(Feat) - Add GCS Pub/Sub Logging integration for sending DB SpendLogs to BigQuery (#7976)

* add pub_sub

* fix custom batch logger for GCS PUB/SUB

* GCS_PUBSUB_PROJECT_ID

* e2e gcs pub sub

* add gcs pub sub

* fix logging

* add GcsPubSubLogger

* fix pub sub

* add pub sub

* docs gcs pub / sub

* docs on pub sub controls

* test_gcs_pub_sub

* fix publish_message

* test_async_gcs_pub_sub

* test_async_gcs_pub_sub

docs/my-website/docs/proxy/config_settings.md

@@ -367,6 +367,8 @@ router_settings:
 | GCS_PATH_SERVICE_ACCOUNT | Path to the Google Cloud service account JSON file
 | GCS_FLUSH_INTERVAL | Flush interval for GCS logging (in seconds). Specify how often you want a log to be sent to GCS. **Default is 20 seconds**
 | GCS_BATCH_SIZE | Batch size for GCS logging. Specify after how many logs you want to flush to GCS. If `BATCH_SIZE` is set to 10, logs are flushed every 10 logs. **Default is 2048**
+| GCS_PUBSUB_TOPIC_ID | PubSub Topic ID to send LiteLLM SpendLogs to.
+| GCS_PUBSUB_PROJECT_ID | PubSub Project ID to send LiteLLM SpendLogs to.
 | GENERIC_AUTHORIZATION_ENDPOINT | Authorization endpoint for generic OAuth providers
 | GENERIC_CLIENT_ID | Client ID for generic OAuth providers
 | GENERIC_CLIENT_SECRET | Client secret for generic OAuth providers

docs/my-website/docs/proxy/logging.md

@@ -1025,6 +1025,74 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 6. Save the JSON file and add the path to `GCS_PATH_SERVICE_ACCOUNT`
 
 
+
+## Google Cloud Storage - PubSub Topic
+
+Log LLM Logs/SpendLogs to [Google Cloud Storage PubSub Topic](https://cloud.google.com/pubsub/docs/reference/rest)
+
+:::info
+
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
+
+:::
+
+
+| Property | Details |
+|----------|---------|
+| Description | Log LiteLLM `SpendLogs Table` to Google Cloud Storage PubSub Topic |
+
+When to use `gcs_pubsub`?
+
+- If your LiteLLM Database has crossed 1M+ spend logs and you want to send `SpendLogs` to a PubSub Topic that can be consumed by GCS BigQuery
+
+
+#### Usage
+
+1. Add `gcs_pubsub` to LiteLLM Config.yaml
+```yaml
+model_list:
+- litellm_params:
+    api_base: https://exampleopenaiendpoint-production.up.railway.app/
+    api_key: my-fake-key
+    model: openai/my-fake-model
+  model_name: fake-openai-endpoint
+
+litellm_settings:
+  callbacks: ["gcs_pubsub"] # 👈 KEY CHANGE # 👈 KEY CHANGE
+```
+
+2. Set required env variables
+
+```shell
+GCS_PUBSUB_TOPIC_ID="litellmDB"
+GCS_PUBSUB_PROJECT_ID="reliableKeys"
+```
+
+3. Start Proxy
+
+```
+litellm --config /path/to/config.yaml
+```
+
+4. Test it! 
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--data ' {
+      "model": "fake-openai-endpoint",
+      "messages": [
+        {
+          "role": "user",
+          "content": "what llm are you"
+        }
+      ],
+    }
+'
+```
+
+
+
 ## s3 Buckets
 
 We will use the `--config` to set 
@@ -1301,7 +1369,7 @@ LiteLLM supports customizing the following Datadog environment variables
 
 
 ## Lunary
-### Step1: Install dependencies and set your environment variables 
+#### Step1: Install dependencies and set your environment variables 
 Install the dependencies
 ```shell
 pip install litellm lunary
@@ -1312,7 +1380,7 @@ Get you Lunary public key from from https://app.lunary.ai/settings
 export LUNARY_PUBLIC_KEY="<your-public-key>"
 ```
 
