mirror of
https://github.com/BerriAI/litellm.git
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053b0e741f
* test: fix import for test
* fix: fix bad error string
* docs: cleanup files docs
* fix(files/main.py): cleanup error string
* style: initial commit with a provider/config pattern for files api
google ai studio files api onboarding
* fix: test
* feat(gemini/files/transformation.py): support gemini files api response transformation
* fix(gemini/files/transformation.py): return file id as gemini uri
allows id to be passed in to chat completion request, just like openai
* feat(llm_http_handler.py): support async route for files api on llm_http_handler
* fix: fix linting errors
* fix: fix model info check
* fix: fix ruff errors
* fix: fix linting errors
* Revert "fix: fix linting errors"
This reverts commit 926a5a527f.
* fix: fix linting errors
* test: fix test
* test: fix tests
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
"""
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Translation from OpenAI's `/chat/completions` endpoint to IBM WatsonX's `/text/chat` endpoint.
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Docs: https://cloud.ibm.com/apidocs/watsonx-ai#text-chat
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"""
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from typing import List, Optional, Tuple, Union
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.watsonx import WatsonXAIEndpoint
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from ....utils import _remove_additional_properties, _remove_strict_from_schema
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from ...openai.chat.gpt_transformation import OpenAIGPTConfig
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from ..common_utils import IBMWatsonXMixin
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class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
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def get_supported_openai_params(self, model: str) -> List:
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return [
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"temperature", # equivalent to temperature
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"max_tokens", # equivalent to max_new_tokens
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"top_p", # equivalent to top_p
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"frequency_penalty", # equivalent to repetition_penalty
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"stop", # equivalent to stop_sequences
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"seed", # equivalent to random_seed
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"stream", # equivalent to stream
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"tools",
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"tool_choice", # equivalent to tool_choice + tool_choice_options
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"logprobs",
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"top_logprobs",
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"n",
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"presence_penalty",
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"response_format",
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]
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def is_tool_choice_option(self, tool_choice: Optional[Union[str, dict]]) -> bool:
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if tool_choice is None:
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return False
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if isinstance(tool_choice, str):
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return tool_choice in ["auto", "none", "required"]
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return False
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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## TOOLS ##
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_tools = non_default_params.pop("tools", None)
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if _tools is not None:
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# remove 'additionalProperties' from tools
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_tools = _remove_additional_properties(_tools)
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# remove 'strict' from tools
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_tools = _remove_strict_from_schema(_tools)
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if _tools is not None:
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non_default_params["tools"] = _tools
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## TOOL CHOICE ##
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_tool_choice = non_default_params.pop("tool_choice", None)
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if self.is_tool_choice_option(_tool_choice):
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optional_params["tool_choice_options"] = _tool_choice
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elif _tool_choice is not None:
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optional_params["tool_choice"] = _tool_choice
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return super().map_openai_params(
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non_default_params, optional_params, model, drop_params
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)
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def _get_openai_compatible_provider_info(
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self, api_base: Optional[str], api_key: Optional[str]
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) -> Tuple[Optional[str], Optional[str]]:
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api_base = api_base or get_secret_str("HOSTED_VLLM_API_BASE") # type: ignore
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dynamic_api_key = (
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api_key or get_secret_str("HOSTED_VLLM_API_KEY") or ""
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) # vllm does not require an api key
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return api_base, dynamic_api_key
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def get_complete_url(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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model: str,
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optional_params: dict,
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litellm_params: dict,
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stream: Optional[bool] = None,
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) -> str:
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url = self._get_base_url(api_base=api_base)
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if model.startswith("deployment/"):
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deployment_id = "/".join(model.split("/")[1:])
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endpoint = (
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WatsonXAIEndpoint.DEPLOYMENT_CHAT_STREAM.value
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if stream
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else WatsonXAIEndpoint.DEPLOYMENT_CHAT.value
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)
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endpoint = endpoint.format(deployment_id=deployment_id)
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else:
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endpoint = (
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WatsonXAIEndpoint.CHAT_STREAM.value
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if stream
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else WatsonXAIEndpoint.CHAT.value
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)
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url = url.rstrip("/") + endpoint
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## add api version
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url = self._add_api_version_to_url(
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url=url, api_version=optional_params.pop("api_version", None)
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)
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return url
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