mirror of
https://github.com/BerriAI/litellm.git
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128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
import os
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import json
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from enum import Enum
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import requests
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import time
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from typing import Callable
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from litellm.utils import ModelResponse
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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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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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def validate_environment(api_key):
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if api_key is None:
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raise ValueError(
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"Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params"
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)
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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"Authorization": "Bearer " + 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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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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custom_prompt_dict={},
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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headers = validate_environment(api_key)
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if model in custom_prompt_dict:
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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["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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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)
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data = {
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"model": model,
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"prompt": prompt,
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"request_type": "language-model-inference",
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**optional_params,
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}
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## LOGGING
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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},
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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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response = requests.post(
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"https://api.together.xyz/inference",
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream_tokens"],
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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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"https://api.together.xyz/inference",
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headers=headers,
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data=json.dumps(data)
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)
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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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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise TogetherAIError(
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message=json.dumps(completion_response),
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status_code=response.status_code,
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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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)
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completion_text = completion_response["output"]["choices"][0]["text"]
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## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(completion_text)
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)
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model_response["choices"][0]["message"]["content"] = completion_text
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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["created"] = time.time()
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model_response["model"] = model
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model_response["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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}
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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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