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from langflow.base.models.aws_constants import AWS_REGIONS, AWS_MODEL_IDs |
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from langflow.base.models.model import LCModelComponent |
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from langflow.field_typing import LanguageModel |
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from langflow.inputs import MessageTextInput, SecretStrInput |
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from langflow.inputs.inputs import HandleInput |
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from langflow.io import DictInput, DropdownInput |
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class AmazonBedrockComponent(LCModelComponent): |
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display_name: str = "Amazon Bedrock" |
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description: str = "Generate text using Amazon Bedrock LLMs." |
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icon = "Amazon" |
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name = "AmazonBedrockModel" |
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inputs = [ |
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*LCModelComponent._base_inputs, |
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DropdownInput( |
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name="model_id", |
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display_name="Model ID", |
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options=AWS_MODEL_IDs, |
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value="anthropic.claude-3-haiku-20240307-v1:0", |
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info="List of available model IDs to choose from.", |
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), |
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SecretStrInput( |
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name="aws_access_key_id", |
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display_name="AWS Access Key ID", |
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info="The access key for your AWS account." |
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"Usually set in Python code as the environment variable 'AWS_ACCESS_KEY_ID'.", |
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value="AWS_ACCESS_KEY_ID", |
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), |
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SecretStrInput( |
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name="aws_secret_access_key", |
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display_name="AWS Secret Access Key", |
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info="The secret key for your AWS account. " |
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"Usually set in Python code as the environment variable 'AWS_SECRET_ACCESS_KEY'.", |
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value="AWS_SECRET_ACCESS_KEY", |
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), |
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SecretStrInput( |
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name="aws_session_token", |
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display_name="AWS Session Token", |
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advanced=False, |
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info="The session key for your AWS account. " |
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"Only needed for temporary credentials. " |
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"Usually set in Python code as the environment variable 'AWS_SESSION_TOKEN'.", |
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load_from_db=False, |
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), |
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SecretStrInput( |
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name="credentials_profile_name", |
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display_name="Credentials Profile Name", |
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advanced=True, |
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info="The name of the profile to use from your " |
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"~/.aws/credentials file. " |
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"If not provided, the default profile will be used.", |
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load_from_db=False, |
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), |
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DropdownInput( |
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name="region_name", |
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display_name="Region Name", |
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value="us-east-1", |
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options=AWS_REGIONS, |
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info="The AWS region where your Bedrock resources are located.", |
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), |
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DictInput( |
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name="model_kwargs", |
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display_name="Model Kwargs", |
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advanced=True, |
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is_list=True, |
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info="Additional keyword arguments to pass to the model.", |
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), |
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MessageTextInput( |
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name="endpoint_url", |
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display_name="Endpoint URL", |
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advanced=True, |
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info="The URL of the Bedrock endpoint to use.", |
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), |
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HandleInput( |
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name="output_parser", |
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display_name="Output Parser", |
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info="The parser to use to parse the output of the model", |
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advanced=True, |
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input_types=["OutputParser"], |
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), |
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] |
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def build_model(self) -> LanguageModel: |
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try: |
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from langchain_aws import ChatBedrock |
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except ImportError as e: |
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msg = "langchain_aws is not installed. Please install it with `pip install langchain_aws`." |
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raise ImportError(msg) from e |
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try: |
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import boto3 |
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except ImportError as e: |
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msg = "boto3 is not installed. Please install it with `pip install boto3`." |
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raise ImportError(msg) from e |
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if self.aws_access_key_id or self.aws_secret_access_key: |
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try: |
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session = boto3.Session( |
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aws_access_key_id=self.aws_access_key_id, |
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aws_secret_access_key=self.aws_secret_access_key, |
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aws_session_token=self.aws_session_token, |
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) |
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except Exception as e: |
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msg = "Could not create a boto3 session." |
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raise ValueError(msg) from e |
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elif self.credentials_profile_name: |
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session = boto3.Session(profile_name=self.credentials_profile_name) |
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else: |
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session = boto3.Session() |
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client_params = {} |
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if self.endpoint_url: |
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client_params["endpoint_url"] = self.endpoint_url |
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if self.region_name: |
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client_params["region_name"] = self.region_name |
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boto3_client = session.client("bedrock-runtime", **client_params) |
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try: |
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output = ChatBedrock( |
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client=boto3_client, |
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model_id=self.model_id, |
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region_name=self.region_name, |
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model_kwargs=self.model_kwargs, |
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endpoint_url=self.endpoint_url, |
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streaming=self.stream, |
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) |
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except Exception as e: |
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msg = "Could not connect to AmazonBedrock API." |
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raise ValueError(msg) from e |
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return output |
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