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| from langchain.base_language import BaseLanguageModel | |
| from langchain.chains import LLMChain, SequentialChain | |
| from langchain.chat_models import ChatAnthropic | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.prompts import ( | |
| PromptTemplate, | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| class GenerateStyleText: | |
| example: str | |
| prompt: str | |
| llm: BaseLanguageModel | |
| def __init__(self, example=None, prompt=None, llm=None): | |
| self.example = example | |
| self.prompt = prompt | |
| self.llm = llm | |
| def set_imp_llm(self, model): | |
| if model == 'GPT3': | |
| self.llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k") | |
| elif model == "GPT4": | |
| self.llm = ChatOpenAI(model_name="gpt-4") | |
| elif model == "Claude": | |
| self.llm = ChatAnthropic() | |
| else: | |
| self.llm = HuggingFaceHub(repo_id=model) | |
| def run(self): | |
| return self.process() | |
| def process(self): | |
| seq_chain = SequentialChain( | |
| chains=[self.get_extract_tone_chain(), self.get_generate_text_chain(self.prompt), | |
| self.get_apply_style_chain()], | |
| input_variables=["text"], verbose=True) | |
| result = seq_chain({'text': self.example, "style": ""}) | |
| return str(result.get('result')) | |
| def create_chain(self, chat_prompt, output_key): | |
| return LLMChain(llm=self.llm, | |
| prompt=chat_prompt, output_key=output_key) | |
| def get_extract_tone_chain(self): | |
| template = """Based on the tone and writing style in the seed text, create a style guide for a blog or | |
| publication that captures the essence of the seed’s tone. Emphasize engaging techniques that help readers | |
| feel connected to the content. | |
| """ | |
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | |
| human_template = "{text}" | |
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | |
| chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) | |
| return self.create_chain(chat_prompt, "style") | |
| def get_generate_text_chain(self, prompt): | |
| template = """Generate a text following the user_request(use same language of the request): | |
| {user_request} | |
| """.replace("{user_request}", prompt) | |
| return self.create_chain(PromptTemplate.from_template(template), | |
| "generated_text") | |
| def get_apply_style_chain(self): | |
| template = """STYLE: | |
| {style} | |
| REWRITE THE TEXT BELLOW APPLYING THE STYLE ABOVE(use same language of the request), | |
| ONLY GENERATE NEW TEXT BASED ON THE STYLE CONTEXT, DO NOT COPY STYLE EXACT PARTS: | |
| {generated_text} | |
| """ | |
| prompt = PromptTemplate.from_template(template=template) | |
| prompt.partial(style="") | |
| return self.create_chain(prompt, "result") | |