Fix linting errors

README.md

@@ -334,7 +334,7 @@ with open("./newText.txt") as f:
 We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords.
 
 ##### How It Works?
-The function operates by dividing the input into two parts: 
+The function operates by dividing the input into two parts:
 - `User Query`
 - `Prompt`
 

examples/query_keyword_separation_example.py

@@ -95,20 +95,22 @@ book2 = open("./book_2.txt", encoding="utf-8")
 
 rag.insert([book1.read(), book2.read()])
 
+
 # Example function demonstrating the new query_with_separate_keyword_extraction usage
 async def run_example():
     query = "What are the top themes in this story?"
     prompt = "Please simplify the response for a young audience."
-    
+
     # Using the new method to ensure the keyword extraction is only applied to the query
     response = rag.query_with_separate_keyword_extraction(
         query=query,
         prompt=prompt,
-        param=QueryParam(mode="hybrid")  # Adjust QueryParam mode as necessary
+        param=QueryParam(mode="hybrid"),  # Adjust QueryParam mode as necessary
     )
-    
+
     print("Extracted Response:", response)
 
+
 # Run the example asynchronously
 if __name__ == "__main__":
     asyncio.run(run_example())