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https://github.com/BerriAI/litellm.git
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refactor: add black formatting
This commit is contained in:
Krrish Dholakia
parent
b87d630b0a
commit
4905929de3
156 changed files with 19723 additions and 10869 deletions
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@ -9,17 +9,21 @@ import httpx
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from litellm.utils import ModelResponse, Usage
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class TogetherAIError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(method="POST", url="https://api.together.xyz/inference")
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self.request = httpx.Request(
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method="POST", url="https://api.together.xyz/inference"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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class TogetherAIConfig():
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class TogetherAIConfig:
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"""
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Reference: https://docs.together.ai/reference/inference
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@ -37,35 +41,49 @@ class TogetherAIConfig():
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- `repetition_penalty` (float, optional): A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition.
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- `logprobs` (int32, optional): This parameter is not described in the prompt.
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- `logprobs` (int32, optional): This parameter is not described in the prompt.
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"""
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max_tokens: Optional[int]=None
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stop: Optional[str]=None
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temperature:Optional[int]=None
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top_p: Optional[float]=None
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top_k: Optional[int]=None
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repetition_penalty: Optional[float]=None
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logprobs: Optional[int]=None
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def __init__(self,
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max_tokens: Optional[int]=None,
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stop: Optional[str]=None,
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temperature:Optional[int]=None,
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top_p: Optional[float]=None,
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top_k: Optional[int]=None,
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repetition_penalty: Optional[float]=None,
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logprobs: Optional[int]=None) -> None:
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max_tokens: Optional[int] = None
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stop: Optional[str] = None
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temperature: Optional[int] = None
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top_p: Optional[float] = None
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top_k: Optional[int] = None
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repetition_penalty: Optional[float] = None
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logprobs: Optional[int] = None
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def __init__(
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self,
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max_tokens: Optional[int] = None,
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stop: Optional[str] = None,
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temperature: Optional[int] = None,
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top_p: Optional[float] = None,
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top_k: Optional[int] = None,
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repetition_penalty: Optional[float] = None,
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logprobs: Optional[int] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != 'self' and value is not None:
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {k: v for k, v in cls.__dict__.items()
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if not k.startswith('__')
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod))
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and v is not None}
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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 validate_environment(api_key):
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@ -80,10 +98,11 @@ def validate_environment(api_key):
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}
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return headers
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def completion(
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model: str,
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messages: list,
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api_base: str,
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api_base: str,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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@ -97,9 +116,11 @@ def completion(
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headers = validate_environment(api_key)
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## Load Config
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config = litellm.TogetherAIConfig.get_config()
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for k, v in config.items():
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if k not in optional_params: # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
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config = litellm.TogetherAIConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
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@ -107,15 +128,20 @@ def completion(
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details.get("roles", {}),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
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bos_token=model_prompt_details.get("bos_token", ""),
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eos_token=model_prompt_details.get("eos_token", ""),
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messages=messages,
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)
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role_dict=model_prompt_details.get("roles", {}),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
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bos_token=model_prompt_details.get("bos_token", ""),
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eos_token=model_prompt_details.get("eos_token", ""),
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messages=messages,
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)
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else:
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prompt = prompt_factory(model=model, messages=messages, api_key=api_key, custom_llm_provider="together_ai") # api key required to query together ai model list
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prompt = prompt_factory(
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model=model,
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messages=messages,
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api_key=api_key,
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custom_llm_provider="together_ai",
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) # api key required to query together ai model list
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data = {
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"model": model,
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@ -128,13 +154,14 @@ def completion(
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logging_obj.pre_call(
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input=prompt,
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api_key=api_key,
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additional_args={"complete_input_dict": data, "headers": headers, "api_base": api_base},
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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if (
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"stream_tokens" in optional_params
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and optional_params["stream_tokens"] == True
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):
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if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
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response = requests.post(
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api_base,
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headers=headers,
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@ -143,18 +170,14 @@ def completion(
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)
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return response.iter_lines()
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else:
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response = requests.post(
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api_base,
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headers=headers,
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data=json.dumps(data)
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)
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response = requests.post(api_base, headers=headers, data=json.dumps(data))
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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input=prompt,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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if response.status_code != 200:
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@ -170,30 +193,38 @@ def completion(
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)
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elif "error" in completion_response["output"]:
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raise TogetherAIError(
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message=json.dumps(completion_response["output"]), status_code=response.status_code
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message=json.dumps(completion_response["output"]),
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status_code=response.status_code,
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)
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if len(completion_response["output"]["choices"][0]["text"]) >= 0:
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model_response["choices"][0]["message"]["content"] = completion_response["output"]["choices"][0]["text"]
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model_response["choices"][0]["message"]["content"] = completion_response[
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"output"
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]["choices"][0]["text"]
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## CALCULATING USAGE
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print_verbose(f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}")
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print_verbose(
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f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}"
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)
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content", ""))
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)
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if "finish_reason" in completion_response["output"]["choices"][0]:
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model_response.choices[0].finish_reason = completion_response["output"]["choices"][0]["finish_reason"]
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model_response.choices[0].finish_reason = completion_response["output"][
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"choices"
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][0]["finish_reason"]
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model_response["created"] = int(time.time())
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model_response["model"] = model
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usage = Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens
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total_tokens=prompt_tokens + completion_tokens,
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)
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model_response.usage = usage
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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