https://github.com/xavidop/genkitx-aws-bedrock
About Community Plugin for Genkit to use AWS Bedrock
https://github.com/xavidop/genkitx-aws-bedrock
ai aws aws-bedrock bedrock firebase gen-ai generative-ai genkit genkit-plugin genkitx plugin
Last synced: 5 months ago
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About Community Plugin for Genkit to use AWS Bedrock
- Host: GitHub
- URL: https://github.com/xavidop/genkitx-aws-bedrock
- Owner: xavidop
- License: apache-2.0
- Created: 2024-12-24T10:36:30.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T04:48:28.000Z (6 months ago)
- Last Synced: 2025-05-06T05:31:49.929Z (6 months ago)
- Topics: ai, aws, aws-bedrock, bedrock, firebase, gen-ai, generative-ai, genkit, genkit-plugin, genkitx, plugin
- Language: TypeScript
- Homepage: https://xavidop.github.io/genkitx-aws-bedrock/
- Size: 1.83 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
- awesome-genkit - `genkitx-aws-bedrock` - Plugin for AWS Bedrock APIs. (Plugins / JavaScript - Community)
README
Firebase Genkit <> AWS Bedrock Plugin
AWS Bedrock Community Plugin for Google Firebase Genkit
**`genkitx-aws-bedrock`** is a community plugin for using AWS Bedrock APIs with
[Firebase Genkit](https://github.com/firebase/genkit). Built by [**Xavier Portilla Edo**](https://github.com/xavidop).
This Genkit plugin allows to use AWS Bedrock through their official APIs.
## Installation
Install the plugin in your project with your favorite package manager:
- `npm install genkitx-aws-bedrock`
- `pnpm add genkitx-aws-bedrock`
### Versions
if you are using Genkit version `=v0.9.0`, please use the plugin version `>=v1.10.0`.
## Usage
### Configuration
To use the plugin, you need to configure it with your AWS credentials. There are several approaches depending on your environment.
#### Standard Initialization
You can configure the plugin by calling the `genkit` function with your AWS region and model:
```typescript
import { genkit, z } from 'genkit';
import { awsBedrock, amazonNovaProV1 } from "genkitx-aws-bedrock";
const ai = genkit({
plugins: [
awsBedrock({ region: "" }),
],
model: amazonNovaProV1,
});
```
If you have set the `AWS_` environment variables, you can initialize it like this:
```typescript
import { genkit, z } from 'genkit';
import { awsBedrock, amazonNovaProV1 } from "genkitx-aws-bedrock";
const ai = genkit({
plugins: [
awsBedrock(),
],
model: amazonNovaProV1,
});
```
#### Production Environment Authentication
In production environments, it is often necessary to install an additional library to handle authentication. One approach is to use the `@aws-sdk/credential-providers` package:
```typescript
import { fromEnv } from "@aws-sdk/credential-providers";
const ai = genkit({
plugins: [
awsBedrock({
region: "us-east-1",
credentials: fromEnv(),
}),
],
});
```
Ensure you have a `.env` file with the necessary AWS credentials. Remember that the .env file must be added to your .gitignore to prevent sensitive credentials from being exposed.
```
AWS_ACCESS_KEY_ID =
AWS_SECRET_ACCESS_KEY =
```
#### Local Environment Authentication
For local development, you can directly supply the credentials:
```typescript
const ai = genkit({
plugins: [
awsBedrock({
region: "us-east-1",
credentials: {
accessKeyId: awsAccessKeyId.value(),
secretAccessKey: awsSecretAccessKey.value(),
},
}),
],
});
```
Each approach allows you to manage authentication effectively based on your environment needs.
### Configuration with Inference Endpoint
If you want to use a model that uses [Cross-region Inference Endpoints](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html), you can specify the region in the model configuration. Cross-region inference uses inference profiles to increase throughput and improve resiliency by routing your requests across multiple AWS Regions during peak utilization bursts:
```typescript
import { genkit, z } from 'genkit';
import {awsBedrock, amazonNovaProV1, anthropicClaude35SonnetV2} from "genkitx-aws-bedrock";
const ai = genkit({
plugins: [
awsBedrock(),
],
model: anthropicClaude35SonnetV2("us"),
});
```
You can check more information about the available models in the [AWS Bedrock PLugin documentation](https://xavidop.github.io/genkitx-aws-bedrock/).
### Basic examples
The simplest way to call the text generation model is by using the helper function `generate`:
```typescript
import { genkit, z } from 'genkit';
import {awsBedrock, amazonNovaProV1} from "genkitx-aws-bedrock";
// Basic usage of an LLM
const response = await ai.generate({
prompt: 'Tell me a joke.',
});
console.log(await response.text);
```
### Within a flow
```typescript
// ...configure Genkit (as shown above)...
export const myFlow = ai.defineFlow(
{
name: 'menuSuggestionFlow',
inputSchema: z.string(),
outputSchema: z.string(),
},
async (subject) => {
const llmResponse = await ai.generate({
prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
});
return llmResponse.text;
}
);
```
### Tool use
```typescript
// ...configure Genkit (as shown above)...
const specialToolInputSchema = z.object({ meal: z.enum(["breakfast", "lunch", "dinner"]) });
const specialTool = ai.defineTool(
{
name: "specialTool",
description: "Retrieves today's special for the given meal",
inputSchema: specialToolInputSchema,
outputSchema: z.string(),
},
async ({ meal }): Promise => {
// Retrieve up-to-date information and return it. Here, we just return a
// fixed value.
return "Baked beans on toast";
}
);
const result = ai.generate({
tools: [specialTool],
prompt: "What's for breakfast?",
});
console.log(result.then((res) => res.text));
```
For more detailed examples and the explanation of other functionalities, refer to the [official Genkit documentation](https://firebase.google.com/docs/genkit/get-started).
## Supported models
This plugin supports all currently available **Chat/Completion** and **Embeddings** models from AWS Bedrock. This plugin supports image input and multimodal models.
## API Reference
You can find the full API reference in the [API Reference Documentation](https://xavidop.github.io/genkitx-aws-bedrock/)
## Contributing
Want to contribute to the project? That's awesome! Head over to our [Contribution Guidelines](https://github.com/xavidop/genkitx-aws-bedrock/blob/main/CONTRIBUTING.md).
## Need support?
> [!NOTE]
> This repository depends on Google's Firebase Genkit. For issues and questions related to Genkit, please refer to instructions available in [Genkit's repository](https://github.com/firebase/genkit).
Reach out by opening a discussion on [GitHub Discussions](https://github.com/xavidop/genkitx-aws-bedrock/discussions).
## Credits
This plugin is proudly maintained by Xavier Portilla Edo [**Xavier Portilla Edo**](https://github.com/xavidop).
I got the inspiration, structure and patterns to create this plugin from the [Genkit Community Plugins](https://github.com/TheFireCo/genkit-plugins) repository built by the [Fire Compnay](https://github.com/TheFireCo) as well as the [ollama plugin](https://firebase.google.com/docs/genkit/plugins/ollama).
## License
This project is licensed under the [Apache 2.0 License](https://github.com/xavidop/genkitx-aws-bedrock/blob/main/LICENSE).
[](https://github.com/xavidop/genkitx-aws-bedrock/blob/main/LICENSE)