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"""Chain for summarization with self-verification."""
from pathlib import Path
from typing import Dict, List
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential import SequentialChain
from langchain.llms.base import BaseLLM
from langchain.prompts.prompt import PromptTemplate
PROMPTS_DIR = Path(__file__).parent / "prompts"
CREATE_ASSERTIONS_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "create_facts.txt", ["summary"]
)
CHECK_ASSERTIONS_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "check_facts.txt", ["assertions"]
)
REVISED_SUMMARY_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "revise_summary.txt", ["checked_assertions", "summary"]
)
ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(
PROMPTS_DIR / "are_all_true_prompt.txt", ["checked_assertions"]
)
class LLMSummarizationCheckerChain(Chain, BaseModel):
"""Chain for question-answering with self-verification.
Example:
.. code-block:: python
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain(llm=llm)
"""
llm: BaseLLM
"""LLM wrapper to use."""
create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT
check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to double-checking."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
return [self.output_key]
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
all_true = False
count = 0
output = None
original_input = inputs[self.input_key]
chain_input = original_input
while not all_true and count < self.max_checks:
chain = SequentialChain(
chains=[
LLMChain(
llm=self.llm,
prompt=self.create_assertions_prompt,
output_key="assertions",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
prompt=self.check_assertions_prompt,
output_key="checked_assertions",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
prompt=self.revised_summary_prompt,
output_key="revised_summary",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
output_key="all_true",
prompt=self.are_all_true_prompt,
verbose=self.verbose,
),
],
input_variables=["summary"],
output_variables=["all_true", "revised_summary"],
verbose=self.verbose,
)
output = chain({"summary": chain_input})
count += 1
if output["all_true"].strip() == "True":
break
if self.verbose:
print(output["revised_summary"])
chain_input = output["revised_summary"]
if not output:
raise ValueError("No output from chain")
return {self.output_key: output["revised_summary"].strip()}
@property
def _chain_type(self) -> str:
return "llm_summarization_checker_chain"
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