mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 10:44:24 +00:00
11f9df923a
* refactor(vertex_ai_partner_models/anthropic): refactor anthropic to use partner model logic * fix(vertex_ai/): support passing custom api base to partner models Fixes https://github.com/BerriAI/litellm/issues/4317 * fix(proxy_server.py): Fix prometheus premium user check logic * docs(prometheus.md): update quick start docs * fix(custom_llm.py): support passing dynamic api key + api base * fix(realtime_api/main.py): Add request/response logging for realtime api endpoints Closes https://github.com/BerriAI/litellm/issues/6081 * feat(openai/realtime): add openai realtime api logging Closes https://github.com/BerriAI/litellm/issues/6081 * fix(realtime_streaming.py): fix linting errors * fix(realtime_streaming.py): fix linting errors * fix: fix linting errors * fix pattern match router * Add literalai in the sidebar observability category (#6163) * fix: add literalai in the sidebar * fix: typo * update (#6160) * Feat: Add Langtrace integration (#5341) * Feat: Add Langtrace integration * add langtrace service name * fix timestamps for traces * add tests * Discard Callback + use existing otel logger * cleanup * remove print statments * remove callback * add docs * docs * add logging docs * format logging * remove emoji and add litellm proxy example * format logging * format `logging.md` * add langtrace docs to logging.md * sync conflict * docs fix * (perf) move s3 logging to Batch logging + async [94% faster perf under 100 RPS on 1 litellm instance] (#6165) * fix move s3 to use customLogger * add basic s3 logging test * add s3 to custom logger compatible * use batch logger for s3 * s3 set flush interval and batch size * fix s3 logging * add notes on s3 logging * fix s3 logging * add basic s3 logging test * fix s3 type errors * add test for sync logging on s3 * fix: fix to debug log --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: Willy Douhard <willy.douhard@gmail.com> Co-authored-by: yujonglee <yujonglee.dev@gmail.com> Co-authored-by: Ali Waleed <ali@scale3labs.com>
368 lines
14 KiB
Python
368 lines
14 KiB
Python
"""
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s3 Bucket Logging Integration
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async_log_success_event: Processes the event, stores it in memory for 10 seconds or until MAX_BATCH_SIZE and then flushes to s3
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NOTE 1: S3 does not provide a BATCH PUT API endpoint, so we create tasks to upload each element individually
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NOTE 2: We create a httpx client with a concurrent limit of 1 to upload to s3. Files should get uploaded BUT they should not impact latency of LLM calling logic
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"""
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import asyncio
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import json
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from datetime import datetime
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from typing import Dict, List, Optional
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import litellm
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from litellm._logging import print_verbose, verbose_logger
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from litellm.llms.base_aws_llm import BaseAWSLLM
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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httpxSpecialProvider,
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)
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from litellm.types.integrations.s3 import s3BatchLoggingElement
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from litellm.types.utils import StandardLoggingPayload
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from .custom_batch_logger import CustomBatchLogger
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# Default Flush interval and batch size for s3
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# Flush to s3 every 10 seconds OR every 1K requests in memory
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DEFAULT_S3_FLUSH_INTERVAL_SECONDS = 10
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DEFAULT_S3_BATCH_SIZE = 1000
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class S3Logger(CustomBatchLogger, BaseAWSLLM):
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# Class variables or attributes
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def __init__(
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self,
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s3_bucket_name: Optional[str] = None,
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s3_path: Optional[str] = None,
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s3_region_name: Optional[str] = None,
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s3_api_version: Optional[str] = None,
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s3_use_ssl: bool = True,
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s3_verify: Optional[bool] = None,
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s3_endpoint_url: Optional[str] = None,
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s3_aws_access_key_id: Optional[str] = None,
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s3_aws_secret_access_key: Optional[str] = None,
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s3_aws_session_token: Optional[str] = None,
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s3_flush_interval: Optional[int] = DEFAULT_S3_FLUSH_INTERVAL_SECONDS,
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s3_batch_size: Optional[int] = DEFAULT_S3_BATCH_SIZE,
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s3_config=None,
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**kwargs,
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):
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try:
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verbose_logger.debug(
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f"in init s3 logger - s3_callback_params {litellm.s3_callback_params}"
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)
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# IMPORTANT: We use a concurrent limit of 1 to upload to s3
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# Files should get uploaded BUT they should not impact latency of LLM calling logic
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self.async_httpx_client = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.LoggingCallback,
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params={"concurrent_limit": 1},
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)
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if litellm.s3_callback_params is not None:
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# read in .env variables - example os.environ/AWS_BUCKET_NAME
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for key, value in litellm.s3_callback_params.items():
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if type(value) is str and value.startswith("os.environ/"):
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litellm.s3_callback_params[key] = litellm.get_secret(value)
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# now set s3 params from litellm.s3_logger_params
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s3_bucket_name = litellm.s3_callback_params.get("s3_bucket_name")
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s3_region_name = litellm.s3_callback_params.get("s3_region_name")
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s3_api_version = litellm.s3_callback_params.get("s3_api_version")
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s3_use_ssl = litellm.s3_callback_params.get("s3_use_ssl", True)
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s3_verify = litellm.s3_callback_params.get("s3_verify")
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s3_endpoint_url = litellm.s3_callback_params.get("s3_endpoint_url")
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s3_aws_access_key_id = litellm.s3_callback_params.get(
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"s3_aws_access_key_id"
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)
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s3_aws_secret_access_key = litellm.s3_callback_params.get(
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"s3_aws_secret_access_key"
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)
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s3_aws_session_token = litellm.s3_callback_params.get(
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"s3_aws_session_token"
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)
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s3_config = litellm.s3_callback_params.get("s3_config")
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s3_path = litellm.s3_callback_params.get("s3_path")
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# done reading litellm.s3_callback_params
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s3_flush_interval = litellm.s3_callback_params.get(
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"s3_flush_interval", DEFAULT_S3_FLUSH_INTERVAL_SECONDS
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)
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s3_batch_size = litellm.s3_callback_params.get(
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"s3_batch_size", DEFAULT_S3_BATCH_SIZE
