two version of R2R are hereHEADmaster

2 files changed, 783 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog.py b/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog.py
new file mode 100644
index 00000000..4f4b05c8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog.py
@@ -0,0 +1,580 @@
+"""
+DataDog Integration - sends logs to /api/v2/log
+
+DD Reference API: https://docs.datadoghq.com/api/latest/logs
+
+`async_log_success_event` - used by litellm proxy to send logs to datadog
+`log_success_event` - sync version of logging to DataDog, only used on litellm Python SDK, if user opts in to using sync functions
+
+async_log_success_event:  will store batch of DD_MAX_BATCH_SIZE in memory and flush to Datadog once it reaches DD_MAX_BATCH_SIZE or every 5 seconds
+
+async_service_failure_hook: Logs failures from Redis, Postgres (Adjacent systems), as 'WARNING' on DataDog
+
+For batching specific details see CustomBatchLogger class
+"""
+
+import asyncio
+import datetime
+import json
+import os
+import traceback
+import uuid
+from datetime import datetime as datetimeObj
+from typing import Any, List, Optional, Union
+
+import httpx
+from httpx import Response
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.llms.custom_httpx.http_handler import (
+    _get_httpx_client,
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+from litellm.types.integrations.base_health_check import IntegrationHealthCheckStatus
+from litellm.types.integrations.datadog import *
+from litellm.types.services import ServiceLoggerPayload, ServiceTypes
+from litellm.types.utils import StandardLoggingPayload
+
+from ..additional_logging_utils import AdditionalLoggingUtils
+
+# max number of logs DD API can accept
+DD_MAX_BATCH_SIZE = 1000
+
+# specify what ServiceTypes are logged as success events to DD. (We don't want to spam DD traces with large number of service types)
+DD_LOGGED_SUCCESS_SERVICE_TYPES = [
+    ServiceTypes.RESET_BUDGET_JOB,
+]
+
+
+class DataDogLogger(
+    CustomBatchLogger,
+    AdditionalLoggingUtils,
+):
+    # Class variables or attributes
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        """
+        Initializes the datadog logger, checks if the correct env variables are set
+
+        Required environment variables:
+        `DD_API_KEY` - your datadog api key
+        `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"`
+        """
+        try:
+            verbose_logger.debug("Datadog: in init datadog logger")
+            # check if the correct env variables are set
+            if os.getenv("DD_API_KEY", None) is None:
+                raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>")
+            if os.getenv("DD_SITE", None) is None:
+                raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>")
+            self.async_client = get_async_httpx_client(
+                llm_provider=httpxSpecialProvider.LoggingCallback
+            )
+            self.DD_API_KEY = os.getenv("DD_API_KEY")
+            self.intake_url = (
+                f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
+            )
+
+            ###################################
+            # OPTIONAL -only used for testing
+            dd_base_url: Optional[str] = (
+                os.getenv("_DATADOG_BASE_URL")
+                or os.getenv("DATADOG_BASE_URL")
+                or os.getenv("DD_BASE_URL")
+            )
+            if dd_base_url is not None:
+                self.intake_url = f"{dd_base_url}/api/v2/logs"
+            ###################################
+            self.sync_client = _get_httpx_client()
+            asyncio.create_task(self.periodic_flush())
+            self.flush_lock = asyncio.Lock()
+            super().__init__(
+                **kwargs, flush_lock=self.flush_lock, batch_size=DD_MAX_BATCH_SIZE
+            )
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog: Got exception on init Datadog client {str(e)}"
+            )
+            raise e
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        """
+        Async Log success events to Datadog
+
+        - Creates a Datadog payload
+        - Adds the Payload to the in memory logs queue
+        - Payload is flushed every 10 seconds or when batch size is greater than 100
+
+
+        Raises:
+            Raises a NON Blocking verbose_logger.exception if an error occurs
+        """
+        try:
+            verbose_logger.debug(
+                "Datadog: Logging - Enters logging function for model %s", kwargs
+            )
+            await self._log_async_event(kwargs, response_obj, start_time, end_time)
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
+            )
+            pass
+
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+        try:
