If there is no conversation history, output 1.
            Do not answer anything else nor add anything to you answer."""
            )
        prompt = PromptTemplate(
            template=stage_analyzer_inception_prompt_template,
            input_variables=["conversation_history"],
        )
        return cls(prompt=prompt, llm=llm, verbose=verbose)
class SalesConversationChain(LLMChain):
    """Chain to generate the next utterance for the conversation."""
    @classmethod
    def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:
        """Get the response parser."""
        sales_agent_inception_prompt = (
        """Never forget your name is {salesperson_name}. You work as a {salesperson_role}.
        You work at company named {company_name}. {company_name}'s business is the following: {company_business}
        Company values are the following. {company_values}
        You are contacting a potential customer in order to {conversation_purpose}
        Your means of contacting the prospect is {conversation_type}
        If you're asked about where you got the user's contact information, say that you got it from public records.
        Keep your responses in short length to retain the user's attention. Never produce lists, just answers.
        You must respond according to the previous conversation history and the stage of the conversation you are at.
        Only generate one response at a time! When you are done generating, end with '<END_OF_TURN>' to give the user a chance to respond. 
        Example:
        Conversation history: 
        {salesperson_name}: Hey, how are you? This is {salesperson_name} calling from {company_name}. Do you have a minute? <END_OF_TURN>
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/sales_agent_with_context.html