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| import openai | |
| class LlmAgent: | |
| def __init__(self, llm_model: str): | |
| self.llm = llm_model | |
| def generate_paragraph(self, query: str, context: {}, histo: [(str, str)], language='fr') -> str: | |
| """generates the answer""" | |
| template = (f"You are a conversation bot designed to answer to the query from users." | |
| f"Your answer is based on the context delimited by triple backticks :\n ``` {context} ```\n" | |
| f"You are consistent and avoid redundancies with the rest of the initial conversation delimited by triple backticks :\n ``` {histo} ```\n" | |
| f"Your response shall be in {language} and shall be concise." | |
| f"You shall only provide the answer, nothing else before and after." | |
| f"Here is the query you are given :\n" | |
| f"``` {query} ```") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":template}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----") | |
| return str(res) | |
| def generate_paragraph_v2(self, query: str, context: {}, histo: [(str, str)], language='fr') -> str: | |
| """generates the answer""" | |
| context_for_the_ai = (f"You are a conversation bot designed to answer to the query from users." | |
| f"Your answer is based on the context delimited by triple backticks :\n ``` {context} ```\n" | |
| f"You are consistent and avoid redundancies with the rest of the initial conversation delimited by triple backticks :\n ``` {histo} ```\n" | |
| f"Your response shall be in {language} and shall be concise.") | |
| generation = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k", messages=[{"role":"system","content":context_for_the_ai},{"role":"user","content":query}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----") | |
| return str(res) | |
| def translate(self, text: str) -> str: | |
| """translates""" | |
| template = (f"Your task consists in translating in English the following text delimited by triple backticks: ``` {text} ```\n" | |
| f"If the text is already in English, just return it !\n" | |
| f"Your must not provide an answer to the text, just translate it.\n") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":template}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----TRANSLATE----") | |
| return res | |
| def translate_v2(self, text: str) -> str: | |
| """translates""" | |
| task = "Translate in english the text. If it is already in english, just return the text." | |
| generation = openai.ChatCompletion.create(model="gpt-4", messages=[{"role":"system","content":task},{"role":"user","content":text}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----TRANSLATE V2----") | |
| return res | |
| def generate_answer(self, query: str, answer: str, histo: str, context: str,language : str) -> str: | |
| """provides the final answer in {language} based on the initial query and the answer in english""" | |
| template = (f"Your task consists in translating the answer in {language}, if its not already the case, to the query " | |
| f"delimited by triple backticks: ```{query}``` \n" | |
| f"You don't add new content to the answer but: " | |
| f"1 You can use some vocabulary from the context delimited by triple backticks:\n" | |
| f"```{context}```\n" | |
| f"2 You are consistent and avoid redundancies with the rest of the initial" | |
| f"conversation delimited by triple backticks: ```{histo}```\n" | |
| f"Your response shall respect the following format:<response>\n" | |
| f"Here is the answer you are given in {language}:" | |
| f"{answer}") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":template}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----") | |
| return str(res).strip() | |
| def summarize_paragraph(self, prompt : str, title_doc : str = '',title_para : str = ''): | |
| max_tokens = 700 | |
| """summarizes the paragraph""" | |
| template = (f"Your task consists in summarizing the paragraph of the document untitled ```{title_doc}```." | |
| f"The paragraph title is ```{title_para}```." | |
| f"Your response shall be concise and shall respect the following format:" | |
| f"<summary>" | |
| f"If you see that the summary that you are creating will not respect ```{max_tokens}``` tokens, find a way to make it shorter." | |
| f"The paragraph you need to summarize is the following :" | |
| f"{prompt}") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":template}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----") | |
| return str(res).strip() | |
| def summarize_paragraph_v2(self, prompt : str, title_doc : str = '', title_para : str = ''): | |
| max_tokens = 850 | |
| location_of_the_paragraph = prompt.split(" :")[0] | |
| """summarizes the paragraph""" | |
| task = (f"Your task consists in summarizing in English the paragraph of the document untitled ```{title_doc}``` located in the ```{location_of_the_paragraph}``` section of the document." | |
| f"The paragraph title is ```{title_para}```." | |
| f"Your response shall be concise and shall respect the following format:" | |
| f"<summary>" | |
| f"If you see that the summary that you are creating will not respect ```{max_tokens}``` tokens, find a way to make it shorter.") | |
| generation = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k", messages=[{"role":"system","content":task},{"role":"user","content":prompt}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| print(res) | |
| print("----") | |
| return str(res).strip() | |
| def transform_paragraph_into_question(self, prompt : str, title_doc : str = '',title_para : str = '') -> (str, str): | |
| max_tokens = 150 | |
| prompt_template=(f"Your job is to create two questions about a paragraph of a document untitled ```{title_doc}```." | |
| f"The paragraph title is ```{title_para}```." | |
| f"If you see that the questions that you are creating will not respect ```{max_tokens}``` tokens, find a way to make them shorter." | |
| f"If you can't create a question about the paragraph, just rephrase ```{title_para}``` so that it becomes a question." | |
| f"Your response shall contains two questions, shall be concise and shall respect the following format:" | |
| f"`<question1>!=;<question2>`" | |
| f"You should not answer to the questions, just create them. Moreover, you shall include the title of the paragraph in the questions." | |
| f"The paragraph you need to create questions about is the following :" | |
| f"{prompt}") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":prompt_template}]) | |
| res = generation.choices[0].message.content | |
| print("****************") | |
| res = str(res).split("!=;") | |
| if len(res) == 1: | |
| return (res[0],"") | |
| elif len(res) == 2: | |
| return (res[0],res[1]) | |
| else: | |
| return ("","") | |
| def detect_language(self, text: str) -> str: | |
| """detects the language""" | |
| template = (f"Your task consists in detecting the language of the last question or sentence of the text." | |
| f"You should only give the two letters code of the language detected, nothing else." | |
| f"Here is the text you are given delimited by triple backticks : ```{text}```") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"user","content":template}]) | |
| res = generation.choices[0].message.content | |
| return str(res).strip() | |
| def detect_language_v2(self, text: str) -> str: | |
| """detects the language""" | |
| task = (f"Your task consists in detecting the language of the last question or sentence of the text." | |
| f"You should only give the two letters code of the language detected, nothing else.") | |
| generation = openai.ChatCompletion.create(model=self.llm, messages=[{"role":"system","content":task},{"role":"user","content":text}]) | |
| res = generation.choices[0].message.content | |
| return str(res).strip() | |