feat gcs bucket log payload

litellm/integrations/gcs_bucket.py

@@ -0,0 +1,144 @@
+import json
+import os
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+from pydantic import BaseModel, Field
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.proxy._types import SpendLogsPayload
+
+
+class GCSBucketPayload(SpendLogsPayload):
+    messages: Optional[List]
+    output: Optional[Union[Dict, str, List]]
+
+
+class GCSBucketLogger(CustomLogger):
+    def __init__(self) -> None:
+        self.async_httpx_client = AsyncHTTPHandler(
+            timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+        )
+        self.path_service_account_json = os.getenv("GCS_PATH_SERVICE_ACCOUNT", None)
+        self.BUCKET_NAME = os.getenv("GCS_BUCKET_NAME", None)
+
+        if self.BUCKET_NAME is None:
+            raise ValueError(
+                "GCS_BUCKET_NAME is not set in the environment, but GCS Bucket is being used as a logging callback. Please set 'GCS_BUCKET_NAME' in the environment."
+            )
+
+        if self.path_service_account_json is None:
+            raise ValueError(
+                "GCS_PATH_SERVICE_ACCOUNT is not set in the environment, but GCS Bucket is being used as a logging callback. Please set 'GCS_PATH_SERVICE_ACCOUNT' in the environment."
+            )
+        pass
+
+    #### ASYNC ####
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        try:
+            verbose_logger.debug(
+                "GCS Logger: async_log_success_event logging kwargs: %s, response_obj: %s",
+                kwargs,
+                response_obj,
+            )
+            headers = await self.construct_request_headers()
+            logging_payload: GCSBucketPayload = await self.get_gcs_payload(
+                kwargs, response_obj, start_time, end_time
+            )
+
+            object_name = logging_payload["request_id"]
+            response = await self.async_httpx_client.post(
+                headers=headers,
+                url=f"https://storage.googleapis.com/upload/storage/v1/b/{self.BUCKET_NAME}/o?uploadType=media&name={object_name}",
+                json=logging_payload,
+            )
+
+            if response.status_code != 200:
+                verbose_logger.error("GCS Bucket logging error: %s", str(response.text))
+
+            verbose_logger.debug("GCS Bucket response %s", response)
+            verbose_logger.debug("GCS Bucket status code %s", response.status_code)
+            verbose_logger.debug("GCS Bucket response.text %s", response.text)
+        except Exception as e:
+            verbose_logger.error("GCS Bucket logging error: %s", str(e))
+
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+        pass
+
+    async def construct_request_headers(self) -> Dict[str, str]:
+        from litellm import vertex_chat_completion
+
+        auth_header, _ = vertex_chat_completion._get_token_and_url(
+            model="gcs-bucket",
+            vertex_credentials=self.path_service_account_json,
+            vertex_project=None,
+            vertex_location=None,
+            gemini_api_key=None,
+            stream=None,
+            custom_llm_provider="vertex_ai",
+            api_base=None,
+        )
+        verbose_logger.debug("constructed auth_header %s", auth_header)
+        headers = {
+            "Authorization": f"Bearer {auth_header}",  # auth_header
+            "Content-Type": "application/json",
+        }
+
+        return headers
+
+    async def get_gcs_payload(
+        self, kwargs, response_obj, start_time, end_time
+    ) -> GCSBucketPayload:
+        from litellm.proxy.spend_tracking.spend_tracking_utils import (
+            get_logging_payload,
+        )
+
+        spend_logs_payload: GCSBucketPayload = get_logging_payload(
+            kwargs=kwargs,
+            response_obj=response_obj,
+            start_time=start_time,
+            end_time=end_time,
+            end_user_id=kwargs.get("user"),
+        )
+        spend_logs_payload["startTime"] = start_time.isoformat()
+        spend_logs_payload["endTime"] = end_time.isoformat()
+        spend_logs_payload["completionStartTime"] = spend_logs_payload[
+            "completionStartTime"
+        ].isoformat()
+
+        object_name = spend_logs_payload["request_id"]
+        output = None
+        if response_obj is not None and (
+            kwargs.get("call_type", None) == "embedding"
+            or isinstance(response_obj, litellm.EmbeddingResponse)
+        ):
+            output = None
+        elif response_obj is not None and isinstance(
+            response_obj, litellm.ModelResponse
+        ):
+            output_list = []
+            for choice in response_obj.choices:
+                output_list.append(choice.json())
+            output = output_list
+        elif response_obj is not None and isinstance(
+            response_obj, litellm.TextCompletionResponse
+        ):
+            output_list = []
+            for choice in response_obj.choices:
+                output_list.append(choice.json())
+            output = output_list
+        elif response_obj is not None and isinstance(
+            response_obj, litellm.ImageResponse
+        ):
+            output = response_obj["data"]
+        elif response_obj is not None and isinstance(
+            response_obj, litellm.TranscriptionResponse
+        ):
+            output = response_obj["text"]
+
+        spend_logs_payload["output"] = output
+        return spend_logs_payload