+            verbose_logger.debug(
+                "Datadog: Logging - Enters logging function for model %s", kwargs
+            )
+            await self._log_async_event(kwargs, response_obj, start_time, end_time)
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
+            )
+            pass
+
+    async def async_send_batch(self):
+        """
+        Sends the in memory logs queue to datadog api
+
+        Logs sent to /api/v2/logs
+
+        DD Ref: https://docs.datadoghq.com/api/latest/logs/
+
+        Raises:
+            Raises a NON Blocking verbose_logger.exception if an error occurs
+        """
+        try:
+            if not self.log_queue:
+                verbose_logger.exception("Datadog: log_queue does not exist")
+                return
+
+            verbose_logger.debug(
+                "Datadog - about to flush %s events on %s",
+                len(self.log_queue),
+                self.intake_url,
+            )
+
+            response = await self.async_send_compressed_data(self.log_queue)
+            if response.status_code == 413:
+                verbose_logger.exception(DD_ERRORS.DATADOG_413_ERROR.value)
+                return
+
+            response.raise_for_status()
+            if response.status_code != 202:
+                raise Exception(
+                    f"Response from datadog API status_code: {response.status_code}, text: {response.text}"
+                )
+
+            verbose_logger.debug(
+                "Datadog: Response from datadog API status_code: %s, text: %s",
+                response.status_code,
+                response.text,
+            )
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog Error sending batch API - {str(e)}\n{traceback.format_exc()}"
+            )
+
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
+        """
+        Sync Log success events to Datadog
+
+        - Creates a Datadog payload
+        - instantly logs it on DD API
+        """
+        try:
+            if litellm.datadog_use_v1 is True:
+                dd_payload = self._create_v0_logging_payload(
+                    kwargs=kwargs,
+                    response_obj=response_obj,
+                    start_time=start_time,
+                    end_time=end_time,
+                )
+            else:
+                dd_payload = self.create_datadog_logging_payload(
+                    kwargs=kwargs,
+                    response_obj=response_obj,
+                    start_time=start_time,
+                    end_time=end_time,
+                )
+
+            response = self.sync_client.post(
+                url=self.intake_url,
+                json=dd_payload,  # type: ignore
+                headers={
+                    "DD-API-KEY": self.DD_API_KEY,
+                },
+            )
+
+            response.raise_for_status()
+            if response.status_code != 202:
+                raise Exception(
+                    f"Response from datadog API status_code: {response.status_code}, text: {response.text}"
+                )
+
+            verbose_logger.debug(
+                "Datadog: Response from datadog API status_code: %s, text: %s",
+                response.status_code,
+                response.text,
+            )
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
+            )
+            pass
+        pass
+
+    async def _log_async_event(self, kwargs, response_obj, start_time, end_time):
+
+        dd_payload = self.create_datadog_logging_payload(
+            kwargs=kwargs,
+            response_obj=response_obj,
+            start_time=start_time,
+            end_time=end_time,
+        )
+
+        self.log_queue.append(dd_payload)
+        verbose_logger.debug(
+            f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
+        )
+
+        if len(self.log_queue) >= self.batch_size:
+            await self.async_send_batch()
+
+    def _create_datadog_logging_payload_helper(
+        self,
+        standard_logging_object: StandardLoggingPayload,
+        status: DataDogStatus,
+    ) -> DatadogPayload:
+        json_payload = json.dumps(standard_logging_object, default=str)
+        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
+        dd_payload = DatadogPayload(
+            ddsource=self._get_datadog_source(),
+            ddtags=self._get_datadog_tags(
+                standard_logging_object=standard_logging_object
+            ),
+            hostname=self._get_datadog_hostname(),
+            message=json_payload,
+            service=self._get_datadog_service(),
+            status=status,
+        )
+        return dd_payload
+
+    def create_datadog_logging_payload(
+        self,
