mirror of
https://github.com/BerriAI/litellm.git
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Litellm ruff linting enforcement (#5992)
* 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
This commit is contained in:
Krish Dholakia
GitHub
parent
3fc4ae0d65
commit
d57be47b0f
263 changed files with 1687 additions and 3320 deletions
215
litellm/utils.py
215
litellm/utils.py
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@ -246,9 +246,9 @@ def print_verbose(
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verbose_logger.info(print_statement)
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elif log_level == "ERROR":
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verbose_logger.error(print_statement)
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if litellm.set_verbose == True and logger_only == False:
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if litellm.set_verbose is True and logger_only is False:
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print(print_statement) # noqa
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except:
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except Exception:
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pass
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@ -289,10 +289,10 @@ class Rules:
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for rule in litellm.post_call_rules:
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if callable(rule):
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decision = rule(input)
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if type(decision) == bool:
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if isinstance(decision, bool):
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if decision is False:
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raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore
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elif type(decision) == dict:
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elif isinstance(decision, dict):
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decision_val = decision.get("decision", True)
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decision_message = decision.get(
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"message", "LLM Response failed post-call-rule check"
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@ -461,7 +461,7 @@ def function_setup(
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if add_breadcrumb:
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try:
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details_to_log = copy.deepcopy(kwargs)
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except:
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except Exception:
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details_to_log = kwargs
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if litellm.turn_off_message_logging:
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@ -548,7 +548,7 @@ def function_setup(
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messages = kwargs.get("input", "speech")
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else:
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messages = "default-message-value"
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stream = True if "stream" in kwargs and kwargs["stream"] == True else False
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stream = True if "stream" in kwargs and kwargs["stream"] is True else False
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logging_obj = litellm.litellm_core_utils.litellm_logging.Logging(
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model=model,
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messages=messages,
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@ -585,7 +585,7 @@ def function_setup(
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def client(original_function):
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global liteDebuggerClient, get_all_keys
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global liteDebuggerClient
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rules_obj = Rules()
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def check_coroutine(value) -> bool:
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@ -706,13 +706,13 @@ def client(original_function):
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# DO NOT MOVE THIS. It always needs to run first
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# Check if this is an async function. If so only execute the async function
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if (
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kwargs.get("acompletion", False) == True
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or kwargs.get("aembedding", False) == True
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or kwargs.get("aimg_generation", False) == True
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or kwargs.get("amoderation", False) == True
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or kwargs.get("atext_completion", False) == True
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or kwargs.get("atranscription", False) == True
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or kwargs.get("arerank", False) == True
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kwargs.get("acompletion", False) is True
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or kwargs.get("aembedding", False) is True
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or kwargs.get("aimg_generation", False) is True
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or kwargs.get("amoderation", False) is True
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or kwargs.get("atext_completion", False) is True
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or kwargs.get("atranscription", False) is True
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or kwargs.get("arerank", False) is True
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):
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# [OPTIONAL] CHECK MAX RETRIES / REQUEST
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if litellm.num_retries_per_request is not None:
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@ -722,14 +722,14 @@ def client(original_function):
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)
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if previous_models is not None:
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if litellm.num_retries_per_request <= len(previous_models):
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raise Exception(f"Max retries per request hit!")
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raise Exception("Max retries per request hit!")
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# MODEL CALL
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result = original_function(*args, **kwargs)
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if "stream" in kwargs and kwargs["stream"] == True:
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if "stream" in kwargs and kwargs["stream"] is True:
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if (
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"complete_response" in kwargs
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and kwargs["complete_response"] == True
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and kwargs["complete_response"] is True
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):
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chunks = []
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for idx, chunk in enumerate(result):
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@ -803,15 +803,15 @@ def client(original_function):
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kwargs.get("caching", None) is None
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and litellm.cache is not None
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)
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or kwargs.get("caching", False) == True
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or kwargs.get("caching", False) is True
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)
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and kwargs.get("cache", {}).get("no-cache", False) != True
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and kwargs.get("cache", {}).get("no-cache", False) is not True
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)
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and kwargs.get("aembedding", False) != True
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and kwargs.get("atext_completion", False) != True
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and kwargs.get("acompletion", False) != True
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and kwargs.get("aimg_generation", False) != True
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and kwargs.get("atranscription", False) != True
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and kwargs.get("aembedding", False) is not True
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and kwargs.get("atext_completion", False) is not True
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and kwargs.get("acompletion", False) is not True
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and kwargs.get("aimg_generation", False) is not True
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and kwargs.get("atranscription", False) is not True
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): # allow users to control returning cached responses from the completion function
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# checking cache
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print_verbose("INSIDE CHECKING CACHE")
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@ -1057,7 +1057,8 @@ def client(original_function):
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) # DO NOT MAKE THREADED - router retry fallback relies on this!
