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import os
from langchain_openai import ChatOpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_together import Together
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
from dotenv import load_dotenv
load_dotenv()
class PersonalModels:
def get_chat_model(self,
model: str = 'mistralai/Mistral-7B-Instruct-v0.2',
api_key: str = os.environ.get('TOGETHER_API_KEY', ''),
max_tokens: int = 1000,
temperature: float = 0.0,
verbose: bool = True) -> ChatOpenAI:
llm = ChatOpenAI(
api_key=api_key,
base_url="https://api.together.xyz/v1",
model=model,
max_tokens=max_tokens,
temperature=temperature,
verbose=verbose
)
return llm
def get_embedding_model(self,
model: str = 'intfloat/multilingual-e5-large') -> HuggingFaceInferenceAPIEmbeddings:
embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN', ''), model_name=model)
# embeddings = HuggingFaceEmbeddings(model_name=model,model_kwargs = {'device': 'cuda'})
# print('modelo está na gpu?',next(embeddings.model.parameters()).is_cuda)
return embeddings
def get_rag_model(self,
model: str = 'mistralai/Mistral-7B-Instruct-v0.2',
api_key: str = os.environ.get('TOGETHER_API_KEY', ''),
max_tokens: int = 1000,
temperature: float = 0.0,
top_k: int = 20,
verbose: bool = True) -> Together:
llm = Together(
model=model,
temperature=temperature,
max_tokens=max_tokens,
top_k=top_k,
together_api_key=api_key,
verbose=verbose
)
return llm
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