Amazon
Bundles contain custom components that support specific third-party integrations with Langflow.
This page describes the components that are available in the Amazon bundle.
Amazon Bedrock Converse
This component generates text using Amazon Bedrock LLMs with the Bedrock Converse API.
It can output either a Model Response (Message) or a Language Model (LanguageModel).
Specifically, the Language Model output is an instance of ChatBedrockConverse configured according to the component's parameters.
Use the Language Model output when you want to use an Amazon Bedrock model as the LLM for another LLM-driven component, such as an Agent or Smart Transform component.
For more information, see Language model components.
Amazon Bedrock Converse parameters
Some parameters are hidden by default in the visual editor. You can modify all parameters through the Controls in the component's header menu.
| Name | Type | Description |
|---|---|---|
| input_value | String | Input parameter. The input string for text generation. |
| system_message | String | Input parameter. A system message to pass to the model. |
| stream | Boolean | Input parameter. Whether to stream the response. Only works in chat. Default: false. |
| model_id | String | Input parameter. The Amazon Bedrock model to use. |
| aws_access_key_id | SecretString | Input parameter. AWS Access Key for authentication. Required. |
| aws_secret_access_key | SecretString | Input parameter. AWS Secret Key for authentication. Required. |
| aws_session_token | SecretString | Input parameter. The session key for your AWS account. Only needed for temporary credentials. |
| credentials_profile_name | String | Input parameter. Name of the AWS credentials profile to use. If not provided, the default profile will be used. |
| region_name | String | Input parameter. AWS region where your Bedrock resources are located. Default: us-east-1. |
| endpoint_url | String | Input parameter. Custom endpoint URL for a Bedrock service. |
| temperature | Float | Input parameter. Controls randomness in output. Higher values make output more random. Default: 0.7. |
| max_tokens | Integer | Input parameter. Maximum number of tokens to generate. Default: 4096. |
| top_p | Float | Input parameter. Nucleus sampling parameter. Controls diversity of output. Default: 0.9. |
| top_k | Integer | Input parameter. Limits the number of highest probability vocabulary tokens to consider. Note: Not all models support top_k. Default: 250. |
| disable_streaming | Boolean | Input parameter. If True, disables streaming responses. Useful for batch processing. Default: false. |
| additional_model_fields | Dictionary | Input parameter. Additional model-specific parameters for fine-tuning behavior. |
Amazon Bedrock Embeddings
The Amazon Bedrock Embeddings component is used to load embedding models from Amazon Bedrock.
For more information about using embedding model components in flows, see Embedding model components.
Amazon Bedrock Embeddings parameters
Some parameters are hidden by default in the visual editor. You can modify all parameters through the Controls in the component's header menu.
| Name | Type | Description |
|---|---|---|
| model_id | String | Input parameter. The ID of the model to call, such as amazon.titan-embed-text-v1. This is equivalent to the modelId property in the list-foundation-models API. |
| aws_access_key_id | SecretString | Input parameter. AWS Access Key for authentication. |
| aws_secret_access_key | SecretString | Input parameter. AWS Secret Key for authentication. |
| aws_session_token | SecretString | Input parameter. The session key for your AWS account. |
| credentials_profile_name | String | Input parameter. The name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. |
| region_name | String | Input parameter. The AWS region to use, such as us-west-2. Falls back to the AWS_DEFAULT_REGION environment variable or region specified in ~/.aws/config if not provided. |
| endpoint_url | String | Input parameter. The URL to set a specific service endpoint other than the default AWS endpoint. |
S3 Bucket Uploader
The S3 Bucket Uploader component uploads files to an Amazon S3 bucket.
It is designed to process Data input from a Read File or Directory component.
If you upload Data from other components, test the results before running the flow in production.
Requires the boto3 package, which is included in your Langflow installation.
The component produces logs but it doesn't emit output to the flow.
S3 Bucket Uploader parameters
Some parameters are hidden by default in the visual editor. You can modify all parameters through the Controls in the component's header menu.
| Name | Type | Description |
|---|---|---|
| AWS Access Key ID | SecretString | Input parameter. AWS Access Key ID for authentication. |
| AWS Secret Key | SecretString | Input parameter. AWS Secret Key for authentication. |
| Bucket Name | String | Input parameter. The name of the S3 bucket to upload files to. |
| Strategy for file upload | String | Input parameter. The file upload strategy. Store Data (default) iterates over Data inputs, logs the file path and text content, and uploads each file to the specified S3 bucket if both file path and text content are available. Store Original File iterates through the list of data inputs, retrieves the file path from each data item, uploads the file to the specified S3 bucket if the file path is available, and logs the file path being uploaded. |
| Data Inputs | Data | Input parameter. The Data input to iterate over and upload as files in the specified S3 bucket. |
| S3 Prefix | String | Input parameter. Optional prefix (folder path) within the S3 bucket where files will be uploaded. |
| Strip Path | Boolean | Input parameter. Whether to strip the file path when uploading. Default: false. |
Legacy Amazon components
Legacy components are longer supported and can be removed in a future release. You can continue to use them in existing flows, but it is recommended that you replace them with supported components as soon as possible. Suggested replacements are included in the Legacy banner on components in your flows. They are also given in release notes and Langflow documentation whenever possible.
If you aren't sure how to replace a legacy component, Search for components by provider, service, or component name. The component may have been deprecated in favor of a completely new component, a similar component, or a new version of the same component in a different category.
If there is no obvious replacement, consider whether another component can be adapted to your use case. For example, many Core components provide generic functionality that can support multiple providers and use cases, such as the API Request component.
If neither of these options are viable, you could use the legacy component's code to create your own custom component, or start a discussion about the legacy component.
To discourage use of legacy components in new flows, these components are hidden by default. In the visual editor, you can click Component settings to toggle the Legacy filter.
The following Amazon components are in legacy status:
Amazon Bedrock
The Amazon Bedrock component was deprecated in favor of the Amazon Bedrock Converse component, which uses the Bedrock Converse API for conversation handling.
To use Amazon Bedrock models in your flows, use the Amazon Bedrock Converse component instead.
