ai-station/.venv/lib/python3.12/site-packages/litellm/integrations/argilla.py

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2025-12-25 14:54:33 +00:00
"""
Send logs to Argilla for annotation
"""
import asyncio
import json
import os
import random
import types
from typing import Any, Dict, List, Optional
import httpx
from pydantic import BaseModel # type: ignore
import litellm
from litellm._logging import verbose_logger
from litellm.integrations.custom_batch_logger import CustomBatchLogger
from litellm.integrations.custom_logger import CustomLogger
from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
httpxSpecialProvider,
)
from litellm.types.integrations.argilla import (
SUPPORTED_PAYLOAD_FIELDS,
ArgillaCredentialsObject,
ArgillaItem,
)
from litellm.types.utils import StandardLoggingPayload
def is_serializable(value):
non_serializable_types = (
types.CoroutineType,
types.FunctionType,
types.GeneratorType,
BaseModel,
)
return not isinstance(value, non_serializable_types)
class ArgillaLogger(CustomBatchLogger):
def __init__(
self,
argilla_api_key: Optional[str] = None,
argilla_dataset_name: Optional[str] = None,
argilla_base_url: Optional[str] = None,
**kwargs,
):
if litellm.argilla_transformation_object is None:
raise Exception(
"'litellm.argilla_transformation_object' is required, to log your payload to Argilla."
)
self.validate_argilla_transformation_object(
litellm.argilla_transformation_object
)
self.argilla_transformation_object = litellm.argilla_transformation_object
self.default_credentials = self.get_credentials_from_env(
argilla_api_key=argilla_api_key,
argilla_dataset_name=argilla_dataset_name,
argilla_base_url=argilla_base_url,
)
self.sampling_rate: float = (
float(os.getenv("ARGILLA_SAMPLING_RATE")) # type: ignore
if os.getenv("ARGILLA_SAMPLING_RATE") is not None
and os.getenv("ARGILLA_SAMPLING_RATE").strip().isdigit() # type: ignore
else 1.0
)
self.async_httpx_client = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
_batch_size = (
os.getenv("ARGILLA_BATCH_SIZE", None) or litellm.argilla_batch_size
)
if _batch_size:
self.batch_size = int(_batch_size)
asyncio.create_task(self.periodic_flush())
self.flush_lock = asyncio.Lock()
super().__init__(**kwargs, flush_lock=self.flush_lock)
def validate_argilla_transformation_object(
self, argilla_transformation_object: Dict[str, Any]
):
if not isinstance(argilla_transformation_object, dict):
raise Exception(
"'argilla_transformation_object' must be a dictionary, to log your payload to Argilla."
)
for v in argilla_transformation_object.values():
if v not in SUPPORTED_PAYLOAD_FIELDS:
raise Exception(
f"All values in argilla_transformation_object must be a key in SUPPORTED_PAYLOAD_FIELDS, {v} is not a valid key."
)
def get_credentials_from_env(
self,
argilla_api_key: Optional[str],
argilla_dataset_name: Optional[str],
argilla_base_url: Optional[str],
) -> ArgillaCredentialsObject:
_credentials_api_key = argilla_api_key or os.getenv("ARGILLA_API_KEY")
if _credentials_api_key is None:
raise Exception("Invalid Argilla API Key given. _credentials_api_key=None.")
_credentials_base_url = (
argilla_base_url
or os.getenv("ARGILLA_BASE_URL")
or "http://localhost:6900/"
)
if _credentials_base_url is None:
raise Exception(
"Invalid Argilla Base URL given. _credentials_base_url=None."
)
_credentials_dataset_name = (
argilla_dataset_name
or os.getenv("ARGILLA_DATASET_NAME")
or "litellm-completion"
)
if _credentials_dataset_name is None:
raise Exception("Invalid Argilla Dataset give. Value=None.")
else:
dataset_response = litellm.module_level_client.get(
url=f"{_credentials_base_url}/api/v1/me/datasets?name={_credentials_dataset_name}",
headers={"X-Argilla-Api-Key": _credentials_api_key},
)
json_response = dataset_response.json()
if (
"items" in json_response
and isinstance(json_response["items"], list)
and len(json_response["items"]) > 0
):
_credentials_dataset_name = json_response["items"][0]["id"]
return ArgillaCredentialsObject(
ARGILLA_API_KEY=_credentials_api_key,
ARGILLA_BASE_URL=_credentials_base_url,
ARGILLA_DATASET_NAME=_credentials_dataset_name,
)
def get_chat_messages(
self, payload: StandardLoggingPayload
) -> List[Dict[str, Any]]:
payload_messages = payload.get("messages", None)
if payload_messages is None:
raise Exception("No chat messages found in payload.")
if (
isinstance(payload_messages, list)
and len(payload_messages) > 0
and isinstance(payload_messages[0], dict)
):
return payload_messages
elif isinstance(payload_messages, dict):
return [payload_messages]
else:
raise Exception(f"Invalid chat messages format: {payload_messages}")
def get_str_response(self, payload: StandardLoggingPayload) -> str:
response = payload["response"]
if response is None:
raise Exception("No response found in payload.")
if isinstance(response, str):
return response
elif isinstance(response, dict):
return (
response.get("choices", [{}])[0].get("message", {}).get("content", "")
)
else:
raise Exception(f"Invalid response format: {response}")
def _prepare_log_data(
self, kwargs, response_obj, start_time, end_time
) -> Optional[ArgillaItem]:
try:
# Ensure everything in the payload is converted to str
payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object", None
)
if payload is None:
raise Exception("Error logging request payload. Payload=none.")
argilla_message = self.get_chat_messages(payload)
argilla_response = self.get_str_response(payload)
argilla_item: ArgillaItem = {"fields": {}}
for k, v in self.argilla_transformation_object.items():
if v == "messages":
argilla_item["fields"][k] = argilla_message
elif v == "response":
argilla_item["fields"][k] = argilla_response
else:
argilla_item["fields"][k] = payload.get(v, None)
return argilla_item
except Exception:
raise
def _send_batch(self):
if not self.log_queue:
return
argilla_api_base = self.default_credentials["ARGILLA_BASE_URL"]
argilla_dataset_name = self.default_credentials["ARGILLA_DATASET_NAME"]
url = f"{argilla_api_base}/api/v1/datasets/{argilla_dataset_name}/records/bulk"
argilla_api_key = self.default_credentials["ARGILLA_API_KEY"]
headers = {"X-Argilla-Api-Key": argilla_api_key}
try:
response = litellm.module_level_client.post(
url=url,
json=self.log_queue,
headers=headers,
)
if response.status_code >= 300:
verbose_logger.error(
f"Argilla Error: {response.status_code} - {response.text}"
)
else:
verbose_logger.debug(
f"Batch of {len(self.log_queue)} runs successfully created"
)
self.log_queue.clear()
except Exception:
verbose_logger.exception("Argilla Layer Error - Error sending batch.")
def log_success_event(self, kwargs, response_obj, start_time, end_time):
try:
sampling_rate = (
float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore
if os.getenv("LANGSMITH_SAMPLING_RATE") is not None
and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore
else 1.0
)
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
"Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
sampling_rate, random_sample
)
)
return # Skip logging
verbose_logger.debug(
"Langsmith Sync Layer Logging - kwargs: %s, response_obj: %s",
kwargs,
response_obj,
)
data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
if data is None:
return
self.log_queue.append(data)
verbose_logger.debug(
f"Langsmith, event added to queue. Will flush in {self.flush_interval} seconds..."
)
if len(self.log_queue) >= self.batch_size:
self._send_batch()
except Exception:
verbose_logger.exception("Langsmith Layer Error - log_success_event error")
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
try:
sampling_rate = self.sampling_rate
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
"Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
sampling_rate, random_sample
)
)
return # Skip logging
verbose_logger.debug(
"Langsmith Async Layer Logging - kwargs: %s, response_obj: %s",
kwargs,
response_obj,
)
payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object", None
)
data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
## ALLOW CUSTOM LOGGERS TO MODIFY / FILTER DATA BEFORE LOGGING
for callback in litellm.callbacks:
if isinstance(callback, CustomLogger):
try:
if data is None:
break
data = await callback.async_dataset_hook(data, payload)
except NotImplementedError:
pass
if data is None:
return
self.log_queue.append(data)
verbose_logger.debug(
"Langsmith logging: queue length %s, batch size %s",
len(self.log_queue),
self.batch_size,
)
if len(self.log_queue) >= self.batch_size:
await self.flush_queue()
except Exception:
verbose_logger.exception(
"Argilla Layer Error - error logging async success event."
)
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
sampling_rate = self.sampling_rate
random_sample = random.random()
if random_sample > sampling_rate:
verbose_logger.info(
"Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
sampling_rate, random_sample
)
)
return # Skip logging
verbose_logger.info("Langsmith Failure Event Logging!")
try:
data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
self.log_queue.append(data)
verbose_logger.debug(
"Langsmith logging: queue length %s, batch size %s",
len(self.log_queue),
self.batch_size,
)
if len(self.log_queue) >= self.batch_size:
await self.flush_queue()
except Exception:
verbose_logger.exception(
"Langsmith Layer Error - error logging async failure event."
)
async def async_send_batch(self):
"""
sends runs to /batch endpoint
Sends runs from self.log_queue
Returns: None
Raises: Does not raise an exception, will only verbose_logger.exception()
"""
if not self.log_queue:
return
argilla_api_base = self.default_credentials["ARGILLA_BASE_URL"]
argilla_dataset_name = self.default_credentials["ARGILLA_DATASET_NAME"]
url = f"{argilla_api_base}/api/v1/datasets/{argilla_dataset_name}/records/bulk"
argilla_api_key = self.default_credentials["ARGILLA_API_KEY"]
headers = {"X-Argilla-Api-Key": argilla_api_key}
try:
response = await self.async_httpx_client.put(
url=url,
data=json.dumps(
{
"items": self.log_queue,
}
),
headers=headers,
timeout=60000,
)
response.raise_for_status()
if response.status_code >= 300:
verbose_logger.error(
f"Argilla Error: {response.status_code} - {response.text}"
)
else:
verbose_logger.debug(
"Batch of %s runs successfully created", len(self.log_queue)
)
except httpx.HTTPStatusError:
verbose_logger.exception("Argilla HTTP Error")
except Exception:
verbose_logger.exception("Argilla Layer Error")