"""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
