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https://github.com/BerriAI/litellm.git
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94a05ca5d0
* ci(config.yml): add a 'check_code_quality' step Addresses https://github.com/BerriAI/litellm/issues/5991 * ci(config.yml): check why circle ci doesn't pick up this test * ci(config.yml): fix to run 'check_code_quality' tests * fix(__init__.py): fix unprotected import * fix(__init__.py): don't remove unused imports * build(ruff.toml): update ruff.toml to ignore unused imports * fix: fix: ruff + pyright - fix linting + type-checking errors * fix: fix linting errors * fix(lago.py): fix module init error * fix: fix linting errors * ci(config.yml): cd into correct dir for checks * fix(proxy_server.py): fix linting error * fix(utils.py): fix bare except causes ruff linting errors * fix: ruff - fix remaining linting errors * fix(clickhouse.py): use standard logging object * fix(__init__.py): fix unprotected import * fix: ruff - fix linting errors * fix: fix linting errors * ci(config.yml): cleanup code qa step (formatting handled in local_testing) * fix(_health_endpoints.py): fix ruff linting errors * ci(config.yml): just use ruff in check_code_quality pipeline for now * build(custom_guardrail.py): include missing file * style(embedding_handler.py): fix ruff check
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
import datetime
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class AthinaLogger:
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def __init__(self):
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import os
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self.athina_api_key = os.getenv("ATHINA_API_KEY")
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self.headers = {
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"athina-api-key": self.athina_api_key,
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"Content-Type": "application/json",
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}
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self.athina_logging_url = "https://log.athina.ai/api/v1/log/inference"
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self.additional_keys = [
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"environment",
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"prompt_slug",
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"customer_id",
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"customer_user_id",
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"session_id",
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"external_reference_id",
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"context",
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"expected_response",
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"user_query",
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]
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def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
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import json
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import traceback
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import requests # type: ignore
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try:
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is_stream = kwargs.get("stream", False)
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if is_stream:
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if "complete_streaming_response" in kwargs:
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# Log the completion response in streaming mode
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completion_response = kwargs["complete_streaming_response"]
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response_json = (
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completion_response.model_dump() if completion_response else {}
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)
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else:
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# Skip logging if the completion response is not available
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return
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else:
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# Log the completion response in non streaming mode
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response_json = response_obj.model_dump() if response_obj else {}
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data = {
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"language_model_id": kwargs.get("model"),
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"request": kwargs,
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"response": response_json,
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"prompt_tokens": response_json.get("usage", {}).get("prompt_tokens"),
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"completion_tokens": response_json.get("usage", {}).get(
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"completion_tokens"
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),
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"total_tokens": response_json.get("usage", {}).get("total_tokens"),
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}
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if (
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type(end_time) is datetime.datetime
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and type(start_time) is datetime.datetime
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):
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data["response_time"] = int(
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(end_time - start_time).total_seconds() * 1000
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)
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if "messages" in kwargs:
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data["prompt"] = kwargs.get("messages", None)
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# Directly add tools or functions if present
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optional_params = kwargs.get("optional_params", {})
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data.update(
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(k, v)
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for k, v in optional_params.items()
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if k in ["tools", "functions"]
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)
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# Add additional metadata keys
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metadata = kwargs.get("litellm_params", {}).get("metadata", {})
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if metadata:
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for key in self.additional_keys:
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if key in metadata:
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data[key] = metadata[key]
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response = requests.post(
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self.athina_logging_url,
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headers=self.headers,
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data=json.dumps(data, default=str),
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)
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if response.status_code != 200:
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print_verbose(
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f"Athina Logger Error - {response.text}, {response.status_code}"
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)
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else:
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print_verbose(f"Athina Logger Succeeded - {response.text}")
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except Exception as e:
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print_verbose(
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f"Athina Logger Error - {e}, Stack trace: {traceback.format_exc()}"
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)
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pass
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