https://github.com/jparkerweb/bedrock-wrapper
🪨 Bedrock Wrapper is an npm package that simplifies the integration of existing OpenAI-compatible API objects with AWS Bedrock's serverless inference LLMs.
https://github.com/jparkerweb/bedrock-wrapper
aws bedrock inference llm openai-api proxy serverless wrapper
Last synced: 11 months ago
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🪨 Bedrock Wrapper is an npm package that simplifies the integration of existing OpenAI-compatible API objects with AWS Bedrock's serverless inference LLMs.
- Host: GitHub
- URL: https://github.com/jparkerweb/bedrock-wrapper
- Owner: jparkerweb
- License: mit
- Created: 2024-05-01T02:41:54.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-28T23:45:36.000Z (about 1 year ago)
- Last Synced: 2025-05-01T09:57:11.598Z (11 months ago)
- Topics: aws, bedrock, inference, llm, openai-api, proxy, serverless, wrapper
- Language: JavaScript
- Homepage: https://www.equilllabs.com/projects/bedrock-wrapper
- Size: 6.42 MB
- Stars: 5
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 🪨 Bedrock Wrapper
Bedrock Wrapper is an npm package that simplifies the integration of existing OpenAI-compatible API objects with AWS Bedrock's serverless inference LLMs. Follow the steps below to integrate into your own application, or alternativly use the 🔀 [Bedrock Proxy Endpoint](https://github.com/jparkerweb/bedrock-proxy-endpoint) project to spin up your own custom OpenAI server endpoint for even easier inference (using the standard `baseUrl`, and `apiKey` params).
---
### Maintained by
---
### Install
- install package: `npm install bedrock-wrapper`
---
### Usage
1. import `bedrockWrapper`
```javascript
import { bedrockWrapper } from "bedrock-wrapper";
```
2. create an `awsCreds` object and fill in your AWS credentials
```javascript
const awsCreds = {
region: AWS_REGION,
accessKeyId: AWS_ACCESS_KEY_ID,
secretAccessKey: AWS_SECRET_ACCESS_KEY,
};
```
3. clone your openai chat completions object into `openaiChatCompletionsCreateObject` or create a new one and edit the values
```javascript
const openaiChatCompletionsCreateObject = {
"messages": messages,
"model": "Llama-3-1-8b",
"max_tokens": LLM_MAX_GEN_TOKENS,
"stream": true,
"temperature": LLM_TEMPERATURE,
"top_p": LLM_TOP_P,
};
```
the `messages` variable should be in openai's role/content format
```javascript
messages = [
{
role: "system",
content: "You are a helpful AI assistant that follows instructions extremely well. Answer the user questions accurately. Think step by step before answering the question. You will get a $100 tip if you provide the correct answer.",
},
{
role: "user",
content: "Describe why openai api standard used by lots of serverless LLM api providers is better than aws bedrock invoke api offered by aws bedrock. Limit your response to five sentences.",
},
{
role: "assistant",
content: "",
},
]
```
***the `model` value should be the corresponding `modelName` value in the `bedrock_models` section below (see Supported Models below)***
4. call the `bedrockWrapper` function and pass in the previously defined `awsCreds` and `openaiChatCompletionsCreateObject` objects
```javascript
// create a variable to hold the complete response
let completeResponse = "";
// invoke the streamed bedrock api response
for await (const chunk of bedrockWrapper(awsCreds, openaiChatCompletionsCreateObject)) {
completeResponse += chunk;
// ---------------------------------------------------
// -- each chunk is streamed as it is received here --
// ---------------------------------------------------
process.stdout.write(chunk); // ⇠ do stuff with the streamed chunk
}
// console.log(`\n\completeResponse:\n${completeResponse}\n`); // ⇠ optional do stuff with the complete response returned from the API reguardless of stream or not
```
if calling the unstreamed version you can call bedrockWrapper like this
```javascript
// create a variable to hold the complete response
let completeResponse = "";