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if hasattr(e, "message"):
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if (
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liteDebuggerClient and liteDebuggerClient.dashboard_url != None
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liteDebuggerClient
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and liteDebuggerClient.dashboard_url is not None
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): # make it easy to get to the debugger logs if you've initialized it
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e.message += f"\n Check the log in your dashboard - {liteDebuggerClient.dashboard_url}"
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raise e
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@ -1076,7 +1077,7 @@ def client(original_function):
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model = ""
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try:
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model = args[0] if len(args) > 0 else kwargs["model"]
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except:
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except Exception:
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if (
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call_type != CallTypes.aimage_generation.value # model optional
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and call_type != CallTypes.atext_completion.value # can also be engine
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@ -1107,9 +1108,9 @@ def client(original_function):
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if (
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(kwargs.get("caching", None) is None and litellm.cache is not None)
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or kwargs.get("caching", False) == True
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or kwargs.get("caching", False) is True
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) and (
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kwargs.get("cache", {}).get("no-cache", False) != True
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kwargs.get("cache", {}).get("no-cache", False) is not True
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): # allow users to control returning cached responses from the completion function
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# checking cache
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print_verbose("INSIDE CHECKING CACHE")
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@ -1211,7 +1212,7 @@ def client(original_function):
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if call_type == CallTypes.acompletion.value and isinstance(
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cached_result, dict
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):
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if kwargs.get("stream", False) == True:
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if kwargs.get("stream", False) is True:
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cached_result = convert_to_streaming_response_async(
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response_object=cached_result,
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)
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@ -1230,7 +1231,7 @@ def client(original_function):
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call_type == CallTypes.atext_completion.value
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and isinstance(cached_result, dict)
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):
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if kwargs.get("stream", False) == True:
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if kwargs.get("stream", False) is True:
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cached_result = convert_to_streaming_response_async(
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response_object=cached_result,
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)
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@ -1264,7 +1265,7 @@ def client(original_function):
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response_type="audio_transcription",
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hidden_params=hidden_params,
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)
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if kwargs.get("stream", False) == False:
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if kwargs.get("stream", False) is False:
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# LOG SUCCESS
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asyncio.create_task(
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logging_obj.async_success_handler(
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@ -1440,7 +1441,7 @@ def client(original_function):
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str(original_function.__name__)
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in litellm.cache.supported_call_types
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)
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and (kwargs.get("cache", {}).get("no-store", False) != True)
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and (kwargs.get("cache", {}).get("no-store", False) is not True)
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):
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if (
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isinstance(result, litellm.ModelResponse)
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@ -1556,7 +1557,7 @@ def client(original_function):
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elif isinstance(e, openai.APIError): # generic api error
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kwargs["retry_strategy"] = "constant_retry"
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return await litellm.acompletion_with_retries(*args, **kwargs)
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except:
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except Exception:
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pass
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elif (
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isinstance(e, litellm.exceptions.ContextWindowExceededError)
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@ -1715,7 +1716,7 @@ def openai_token_counter(
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num_tokens += calculage_img_tokens(
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data=image_url_str, mode="auto"
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)
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elif text is not None and count_response_tokens == True:
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elif text is not None and count_response_tokens is True:
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# This is the case where we need to count tokens for a streamed response. We should NOT add +3 tokens per message in this branch
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num_tokens = len(encoding.encode(text, disallowed_special=()))
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return num_tokens
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@ -4201,7 +4202,6 @@ def get_api_base(
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except Exception as e:
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verbose_logger.debug("Error occurred in getting api base - {}".format(str(e)))
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custom_llm_provider = None
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dynamic_api_key = None
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dynamic_api_base = None
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if dynamic_api_base is not None:
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@ -4266,7 +4266,7 @@ def get_first_chars_messages(kwargs: dict) -> str:
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_messages = kwargs.get("messages")
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_messages = str(_messages)[:100]
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return _messages
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except:
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except Exception:
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return ""
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@ -4709,7 +4709,7 @@ def get_max_tokens(model: str) -> Optional[int]:
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return max_position_embeddings
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else:
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return None
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except requests.exceptions.RequestException as e:
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except requests.exceptions.RequestException:
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return None
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try:
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@ -4845,7 +4845,7 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
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return max_position_embeddings
