> For the complete documentation index, see [llms.txt](https://docs.flowiseai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flowiseai.com/integrations/langchain/llms/aws-bedrock.md). # AWS Bedrock Wrapper around AWS Bedrock large language models. ## AWS Bedrock The **AWS Bedrock LLM** node integrates with Amazon Bedrock, a managed service that provides access to foundation models for building generative AI applications. To use AWS Bedrock in Flowise, add the **AWS Bedrock LLM** node to your Chatflow. ## Setup 1. Configure AWS credentials for Flowise. In Amazon Bedrock, ensure that your account has access to the model you want to use with Flowise. 2. Add the AWS Bedrock node to your Chatflow. In the Flowise canvas, drag and drop the AWS Bedrock LLM node into your Chatflow. 3. Configure the AWS Bedrock inputs:

AWS Bedrock Node

* AWS Credential: Select an existing AWS credential or create a new one. The associated IAM user or role must have permissions for `bedrock:InvokeModel` and any other required AWS services in your Chatflow. * Region: The AWS region where your Bedrock models are available. * Model Name: The AWS Bedrock foundational model for your conversational AI. 4. Connect the AWS Bedrock node in your Chatflow. After you add other Chatflow components (such as input nodes, output nodes, memory nodes), connect the AWS Bedrock LLM node to the appropriate components to create the Chatflow. For information about deploying Flowise on AWS, see [AWS](/configuration/deployment/aws.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.flowiseai.com/integrations/langchain/llms/aws-bedrock.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.