llm.py

from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate


def generate_prompt(input_variables: list, template_file: str):
    """
    Generate a prompt from a template file and a list of input variables
    """
    with open(template_file, 'r', encoding='utf-8') as source_file:
        template = source_file.read()
    prompt = PromptTemplate(template=template, input_variables=input_variables)
    return prompt


def generate_conversation(memory: object,
                          llm: object,
                          prompt: object,
                          verbose: bool = False):
    """
    Generate a conversation from a memory object, a language model object, and a prompt object
    """
    conversation = ConversationChain(memory=memory,
                                     llm=llm,
                                     prompt=prompt,
                                     verbose=verbose)
    return conversation


def predict(input_text: str, conversation: object):
    '''
    Predict the next response from the conversation object
    '''
    response = conversation(input_text)
    history = response['history']
    history = history.split('\n')
    prediction = response['response']
    return {'history': history, 'prediction': prediction}