-### Step 2: Create a `config.yaml` and set `lunary` callbacks
+#### Step 2: Create a `config.yaml` and set `lunary` callbacks
 
 ```yaml
 model_list:
@@ -1324,12 +1392,12 @@ litellm_settings:
   failure_callback: ["lunary"]
 ```
 
-### Step 3: Start the LiteLLM proxy
+#### Step 3: Start the LiteLLM proxy
 ```shell
 litellm --config config.yaml
 ```
 
-### Step 4: Make a request
+#### Step 4: Make a request
 
 ```shell
 curl -X POST 'http://0.0.0.0:4000/chat/completions' \
@@ -1352,14 +1420,14 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 ## MLflow
 
 
-### Step1: Install dependencies
+#### Step1: Install dependencies
 Install the dependencies.
 
 ```shell
 pip install litellm mlflow
 ```
 
-### Step 2: Create a `config.yaml` with `mlflow` callback
+#### Step 2: Create a `config.yaml` with `mlflow` callback
 
 ```yaml
 model_list:
@@ -1371,12 +1439,12 @@ litellm_settings:
   failure_callback: ["mlflow"]
 ```
 
-### Step 3: Start the LiteLLM proxy
+#### Step 3: Start the LiteLLM proxy
 ```shell
 litellm --config config.yaml
 ```
 
-### Step 4: Make a request
+#### Step 4: Make a request
 
