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