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* LiteLLM Minor Fixes & Improvements (09/16/2024) (#5723) * coverage (#5713) Signed-off-by: dbczumar <corey.zumar@databricks.com> * Move (#5714) Signed-off-by: dbczumar <corey.zumar@databricks.com> * fix(litellm_logging.py): fix logging client re-init (#5710) Fixes https://github.com/BerriAI/litellm/issues/5695 * fix(presidio.py): Fix logging_hook response and add support for additional presidio variables in guardrails config Fixes https://github.com/BerriAI/litellm/issues/5682 * feat(o1_handler.py): fake streaming for openai o1 models Fixes https://github.com/BerriAI/litellm/issues/5694 * docs: deprecated traceloop integration in favor of native otel (#5249) * fix: fix linting errors * fix: fix linting errors * fix(main.py): fix o1 import --------- Signed-off-by: dbczumar <corey.zumar@databricks.com> Co-authored-by: Corey Zumar <39497902+dbczumar@users.noreply.github.com> Co-authored-by: Nir Gazit <nirga@users.noreply.github.com> * feat(spend_management_endpoints.py): expose `/global/spend/refresh` endpoint for updating material view (#5730) * feat(spend_management_endpoints.py): expose `/global/spend/refresh` endpoint for updating material view Supports having `MonthlyGlobalSpend` view be a material view, and exposes an endpoint to refresh it * fix(custom_logger.py): reset calltype * fix: fix linting errors * fix: fix linting error * fix: fix import * test(test_databricks.py): fix databricks tests --------- Signed-off-by: dbczumar <corey.zumar@databricks.com> Co-authored-by: Corey Zumar <39497902+dbczumar@users.noreply.github.com> Co-authored-by: Nir Gazit <nirga@users.noreply.github.com>
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
"""
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Support for o1 model family
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https://platform.openai.com/docs/guides/reasoning
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Translations handled by LiteLLM:
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- modalities: image => drop param (if user opts in to dropping param)
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- role: system ==> translate to role 'user'
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- streaming => faked by LiteLLM
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- Tools, response_format => drop param (if user opts in to dropping param)
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- Logprobs => drop param (if user opts in to dropping param)
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"""
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import types
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from typing import Any, List, Optional, Union
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import litellm
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from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
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from .gpt_transformation import OpenAIGPTConfig
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class OpenAIO1Config(OpenAIGPTConfig):
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"""
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Reference: https://platform.openai.com/docs/guides/reasoning
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"""
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Get the supported OpenAI params for the given model
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"""
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all_openai_params = super().get_supported_openai_params(model=model)
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non_supported_params = [
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"logprobs",
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"tools",
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"tool_choice",
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"parallel_tool_calls",
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"function_call",
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"functions",
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"temperature",
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"top_p",
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"n",
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"presence_penalty",
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"frequency_penalty",
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"top_logprobs",
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"response_format",
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"stop",
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]
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return [
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param for param in all_openai_params if param not in non_supported_params
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]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict, model: str
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):
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if "max_tokens" in non_default_params:
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optional_params["max_completion_tokens"] = non_default_params.pop(
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"max_tokens"
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)
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return super()._map_openai_params(non_default_params, optional_params, model)
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def is_model_o1_reasoning_model(self, model: str) -> bool:
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if model in litellm.open_ai_chat_completion_models and "o1" in model:
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return True
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return False
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def o1_prompt_factory(self, messages: List[AllMessageValues]):
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"""
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Handles limitations of O-1 model family.
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- modalities: image => drop param (if user opts in to dropping param)
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- role: system ==> translate to role 'user'
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"""
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for i, message in enumerate(messages):
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if message["role"] == "system":
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new_message = ChatCompletionUserMessage(
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content=message["content"], role="user"
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)
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messages[i] = new_message # Replace the old message with the new one
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if "content" in message and isinstance(message["content"], list):
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new_content = []
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for content_item in message["content"]:
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if content_item.get("type") == "image_url":
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if litellm.drop_params is not True:
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raise ValueError(
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"Image content is not supported for O-1 models. Set litellm.drop_param to True to drop image content."
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)
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# If drop_param is True, we simply don't add the image content to new_content
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else:
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new_content.append(content_item)
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message["content"] = new_content
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return messages
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