+        kwargs: Union[dict, Any],
+        response_obj: Any,
+        start_time: datetime.datetime,
+        end_time: datetime.datetime,
+    ) -> DatadogPayload:
+        """
+        Helper function to create a datadog payload for logging
+
+        Args:
+            kwargs (Union[dict, Any]): request kwargs
+            response_obj (Any): llm api response
+            start_time (datetime.datetime): start time of request
+            end_time (datetime.datetime): end time of request
+
+        Returns:
+            DatadogPayload: defined in types.py
+        """
+
+        standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
+            "standard_logging_object", None
+        )
+        if standard_logging_object is None:
+            raise ValueError("standard_logging_object not found in kwargs")
+
+        status = DataDogStatus.INFO
+        if standard_logging_object.get("status") == "failure":
+            status = DataDogStatus.ERROR
+
+        # Build the initial payload
+        self.truncate_standard_logging_payload_content(standard_logging_object)
+
+        dd_payload = self._create_datadog_logging_payload_helper(
+            standard_logging_object=standard_logging_object,
+            status=status,
+        )
+        return dd_payload
+
+    async def async_send_compressed_data(self, data: List) -> Response:
+        """
+        Async helper to send compressed data to datadog self.intake_url
+
+        Datadog recommends using gzip to compress data
+        https://docs.datadoghq.com/api/latest/logs/
+
+        "Datadog recommends sending your logs compressed. Add the Content-Encoding: gzip header to the request when sending"
+        """
+
+        import gzip
+        import json
+
+        compressed_data = gzip.compress(json.dumps(data, default=str).encode("utf-8"))
+        response = await self.async_client.post(
+            url=self.intake_url,
+            data=compressed_data,  # type: ignore
+            headers={
+                "DD-API-KEY": self.DD_API_KEY,
+                "Content-Encoding": "gzip",
+                "Content-Type": "application/json",
+            },
+        )
+        return response
+
+    async def async_service_failure_hook(
+        self,
+        payload: ServiceLoggerPayload,
+        error: Optional[str] = "",
+        parent_otel_span: Optional[Any] = None,
+        start_time: Optional[Union[datetimeObj, float]] = None,
+        end_time: Optional[Union[float, datetimeObj]] = None,
+        event_metadata: Optional[dict] = None,
+    ):
+        """
+        Logs failures from Redis, Postgres (Adjacent systems), as 'WARNING' on DataDog
+
+        - example - Redis is failing / erroring, will be logged on DataDog
+        """
+        try:
+            _payload_dict = payload.model_dump()
+            _payload_dict.update(event_metadata or {})
+            _dd_message_str = json.dumps(_payload_dict, default=str)
+            _dd_payload = DatadogPayload(
+                ddsource=self._get_datadog_source(),
+                ddtags=self._get_datadog_tags(),
+                hostname=self._get_datadog_hostname(),
+                message=_dd_message_str,
+                service=self._get_datadog_service(),
+                status=DataDogStatus.WARN,
+            )
+
+            self.log_queue.append(_dd_payload)
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog: Logger - Exception in async_service_failure_hook: {e}"
+            )
+        pass
+
+    async def async_service_success_hook(
+        self,
+        payload: ServiceLoggerPayload,
+        error: Optional[str] = "",
+        parent_otel_span: Optional[Any] = None,
+        start_time: Optional[Union[datetimeObj, float]] = None,
+        end_time: Optional[Union[float, datetimeObj]] = None,
+        event_metadata: Optional[dict] = None,
+    ):
+        """
+        Logs success from Redis, Postgres (Adjacent systems), as 'INFO' on DataDog
+
+        No user has asked for this so far, this might be spammy on datatdog. If need arises we can implement this
+        """
+        try:
+            # intentionally done. Don't want to log all service types to DD
+            if payload.service not in DD_LOGGED_SUCCESS_SERVICE_TYPES:
+                return
+
+            _payload_dict = payload.model_dump()
+            _payload_dict.update(event_metadata or {})
+
+            _dd_message_str = json.dumps(_payload_dict, default=str)
+            _dd_payload = DatadogPayload(
+                ddsource=self._get_datadog_source(),
+                ddtags=self._get_datadog_tags(),
+                hostname=self._get_datadog_hostname(),
+                message=_dd_message_str,
+                service=self._get_datadog_service(),