if (!openaiChatCompletionsCreateObject.stream){ // invoke the unstreamed bedrock api response
const response = await bedrockWrapper(awsCreds, openaiChatCompletionsCreateObject);
for await (const data of response) {
completeResponse += data;
}
// ----------------------------------------------------
// -- unstreamed complete response is available here --
// ----------------------------------------------------
console.log(`\n\completeResponse:\n${completeResponse}\n`); // ⇠ do stuff with the complete response
}
---
### Supported Models
| modelName | AWS Model Id | Image |
|----------------------------|----------------------------------------------|-------|
| Claude-3-7-Sonnet-Thinking | us.anthropic.claude-3-7-sonnet-20250219-v1:0 | ✅ |
| Claude-3-7-Sonnet | us.anthropic.claude-3-7-sonnet-20250219-v1:0 | ✅ |
| Claude-3-5-Sonnet-v2 | anthropic.claude-3-5-sonnet-20241022-v2:0 | ✅ |
| Claude-3-5-Sonnet | anthropic.claude-3-5-sonnet-20240620-v1:0 | ✅ |
| Claude-3-5-Haiku | anthropic.claude-3-5-haiku-20241022-v1:0 | ❌ |
| Claude-3-Haiku | anthropic.claude-3-haiku-20240307-v1:0 | ❌ |
| Llama-3-3-70b | us.meta.llama3-3-70b-instruct-v1:0 | ❌ |
| Llama-3-2-1b | us.meta.llama3-2-1b-instruct-v1:0 | ❌ |
| Llama-3-2-3b | us.meta.llama3-2-3b-instruct-v1:0 | ❌ |
| Llama-3-2-11b | us.meta.llama3-2-11b-instruct-v1:0 | ❌ |
| Llama-3-2-90b | us.meta.llama3-2-90b-instruct-v1:0 | ❌ |
| Llama-3-1-8b | meta.llama3-1-8b-instruct-v1:0 | ❌ |
| Llama-3-1-70b | meta.llama3-1-70b-instruct-v1:0 | ❌ |
| Llama-3-1-405b | meta.llama3-1-405b-instruct-v1:0 | ❌ |
| Llama-3-8b | meta.llama3-8b-instruct-v1:0 | ❌ |
| Llama-3-70b | meta.llama3-70b-instruct-v1:0 | ❌ |
| Mistral-7b | mistral.mistral-7b-instruct-v0:2 | ❌ |
| Mixtral-8x7b | mistral.mixtral-8x7b-instruct-v0:1 | ❌ |
| Mistral-Large | mistral.mistral-large-2402-v1:0 | ❌ |
To return the list progrmatically you can import and call `listBedrockWrapperSupportedModels`:
```javascript
import { listBedrockWrapperSupportedModels } from 'bedrock-wrapper';
console.log(`\nsupported models:\n${JSON.stringify(await listBedrockWrapperSupportedModels())}\n`);
```
Additional Bedrock model support can be added.
Please modify the `bedrock_models.js` file and submit a PR 🏆 or create an Issue.
---
### Image Support
For models with image support (Claude 3.5 Sonnet, Claude 3.7 Sonnet, and Claude 3.7 Sonnet Thinking), you can include images in your messages using the following format:
```javascript
messages = [
{
role: "system",
content: "You are a helpful AI assistant that can analyze images.",
},
{
role: "user",
content: [
{ type: "text", text: "What's in this image?" },
{
type: "image_url",
image_url: {
url: "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEA..." // base64 encoded image
}
}
]
}
]
```
You can also use a direct URL to an image instead of base64 encoding:
```javascript
messages = [
{
role: "user",
content: [
{ type: "text", text: "Describe this image in detail." },
{
type: "image_url",
image_url: {
url: "https://example.com/path/to/image.jpg" // direct URL to image
}
}
]
}
]
```
You can include multiple images in a single message by adding more image_url objects to the content array.
---
### 📢 P.S.
In case you missed it at the beginning of this doc, for an even easier setup, use the 🔀 [Bedrock Proxy Endpoint](https://github.com/jparkerweb/bedrock-proxy-endpoint) project to spin up your own custom OpenAI server endpoint (using the standard `baseUrl`, and `apiKey` params).
---
### 📚 References
- [AWS Meta Llama Models User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html)
- [AWS Mistral Models User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral.html)
- [OpenAI API](https://platform.openai.com/docs/api-reference/chat/create)
- [AWS Bedrock](https://aws.amazon.com/bedrock/)
- [AWS SDK for JavaScript](https://aws.amazon.com/sdk-for-javascript/)
---
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# [🍵 tip me here](https://ko-fi.com/jparkerweb)