feat(TokenTracker): Add context manager support to simplify token tracking

examples/lightrag_gemini_track_token_demo.py

@@ -115,38 +115,36 @@ def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
 
-    # Reset tracker before processing queries
-    token_tracker.reset()
-
     with open("./book.txt", "r", encoding="utf-8") as f:
         rag.insert(f.read())
 
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="naive")
+    # Context Manager Method
+    with token_tracker:
+        print(
+            rag.query(
+                "What are the top themes in this story?", param=QueryParam(mode="naive")
+            )
         )
-    )
 
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="local")
+        print(
+            rag.query(
+                "What are the top themes in this story?", param=QueryParam(mode="local")
+            )
         )
-    )
 
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="global")
+        print(
+            rag.query(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="global"),
+            )
         )
-    )
 
-    print(
-        rag.query(
-            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        print(
+            rag.query(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="hybrid"),
+            )
         )
-    )
-
-    # Display final token usage after main query
-    print("Token usage:", token_tracker.get_usage())
 
 
 if __name__ == "__main__":

examples/lightrag_siliconcloud_track_token_demo.py

@@ -44,14 +44,10 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
 
 # function test
 async def test_funcs():
-    # Reset tracker before processing queries
-    token_tracker.reset()
-
-    result = await llm_model_func("How are you?")
-    print("llm_model_func: ", result)
-
-    # Display final token usage after main query
-    print("Token usage:", token_tracker.get_usage())
+    # Context Manager Method
+    with token_tracker:
+        result = await llm_model_func("How are you?")
+        print("llm_model_func: ", result)
 
 
 asyncio.run(test_funcs())

lightrag/utils.py

@@ -962,6 +962,13 @@ class TokenTracker:
     def __init__(self):
         self.reset()
 
+    def __enter__(self):
+        self.reset()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        print(self)
+
     def reset(self):
         self.prompt_tokens = 0
         self.completion_tokens = 0