| from typing import Tuple |
|
|
| from langchain_core.output_parsers import BaseOutputParser |
| from langchain_core.prompts import PromptTemplate |
|
|
|
|
| class FinishedOutputParser(BaseOutputParser[Tuple[str, bool]]): |
| """Output parser that checks if the output is finished.""" |
|
|
| finished_value: str = "FINISHED" |
| """Value that indicates the output is finished.""" |
|
|
| def parse(self, text: str) -> Tuple[str, bool]: |
| cleaned = text.strip() |
| finished = self.finished_value in cleaned |
| return cleaned.replace(self.finished_value, ""), finished |
|
|
|
|
| PROMPT_TEMPLATE = """\ |
| Respond to the user message using any relevant context. \ |
| If context is provided, you should ground your answer in that context. \ |
| Once you're done responding return FINISHED. |
| |
| >>> CONTEXT: {context} |
| >>> USER INPUT: {user_input} |
| >>> RESPONSE: {response}\ |
| """ |
|
|
| PROMPT = PromptTemplate( |
| template=PROMPT_TEMPLATE, |
| input_variables=["user_input", "context", "response"], |
| ) |
|
|
|
|
| QUESTION_GENERATOR_PROMPT_TEMPLATE = """\ |
| Given a user input and an existing partial response as context, \ |
| ask a question to which the answer is the given term/entity/phrase: |
| |
| >>> USER INPUT: {user_input} |
| >>> EXISTING PARTIAL RESPONSE: {current_response} |
| |
| The question to which the answer is the term/entity/phrase "{uncertain_span}" is:""" |
| QUESTION_GENERATOR_PROMPT = PromptTemplate( |
| template=QUESTION_GENERATOR_PROMPT_TEMPLATE, |
| input_variables=["user_input", "current_response", "uncertain_span"], |
| ) |
|
|
