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"""The code borrowed from https://colab.research.google.com/drive/1RW2yTxh5b9w7F3IrK00Iz51FTO5W01Rx?usp=sharing#scrollTo=RgbLVmf-o4j7"""
import os
from typing import Any, Dict
import together
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
from toolkit.utils import Config
configs = Config("configparser.ini")
os.environ["TOGETHER_API_KEY"] = configs.together_api_key
# together.api_key = configs.together_api_key
# models = together.Models.list()
# for idx, model in enumerate(models):
# print(idx, model["name"])
class TogetherLLM(LLM):
"""Together large language models."""
model: str = "togethercomputer/llama-2-70b-chat"
"""model endpoint to use"""
together_api_key: str = os.environ["TOGETHER_API_KEY"]
"""Together API key"""
temperature: float = 0
"""What sampling temperature to use."""
max_tokens: int = 512
"""The maximum number of tokens to generate in the completion."""
class Config:
extra = "forbid"
# @root_validator()
# def validate_environment(cls, values: Dict) -> Dict:
# """Validate that the API key is set."""
# api_key = get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
# values["together_api_key"] = api_key
# return values
@property
def _llm_type(self) -> str:
"""Return type of LLM."""
return "together"
def _call(
self,
prompt: str,
**kwargs: Any,
) -> str:
"""Call to Together endpoint."""
together.api_key = self.together_api_key
output = together.Complete.create(
prompt,
model=self.model,
max_tokens=self.max_tokens,
temperature=self.temperature,
)
text = output["output"]["choices"][0]["text"]
return text
# if __name__ == "__main__":
# test_llm = TogetherLLM(
# model="togethercomputer/llama-2-70b-chat", temperature=0, max_tokens=1000
# )
# print(test_llm("What are the olympics? "))
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