47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
from langchain_core.output_parsers import BaseOutputParser
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from langchain_core.prompts import PromptTemplate
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from typing_extensions import override
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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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@override
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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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