+                status=DataDogStatus.INFO,
+            )
+
+            self.log_queue.append(_dd_payload)
+
+        except Exception as e:
+            verbose_logger.exception(
+                f"Datadog: Logger - Exception in async_service_failure_hook: {e}"
+            )
+
+    def _create_v0_logging_payload(
+        self,
+        kwargs: Union[dict, Any],
+        response_obj: Any,
+        start_time: datetime.datetime,
+        end_time: datetime.datetime,
+    ) -> DatadogPayload:
+        """
+        Note: This is our V1 Version of DataDog Logging Payload
+
+
+        (Not Recommended) If you want this to get logged set `litellm.datadog_use_v1 = True`
+        """
+        import json
+
+        litellm_params = kwargs.get("litellm_params", {})
+        metadata = (
+            litellm_params.get("metadata", {}) or {}
+        )  # if litellm_params['metadata'] == None
+        messages = kwargs.get("messages")
+        optional_params = kwargs.get("optional_params", {})
+        call_type = kwargs.get("call_type", "litellm.completion")
+        cache_hit = kwargs.get("cache_hit", False)
+        usage = response_obj["usage"]
+        id = response_obj.get("id", str(uuid.uuid4()))
+        usage = dict(usage)
+        try:
+            response_time = (end_time - start_time).total_seconds() * 1000
+        except Exception:
+            response_time = None
+
+        try:
+            response_obj = dict(response_obj)
+        except Exception:
+            response_obj = response_obj
+
+        # Clean Metadata before logging - never log raw metadata
+        # the raw metadata can contain circular references which leads to infinite recursion
+        # we clean out all extra litellm metadata params before logging
+        clean_metadata = {}
+        if isinstance(metadata, dict):
+            for key, value in metadata.items():
+                # clean litellm metadata before logging
+                if key in [
+                    "endpoint",
+                    "caching_groups",
+                    "previous_models",
+                ]:
+                    continue
+                else:
+                    clean_metadata[key] = value
+
+        # Build the initial payload
+        payload = {
+            "id": id,
+            "call_type": call_type,
+            "cache_hit": cache_hit,
+            "start_time": start_time,
+            "end_time": end_time,
+            "response_time": response_time,
+            "model": kwargs.get("model", ""),
+            "user": kwargs.get("user", ""),
+            "model_parameters": optional_params,
+            "spend": kwargs.get("response_cost", 0),
+            "messages": messages,
+            "response": response_obj,
+            "usage": usage,
+            "metadata": clean_metadata,
+        }
+
+        json_payload = json.dumps(payload, default=str)
+
+        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
+
+        dd_payload = DatadogPayload(
+            ddsource=self._get_datadog_source(),
+            ddtags=self._get_datadog_tags(),
+            hostname=self._get_datadog_hostname(),
+            message=json_payload,
+            service=self._get_datadog_service(),
+            status=DataDogStatus.INFO,
+        )
+        return dd_payload
+
+    @staticmethod
+    def _get_datadog_tags(
+        standard_logging_object: Optional[StandardLoggingPayload] = None,
+    ) -> str:
+        """
+        Get the datadog tags for the request
+
+        DD tags need to be as follows:
+            - tags: ["user_handle:dog@gmail.com", "app_version:1.0.0"]
+        """
+        base_tags = {
+            "env": os.getenv("DD_ENV", "unknown"),
+            "service": os.getenv("DD_SERVICE", "litellm"),
+            "version": os.getenv("DD_VERSION", "unknown"),
+            "HOSTNAME": DataDogLogger._get_datadog_hostname(),
+            "POD_NAME": os.getenv("POD_NAME", "unknown"),
+        }
+
+        tags = [f"{k}:{v}" for k, v in base_tags.items()]
+
+        if standard_logging_object:
+            _request_tags: List[str] = (
+                standard_logging_object.get("request_tags", []) or []
+            )
+            request_tags = [f"request_tag:{tag}" for tag in _request_tags]
+            tags.extend(request_tags)
+
+        return ",".join(tags)
+
+    @staticmethod
+    def _get_datadog_source():
+        return os.getenv("DD_SOURCE", "litellm")
+
+    @staticmethod
+    def _get_datadog_service():
+        return os.getenv("DD_SERVICE", "litellm-server")
+
+    @staticmethod
+    def _get_datadog_hostname():
+        return os.getenv("HOSTNAME", "")
+
+    @staticmethod
+    def _get_datadog_env():
+        return os.getenv("DD_ENV", "unknown")
+
+    @staticmethod