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)
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self.bucket_name = s3_bucket_name
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self.s3_path = s3_path
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verbose_logger.debug(f"s3 logger using endpoint url {s3_endpoint_url}")
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self.s3_bucket_name = s3_bucket_name
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self.s3_region_name = s3_region_name
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self.s3_api_version = s3_api_version
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self.s3_use_ssl = s3_use_ssl
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self.s3_verify = s3_verify
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self.s3_endpoint_url = s3_endpoint_url
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self.s3_aws_access_key_id = s3_aws_access_key_id
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self.s3_aws_secret_access_key = s3_aws_secret_access_key
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self.s3_aws_session_token = s3_aws_session_token
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self.s3_config = s3_config
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self.init_kwargs = kwargs
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asyncio.create_task(self.periodic_flush())
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self.flush_lock = asyncio.Lock()
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verbose_logger.debug(
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f"s3 flush interval: {s3_flush_interval}, s3 batch size: {s3_batch_size}"
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)
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# Call CustomLogger's __init__
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CustomBatchLogger.__init__(
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self,
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flush_lock=self.flush_lock,
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flush_interval=s3_flush_interval,
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batch_size=s3_batch_size,
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)
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self.log_queue: List[s3BatchLoggingElement] = []
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# Call BaseAWSLLM's __init__
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BaseAWSLLM.__init__(self)
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except Exception as e:
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print_verbose(f"Got exception on init s3 client {str(e)}")
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raise e
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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try:
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verbose_logger.debug(
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f"s3 Logging - Enters logging function for model {kwargs}"
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)
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s3_batch_logging_element = self.create_s3_batch_logging_element(
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start_time=start_time,
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standard_logging_payload=kwargs.get("standard_logging_object", None),
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s3_path=self.s3_path,
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)
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if s3_batch_logging_element is None:
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raise ValueError("s3_batch_logging_element is None")
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verbose_logger.debug(
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"\ns3 Logger - Logging payload = %s", s3_batch_logging_element
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)
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self.log_queue.append(s3_batch_logging_element)
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verbose_logger.debug(
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"s3 logging: queue length %s, batch size %s",
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len(self.log_queue),
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self.batch_size,
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)
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if len(self.log_queue) >= self.batch_size:
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await self.flush_queue()
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except Exception as e:
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verbose_logger.exception(f"s3 Layer Error - {str(e)}")
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pass
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def log_success_event(self, kwargs, response_obj, start_time, end_time):
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"""
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Synchronous logging function to log to s3
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Does not batch logging requests, instantly logs on s3 Bucket
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"""
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try:
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s3_batch_logging_element = self.create_s3_batch_logging_element(
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start_time=start_time,
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standard_logging_payload=kwargs.get("standard_logging_object", None),
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s3_path=self.s3_path,
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)
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if s3_batch_logging_element is None:
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raise ValueError("s3_batch_logging_element is None")
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verbose_logger.debug(
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"\ns3 Logger - Logging payload = %s", s3_batch_logging_element
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)
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# log the element sync httpx client
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self.upload_data_to_s3(s3_batch_logging_element)
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except Exception as e:
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verbose_logger.exception(f"s3 Layer Error - {str(e)}")
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pass
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async def async_upload_data_to_s3(
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self, batch_logging_element: s3BatchLoggingElement
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):
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try:
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import hashlib
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import boto3
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import requests
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from botocore.auth import SigV4Auth
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from botocore.awsrequest import AWSRequest
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from botocore.credentials import Credentials
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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try:
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=self.s3_aws_access_key_id,
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aws_secret_access_key=self.s3_aws_secret_access_key,
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aws_session_token=self.s3_aws_session_token,
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aws_region_name=self.s3_region_name,
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)
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# Prepare the URL
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url = f"https://{self.bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}"
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if self.s3_endpoint_url:
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url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key
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# Convert JSON to string
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json_string = json.dumps(batch_logging_element.payload)
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# Calculate SHA256 hash of the content
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content_hash = hashlib.sha256(json_string.encode("utf-8")).hexdigest()
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# Prepare the request
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headers = {
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"Content-Type": "application/json",
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"x-amz-content-sha256": content_hash,
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"Content-Language": "en",
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"Content-Disposition": f'inline; filename="{batch_logging_element.s3_object_download_filename}"',
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"Cache-Control": "private, immutable, max-age=31536000, s-maxage=0",
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}
