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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
from langchain.output_parsers import GuardrailsOutputParser
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from langchain.prompts import PromptTemplate
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from langchain_community.llms import OpenAI
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# Define rail string
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rail_str = """
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<rail version="0.1">
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<output>
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<string
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description="Profanity-free translation"
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format="is-profanity-free"
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name="translated_statement"
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on-fail-is-profanity-free="fix">
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</string>
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</output>
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<prompt>
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Translate the given statement into English:
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${statement_to_be_translated}
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${gr.complete_json_suffix}
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</prompt>
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</rail>
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"""
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# Create the GuardrailsOutputParser object from the rail string
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output_parser = GuardrailsOutputParser.from_rail_string(rail_str)
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# Define the prompt, model and chain
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prompt = PromptTemplate(
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template=output_parser.guard.prompt.escape(),
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input_variables=output_parser.guard.prompt.variable_names,
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)
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chain = prompt | OpenAI() | output_parser
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# This is needed because GuardrailsOutputParser does not have an inferrable type
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chain = chain.with_types(output_type=dict)
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