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from langchain.output_parsers import GuardrailsOutputParser
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
# Define rail string
rail_str = """
<rail version="0.1">
<output>
<string
description="Profanity-free translation"
format="is-profanity-free"
name="translated_statement"
on-fail-is-profanity-free="fix">
</string>
</output>
<prompt>
Translate the given statement into English:
${statement_to_be_translated}
${gr.complete_json_suffix}
</prompt>
</rail>
"""
# Create the GuardrailsOutputParser object from the rail string
output_parser = GuardrailsOutputParser.from_rail_string(rail_str)
# Define the prompt, model and chain
prompt = PromptTemplate(
template=output_parser.guard.prompt.escape(),
input_variables=output_parser.guard.prompt.variable_names,
)
chain = prompt | OpenAI() | output_parser
# This is needed because GuardrailsOutputParser does not have an inferrable type
chain = chain.with_types(output_type=dict)
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