fix(gcs_bucket.py): show error response text in exception

litellm/integrations/gcs_bucket.py

@@ -83,11 +83,14 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
 
             # Modify the object_name to include the date-based folder
             object_name = f"{current_date}/{response_obj['id']}"
-            response = await self.async_httpx_client.post(
-                headers=headers,
-                url=f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}",
-                data=json_logged_payload,
-            )
+            try:
+                response = await self.async_httpx_client.post(
+                    headers=headers,
+                    url=f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}",
+                    data=json_logged_payload,
+                )
+            except httpx.HTTPStatusError as e:
+                raise Exception(f"GCS Bucket logging error: {e.response.text}")
 
             if response.status_code != 200:
                 verbose_logger.error("GCS Bucket logging error: %s", str(response.text))

litellm/proxy/_new_secret_config.yaml

@@ -1,73 +1,9 @@
 model_list:
-  - model_name: fake-claude-endpoint
-    litellm_params:
-      model: anthropic.claude-3-sonnet-20240229-v1:0
-      api_base: https://exampleopenaiendpoint-production.up.railway.app
-      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
-      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
-  - model_name: gemini-vision
-    litellm_params:
-      model: vertex_ai/gemini-1.0-pro-vision-001
-      api_base: https://exampleopenaiendpoint-production.up.railway.app/v1/projects/adroit-crow-413218/locations/us-central1/publishers/google/models/gemini-1.0-pro-vision-001
-      vertex_project: "adroit-crow-413218"
-      vertex_location: "us-central1"
-  - model_name: fake-azure-endpoint
-    litellm_params:
-      model: openai/429
-      api_key: fake-key
-      api_base: https://exampleopenaiendpoint-production.up.railway.app
   - model_name: fake-openai-endpoint
     litellm_params:
-      model: gpt-3.5-turbo
-      api_base: https://exampleopenaiendpoint-production.up.railway.app
-  - model_name: o1-preview
-    litellm_params:
-      model: o1-preview
-  - model_name: rerank-english-v3.0
-    litellm_params:
-      model: cohere/rerank-english-v3.0
-      api_key: os.environ/COHERE_API_KEY
-  - model_name: azure-rerank-english-v3.0
-    litellm_params:
-      model: azure_ai/rerank-english-v3.0
-      api_base: os.environ/AZURE_AI_COHERE_API_BASE
-      api_key: os.environ/AZURE_AI_COHERE_API_KEY
-  - model_name: "databricks/*"
-    litellm_params:
-      model: "databricks/*"
-      api_key: os.environ/DATABRICKS_API_KEY
-      api_base: os.environ/DATABRICKS_API_BASE
-  - model_name: "anthropic/*"
-    litellm_params:
-      model: "anthropic/*"
-  - model_name: "*"
-    litellm_params:
-      model: "openai/*"
-  - model_name: "fireworks_ai/*"
-    litellm_params:
-      model: "fireworks_ai/*"
-      configurable_clientside_auth_params: ["api_base"]
-  - model_name: "gemini-flash-experimental"
-    litellm_params:
-      model: "vertex_ai/gemini-flash-experimental"
+      model: openai/fake
+      api_key: fake-key
+      api_base: https://exampleopenaiendpoint-production.up.railway.app/
 
 litellm_settings:
-  turn_off_message_logging: true
-  # callbacks:
-    # - prometheus
-    # - otel
-  failure_callback:
-    - sentry
-    - prometheus
-  success_callback:
-    - prometheus
-    - s3
-  s3_callback_params:
-    s3_bucket_name: mytestbucketlitellm   # AWS Bucket Name for S3
-    s3_region_name: us-west-2              # AWS Region Name for S3
-    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
-    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
-
-general_settings:
-  db_url: os.environ/DATABASE_URL
-  # disable_prisma_schema_update: true
+  callbacks: ["gcs_bucket"]