lint

Author: framsouza

supporting-blog-content/github-assistant/evaluation.py

@@ -23,25 +23,35 @@
 parser = argparse.ArgumentParser(
     description="Process documents and questions for evaluation."
 )
-parser.add_argument("--num_documents",
+parser.add_argument(
+                    "--num_documents",
                     type=int, 
                     default=None,
-                    help="Number of documents to process (default: all)")
-parser.add_argument("--skip_documents", 
+                    help="Number of documents to process (default: all)"
+)
+parser.add_argument(
+                    "--skip_documents", 
                     type=int, 
                     default=0,
-                    help="Number of documents to skip at the beginning (default: 0)")
-parser.add_argument("--num_questions", 
+                    help="Number of documents to skip at the beginning (default: 0)"
+)
+parser.add_argument(
+                    "--num_questions", 
                     type=int, 
                     default=None,
-                    help="Number of questions to process (default: all)")
-parser.add_argument("--skip_questions",
+                    help="Number of questions to process (default: all)"
+)
+parser.add_argument(
+                    "--skip_questions",
                     type=int, 
                     default=0,
-                    help="Number of questions to skip at the beginning (default: 0)")
-parser.add_argument("--process_last_questions",
+                    help="Number of questions to skip at the beginning (default: 0)"
+)
+parser.add_argument(
+                    "--process_last_questions",
                     action="store_true",
-                    help="Process last N questions instead of first N")
+                    help="Process last N questions instead of first N"
+)
 args = parser.parse_args()
 
 load_dotenv(".env")

supporting-blog-content/github-assistant/query.py

@@ -20,24 +20,24 @@ def run_query_sync():
         query_engine = index.as_query_engine(
             llm=openai_llm,
             similarity_top_k=3,
-            streaming=False, 
+            streaming=False,
             response_mode="tree_summarize",
         )
 
         bundle = QueryBundle(query, embedding=embed_model.get_query_embedding(query))
 
         result = query_engine.query(bundle)
-        return result.response  
+        return result.response
     except Exception as e:
         print(f"An error occurred while running the query: {e}")
     finally:
         if hasattr(openai_llm, "client") and isinstance(
             openai_llm.client, httpx.Client
-            ):
+        ):
             openai_llm.client.close()
         if hasattr(embed_model, "client") and isinstance(
             embed_model.client, httpx.Client
-            ):
+        ):
             embed_model.client.close()
         if hasattr(es_vector_store, "close"):
             es_vector_store.close()