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https://github.com/BerriAI/litellm.git
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* 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>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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Custom Logger that handles batching logic
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Use this if you want your logs to be stored in memory and flushed periodically
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"""
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import asyncio
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import time
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from typing import List, Literal, Optional
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from litellm._logging import verbose_logger
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from litellm.integrations.custom_logger import CustomLogger
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DEFAULT_BATCH_SIZE = 512
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DEFAULT_FLUSH_INTERVAL_SECONDS = 5
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class CustomBatchLogger(CustomLogger):
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def __init__(
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self,
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flush_lock: Optional[asyncio.Lock] = None,
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batch_size: Optional[int] = DEFAULT_BATCH_SIZE,
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flush_interval: Optional[int] = DEFAULT_FLUSH_INTERVAL_SECONDS,
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**kwargs,
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) -> None:
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"""
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Args:
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flush_lock (Optional[asyncio.Lock], optional): Lock to use when flushing the queue. Defaults to None. Only used for custom loggers that do batching
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"""
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self.log_queue: List = []
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self.flush_interval = flush_interval or DEFAULT_FLUSH_INTERVAL_SECONDS
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self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE
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self.last_flush_time = time.time()
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self.flush_lock = flush_lock
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super().__init__(**kwargs)
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pass
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async def periodic_flush(self):
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while True:
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await asyncio.sleep(self.flush_interval)
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verbose_logger.debug(
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f"CustomLogger periodic flush after {self.flush_interval} seconds"
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)
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await self.flush_queue()
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async def flush_queue(self):
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if self.flush_lock is None:
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return
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async with self.flush_lock:
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if self.log_queue:
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verbose_logger.debug(
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"CustomLogger: Flushing batch of %s events", len(self.log_queue)
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)
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await self.async_send_batch()
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self.log_queue.clear()
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self.last_flush_time = time.time()
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async def async_send_batch(self, *args, **kwargs):
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pass
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