Added AWS-Bedrock LLM setup section

[cheriepwoo]

Also added link to Flowise deployment on AWS.

[gemini-code-assist]

Summary of Changes

Hello @cheriepwoo, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the documentation for integrating AWS Bedrock LLMs into Flowise by providing a comprehensive setup guide. It clarifies the steps required for users to leverage Amazon Bedrock's foundational models within their Flowise applications and also adds a helpful link to Flowise deployment instructions on AWS, streamlining the overall user experience.

Highlights

• AWS Bedrock LLM Setup: Detailed instructions for integrating and configuring AWS Bedrock Large Language Models within Flowise chatflows have been added. This includes steps for setting up AWS credentials, adding the Bedrock LLM node, configuring its inputs (credentials, region, model name), and connecting it within a chatflow.
• Flowise Deployment on AWS Link: A direct link to documentation for deploying Flowise on AWS has been included for user convenience.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request adds essential documentation for setting up the AWS Bedrock LLM node, replacing the previous placeholder content. The new guide is well-structured and provides a step-by-step process. My review includes a few suggestions to enhance the clarity and conciseness of the instructions, making them even more user-friendly.