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class LlmAgent:
def __init__(self, llm):
self.llm = llm
def generate_paragraph(self, query: str, context: {}, language='fr') -> str:
"""generates the final answer"""
template = (f" You are an agent designed to answer to the {query} based on the context delimited by triple backticks:\n"
f"``` {context}```\n"
f" The response shall be in {language} and shall be concise and based on the context provided\n"
f" In case the provided context is not relevant to answer to the question, just return that you "
f" don't know the answer ")
p = self.llm(template)
return p
def translate(self, text: str, language="en") -> str:
"""translates"""
languages = "french to english" if language == "en" else "english to french"
template = (f" Your task consists in translating {languages}\\n"
f" the following text delimited by by triple backticks: ```{text}```\n"
)
p = self.llm(template)
return p
def generate_answer(self, query: str, answer_en: str, context_fr: str) -> str:
"""provides the final answer in french based on the initial query and the answer in english"""
def _cut_unfinished_sentence(s: str):
return '.'.join(p.split('.')[:-1])
template = (f"Your task consists in providing the answer in french to the query "
f"delimited by triple backticks: ```{query}``` given the informations here delimited "
f"by triple backticks: ```{context_fr}``` and the answer in english delimited by triple "
f"backticks: ```{answer_en}```"
)
print(template)
p = self.llm(template)
p = _cut_unfinished_sentence(p)
print(p)
return p
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