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# {'prompts': [
# {'id': 1, 'prompt_template': 'Question: {question}\nAnswer: \n', 'description': 'simple question without a prompt', 'rate': 1},
# {'id': 2, 'prompt_template': "Question: {question}\nAnswer: Write a concise answer on the question with
# one example if it's possible. CONCISE ANSWER.\n", 'description': 'simple concise prompt', 'rate': 3}]}
import os
import yaml
import logging
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import HuggingFaceHub
from .config import config
class LLM_chain:
def __init__(self):
self.llm = HuggingFaceHub(
repo_id=config["model"],
model_kwargs={"temperature": config["temperature"], "max_new_tokens": config["max_new_tokens"], "top_k": config["top_k"], "load_in_8bit": config["load_in_8bit"]})
def __read_yaml(self):
try:
# get current dir
current_dir = os.path.dirname(os.path.realpath(__file__))
yaml_file = os.path.join(current_dir, 'prompts.yaml')
with open(yaml_file, 'r') as file:
data = yaml.safe_load(file)
return data
except Exception as e:
logging.error(e)
def __call__(self, entity: str, id: int = 0):
try:
data = self.__read_yaml()
prompts = data["prompts"][id] # get second prompt from yaml, need change id parameter to get other prompt
template = prompts["prompt_template"]
prompt = PromptTemplate(template=template, input_variables=["entity"])
llm_chain = LLMChain(prompt=prompt, llm=self.llm, verbose=True)
output = llm_chain.invoke(entity)
return output["text"]
except Exception as e:
logging.error(e) |