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else:
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return None
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except requests.exceptions.RequestException as e:
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except requests.exceptions.RequestException:
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return None
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try:
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@ -4864,7 +4864,7 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
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# Get custom_llm_provider
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try:
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split_model, custom_llm_provider, _, _ = get_llm_provider(model=model)
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except:
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except Exception:
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pass
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combined_model_name = model
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stripped_model_name = _strip_model_name(model=model)
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@ -5335,7 +5335,7 @@ def validate_environment(
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## EXTRACT LLM PROVIDER - if model name provided
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try:
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_, custom_llm_provider, _, _ = get_llm_provider(model=model)
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except:
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except Exception:
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custom_llm_provider = None
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# # check if llm provider part of model name
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# if model.split("/",1)[0] in litellm.provider_list:
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@ -5714,7 +5714,7 @@ def convert_to_streaming_response(response_object: Optional[dict] = None):
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tool_calls=choice["message"].get("tool_calls", None),
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)
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finish_reason = choice.get("finish_reason", None)
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if finish_reason == None:
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if finish_reason is None:
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# gpt-4 vision can return 'finish_reason' or 'finish_details'
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finish_reason = choice.get("finish_details")
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logprobs = choice.get("logprobs", None)
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@ -6005,7 +6005,7 @@ def convert_to_model_response_object(
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model_response_object._response_headers = _response_headers
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return model_response_object
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except Exception as e:
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except Exception:
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raise Exception(
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f"Invalid response object {traceback.format_exc()}\n\nreceived_args={received_args}"
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)
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@ -6022,12 +6022,12 @@ def prompt_token_calculator(model, messages):
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if "claude" in model:
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try:
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import anthropic
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except:
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except Exception:
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Exception("Anthropic import failed please run `pip install anthropic`")
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from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic
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anthropic = Anthropic()
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num_tokens = anthropic.count_tokens(text)
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anthropic_obj = Anthropic()
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num_tokens = anthropic_obj.count_tokens(text)
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else:
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num_tokens = len(encoding.encode(text))
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return num_tokens
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|
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@ -6044,7 +6044,7 @@ def valid_model(model):
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else:
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messages = [{"role": "user", "content": "Hello World"}]
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litellm.completion(model=model, messages=messages)
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except:
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except Exception:
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raise BadRequestError(message="", model=model, llm_provider="")
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@ -6065,9 +6065,9 @@ def check_valid_key(model: str, api_key: str):
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model=model, messages=messages, api_key=api_key, max_tokens=10
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)
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return True
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except AuthenticationError as e:
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except AuthenticationError:
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return False
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except Exception as e:
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except Exception:
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return False
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|
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@ -6160,7 +6160,7 @@ def _get_retry_after_from_exception_header(
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return retry_after
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except Exception as e:
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except Exception:
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retry_after = -1
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@ -6189,14 +6189,6 @@ def _calculate_retry_after(
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return timeout if timeout >= min_timeout else min_timeout
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# integration helper function
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def modify_integration(integration_name, integration_params):
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global supabaseClient
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if integration_name == "supabase":
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if "table_name" in integration_params:
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Supabase.supabase_table_name = integration_params["table_name"]
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# custom prompt helper function
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def register_prompt_template(
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model: str,
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|
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@ -6264,11 +6256,11 @@ def get_all_keys(llm_provider=None):
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)
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if user_email:
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time_delta = 0
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if last_fetched_at_keys != None:
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if last_fetched_at_keys is not None:
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current_time = time.time()
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time_delta = current_time - last_fetched_at_keys
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if (
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time_delta > 300 or last_fetched_at_keys == None or llm_provider
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time_delta > 300 or last_fetched_at_keys is None or llm_provider
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): # if the llm provider is passed in , assume this happening due to an AuthError for that provider
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# make the api call
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last_fetched_at = time.time()
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|
|
@ -6291,7 +6283,7 @@ def get_all_keys(llm_provider=None):
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return "it worked!"
