Amazon sagemaker jumpstart prompt driver

AmazonSageMakerJumpstartPromptDriver

Bases: BasePromptDriver

Source code in griptape/drivers/prompt/amazon_sagemaker_jumpstart_prompt_driver.py

@define
class AmazonSageMakerJumpstartPromptDriver(BasePromptDriver):
    session: boto3.Session = field(default=Factory(lambda: import_optional_dependency("boto3").Session()), kw_only=True)
    sagemaker_client: Any = field(
        default=Factory(lambda self: self.session.client("sagemaker-runtime"), takes_self=True), kw_only=True
    )
    endpoint: str = field(kw_only=True, metadata={"serializable": True})
    custom_attributes: str = field(default="accept_eula=true", kw_only=True, metadata={"serializable": True})
    inference_component_name: Optional[str] = field(default=None, kw_only=True, metadata={"serializable": True})
    stream: bool = field(default=False, kw_only=True, metadata={"serializable": True})
    max_tokens: int = field(default=250, kw_only=True, metadata={"serializable": True})
    tokenizer: HuggingFaceTokenizer = field(
        default=Factory(
            lambda self: HuggingFaceTokenizer(model=self.model, max_output_tokens=self.max_tokens), takes_self=True
        ),
        kw_only=True,
    )

    @stream.validator  # pyright: ignore
    def validate_stream(self, _, stream):
        if stream:
            raise ValueError("streaming is not supported")

    def try_run(self, prompt_stack: PromptStack) -> TextArtifact:
        payload = {"inputs": self._to_model_input(prompt_stack), "parameters": self._to_model_params(prompt_stack)}

        response = self.sagemaker_client.invoke_endpoint(
            EndpointName=self.endpoint,
            ContentType="application/json",
            Body=json.dumps(payload),
            CustomAttributes=self.custom_attributes,
            **(
                {"InferenceComponentName": self.inference_component_name}
                if self.inference_component_name is not None
                else {}
            ),
        )

        decoded_body = json.loads(response["Body"].read().decode("utf8"))

        if isinstance(decoded_body, list):
            if decoded_body:
                return TextArtifact(decoded_body[0]["generated_text"])
            else:
                raise ValueError("model response is empty")
        else:
            return TextArtifact(decoded_body["generated_text"])

    def try_stream(self, prompt_stack: PromptStack) -> Iterator[TextArtifact]:
        raise NotImplementedError("streaming is not supported")

    def _prompt_stack_input_to_message(self, prompt_input: PromptStack.Input) -> dict:
        return {"role": prompt_input.role, "content": prompt_input.content}

    def _to_model_input(self, prompt_stack: PromptStack) -> str:
        prompt = self.tokenizer.tokenizer.apply_chat_template(
            [self._prompt_stack_input_to_message(i) for i in prompt_stack.inputs],
            tokenize=False,
            add_generation_prompt=True,
        )

        if isinstance(prompt, str):
            return prompt
        else:
            raise ValueError("Invalid output type.")

    def _to_model_params(self, prompt_stack: PromptStack) -> dict:
        return {
            "temperature": self.temperature,
            "max_new_tokens": self.max_tokens,
            "do_sample": True,
            "eos_token_id": self.tokenizer.tokenizer.eos_token_id,
            "stop_strings": self.tokenizer.stop_sequences,
            "return_full_text": False,
        }

custom_attributes: str = field(default='accept_eula=true', kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

endpoint: str = field(kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

inference_component_name: Optional[str] = field(default=None, kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

max_tokens: int = field(default=250, kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

sagemaker_client: Any = field(default=Factory(lambda self: self.session.client('sagemaker-runtime'), takes_self=True), kw_only=True) class-attribute instance-attribute

session: boto3.Session = field(default=Factory(lambda: import_optional_dependency('boto3').Session()), kw_only=True) class-attribute instance-attribute

stream: bool = field(default=False, kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

tokenizer: HuggingFaceTokenizer = field(default=Factory(lambda self: HuggingFaceTokenizer(model=self.model, max_output_tokens=self.max_tokens), takes_self=True), kw_only=True) class-attribute instance-attribute

try_run(prompt_stack)

Source code in griptape/drivers/prompt/amazon_sagemaker_jumpstart_prompt_driver.py

def try_run(self, prompt_stack: PromptStack) -> TextArtifact:
    payload = {"inputs": self._to_model_input(prompt_stack), "parameters": self._to_model_params(prompt_stack)}

    response = self.sagemaker_client.invoke_endpoint(
        EndpointName=self.endpoint,
        ContentType="application/json",
        Body=json.dumps(payload),
        CustomAttributes=self.custom_attributes,
        **(
            {"InferenceComponentName": self.inference_component_name}
            if self.inference_component_name is not None
            else {}
        ),
    )

    decoded_body = json.loads(response["Body"].read().decode("utf8"))

    if isinstance(decoded_body, list):
        if decoded_body:
            return TextArtifact(decoded_body[0]["generated_text"])
        else:
            raise ValueError("model response is empty")
    else:
        return TextArtifact(decoded_body["generated_text"])

try_stream(prompt_stack)

Source code in griptape/drivers/prompt/amazon_sagemaker_jumpstart_prompt_driver.py

def try_stream(self, prompt_stack: PromptStack) -> Iterator[TextArtifact]:
    raise NotImplementedError("streaming is not supported")

validate_stream(_, stream)

Source code in griptape/drivers/prompt/amazon_sagemaker_jumpstart_prompt_driver.py

@stream.validator  # pyright: ignore
def validate_stream(self, _, stream):
    if stream:
        raise ValueError("streaming is not supported")