Merge branch 'main' into workspace-isolation

lightrag/utils.py

@@ -353,6 +353,17 @@ class TaskState:
 
 @dataclass
 class EmbeddingFunc:
+    """Embedding function wrapper with dimension validation
+    This class wraps an embedding function to ensure that the output embeddings have the correct dimension.
+    This class should not be wrapped multiple times.
+
+    Args:
+        embedding_dim: Expected dimension of the embeddings
+        func: The actual embedding function to wrap
+        max_token_size: Optional token limit for the embedding model
+        send_dimensions: Whether to inject embedding_dim as a keyword argument
+    """
+
     embedding_dim: int
     func: callable
     max_token_size: int | None = None  # Token limit for the embedding model
@@ -379,7 +390,32 @@ class EmbeddingFunc:
             # Inject embedding_dim from decorator
             kwargs["embedding_dim"] = self.embedding_dim
 
-        return await self.func(*args, **kwargs)
+        # Call the actual embedding function
+        result = await self.func(*args, **kwargs)
+
+        # Validate embedding dimensions using total element count
+        total_elements = result.size  # Total number of elements in the numpy array
+        expected_dim = self.embedding_dim
+
+        # Check if total elements can be evenly divided by embedding_dim
+        if total_elements % expected_dim != 0:
+            raise ValueError(
+                f"Embedding dimension mismatch detected: "
+                f"total elements ({total_elements}) cannot be evenly divided by "
+                f"expected dimension ({expected_dim}). "
+            )
+
+        # Optional: Verify vector count matches input text count
+        actual_vectors = total_elements // expected_dim
+        if args and isinstance(args[0], (list, tuple)):
+            expected_vectors = len(args[0])
+            if actual_vectors != expected_vectors:
+                raise ValueError(
+                    f"Vector count mismatch: "
+                    f"expected {expected_vectors} vectors but got {actual_vectors} vectors (from embedding result)."
+                )
+
+        return result
 
 
 def compute_args_hash(*args: Any) -> str: