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