECKING:
+    import numpy as np
 
 
 class BaseFilter(ABC):
@@ -15,7 +16,7 @@ def filter(self, data: Any) -> bool:
 
 class BaseValueFilter(BaseFilter, ABC):
     @abstractmethod
-    def filter(self, data: Union[int, float, np.number]) -> bool:
+    def filter(self, data: Union[int, float, "np.number"]) -> bool:
         """
         Filter the numeric value and return True if it passes the filter, False otherwise.
         """

graphgen/bases/base_operator.py

@@ -1,14 +1,18 @@
+from __future__ import annotations
+
 import inspect
 import os
 from abc import ABC, abstractmethod
-from typing import Iterable, Tuple, Union
+from typing import TYPE_CHECKING, Iterable, Tuple, Union
 
-import numpy as np
-import pandas as pd
-import ray
+if TYPE_CHECKING:
+    import numpy as np
+    import pandas as pd
 
 
 def convert_to_serializable(obj):
+    import numpy as np
+
     if isinstance(obj, np.ndarray):
         return obj.tolist()
     if isinstance(obj, np.generic):
@@ -40,6 +44,8 @@ def __init__(
         )
 
         try:
+            import ray
+
             ctx = ray.get_runtime_context()
             worker_id = ctx.get_actor_id() or ctx.get_worker_id()
             worker_id_short = worker_id[-6:] if worker_id else "driver"
@@ -62,9 +68,11 @@ def __init__(
         )
 
     def __call__(
-        self, batch: pd.DataFrame
-    ) -> Union[pd.DataFrame, Iterable[pd.DataFrame]]:
+        self, batch: "pd.DataFrame"
+    ) -> Union["pd.DataFrame", Iterable["pd.DataFrame"]]:
         # lazy import to avoid circular import
+        import pandas as pd
+
         from graphgen.utils import CURRENT_LOGGER_VAR
 
         logger_token = CURRENT_LOGGER_VAR.set(self.logger)
@@ -106,14 +114,16 @@ def get_trace_id(self, content: dict) -> str:
 
         return compute_dict_hash(content, prefix=f"{self.op_name}-")
 
-    def split(self, batch: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    def split(self, batch: "pd.DataFrame") -> tuple["pd.DataFrame", "pd.DataFrame"]:
         """
         Split the input batch into to_process & processed based on _meta data in KV_storage
         :param batch
         :return:
             to_process: DataFrame of documents to be chunked
             recovered: Result DataFrame of already chunked documents
         """
+        import pandas as pd
+
         meta_forward = self.get_meta_forward()
         meta_ids = set(meta_forward.keys())
         mask = batch["_trace_id"].isin(meta_ids)

graphgen/bases/base_reader.py

@@ -1,10 +1,14 @@
+from __future__ import annotations
+
 import os
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Union
 
-import pandas as pd
 import requests
-from ray.data import Dataset
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from ray.data import Dataset
 
 
 class BaseReader(ABC):
@@ -51,6 +55,7 @@ def _validate_batch(self, batch: pd.DataFrame) -> pd.DataFrame:
         """
         Validate data format.
         """
+
         if "type" not in batch.columns:
             raise ValueError(f"Missing 'type' column. Found: {list(batch.columns)}")
 

graphgen/common/init_llm.py

@@ -1,11 +1,12 @@
 import os
-from typing import Any, Dict, Optional
-
-import ray
+from typing import TYPE_CHECKING, Any, Dict, Optional
 
 from graphgen.bases import BaseLLMWrapper
 from graphgen.models import Tokenizer
 
+if TYPE_CHECKING:
+    import ray
+
 
 class LLMServiceActor:
     """
@@ -73,7 +74,7 @@ class LLMServiceProxy(BaseLLMWrapper):
     A proxy class to interact with the LLMServiceActor for distributed LLM operations.
     """
 
