diff --git a/surfsense_backend/app/utils/document_converters.py b/surfsense_backend/app/utils/document_converters.py
index 0cacdd1d3..ed52c1b7b 100644
--- a/surfsense_backend/app/utils/document_converters.py
+++ b/surfsense_backend/app/utils/document_converters.py
@@ -1,5 +1,6 @@
 import hashlib
 import logging
+import threading
 import warnings
 
 import numpy as np
@@ -11,6 +12,12 @@ from app.prompts import SUMMARY_PROMPT_TEMPLATE
 
 logger = logging.getLogger(__name__)
 
+# HuggingFace fast tokenizers (Rust-backed) are not thread-safe — concurrent
+# access from multiple threads causes "RuntimeError: Already borrowed".
+# This reentrant lock serialises tokenizer + embedding model access so that
+# asyncio.to_thread calls from index_batch_parallel don't collide.
+_embedding_lock = threading.RLock()
+
 
 def _get_embedding_max_tokens() -> int:
     """Get the max token limit for the configured embedding model.
@@ -36,23 +43,25 @@ def truncate_for_embedding(text: str) -> str:
     if len(text) // 3 <= max_tokens:
         return text
 
-    tokenizer = config.embedding_model_instance.get_tokenizer()
-    tokens = tokenizer.encode(text)
-    if len(tokens) <= max_tokens:
-        return text
+    with _embedding_lock:
+        tokenizer = config.embedding_model_instance.get_tokenizer()
+        tokens = tokenizer.encode(text)
+        if len(tokens) <= max_tokens:
+            return text
 
-    warnings.warn(
-        f"Truncating text from {len(tokens)} to {max_tokens} tokens for embedding.",
-        stacklevel=2,
-    )
-    return tokenizer.decode(tokens[:max_tokens])
+        warnings.warn(
+            f"Truncating text from {len(tokens)} to {max_tokens} tokens for embedding.",
+            stacklevel=2,
+        )
+        return tokenizer.decode(tokens[:max_tokens])
 
 
 def embed_text(text: str) -> np.ndarray:
     """Truncate text to fit and embed it. Drop-in replacement for
     ``config.embedding_model_instance.embed(text)`` that never exceeds the
     model's context window."""
-    return config.embedding_model_instance.embed(truncate_for_embedding(text))
+    with _embedding_lock:
+        return config.embedding_model_instance.embed(truncate_for_embedding(text))
 
 
 def embed_texts(texts: list[str]) -> list[np.ndarray]:
@@ -66,10 +75,11 @@ def embed_texts(texts: list[str]) -> list[np.ndarray]:
     """
     if not texts:
         return []
-    truncated = [truncate_for_embedding(t) for t in texts]
-    if config.is_local_embedding_model:
-        return [config.embedding_model_instance.embed(t) for t in truncated]
-    return config.embedding_model_instance.embed_batch(truncated)
+    with _embedding_lock:
+        truncated = [truncate_for_embedding(t) for t in texts]
+        if config.is_local_embedding_model:
+            return [config.embedding_model_instance.embed(t) for t in truncated]
+        return config.embedding_model_instance.embed_batch(truncated)
 
 
 def get_model_context_window(model_name: str) -> int: