Source code for langchain_experimental.fallacy_removal.base

"""Chain for applying removals of logical fallacies."""

from __future__ import annotations

from typing import Any, Dict, List, Optional

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.schema import BasePromptTemplate
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel

from langchain_experimental.fallacy_removal.fallacies import FALLACIES
from langchain_experimental.fallacy_removal.models import LogicalFallacy
from langchain_experimental.fallacy_removal.prompts import (
    FALLACY_CRITIQUE_PROMPT,
    FALLACY_REVISION_PROMPT,
)



[docs]
class FallacyChain(Chain):
    """Chain for applying logical fallacy evaluations.

    It is modeled after Constitutional AI and in same format, but
    applying logical fallacies as generalized rules to remove in output.

    Example:
        .. code-block:: python

            from langchain_community.llms import OpenAI
            from langchain.chains import LLMChain
            from langchain_experimental.fallacy import FallacyChain
            from langchain_experimental.fallacy_removal.models import LogicalFallacy

            llm = OpenAI()

            qa_prompt = PromptTemplate(
                template="Q: {question} A:",
                input_variables=["question"],
            )
            qa_chain = LLMChain(llm=llm, prompt=qa_prompt)

            fallacy_chain = FallacyChain.from_llm(
                llm=llm,
                chain=qa_chain,
                logical_fallacies=[
                    LogicalFallacy(
                        fallacy_critique_request="Tell if this answer meets criteria.",
                        fallacy_revision_request=\
                        "Give an answer that meets better criteria.",
                    )
                ],
            )

            fallacy_chain.run(question="How do I know if the earth is round?")
    """

    chain: LLMChain
    logical_fallacies: List[LogicalFallacy]
    fallacy_critique_chain: LLMChain
    fallacy_revision_chain: LLMChain
    return_intermediate_steps: bool = False


[docs]
    @classmethod
    def get_fallacies(cls, names: Optional[List[str]] = None) -> List[LogicalFallacy]:
        if names is None:
            return list(FALLACIES.values())
        else:
            return [FALLACIES[name] for name in names]



[docs]
    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        chain: LLMChain,
        fallacy_critique_prompt: BasePromptTemplate = FALLACY_CRITIQUE_PROMPT,
        fallacy_revision_prompt: BasePromptTemplate = FALLACY_REVISION_PROMPT,
        **kwargs: Any,
    ) -> "FallacyChain":
        """Create a chain from an LLM."""
        fallacy_critique_chain = LLMChain(llm=llm, prompt=fallacy_critique_prompt)
        fallacy_revision_chain = LLMChain(llm=llm, prompt=fallacy_revision_prompt)
        return cls(
            chain=chain,
            fallacy_critique_chain=fallacy_critique_chain,
            fallacy_revision_chain=fallacy_revision_chain,
            **kwargs,
        )


    @property
    def input_keys(self) -> List[str]:
        """Input keys."""
        return self.chain.input_keys

    @property
    def output_keys(self) -> List[str]:
        """Output keys."""
        if self.return_intermediate_steps:
            return ["output", "fallacy_critiques_and_revisions", "initial_output"]
        return ["output"]

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        response = self.chain.run(
            **inputs,
            callbacks=_run_manager.get_child("original"),
        )
        initial_response = response
        input_prompt = self.chain.prompt.format(**inputs)

        _run_manager.on_text(
            text="Initial response: " + response + "\n\n",
            verbose=self.verbose,
            color="yellow",
        )
        fallacy_critiques_and_revisions = []
        for logical_fallacy in self.logical_fallacies:
            # Fallacy critique below

            fallacy_raw_critique = self.fallacy_critique_chain.run(
                input_prompt=input_prompt,
                output_from_model=response,
                fallacy_critique_request=logical_fallacy.fallacy_critique_request,
                callbacks=_run_manager.get_child("fallacy_critique"),
            )
            fallacy_critique = self._parse_critique(
                output_string=fallacy_raw_critique,
            ).strip()

            # if fallacy critique contains "No fallacy critique needed" then done
            if "no fallacy critique needed" in fallacy_critique.lower():
                fallacy_critiques_and_revisions.append((fallacy_critique, ""))
                continue

            fallacy_revision = self.fallacy_revision_chain.run(
                input_prompt=input_prompt,
                output_from_model=response,
                fallacy_critique_request=logical_fallacy.fallacy_critique_request,
                fallacy_critique=fallacy_critique,
                revision_request=logical_fallacy.fallacy_revision_request,
                callbacks=_run_manager.get_child("fallacy_revision"),
            ).strip()
            response = fallacy_revision
            fallacy_critiques_and_revisions.append((fallacy_critique, fallacy_revision))

            _run_manager.on_text(
                text=f"Applying {logical_fallacy.name}..." + "\n\n",
                verbose=self.verbose,
                color="green",
            )

            _run_manager.on_text(
                text="Logical Fallacy: " + fallacy_critique + "\n\n",
                verbose=self.verbose,
                color="blue",
            )

            _run_manager.on_text(
                text="Updated response: " + fallacy_revision + "\n\n",
                verbose=self.verbose,
                color="yellow",
            )

        final_output: Dict[str, Any] = {"output": response}
        if self.return_intermediate_steps:
            final_output["initial_output"] = initial_response
            final_output["fallacy_critiques_and_revisions"] = (
                fallacy_critiques_and_revisions
            )
        return final_output

    @staticmethod
    def _parse_critique(output_string: str) -> str:
        if "Fallacy Revision request:" not in output_string:
            return output_string
        output_string = output_string.split("Fallacy Revision request:")[0]
        if "\n\n" in output_string:
            output_string = output_string.split("\n\n")[0]
        return output_string