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from application.llm.openai import OpenAILLM, AzureOpenAILLM | ||
from application.llm.sagemaker import SagemakerAPILLM | ||
from application.llm.huggingface import HuggingFaceLLM | ||
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class LLMCreator: | ||
llms = { | ||
'openai': OpenAILLM, | ||
'azure_openai': AzureOpenAILLM, | ||
'sagemaker': SagemakerAPILLM, | ||
'huggingface': HuggingFaceLLM | ||
} | ||
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@classmethod | ||
def create_llm(cls, type, *args, **kwargs): | ||
llm_class = cls.llms.get(type.lower()) | ||
if not llm_class: | ||
raise ValueError(f"No LLM class found for type {type}") | ||
return llm_class(*args, **kwargs) |
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from application.llm.base import BaseLLM | ||
from application.core.settings import settings | ||
import requests | ||
import json | ||
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class SagemakerAPILLM(BaseLLM): | ||
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def __init__(self, *args, **kwargs): | ||
self.url = settings.SAGEMAKER_API_URL | ||
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def gen(self, model, engine, messages, stream=False, **kwargs): | ||
context = messages[0]['content'] | ||
user_question = messages[-1]['content'] | ||
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n" | ||
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response = requests.post( | ||
url=self.url, | ||
headers={ | ||
"Content-Type": "application/json; charset=utf-8", | ||
}, | ||
data=json.dumps({"input": prompt}) | ||
) | ||
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return response.json()['answer'] | ||
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def gen_stream(self, model, engine, messages, stream=True, **kwargs): | ||
raise NotImplementedError("Sagemaker does not support streaming") |