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Prompt summary engine

PromptSummaryEngine

Bases: BaseSummaryEngine

Source code in griptape/griptape/engines/summary/prompt_summary_engine.py 
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@define
class PromptSummaryEngine(BaseSummaryEngine):
    chunk_joiner: str = field(default="\n\n", kw_only=True)
    max_token_multiplier: float = field(default=0.5, kw_only=True)
    template_generator: J2 = field(default=Factory(lambda: J2("engines/summary/prompt_summary.j2")), kw_only=True)
    prompt_driver: BasePromptDriver = field(
        default=Factory(lambda: OpenAiChatPromptDriver(model=OpenAiTokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL)),
        kw_only=True,
    )
    chunker: BaseChunker = field(
        default=Factory(
            lambda self: TextChunker(tokenizer=self.prompt_driver.tokenizer, max_tokens=self.max_chunker_tokens),
            takes_self=True,
        ),
        kw_only=True,
    )

    @max_token_multiplier.validator
    def validate_allowlist(self, _, max_token_multiplier: int) -> None:
        if max_token_multiplier > 1:
            raise ValueError("has to be less than or equal to 1")
        elif max_token_multiplier <= 0:
            raise ValueError("has to be greater than 0")

    @property
    def max_chunker_tokens(self) -> int:
        return round(self.prompt_driver.tokenizer.max_tokens * self.max_token_multiplier)

    @property
    def min_response_tokens(self) -> int:
        return round(
            self.prompt_driver.tokenizer.max_tokens
            - self.prompt_driver.tokenizer.max_tokens * self.max_token_multiplier
        )

    def summarize_artifacts(self, artifacts: ListArtifact, rulesets: Optional[Ruleset] = None) -> TextArtifact:
        return self.summarize_artifacts_rec(artifacts.value, None, rulesets=rulesets)

    def summarize_artifacts_rec(
        self, artifacts: list[BaseArtifact], summary: Optional[str], rulesets: Optional[Ruleset] = None
    ) -> TextArtifact:
        artifacts_text = self.chunk_joiner.join([a.to_text() for a in artifacts])

        full_text = self.template_generator.render(
            summary=summary, text=artifacts_text, rulesets=J2("rulesets/rulesets.j2").render(rulesets=rulesets)
        )

        if self.prompt_driver.tokenizer.count_tokens_left(full_text) >= self.min_response_tokens:
            return self.prompt_driver.run(
                PromptStack(inputs=[PromptStack.Input(full_text, role=PromptStack.USER_ROLE)])
            )
        else:
            chunks = self.chunker.chunk(artifacts_text)

            partial_text = self.template_generator.render(
                summary=summary, text=chunks[0].value, rulesets=J2("rulesets/rulesets.j2").render(rulesets=rulesets)
            )

            return self.summarize_artifacts_rec(
                chunks[1:],
                self.prompt_driver.run(
                    PromptStack(inputs=[PromptStack.Input(partial_text, role=PromptStack.USER_ROLE)])
                ).value,
                rulesets=rulesets,
            )

chunk_joiner: str = field(default='\n\n', kw_only=True) class-attribute instance-attribute

chunker: BaseChunker = field(default=Factory(lambda : TextChunker(tokenizer=self.prompt_driver.tokenizer, max_tokens=self.max_chunker_tokens), takes_self=True), kw_only=True) class-attribute instance-attribute

max_chunker_tokens: int property

max_token_multiplier: float = field(default=0.5, kw_only=True) class-attribute instance-attribute

min_response_tokens: int property

prompt_driver: BasePromptDriver = field(default=Factory(lambda : OpenAiChatPromptDriver(model=OpenAiTokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL)), kw_only=True) class-attribute instance-attribute

template_generator: J2 = field(default=Factory(lambda : J2('engines/summary/prompt_summary.j2')), kw_only=True) class-attribute instance-attribute

summarize_artifacts(artifacts, rulesets=None)

Source code in griptape/griptape/engines/summary/prompt_summary_engine.py 
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def summarize_artifacts(self, artifacts: ListArtifact, rulesets: Optional[Ruleset] = None) -> TextArtifact:
    return self.summarize_artifacts_rec(artifacts.value, None, rulesets=rulesets)

summarize_artifacts_rec(artifacts, summary, rulesets=None)

Source code in griptape/griptape/engines/summary/prompt_summary_engine.py 
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def summarize_artifacts_rec(
    self, artifacts: list[BaseArtifact], summary: Optional[str], rulesets: Optional[Ruleset] = None
) -> TextArtifact:
    artifacts_text = self.chunk_joiner.join([a.to_text() for a in artifacts])

    full_text = self.template_generator.render(
        summary=summary, text=artifacts_text, rulesets=J2("rulesets/rulesets.j2").render(rulesets=rulesets)
    )

    if self.prompt_driver.tokenizer.count_tokens_left(full_text) >= self.min_response_tokens:
        return self.prompt_driver.run(
            PromptStack(inputs=[PromptStack.Input(full_text, role=PromptStack.USER_ROLE)])
        )
    else:
        chunks = self.chunker.chunk(artifacts_text)

        partial_text = self.template_generator.render(
            summary=summary, text=chunks[0].value, rulesets=J2("rulesets/rulesets.j2").render(rulesets=rulesets)
        )

        return self.summarize_artifacts_rec(
            chunks[1:],
            self.prompt_driver.run(
                PromptStack(inputs=[PromptStack.Input(partial_text, role=PromptStack.USER_ROLE)])
            ).value,
            rulesets=rulesets,
        )

validate_allowlist(_, max_token_multiplier)

Source code in griptape/griptape/engines/summary/prompt_summary_engine.py 
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@max_token_multiplier.validator
def validate_allowlist(self, _, max_token_multiplier: int) -> None:
    if max_token_multiplier > 1:
        raise ValueError("has to be less than or equal to 1")
    elif max_token_multiplier <= 0:
        raise ValueError("has to be greater than 0")