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return None
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return None
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except:
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except Exception:
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print_verbose(
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f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}"
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)
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|
|
@ -6332,7 +6324,7 @@ def get_model_list():
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# threading.Thread(target=get_all_keys, args=(missing_llm_provider)).start()
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return model_list
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return [] # return empty list by default
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except:
|
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except Exception:
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print_verbose(
|
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f"[Non-Blocking Error] get_model_list error - {traceback.format_exc()}"
|
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)
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|
|
@ -6647,7 +6639,7 @@ class CustomStreamWrapper:
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|
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def handle_predibase_chunk(self, chunk):
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try:
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if type(chunk) != str:
|
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if not isinstance(chunk, str):
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chunk = chunk.decode(
|
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"utf-8"
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) # DO NOT REMOVE this: This is required for HF inference API + Streaming
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|
|
@ -6690,7 +6682,7 @@ class CustomStreamWrapper:
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|||
|
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def handle_huggingface_chunk(self, chunk):
|
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try:
|
||||
if type(chunk) != str:
|
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if not isinstance(chunk, str):
|
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chunk = chunk.decode(
|
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"utf-8"
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||||
) # DO NOT REMOVE this: This is required for HF inference API + Streaming
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|
|
@ -6743,7 +6735,7 @@ class CustomStreamWrapper:
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|||
"is_finished": is_finished,
|
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"finish_reason": finish_reason,
|
||||
}
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||||
except:
|
||||
except Exception:
|
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raise ValueError(f"Unable to parse response. Original response: {chunk}")
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|
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def handle_maritalk_chunk(self, chunk): # fake streaming
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|
|
@ -6758,7 +6750,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
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"finish_reason": finish_reason,
|
||||
}
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||||
except:
|
||||
except Exception:
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raise ValueError(f"Unable to parse response. Original response: {chunk}")
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|
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def handle_nlp_cloud_chunk(self, chunk):
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|
|
@ -6781,7 +6773,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
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"finish_reason": finish_reason,
|
||||
}
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||||
except Exception as e:
|
||||
except Exception:
|
||||
raise ValueError(f"Unable to parse response. Original response: {chunk}")
|
||||
|
||||
def handle_aleph_alpha_chunk(self, chunk):
|
||||
|
|
@ -6796,7 +6788,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
except:
|
||||
except Exception:
|
||||
raise ValueError(f"Unable to parse response. Original response: {chunk}")
|
||||
|
||||
def handle_cohere_chunk(self, chunk):
|
||||
|
|
@ -6822,7 +6814,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
except:
|
||||
except Exception:
|
||||
raise ValueError(f"Unable to parse response. Original response: {chunk}")
|
||||
|
||||
def handle_cohere_chat_chunk(self, chunk):
|
||||
|
|
@ -6835,7 +6827,7 @@ class CustomStreamWrapper:
|
|||
finish_reason = ""
|
||||
if "text" in data_json:
|
||||
text = data_json["text"]