 ```shell
 curl -X POST 'http://0.0.0.0:4000/chat/completions' \
@@ -1392,7 +1460,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 }'
 ```
 
-### Step 5: Review traces
+#### Step 5: Review traces
 
 Run the following command to start MLflow UI and review recorded traces.
 

litellm/__init__.py

@@ -77,6 +77,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
     "langfuse",
     "pagerduty",
     "humanloop",
+    "gcs_pubsub",
 ]
 logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
 _known_custom_logger_compatible_callbacks: List = list(

litellm/integrations/Readme.md

@@ -0,0 +1,5 @@
+# Integrations
+
+This folder contains logging integrations for litellm
+
+eg. logging to Datadog, Langfuse, Prometheus, s3, GCS Bucket, etc.

litellm/integrations/gcs_pubsub/pub_sub.py

@@ -0,0 +1,202 @@
+"""
+BETA
+
+This is the PubSub logger for GCS PubSub, this sends LiteLLM SpendLogs Payloads to GCS PubSub.
+
+Users can use this instead of sending their SpendLogs to their Postgres database.
+"""
+
+import asyncio
+import json
+import os
+import traceback
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+if TYPE_CHECKING:
+    from litellm.proxy._types import SpendLogsPayload
+else:
+    SpendLogsPayload = Any
+
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+
+
+class GcsPubSubLogger(CustomBatchLogger):
+    def __init__(
+        self,
+        project_id: Optional[str] = None,
+        topic_id: Optional[str] = None,
+        credentials_path: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Initialize Google Cloud Pub/Sub publisher
+
+        Args:
+            project_id (str): Google Cloud project ID
+            topic_id (str): Pub/Sub topic ID
+            credentials_path (str, optional): Path to Google Cloud credentials JSON file
+        """
+        from litellm.proxy.utils import _premium_user_check
+
+        _premium_user_check()
+
+        self.async_httpx_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.LoggingCallback
+        )
+
+        self.project_id = project_id or os.getenv("GCS_PUBSUB_PROJECT_ID")
+        self.topic_id = topic_id or os.getenv("GCS_PUBSUB_TOPIC_ID")
+        self.path_service_account_json = credentials_path or os.getenv(
+            "GCS_PATH_SERVICE_ACCOUNT"
+        )
+
+        if not self.project_id or not self.topic_id:
+            raise ValueError("Both project_id and topic_id must be provided")
+
+        self.flush_lock = asyncio.Lock()
+        super().__init__(**kwargs, flush_lock=self.flush_lock)
+        asyncio.create_task(self.periodic_flush())
+        self.log_queue: List[SpendLogsPayload] = []
+
+    async def construct_request_headers(self) -> Dict[str, str]:
+        """Construct authorization headers using Vertex AI auth"""
+        from litellm import vertex_chat_completion
+
+        _auth_header, vertex_project = (
+            await vertex_chat_completion._ensure_access_token_async(
+                credentials=self.path_service_account_json,
+                project_id=None,
+                custom_llm_provider="vertex_ai",
+            )
+        )
+
+        auth_header, _ = vertex_chat_completion._get_token_and_url(
+            model="pub-sub",
+            auth_header=_auth_header,
+            vertex_credentials=self.path_service_account_json,
+            vertex_project=vertex_project,
+            vertex_location=None,
+            gemini_api_key=None,
+            stream=None,
+            custom_llm_provider="vertex_ai",
+            api_base=None,
+        )
+
+        headers = {
+            "Authorization": f"Bearer {auth_header}",
+            "Content-Type": "application/json",
+        }
+        return headers
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        """
+        Async Log success events to GCS PubSub Topic
+
+        - Creates a SpendLogsPayload
+        - Adds to batch queue
+        - Flushes based on CustomBatchLogger settings
+
+        Raises:
+            Raises a NON Blocking verbose_logger.exception if an error occurs
+        """
+        from litellm.proxy.spend_tracking.spend_tracking_utils import (
+            get_logging_payload,
+        )
+        from litellm.proxy.utils import _premium_user_check
+
+        _premium_user_check()
+
+        try:
+            verbose_logger.debug(
+                "PubSub: Logging - Enters logging function for model %s", kwargs
+            )
+            spend_logs_payload = get_logging_payload(
+                kwargs=kwargs,
+                response_obj=response_obj,
+                start_time=start_time,
+                end_time=end_time,
+            )
+            self.log_queue.append(spend_logs_payload)
+
+            if len(self.log_queue) >= self.batch_size:
+                await self.async_send_batch()
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"PubSub Layer Error - {str(e)}\n{traceback.format_exc()}"
+            )
+            pass
+
+    async def async_send_batch(self):
+        """
+        Sends the batch of messages to Pub/Sub
+        """
+        try:
+            if not self.log_queue:
+                return
+
+            verbose_logger.debug(
+                f"PubSub - about to flush {len(self.log_queue)} events"
+            )
+
