bedrock-tunnel

Bedrock Tunnel

Bedrock Tunnel is an npm package that simplifies the integration of existing OpenAI-compatible API objects AWS Bedrock's serverless inference LLMs. Follow the steps below to integrate into your own application, or alternativly use the Bedrock Tunnel Endpoint project to spin up your own custom OpenAI server endpoint for even easier inference (using the standard baseUrl, and apiKey params).

Install

• install package: npm install bedrock-tunnel

Usage

1. import awsBedrockTunnel

   import { awsBedrockTunnel } from "bedrock-tunnel";

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-3-8b",
       "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 aws_models (see the Supported Models section below)

4. call the awsBedrockTunnel 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 awsBedrockTunnel(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 awsBedrockTunnel 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 awsBedrockTunnel(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
   }

Supported Models

To return the list progrmatically you can import and call listBedrockTunnelSupportedModels:

import { listBedrockTunnelSupportedModels } from 'bedrock-tunnel';
console.log(`\nsupported models:\n${JSON.stringify(await listBedrockTunnelSupportedModels())}\n`);

Additional Bedrock model support can be added.
Please modify the aws_models.js file and submit a PR or create an Issue.

P.S.

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