+    def _get_datadog_pod_name():
+        return os.getenv("POD_NAME", "unknown")
+
+    async def async_health_check(self) -> IntegrationHealthCheckStatus:
+        """
+        Check if the service is healthy
+        """
+        from litellm.litellm_core_utils.litellm_logging import (
+            create_dummy_standard_logging_payload,
+        )
+
+        standard_logging_object = create_dummy_standard_logging_payload()
+        dd_payload = self._create_datadog_logging_payload_helper(
+            standard_logging_object=standard_logging_object,
+            status=DataDogStatus.INFO,
+        )
+        log_queue = [dd_payload]
+        response = await self.async_send_compressed_data(log_queue)
+        try:
+            response.raise_for_status()
+            return IntegrationHealthCheckStatus(
+                status="healthy",
+                error_message=None,
+            )
+        except httpx.HTTPStatusError as e:
+            return IntegrationHealthCheckStatus(
+                status="unhealthy",
+                error_message=e.response.text,
+            )
+        except Exception as e:
+            return IntegrationHealthCheckStatus(
+                status="unhealthy",
+                error_message=str(e),
+            )
+
+    async def get_request_response_payload(
+        self,
+        request_id: str,
+        start_time_utc: Optional[datetimeObj],
+        end_time_utc: Optional[datetimeObj],
+    ) -> Optional[dict]:
+        pass
diff --git a/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog_llm_obs.py b/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog_llm_obs.py
new file mode 100644
index 00000000..e4e074ba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/integrations/datadog/datadog_llm_obs.py
@@ -0,0 +1,203 @@
+"""
+Implements logging integration with Datadog's LLM Observability Service
+
+
+API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=example#api-standards
+
+"""
+
+import asyncio
+import json
+import os
+import uuid
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Union
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.integrations.datadog.datadog import DataDogLogger
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+from litellm.types.integrations.datadog_llm_obs import *
+from litellm.types.utils import StandardLoggingPayload
+
+
+class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
+    def __init__(self, **kwargs):
+        try:
+            verbose_logger.debug("DataDogLLMObs: Initializing logger")
+            if os.getenv("DD_API_KEY", None) is None:
+                raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>'")
+            if os.getenv("DD_SITE", None) is None:
+                raise Exception(
+                    "DD_SITE is not set, set 'DD_SITE=<>', example sit = `us5.datadoghq.com`"
+                )
+
+            self.async_client = get_async_httpx_client(
+                llm_provider=httpxSpecialProvider.LoggingCallback
+            )
+            self.DD_API_KEY = os.getenv("DD_API_KEY")
+            self.DD_SITE = os.getenv("DD_SITE")
+            self.intake_url = (
+                f"https://api.{self.DD_SITE}/api/intake/llm-obs/v1/trace/spans"
+            )
+
+            # testing base url
+            dd_base_url = os.getenv("DD_BASE_URL")
+            if dd_base_url:
+                self.intake_url = f"{dd_base_url}/api/intake/llm-obs/v1/trace/spans"
+
+            asyncio.create_task(self.periodic_flush())
+            self.flush_lock = asyncio.Lock()
+            self.log_queue: List[LLMObsPayload] = []
+            CustomBatchLogger.__init__(self, **kwargs, flush_lock=self.flush_lock)
+        except Exception as e:
+            verbose_logger.exception(f"DataDogLLMObs: Error initializing - {str(e)}")
+            raise e
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        try:
+            verbose_logger.debug(
+                f"DataDogLLMObs: Logging success event for model {kwargs.get('model', 'unknown')}"
+            )
+            payload = self.create_llm_obs_payload(
+                kwargs, response_obj, start_time, end_time
+            )
+            verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}")
+            self.log_queue.append(payload)
+
+            if len(self.log_queue) >= self.batch_size:
+                await self.async_send_batch()
+        except Exception as e:
+            verbose_logger.exception(
+                f"DataDogLLMObs: Error logging success event - {str(e)}"
+            )
+
+    async def async_send_batch(self):
+        try:
+            if not self.log_queue:
+                return
+
+            verbose_logger.debug(
+                f"DataDogLLMObs: Flushing {len(self.log_queue)} events"