+            for message in self.log_queue:
+                await self.publish_message(message)
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"PubSub Error sending batch - {str(e)}\n{traceback.format_exc()}"
+            )
+        finally:
+            self.log_queue.clear()
+
+    async def publish_message(
+        self, message: SpendLogsPayload
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Publish message to Google Cloud Pub/Sub using REST API
+
+        Args:
+            message: Message to publish (dict or string)
+
+        Returns:
+            dict: Published message response
+        """
+        try:
+            headers = await self.construct_request_headers()
+
+            # Prepare message data
+            if isinstance(message, str):
+                message_data = message
+            else:
+                message_data = json.dumps(message, default=str)
+
+            # Base64 encode the message
+            import base64
+
+            encoded_message = base64.b64encode(message_data.encode("utf-8")).decode(
+                "utf-8"
+            )
+
+            # Construct request body
+            request_body = {"messages": [{"data": encoded_message}]}
+
+            url = f"https://pubsub.googleapis.com/v1/projects/{self.project_id}/topics/{self.topic_id}:publish"
+
+            response = await self.async_httpx_client.post(
+                url=url, headers=headers, json=request_body
+            )
+
+            if response.status_code not in [200, 202]:
+                verbose_logger.error("Pub/Sub publish error: %s", str(response.text))
+                raise Exception(f"Failed to publish message: {response.text}")
+
+            verbose_logger.debug("Pub/Sub response: %s", response.text)
+            return response.json()
+
+        except Exception as e:
+            verbose_logger.error("Pub/Sub publish error: %s", str(e))

litellm/litellm_core_utils/litellm_logging.py

@@ -77,6 +77,7 @@ from ..integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger
 from ..integrations.dynamodb import DyanmoDBLogger
 from ..integrations.galileo import GalileoObserve
 from ..integrations.gcs_bucket.gcs_bucket import GCSBucketLogger
+from ..integrations.gcs_pubsub.pub_sub import GcsPubSubLogger
 from ..integrations.greenscale import GreenscaleLogger
 from ..integrations.helicone import HeliconeLogger
 from ..integrations.humanloop import HumanloopLogger
@@ -2571,6 +2572,13 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
             pagerduty_logger = PagerDutyAlerting(**custom_logger_init_args)
             _in_memory_loggers.append(pagerduty_logger)
             return pagerduty_logger  # type: ignore
+        elif logging_integration == "gcs_pubsub":
+            for callback in _in_memory_loggers:
+                if isinstance(callback, GcsPubSubLogger):
+                    return callback
+            _gcs_pubsub_logger = GcsPubSubLogger()
+            _in_memory_loggers.append(_gcs_pubsub_logger)
+            return _gcs_pubsub_logger  # type: ignore
         elif logging_integration == "humanloop":
             for callback in _in_memory_loggers:
                 if isinstance(callback, HumanloopLogger):
@@ -2704,6 +2712,10 @@ def get_custom_logger_compatible_class(  # noqa: PLR0915
             for callback in _in_memory_loggers:
                 if isinstance(callback, PagerDutyAlerting):
                     return callback
+        elif logging_integration == "gcs_pubsub":
+            for callback in _in_memory_loggers:
+                if isinstance(callback, GcsPubSubLogger):
+                    return callback
 
         return None
     except Exception as e:

litellm/proxy/management_endpoints/team_endpoints.py

@@ -58,7 +58,7 @@ from litellm.proxy.management_helpers.utils import (
     add_new_member,
     management_endpoint_wrapper,
 )
-from litellm.proxy.utils import PrismaClient
+from litellm.proxy.utils import PrismaClient, _premium_user_check
 
 router = APIRouter()
 
@@ -1527,15 +1527,3 @@ def _set_team_metadata_field(
         _premium_user_check()
     team_data.metadata = team_data.metadata or {}
     team_data.metadata[field_name] = value
-
-
-def _premium_user_check():
-    from litellm.proxy.proxy_server import premium_user
-
-    if not premium_user:
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "error": f"This feature is only available for LiteLLM Enterprise users. {CommonProxyErrors.not_premium_user.value}"
-            },
-        )

litellm/proxy/proxy_config.yaml

@@ -15,8 +15,7 @@ model_list:
 
 
 litellm_settings:
-  callbacks: ["prometheus"]
-
+  callbacks: ["gcs_pubsub"]
 
 guardrails:
   - guardrail_name: "bedrock-pre-guard"
@@ -26,3 +25,4 @@ guardrails:
       guardrailIdentifier: gf3sc1mzinjw
       guardrailVersion: "DRAFT"
       default_on: true
+

litellm/proxy/utils.py

@@ -16,6 +16,7 @@ from typing import TYPE_CHECKING, Any, List, Literal, Optional, Union, overload
 from litellm.litellm_core_utils.duration_parser import duration_in_seconds
 from litellm.proxy._types import (
     DB_CONNECTION_ERROR_TYPES,
+    CommonProxyErrors,
     ProxyErrorTypes,
     ProxyException,
 )
@@ -2959,3 +2960,18 @@ def handle_exception_on_proxy(e: Exception) -> ProxyException:
