Bedrock invoke

def __init__(
    self,
    model_id: str,
    endpoint_name: str | None = None,
    region: str | None = None,
    bedrock_boto3_client: Any = None,
    max_attempts: int = 3,
    generated_text_jmespath: str = "choices[0].message.content",
    generated_token_count_jmespath: str | None = "usage.completion_tokens",
    input_text_jmespath: str = "messages[].content[].text",
    input_token_count_jmespath: str | None = "usage.prompt_tokens",
):
    """Create a BedrockInvoke Endpoint

    The default ..._jmespath parameters assume your target model uses an OpenAI
    ChatCompletions-like API, which is true for many (but not all) Bedrock models. You'll need
    to override these if targeting a model with different request/response format.

    Args:
        model_id:
            The identifier for the model to use
        endpoint_name:
            Name of the endpoint. Defaults to None.
        region:
            AWS region to use. Defaults to bedrock_boto3_client's, or configured from AWS CLI.
        bedrock_boto3_client:
            Optional pre-configured boto3 client, otherwise one will be created.
        max_attempts:
            Maximum number of retry attempts. Defaults to 3.
        generated_text_jmespath:
            JMESPath query to extract generated text from model response.
        generated_token_count_jmespath:
            JMESPath query to extract generated token count from model response.
        input_text_jmespath:
            JMESPath query to extract input text from the model request payload.
        input_token_count_jmespath:
            JMESPath query to extract input token count from the response.
    """
    super().__init__(
        model_id=model_id,
        endpoint_name=endpoint_name or "amazon bedrock",
        provider="bedrock",
    )

    self.generated_text_jmespath = generated_text_jmespath
    self.generated_token_count_jmespath = generated_token_count_jmespath
    self.input_text_jmespath = input_text_jmespath
    self.input_token_count_jmespath = input_token_count_jmespath

    self.region = (
        region
        or (bedrock_boto3_client and bedrock_boto3_client.meta.region_name)
        or boto3.session.Session().region_name
    )
    logger.info(f"Using AWS region: {self.region}")

    self._bedrock_client = bedrock_boto3_client
    if self._bedrock_client is None:
        config = Config(retries={"max_attempts": max_attempts, "mode": "standard"})
        self._bedrock_client = boto3.client(
            "bedrock-runtime", region_name=self.region, config=config
        )

def __init__(
    self,
    model_id: str,
    endpoint_name: str | None = None,
    region: str | None = None,
    bedrock_boto3_client: Any = None,
    max_attempts: int = 3,
    generated_text_jmespath: str = "choices[0].delta.content",
    generated_token_count_jmespath: str
    | None = '"amazon-bedrock-invocationMetrics".outputTokenCount',
    input_text_jmespath: str = "messages[].content[].text",
    input_token_count_jmespath: str
    | None = '"amazon-bedrock-invocationMetrics".inputTokenCount',
):
    """Create a BedrockInvokeStream Endpoint

    The default ..._jmespath parameters assume your target model uses an OpenAI
    ChatCompletions-like streaming API, which is true for many (but not all) Bedrock models.
    You'll need to override these if targeting a model with different request/response format.

    Args:
        model_id:
            The identifier for the model to use
        endpoint_name:
            Name of the endpoint. Defaults to None.
        region:
            AWS region to use. Defaults to bedrock_boto3_client's, or configured from AWS CLI.
        bedrock_boto3_client:
            Optional pre-configured boto3 client, otherwise one will be created.
        max_attempts:
            Maximum number of retry attempts. Defaults to 3.
        generated_text_jmespath:
            JMESPath query to extract incremental text from *a chunk of* the model response.
        generated_token_count_jmespath:
            JMESPath query to extract generated token count from *a chunk of* model response.
        input_text_jmespath:
            JMESPath query to extract input text from the model request payload.
        input_token_count_jmespath:
            JMESPath query to extract input token count from *a chunk of* the model response.
    """
    super().__init__(
        model_id=model_id,
        endpoint_name=endpoint_name,
        region=region,
        bedrock_boto3_client=bedrock_boto3_client,
        max_attempts=max_attempts,
        generated_text_jmespath=generated_text_jmespath,
        generated_token_count_jmespath=generated_token_count_jmespath,
        input_text_jmespath=input_text_jmespath,
        input_token_count_jmespath=input_token_count_jmespath,
    )