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).

• install package: npm install bedrock-wrapper

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 = "";
   // invoke the streamed bedrock api response
   if (!openaiChatCompletionsCreateObject.stream){ // invoke the unstreamed bedrock api response
       const response = await bedrockWrapper(awsCreds, openaiChatCompletionsCreateObject);
       for await (const data of response) {
           const jsonString = new TextDecoder().decode(data.body);
           const jsonResponse = JSON.parse(jsonString);
           completeResponse += jsonResponse.generation;
       }
       // ----------------------------------------------------
       // -- unstreamed complete response is available here --
       // ----------------------------------------------------
       console.log(`\n\completeResponse:\n${completeResponse}\n`); // ⇠ do stuff with the complete response
   }

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.

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).