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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from __future__ import annotations
from typing import List, Optional, Union
from camel.configs.base_config import BaseConfig
class LiteLLMConfig(BaseConfig):
r"""Defines the parameters for generating chat completions using the
LiteLLM API.
Args:
timeout (Optional[Union[float, str]], optional): Request timeout.
(default: None)
temperature (Optional[float], optional): Temperature parameter for
controlling randomness. (default: None)
top_p (Optional[float], optional): Top-p parameter for nucleus
sampling. (default: None)
n (Optional[int], optional): Number of completions to generate.
(default: None)
stream (Optional[bool], optional): Whether to return a streaming
response. (default: None)
stream_options (Optional[dict], optional): Options for the streaming
response. (default: None)
stop (Optional[Union[str, List[str]]], optional): Sequences where the
API will stop generating further tokens. (default: None)
max_tokens (Optional[int], optional): Maximum number of tokens to
generate. (default: None)
presence_penalty (Optional[float], optional): Penalize new tokens
based on their existence in the text so far. (default: None)
frequency_penalty (Optional[float], optional): Penalize new tokens
based on their frequency in the text so far. (default: None)
logit_bias (Optional[dict], optional): Modify the probability of
specific tokens appearing in the completion. (default: None)
user (Optional[str], optional): A unique identifier representing the
end-user. (default: None)
response_format (Optional[dict], optional): Response format
parameters. (default: None)
seed (Optional[int], optional): Random seed. (default: None)
tools (Optional[List], optional): List of tools. (default: None)
tool_choice (Optional[Union[str, dict]], optional): Tool choice
parameters. (default: None)
logprobs (Optional[bool], optional): Whether to return log
probabilities of the output tokens. (default: None)
top_logprobs (Optional[int], optional): Number of most likely tokens
to return at each token position. (default: None)
deployment_id (Optional[str], optional): Deployment ID. (default: None)
extra_headers (Optional[dict], optional): Additional headers for the
request. (default: None)
api_version (Optional[str], optional): API version. (default: None)
mock_response (Optional[str], optional): Mock completion response for
testing or debugging. (default: None)
custom_llm_provider (Optional[str], optional): Non-OpenAI LLM
provider. (default: None)
max_retries (Optional[int], optional): Maximum number of retries.
(default: None)
"""
timeout: Optional[Union[float, str]] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
n: Optional[int] = None
stream: Optional[bool] = None
stream_options: Optional[dict] = None
stop: Optional[Union[str, List[str]]] = None
max_tokens: Optional[int] = None
presence_penalty: Optional[float] = None
frequency_penalty: Optional[float] = None
logit_bias: Optional[dict] = None
user: Optional[str] = None
response_format: Optional[dict] = None
seed: Optional[int] = None
tool_choice: Optional[Union[str, dict]] = None
logprobs: Optional[bool] = None
top_logprobs: Optional[int] = None
deployment_id: Optional[str] = None
extra_headers: Optional[dict] = None
api_version: Optional[str] = None
mock_response: Optional[str] = None
custom_llm_provider: Optional[str] = None
max_retries: Optional[int] = None
LITELLM_API_PARAMS = {param for param in LiteLLMConfig.model_fields.keys()}
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