Enforce embedding_func validation in BaseVectorStorage

- Add validation in BaseVectorStorage
- Call super().__post_init__ in subclasses
- Simplify collection suffix logic

lightrag/base.py

@@ -220,33 +220,32 @@ class BaseVectorStorage(StorageNameSpace, ABC):
     cosine_better_than_threshold: float = field(default=0.2)
     meta_fields: set[str] = field(default_factory=set)
 
+    def __post_init__(self):
+        """Validate required embedding_func for vector storage."""
+        if self.embedding_func is None:
+            raise ValueError(
+                "embedding_func is required for vector storage. "
+                "Please provide a valid EmbeddingFunc instance."
+            )
+
     def _generate_collection_suffix(self) -> str | None:
         """Generates collection/table suffix from embedding_func.
 
         Return suffix if model_name exists in embedding_func, otherwise return None.
+        Note: embedding_func is guaranteed to exist (validated in __post_init__).
 
         Returns:
             str | None: Suffix string e.g. "text_embedding_3_large_3072d", or None if model_name not available
         """
         import re
 
-        # Try to get embedding_func from self or global_config
-        embedding_func = self.embedding_func
-        if embedding_func is None and "embedding_func" in self.global_config:
-            embedding_func = self.global_config["embedding_func"]
-
-        if embedding_func is None:
-            return None
-
-        # Check if model_name exists
-        model_name = getattr(embedding_func, "model_name", None)
+        # Check if model_name exists (model_name is optional in EmbeddingFunc)
+        model_name = getattr(self.embedding_func, "model_name", None)
         if not model_name:
             return None
 
-        # Get embedding_dim
-        embedding_dim = getattr(embedding_func, "embedding_dim", None)
-        if embedding_dim is None:
-            return None
+        # embedding_dim is required in EmbeddingFunc
+        embedding_dim = self.embedding_func.embedding_dim
 
         # Generate suffix: clean model name and append dimension
         safe_model_name = re.sub(r"[^a-zA-Z0-9_]", "_", model_name.lower())

lightrag/kg/faiss_impl.py

@@ -28,6 +28,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
     """
 
     def __post_init__(self):
+        super().__post_init__()
         # Grab config values if available
         kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
         cosine_threshold = kwargs.get("cosine_better_than_threshold")

lightrag/kg/milvus_impl.py

@@ -934,6 +934,7 @@ class MilvusVectorDBStorage(BaseVectorStorage):
                 raise
 
     def __post_init__(self):
+        super().__post_init__()
         # Check for MILVUS_WORKSPACE environment variable first (higher priority)
         # This allows administrators to force a specific workspace for all Milvus storage instances
         milvus_workspace = os.environ.get("MILVUS_WORKSPACE")

lightrag/kg/nano_vector_db_impl.py

@@ -25,6 +25,7 @@ from .shared_storage import (
 @dataclass
 class NanoVectorDBStorage(BaseVectorStorage):
     def __post_init__(self):
+        super().__post_init__()
         # Initialize basic attributes
         self._client = None
         self._storage_lock = None

lightrag/kg/postgres_impl.py

@@ -2298,6 +2298,7 @@ class PGVectorStorage(BaseVectorStorage):
     db: PostgreSQLDB | None = field(default=None)
 
     def __post_init__(self):
+        super().__post_init__()
         self._max_batch_size = self.global_config["embedding_batch_num"]
         config = self.global_config.get("vector_db_storage_cls_kwargs", {})
         cosine_threshold = config.get("cosine_better_than_threshold")