|
||||
elif "is_finished" in data_json and data_json["is_finished"] == True:
|
||||
elif "is_finished" in data_json and data_json["is_finished"] is True:
|
||||
is_finished = data_json["is_finished"]
|
||||
finish_reason = data_json["finish_reason"]
|
||||
else:
|
||||
|
|
@ -6845,7 +6837,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
except:
|
||||
except Exception:
|
||||
raise ValueError(f"Unable to parse response. Original response: {chunk}")
|
||||
|
||||
def handle_azure_chunk(self, chunk):
|
||||
|
|
@ -6879,7 +6871,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
except:
|
||||
except Exception:
|
||||
raise ValueError(
|
||||
f"Unable to parse response. Original response: {chunk}"
|
||||
)
|
||||
|
|
@ -6910,7 +6902,7 @@ class CustomStreamWrapper:
|
|||
"is_finished": is_finished,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
except:
|
||||
except Exception:
|
||||
raise ValueError(f"Unable to parse response. Original response: {chunk}")
|
||||
|
||||
def handle_openai_chat_completion_chunk(self, chunk):
|
||||
|
|
@ -6922,7 +6914,6 @@ class CustomStreamWrapper:
|
|||
finish_reason = None
|
||||
logprobs = None
|
||||
usage = None
|
||||
original_chunk = None # this is used for function/tool calling
|
||||
if str_line and str_line.choices and len(str_line.choices) > 0:
|
||||
if (
|
||||
str_line.choices[0].delta is not None
|
||||
|
|
@ -6930,7 +6921,7 @@ class CustomStreamWrapper:
|
|||
):
|
||||
text = str_line.choices[0].delta.content
|
||||
else: # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai
|
||||
original_chunk = str_line
|
||||
pass
|
||||
if str_line.choices[0].finish_reason:
|
||||
is_finished = True
|
||||
finish_reason = str_line.choices[0].finish_reason
|
||||
|
|
@ -7084,7 +7075,7 @@ class CustomStreamWrapper:
|
|||
text = ""
|
||||
is_finished = False
|
||||
finish_reason = None
|
||||
if json_chunk["done"] == True:
|
||||
if json_chunk["done"] is True:
|
||||
text = ""
|
||||
is_finished = True
|
||||
finish_reason = "stop"
|
||||
|
|
@ -7119,7 +7110,7 @@ class CustomStreamWrapper:
|
|||
text = ""
|
||||
is_finished = False
|
||||
finish_reason = None
|
||||
if json_chunk["done"] == True:
|
||||
if json_chunk["done"] is True:
|
||||
text = ""
|
||||
is_finished = True
|
||||
finish_reason = "stop"
|
||||
|
|
@ -7159,7 +7150,7 @@ class CustomStreamWrapper:
|
|||
"finish_reason": finish_reason,
|
||||
}
|
||||
elif isinstance(chunk, dict):
|
||||
if chunk["is_finished"] == True:
|
||||
if chunk["is_finished"] is True:
|
||||
finish_reason = "stop"
|
||||
else:
|
||||
finish_reason = ""
|
||||
|
|
@ -7259,7 +7250,7 @@ class CustomStreamWrapper:
|
|||
.get("text", "")
|
||||
.get("raw", "")
|
||||
)
|
||||
prompt_tokens = len(
|
||||
len(
|
||||
encoding.encode(
|
||||
data_json.get("outputs", "")[0]
|
||||
.get("input", "")
|
||||
|
|
@ -7268,7 +7259,7 @@ class CustomStreamWrapper:
|
|||
.get("raw", "")
|
||||
)
|
||||
)
|
||||
completion_tokens = len(encoding.encode(text))
|
||||
len(encoding.encode(text))
|
||||
return {
|
||||
"text": text,
|
||||
"is_finished": True,
|
||||
|
|
@ -7459,7 +7450,7 @@ class CustomStreamWrapper:
|
|||
raise Exception("An unknown error occurred with the stream")
|
||||
self.received_finish_reason = "stop"
|
||||
elif self.custom_llm_provider == "gemini":
|
||||
if hasattr(chunk, "parts") == True:
|
||||
if hasattr(chunk, "parts") is True:
|
||||
try:
|
||||
if len(chunk.parts) > 0:
|
||||
completion_obj["content"] = chunk.parts[0].text
|
||||
|
|
@ -7469,7 +7460,7 @@ class CustomStreamWrapper:
|
|||
self.received_finish_reason = chunk.parts[
|
||||
0
|
||||
].finish_reason.name
|
||||
except:
|
||||
except Exception:
|
||||
if chunk.parts[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
|
|
@ -7533,7 +7524,7 @@ class CustomStreamWrapper:
|
|||
|
||||
if response_obj["is_finished"]:
|
||||
self.received_finish_reason = response_obj["finish_reason"]
|
||||
elif hasattr(chunk, "candidates") == True:
|
||||
elif hasattr(chunk, "candidates") is True:
|
||||
try:
|
||||
try:
|
||||
completion_obj["content"] = chunk.text
|
||||
|
|
@ -7588,7 +7579,7 @@ class CustomStreamWrapper:
|
|||
self.received_finish_reason = chunk.candidates[
|
||||
0
|
||||
].finish_reason.name
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
if chunk.candidates[0].finish_reason.name == "SAFETY":
|
||||
raise Exception(
|
||||
f"The response was blocked by VertexAI. {str(chunk)}"
|
||||
|
|
@ -7905,7 +7896,7 @@ class CustomStreamWrapper:
|
|||
print_verbose(
|
||||
f"new delta: {model_response.choices[0].delta}"
|
||||
)
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
model_response.choices[0].delta = Delta()
|
||||
else:
|
||||
if (
|
||||
|
|
@ -7967,14 +7958,14 @@ class CustomStreamWrapper:
|
|||
if isinstance(choice, BaseModel):
|
||||
try:
|
||||
choice_json = choice.model_dump()
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
choice_json = choice.dict()
|
||||
choice_json.pop(
|
||||
"finish_reason", None
|
||||
) # for mistral etc. which return a value in their last chunk (not-openai compatible).
|
||||
print_verbose(f"choice_json: {choice_json}")
|
||||
choices.append(StreamingChoices(**choice_json))
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
choices.append(StreamingChoices())
|
||||
print_verbose(f"choices in streaming: {choices}")
|
||||
model_response.choices = choices
|
||||
|
|
@ -8066,7 +8057,7 @@ class CustomStreamWrapper:
|
|||
except StopIteration:
|
||||
raise StopIteration
|
||||
except Exception as e:
|
||||
traceback_exception = traceback.format_exc()
|
||||
traceback.format_exc()
|
||||
e.message = str(e)
|
||||
raise exception_type(
|
||||
model=self.model,
|
||||
|
|
@ -8107,7 +8098,7 @@ class CustomStreamWrapper:
|
|||
),
|
||||
loop=self.logging_loop,
|
||||
)
|
||||
result = future.result()
|
||||
future.result()
|
||||
else:
|
||||
asyncio.run(
|
||||
self.logging_obj.async_success_handler(
|
||||
|
|
@ -8555,7 +8546,7 @@ class TextCompletionStreamWrapper:
|
|||
# only pass usage when stream_options["include_usage"] is True
|
||||
if (
|
||||
self.stream_options
|
||||
and self.stream_options.get("include_usage", False) == True
|
||||
and self.stream_options.get("include_usage", False) is True
|
||||
):
|
||||
response["usage"] = chunk.get("usage", None)
|
||||
|
||||
|
|
@ -8567,7 +8558,7 @@ class TextCompletionStreamWrapper:
|
|||
|
||||
def __next__(self):
|
||||
# model_response = ModelResponse(stream=True, model=self.model)
|
||||
response = TextCompletionResponse()
|
||||
TextCompletionResponse()
|
||||
try:
|
||||
for chunk in self.completion_stream:
|
||||
if chunk == "None" or chunk is None:
|
||||
|
|
@ -8649,7 +8640,7 @@ def read_config_args(config_path) -> dict:
|
|||
try:
|
||||
import os
|
||||
|
||||
current_path = os.getcwd()
|
||||
os.getcwd()
|
||||
with open(config_path, "r") as config_file:
|
||||
config = json.load(config_file)
|
||||
|
||||
|
|
@ -8675,7 +8666,7 @@ def completion_with_fallbacks(**kwargs):
|
|||
litellm_call_id = str(uuid.uuid4())
|
||||
|
||||
# max time to process a request with fallbacks: default 45s
|
||||
while response == None and time.time() - start_time < 45:
|
||||
while response is None and time.time() - start_time < 45:
|
||||
for model in fallbacks:
|
||||
# loop thru all models
|
||||
try:
|
||||
|
|
@ -8711,7 +8702,7 @@ def completion_with_fallbacks(**kwargs):
|
|||
} # combine the openai + litellm params at the same level
|
||||
response = litellm.completion(**kwargs, model=model)
|
||||
print_verbose(f"response: {response}")
|
||||
if response != None:
|
||||
if response is not None:
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -8965,7 +8956,7 @@ def get_valid_models() -> List[str]:
|
|||
models_for_provider = litellm.models_by_provider.get(provider, [])
|
||||
valid_models.extend(models_for_provider)
|
||||
return valid_models
|
||||
except:
|
||||
except Exception:
|
||||
return [] # NON-Blocking
|
||||
|
||||
|
||||
|
|
@ -9000,7 +8991,7 @@ def transform_logprobs(hf_response):
|
|||
token_info["token_logprobs"].append(token_logprob)
|
||||
|
||||