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req = requests.Request("PUT", url, data=json_string, headers=headers)
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prepped = req.prepare()
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# Sign the request
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aws_request = AWSRequest(
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method=prepped.method,
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url=prepped.url,
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data=prepped.body,
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headers=prepped.headers,
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)
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SigV4Auth(credentials, "s3", self.s3_region_name).add_auth(aws_request)
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# Prepare the signed headers
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signed_headers = dict(aws_request.headers.items())
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# Make the request
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response = await self.async_httpx_client.put(
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url, data=json_string, headers=signed_headers
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)
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response.raise_for_status()
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except Exception as e:
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verbose_logger.exception(f"Error uploading to s3: {str(e)}")
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async def async_send_batch(self):
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"""
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Sends runs from self.log_queue
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Returns: None
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Raises: Does not raise an exception, will only verbose_logger.exception()
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"""
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if not self.log_queue:
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return
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for payload in self.log_queue:
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asyncio.create_task(self.async_upload_data_to_s3(payload))
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def create_s3_batch_logging_element(
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self,
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start_time: datetime,
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standard_logging_payload: Optional[StandardLoggingPayload],
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s3_path: Optional[str],
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) -> Optional[s3BatchLoggingElement]:
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"""
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Helper function to create an s3BatchLoggingElement.
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Args:
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start_time (datetime): The start time of the logging event.
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standard_logging_payload (Optional[StandardLoggingPayload]): The payload to be logged.
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s3_path (Optional[str]): The S3 path prefix.
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Returns:
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Optional[s3BatchLoggingElement]: The created s3BatchLoggingElement, or None if payload is None.
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"""
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if standard_logging_payload is None:
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return None
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s3_file_name = (
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litellm.utils.get_logging_id(start_time, standard_logging_payload) or ""
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)
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s3_object_key = (
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(s3_path.rstrip("/") + "/" if s3_path else "")
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+ start_time.strftime("%Y-%m-%d")
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+ "/"
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+ s3_file_name
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+ ".json"
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)
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s3_object_download_filename = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{standard_logging_payload['id']}.json"
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return s3BatchLoggingElement(
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payload=standard_logging_payload, # type: ignore
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s3_object_key=s3_object_key,
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s3_object_download_filename=s3_object_download_filename,
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)
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def upload_data_to_s3(self, batch_logging_element: s3BatchLoggingElement):
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try:
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import hashlib
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import boto3
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import requests
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from botocore.auth import SigV4Auth
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from botocore.awsrequest import AWSRequest
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from botocore.credentials import Credentials
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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try:
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=self.s3_aws_access_key_id,
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aws_secret_access_key=self.s3_aws_secret_access_key,
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aws_session_token=self.s3_aws_session_token,
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aws_region_name=self.s3_region_name,
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)
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# Prepare the URL
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url = f"https://{self.bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}"
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if self.s3_endpoint_url:
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url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key
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# Convert JSON to string
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json_string = json.dumps(batch_logging_element.payload)
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# Calculate SHA256 hash of the content
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content_hash = hashlib.sha256(json_string.encode("utf-8")).hexdigest()
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# Prepare the request
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headers = {
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"Content-Type": "application/json",
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"x-amz-content-sha256": content_hash,
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"Content-Language": "en",
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"Content-Disposition": f'inline; filename="{batch_logging_element.s3_object_download_filename}"',
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"Cache-Control": "private, immutable, max-age=31536000, s-maxage=0",
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}
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req = requests.Request("PUT", url, data=json_string, headers=headers)
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prepped = req.prepare()
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# Sign the request
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aws_request = AWSRequest(
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method=prepped.method,
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url=prepped.url,
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data=prepped.body,
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headers=prepped.headers,
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)
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SigV4Auth(credentials, "s3", self.s3_region_name).add_auth(aws_request)
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# Prepare the signed headers
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signed_headers = dict(aws_request.headers.items())
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httpx_client = _get_httpx_client()
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# Make the request
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response = httpx_client.put(url, data=json_string, headers=signed_headers)
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response.raise_for_status()
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except Exception as e:
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verbose_logger.exception(f"Error uploading to s3: {str(e)}")
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