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
-
import
bedrockWrapper
import { bedrockWrapper } from "bedrock-wrapper";
-
create an
awsCreds
object and fill in your AWS credentialsconst awsCreds = { region: AWS_REGION, accessKeyId: AWS_ACCESS_KEY_ID, secretAccessKey: AWS_SECRET_ACCESS_KEY, };
-
clone your openai chat completions object into
openaiChatCompletionsCreateObject
or create a new one and edit the valuesconst 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 formatmessages = [ { 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 $100 tip if you provide the correct answer.", }, { role: "user", content: "Describe why openai api standard used by lots of serverless LLM api providers is better than aws bedrock invoke api offered by aws bedrock. Limit your response to five sentences.", }, { role: "assistant", content: "", }, ]
the
model
value should be either a correspondingmodelName
ormodelId
for the supportedbedrock_models
(see the Supported Models section below) -
call the
bedrockWrapper
function and pass in the previously definedawsCreds
andopenaiChatCompletionsCreateObject
objects// create a variable to hold the complete response let completeResponse = ""; // invoke the streamed bedrock api response for await (const chunk of bedrockWrapper(awsCreds, openaiChatCompletionsCreateObject)) { completeResponse += chunk; // --------------------------------------------------- // -- each chunk is streamed as it is received here -- // --------------------------------------------------- process.stdout.write(chunk); // ⇠ do stuff with the streamed chunk } // console.log(`\n\completeResponse:\n${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${completeResponse}\n`); // ⇠ do stuff with the complete response }
modelName | modelId |
---|---|
Llama-3-1-8b | meta.llama3-1-8b-instruct-v1:0 |
Llama-3-1-70b | meta.llama3-1-70b-instruct-v1:0 |
Llama-3-1-405b | meta.llama3-1-405b-instruct-v1:0 |
Llama-3-8b | meta.llama3-8b-instruct-v1:0 |
Llama-3-70b | meta.llama3-70b-instruct-v1:0 |
Mistral-7b | mistral.mistral-7b-instruct-v0:2 |
Mixtral-8x7b | mistral.mixtral-8x7b-instruct-v0:1 |
Mistral-Large | mistral.mistral-large-2402-v1:0 |
To return the list progrmatically you can import and call listBedrockWrapperSupportedModels
:
import { listBedrockWrapperSupportedModels } from 'bedrock-wrapper';
console.log(`\nsupported models:\n${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.
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).