# stub this to work with llm eval harness
|
||||
top_alt_tokens = {"": -1, "": -2, "": -3}
|
||||
top_alt_tokens = {"": -1, "": -2, "": -3} # noqa: F601
|
||||
token_info["top_logprobs"].append(top_alt_tokens)
|
||||
|
||||
# For each element in the 'tokens' list, extract the relevant information
|
||||
|
|
@ -9044,19 +9035,19 @@ def print_args_passed_to_litellm(original_function, args, kwargs):
|
|||
# we've already printed this for acompletion, don't print for completion
|
||||
if (
|
||||
"acompletion" in kwargs
|
||||
and kwargs["acompletion"] == True
|
||||
and kwargs["acompletion"] is True
|
||||
and original_function.__name__ == "completion"
|
||||
):
|
||||
return
|
||||
elif (
|
||||
"aembedding" in kwargs
|
||||
and kwargs["aembedding"] == True
|
||||
and kwargs["aembedding"] is True
|
||||
and original_function.__name__ == "embedding"
|
||||
):
|
||||
return
|
||||
elif (
|
||||
"aimg_generation" in kwargs
|
||||
and kwargs["aimg_generation"] == True
|
||||
and kwargs["aimg_generation"] is True
|
||||
and original_function.__name__ == "img_generation"
|
||||
):
|
||||
return
|
||||
|
|
@ -9084,7 +9075,7 @@ def print_args_passed_to_litellm(original_function, args, kwargs):
|
|||
else:
|
||||
print_verbose(f"\033[92mlitellm.{original_function.__name__}()\033[0m")
|
||||
print_verbose("\n") # new line after
|
||||
except:
|
||||
except Exception:
|
||||
# This should always be non blocking
|
||||
pass
|
||||
|
||||
|
|
@ -9095,7 +9086,7 @@ def get_logging_id(start_time, response_obj):
|
|||
"time-" + start_time.strftime("%H-%M-%S-%f") + "_" + response_obj.get("id")
|
||||
)
|
||||
return response_id
|
||||
except:
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
|
|
@ -9127,31 +9118,9 @@ def _get_base_model_from_metadata(model_call_details=None):
|
|||
return None
|
||||
|
||||
|
||||
def _add_key_name_and_team_to_alert(request_info: str, metadata: dict) -> str:
|
||||
"""
|
||||
Internal helper function for litellm proxy
|
||||
Add the Key Name + Team Name to the error
|
||||
Only gets added if the metadata contains the user_api_key_alias and user_api_key_team_alias
|
||||
|
||||
[Non-Blocking helper function]
|
||||
"""
|
||||
try:
|
||||
_api_key_name = metadata.get("user_api_key_alias", None)
|
||||
_user_api_key_team_alias = metadata.get("user_api_key_team_alias", None)
|
||||
if _api_key_name is not None:
|
||||
request_info = (
|
||||
f"\n\nKey Name: `{_api_key_name}`\nTeam: `{_user_api_key_team_alias}`"
|
||||
+ request_info
|
||||
)
|
||||
|
||||
return request_info
|
||||
except:
|
||||
return request_info
|
||||
|
||||
|
||||
class ModelResponseIterator:
|
||||
def __init__(self, model_response: ModelResponse, convert_to_delta: bool = False):
|
||||
if convert_to_delta == True:
|
||||
if convert_to_delta is True:
|
||||
self.model_response = ModelResponse(stream=True)
|
||||
_delta = self.model_response.choices[0].delta # type: ignore
|
||||
_delta.content = model_response.choices[0].message.content # type: ignore
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue