Add configurable embedding token limit with validation

- Add EMBEDDING_TOKEN_LIMIT env var
- Set max_token_size on embedding func
- Add token limit property to LightRAG
- Validate summary length vs limit
- Log warning when limit exceeded

lightrag/api/config.py

@@ -445,6 +445,11 @@ def parse_args() -> argparse.Namespace:
         "EMBEDDING_BATCH_NUM", DEFAULT_EMBEDDING_BATCH_NUM, int
     )
 
+    # Embedding token limit configuration
+    args.embedding_token_limit = get_env_value(
+        "EMBEDDING_TOKEN_LIMIT", None, int, special_none=True
+    )
+
     ollama_server_infos.LIGHTRAG_NAME = args.simulated_model_name
     ollama_server_infos.LIGHTRAG_TAG = args.simulated_model_tag
 

lightrag/api/lightrag_server.py

@@ -784,6 +784,11 @@ def create_app(args):
         send_dimensions=send_dimensions,
     )
 
+    # Set max_token_size if EMBEDDING_TOKEN_LIMIT is provided
+    if args.embedding_token_limit is not None:
+        embedding_func.max_token_size = args.embedding_token_limit
+        logger.info(f"Set embedding max_token_size to {args.embedding_token_limit}")
+
     # Configure rerank function based on args.rerank_bindingparameter
     rerank_model_func = None
     if args.rerank_binding != "null":

lightrag/lightrag.py

@@ -276,6 +276,13 @@ class LightRAG:
     embedding_func: EmbeddingFunc | None = field(default=None)
     """Function for computing text embeddings. Must be set before use."""
 
+    @property
+    def embedding_token_limit(self) -> int | None:
+        """Get the token limit for embedding model from embedding_func."""
+        if self.embedding_func and hasattr(self.embedding_func, "max_token_size"):
+            return self.embedding_func.max_token_size
+        return None
+
     embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10)))
     """Batch size for embedding computations."""
 

lightrag/operate.py

@@ -345,6 +345,20 @@ async def _summarize_descriptions(
         llm_response_cache=llm_response_cache,
         cache_type="summary",
     )
+
+    # Check summary token length against embedding limit
+    embedding_token_limit = global_config.get("embedding_token_limit")
+    if embedding_token_limit is not None and summary:
+        tokenizer = global_config["tokenizer"]
+        summary_token_count = len(tokenizer.encode(summary))
+        threshold = int(embedding_token_limit * 0.9)
+
+        if summary_token_count > threshold:
+            logger.warning(
+                f"Summary tokens ({summary_token_count}) exceeds 90% of embedding limit "
+                f"({embedding_token_limit}) for {description_type}: {description_name}"
+            )
+
     return summary