-    def __init__(self, actor_handle: ray.actor.ActorHandle):
+    def __init__(self, actor_handle: "ray.actor.ActorHandle"):
         super().__init__()
         self.actor_handle = actor_handle
         self._create_local_tokenizer()
@@ -120,6 +121,8 @@ class LLMFactory:
     def create_llm(
         model_type: str, backend: str, config: Dict[str, Any]
     ) -> BaseLLMWrapper:
+        import ray
+
         if not config:
             raise ValueError(
                 f"No configuration provided for LLM {model_type} with backend {backend}."

graphgen/common/init_storage.py

@@ -146,7 +146,7 @@ def ready(self) -> bool:
 
 
 class RemoteKVStorageProxy(BaseKVStorage):
-    def __init__(self, actor_handle: ray.actor.ActorHandle):
+    def __init__(self, actor_handle: "ray.actor.ActorHandle"):
         super().__init__()
         self.actor = actor_handle
 
@@ -202,68 +202,87 @@ def get_all_node_degrees(self) -> Dict[str, int]:
         return ray.get(self.actor.get_all_node_degrees.remote())
 
     def get_node_count(self) -> int:
+
         return ray.get(self.actor.get_node_count.remote())
 
     def get_edge_count(self) -> int:
+
         return ray.get(self.actor.get_edge_count.remote())
 
     def get_connected_components(self, undirected: bool = True) -> List[Set[str]]:
+
         return ray.get(self.actor.get_connected_components.remote(undirected))
 
     def has_node(self, node_id: str) -> bool:
+
         return ray.get(self.actor.has_node.remote(node_id))
 
     def has_edge(self, source_node_id: str, target_node_id: str):
+
         return ray.get(self.actor.has_edge.remote(source_node_id, target_node_id))
 
     def node_degree(self, node_id: str) -> int:
+
         return ray.get(self.actor.node_degree.remote(node_id))
 
     def edge_degree(self, src_id: str, tgt_id: str) -> int:
+
         return ray.get(self.actor.edge_degree.remote(src_id, tgt_id))
 
     def get_node(self, node_id: str) -> Any:
+
         return ray.get(self.actor.get_node.remote(node_id))
 
     def update_node(self, node_id: str, node_data: dict[str, str]):
+
         return ray.get(self.actor.update_node.remote(node_id, node_data))
 
     def get_all_nodes(self) -> Any:
+
         return ray.get(self.actor.get_all_nodes.remote())
 
     def get_edge(self, source_node_id: str, target_node_id: str):
+
         return ray.get(self.actor.get_edge.remote(source_node_id, target_node_id))
 
     def update_edge(
         self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
     ):
+
         return ray.get(
             self.actor.update_edge.remote(source_node_id, target_node_id, edge_data)
         )
 
     def get_all_edges(self) -> Any:
+
         return ray.get(self.actor.get_all_edges.remote())
 
     def get_node_edges(self, source_node_id: str) -> Any:
+
         return ray.get(self.actor.get_node_edges.remote(source_node_id))
 
     def upsert_node(self, node_id: str, node_data: dict[str, str]):
+
         return ray.get(self.actor.upsert_node.remote(node_id, node_data))
 
     def upsert_edge(
         self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
     ):
+
         return ray.get(
             self.actor.upsert_edge.remote(source_node_id, target_node_id, edge_data)
         )
 
     def delete_node(self, node_id: str):
+
         return ray.get(self.actor.delete_node.remote(node_id))
 
     def get_neighbors(self, node_id: str) -> List[str]:
+
         return ray.get(self.actor.get_neighbors.remote(node_id))
 
     def reload(self):
+
         return ray.get(self.actor.reload.remote())
 
 
@@ -274,6 +293,7 @@ class StorageFactory:
 
     @staticmethod
     def create_storage(backend: str, working_dir: str, namespace: str):
+
         if backend in ["json_kv", "rocksdb"]:
             actor_name = f"Actor_KV_{namespace}"
             actor_class = KVStorageActor