+            )
+
+            # Prepare the payload
+            payload = {
+                "data": DDIntakePayload(
+                    type="span",
+                    attributes=DDSpanAttributes(
+                        ml_app=self._get_datadog_service(),
+                        tags=[self._get_datadog_tags()],
+                        spans=self.log_queue,
+                    ),
+                ),
+            }
+            verbose_logger.debug("payload %s", json.dumps(payload, indent=4))
+            response = await self.async_client.post(
+                url=self.intake_url,
+                json=payload,
+                headers={
+                    "DD-API-KEY": self.DD_API_KEY,
+                    "Content-Type": "application/json",
+                },
+            )
+
+            response.raise_for_status()
+            if response.status_code != 202:
+                raise Exception(
+                    f"DataDogLLMObs: Unexpected response - status_code: {response.status_code}, text: {response.text}"
+                )
+
+            verbose_logger.debug(
+                f"DataDogLLMObs: Successfully sent batch - status_code: {response.status_code}"
+            )
+            self.log_queue.clear()
+        except Exception as e:
+            verbose_logger.exception(f"DataDogLLMObs: Error sending batch - {str(e)}")
+
+    def create_llm_obs_payload(
+        self, kwargs: Dict, response_obj: Any, start_time: datetime, end_time: datetime
+    ) -> LLMObsPayload:
+        standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
+            "standard_logging_object"
+        )
+        if standard_logging_payload is None:
+            raise Exception("DataDogLLMObs: standard_logging_object is not set")
+
+        messages = standard_logging_payload["messages"]
+        messages = self._ensure_string_content(messages=messages)
+
+        metadata = kwargs.get("litellm_params", {}).get("metadata", {})
+
+        input_meta = InputMeta(messages=messages)  # type: ignore
+        output_meta = OutputMeta(messages=self._get_response_messages(response_obj))
+
+        meta = Meta(
+            kind="llm",
+            input=input_meta,
+            output=output_meta,
+            metadata=self._get_dd_llm_obs_payload_metadata(standard_logging_payload),
+        )
+
+        # Calculate metrics (you may need to adjust these based on available data)
+        metrics = LLMMetrics(
+            input_tokens=float(standard_logging_payload.get("prompt_tokens", 0)),
+            output_tokens=float(standard_logging_payload.get("completion_tokens", 0)),
+            total_tokens=float(standard_logging_payload.get("total_tokens", 0)),
+        )
+
+        return LLMObsPayload(
+            parent_id=metadata.get("parent_id", "undefined"),
+            trace_id=metadata.get("trace_id", str(uuid.uuid4())),
+            span_id=metadata.get("span_id", str(uuid.uuid4())),
+            name=metadata.get("name", "litellm_llm_call"),
+            meta=meta,
+            start_ns=int(start_time.timestamp() * 1e9),
+            duration=int((end_time - start_time).total_seconds() * 1e9),
+            metrics=metrics,
+            tags=[
+                self._get_datadog_tags(standard_logging_object=standard_logging_payload)
+            ],
+        )
+
+    def _get_response_messages(self, response_obj: Any) -> List[Any]:
+        """
+        Get the messages from the response object
+
+        for now this handles logging /chat/completions responses
+        """
+        if isinstance(response_obj, litellm.ModelResponse):
+            return [response_obj["choices"][0]["message"].json()]
+        return []
+
+    def _ensure_string_content(
+        self, messages: Optional[Union[str, List[Any], Dict[Any, Any]]]
+    ) -> List[Any]:
+        if messages is None:
+            return []
+        if isinstance(messages, str):
+            return [messages]
+        elif isinstance(messages, list):
+            return [message for message in messages]
+        elif isinstance(messages, dict):
+            return [str(messages.get("content", ""))]
+        return []
+
+    def _get_dd_llm_obs_payload_metadata(
+        self, standard_logging_payload: StandardLoggingPayload
+    ) -> Dict:
+        _metadata = {
+            "model_name": standard_logging_payload.get("model", "unknown"),
+            "model_provider": standard_logging_payload.get(
+                "custom_llm_provider", "unknown"
+            ),
+        }
+        _standard_logging_metadata: dict = (
+            dict(standard_logging_payload.get("metadata", {})) or {}
+        )
+        _metadata.update(_standard_logging_metadata)
+        return _metadata