         param=getattr(e, "param", "None"),
         code=status.HTTP_500_INTERNAL_SERVER_ERROR,
     )
+
+
+def _premium_user_check():
+    """
+    Raises an HTTPException if the user is not a premium user
+    """
+    from litellm.proxy.proxy_server import premium_user
+
+    if not premium_user:
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "error": f"This feature is only available for LiteLLM Enterprise users. {CommonProxyErrors.not_premium_user.value}"
+            },
+        )

tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json

@@ -0,0 +1,27 @@
+{
+    "request_id": "chatcmpl-2283081b-dc89-41f6-93e6-d4f914774027",
+    "call_type": "acompletion",
+    "api_key": "",
+    "cache_hit": "None",
+    "startTime": "2025-01-24 09:20:46.847371",
+    "endTime": "2025-01-24 09:20:46.851954",
+    "completionStartTime": "2025-01-24 09:20:46.851954",
+    "model": "gpt-4o",
+    "user": "",
+    "team_id": "",
+    "metadata": "{\"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}}",
+    "cache_key": "Cache OFF",
+    "spend": 0.00022500000000000002,
+    "total_tokens": 30,
+    "prompt_tokens": 10,
+    "completion_tokens": 20,
+    "request_tags": "[]",
+    "end_user": "",
+    "api_base": "",
+    "model_group": "",
+    "model_id": "",
+    "requester_ip_address": null,
+    "custom_llm_provider": "openai",
+    "messages": "{}",
+    "response": "{}"
+}

tests/logging_callback_tests/test_gcs_pub_sub.py

@@ -0,0 +1,113 @@
+import io
+import os
+import sys
+
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import asyncio
+import gzip
+import json
+import logging
+import time
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+import litellm
+from litellm import completion
+from litellm._logging import verbose_logger
+from litellm.integrations.gcs_pubsub.pub_sub import *
+from datetime import datetime, timedelta
+from litellm.types.utils import (
+    StandardLoggingPayload,
+    StandardLoggingModelInformation,
+    StandardLoggingMetadata,
+    StandardLoggingHiddenParams,
+)
+
+verbose_logger.setLevel(logging.DEBUG)
+
+
+def assert_gcs_pubsub_request_matches_expected(
+    actual_request_body: dict,
+    expected_file_name: str,
+):
+    """
+    Helper function to compare actual GCS PubSub request body with expected JSON file.
+
+    Args:
+        actual_request_body (dict): The actual request body received from the API call
+        expected_file_name (str): Name of the JSON file containing expected request body
+    """
+    # Get the current directory and read the expected request body
+    pwd = os.path.dirname(os.path.realpath(__file__))
+    expected_body_path = os.path.join(pwd, "gcs_pub_sub_body", expected_file_name)
+
+    with open(expected_body_path, "r") as f:
+        expected_request_body = json.load(f)
+
+    # Replace dynamic values in actual request body
+    time_fields = ["startTime", "endTime", "completionStartTime", "request_id"]
+    for field in time_fields:
+        if field in actual_request_body:
+            actual_request_body[field] = expected_request_body[field]
+
+    # Assert the entire request body matches
+    assert (
+        actual_request_body == expected_request_body
+    ), f"Difference in request bodies: {json.dumps(actual_request_body, indent=2)} != {json.dumps(expected_request_body, indent=2)}"
+
+
+@pytest.mark.asyncio
+async def test_async_gcs_pub_sub():
+    # Create a mock for the async_httpx_client's post method
+    mock_post = AsyncMock()
+    mock_post.return_value.status_code = 202
+    mock_post.return_value.text = "Accepted"
+
+    # Initialize the GcsPubSubLogger and set the mock
+    gcs_pub_sub_logger = GcsPubSubLogger(flush_interval=1)
+    gcs_pub_sub_logger.async_httpx_client.post = mock_post
+
+    mock_construct_request_headers = AsyncMock()
+    mock_construct_request_headers.return_value = {"Authorization": "Bearer mock_token"}
+    gcs_pub_sub_logger.construct_request_headers = mock_construct_request_headers
+    litellm.callbacks = [gcs_pub_sub_logger]
+
+    # Make the completion call
+    response = await litellm.acompletion(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": "Hello, world!"}],
+        mock_response="hi",
+    )
+
+    await asyncio.sleep(3)  # Wait for async flush
+
+    # Assert httpx post was called
+    mock_post.assert_called_once()
+
+    # Get the actual request body from the mock
+    actual_url = mock_post.call_args[1]["url"]
+    print("sent to url", actual_url)
+    assert (
+        actual_url
+        == "https://pubsub.googleapis.com/v1/projects/reliableKeys/topics/litellmDB:publish"
+    )
+    actual_request = mock_post.call_args[1]["json"]
+
+    # Extract and decode the base64 encoded message
+    encoded_message = actual_request["messages"][0]["data"]
+    import base64
+
+    decoded_message = base64.b64decode(encoded_message).decode("utf-8")
+
+    # Parse the JSON string into a dictionary
+    actual_request = json.loads(decoded_message)
+    print("##########\n")
+    print(json.dumps(actual_request, indent=4))
+    print("##########\n")
+    # Verify the request body matches expected format
+    assert_gcs_pubsub_request_matches_expected(
+        actual_request, "spend_logs_payload.json"
+    )