🪨 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 project to spin up your own custom OpenAI server endpoint for even easier inference (using the standard baseUrl, and apiKey params).

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Maintained by

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Install

• install package: npm install bedrock-wrapper

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Usage

1. import bedrockWrapper

   import { bedrockWrapper } from "bedrock-wrapper";

2. create an awsCreds object and fill in your AWS credentials

   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

   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

   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 either a corresponding modelName or modelId for the supported bedrock_models (see the Supported Models section below)

4. call the bedrockWrapper function and pass in the previously defined awsCreds and openaiChatCompletionsCreateObject objects

   // 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

   // 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
   }

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Supported Models

modelName	modelId
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-3-2-1b	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:

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.

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📢 P.S.

In case you missed it at the beginning of this doc, for an even easier setup, use the 🔀 Bedrock Proxy Endpoint project to spin up your own custom OpenAI server endpoint (using the standard baseUrl, and apiKey params).

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📚 References

• AWS Meta Llama Models User Guide
• AWS Mistral Models User Guide
• OpenAI API
• AWS Bedrock
• AWS SDK for JavaScript

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