This will help you get started with IBM text completion models (LLMs) using LangChain. For detailed documentation on IBM watsonx.ai features and configuration options, please refer to the IBM watsonx.ai.

​
Overview

​
Integration details

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
WatsonxLLM@langchain/community❌✅✅NPM - DownloadsNPM - Version

​
Setup

To access IBM WatsonxAI models you’ll need to create an IBM watsonx.ai account, get an API key or any other type of credentials, and install the @langchain/community integration package.

​
Credentials

Head to IBM Cloud to sign up to IBM watsonx.ai and generate an API key or provide any other authentication form as presented below.

​
IAM authentication

export WATSONX_AI_AUTH_TYPE=iam
export WATSONX_AI_APIKEY=<YOUR-APIKEY>

​
Bearer token authentication

export WATSONX_AI_AUTH_TYPE=bearertoken
export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>

​
IBM watsonx.ai software authentication

export WATSONX_AI_AUTH_TYPE=cp4d
export WATSONX_AI_USERNAME=<YOUR_USERNAME>
export WATSONX_AI_PASSWORD=<YOUR_PASSWORD>
export WATSONX_AI_URL=<URL>
Once these are placed in your environment variables and object is initialized authentication will proceed automatically. Authentication can also be accomplished by passing these values as parameters to a new instance.

​
IAM authentication

import { WatsonxLLM } from "@langchain/community/llms/ibm";

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "iam",
  watsonxAIApikey: "<YOUR-APIKEY>",
};
const instance = new WatsonxLLM(props);

​
Bearer token authentication

import { WatsonxLLM } from "@langchain/community/llms/ibm";

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "bearertoken",
  watsonxAIBearerToken: "<YOUR-BEARERTOKEN>",
};
const instance = new WatsonxLLM(props);

​
IBM watsonx.ai software authentication

import { WatsonxLLM } from "@langchain/community/llms/ibm";

const props = {
  version: "YYYY-MM-DD",
  serviceUrl: "<SERVICE_URL>",
  projectId: "<PROJECT_ID>",
  watsonxAIAuthType: "cp4d",
  watsonxAIUsername: "<YOUR-USERNAME>",
  watsonxAIPassword: "<YOUR-PASSWORD>",
  watsonxAIUrl: "<url>",
};
const instance = new WatsonxLLM(props);
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

​
Installation

The LangChain IBM watsonx.ai integration lives in the @langchain/community package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/community @langchain/core
</Npm2Yarn>

​
Instantiation

Now we can instantiate our model object and generate chat completions: 
import { WatsonxLLM } from "@langchain/community/llms/ibm";

const props = {
  decoding_method: "sample",
  maxNewTokens: 100,
  minNewTokens: 1,
  temperature: 0.5,
  topK: 50,
  topP: 1,
};
const instance = new WatsonxLLM({
  version: "YYYY-MM-DD",
  serviceUrl: process.env.API_URL,
  projectId: "<PROJECT_ID>",
  // spaceId: "<SPACE_ID>",
  // idOrName: "<DEPLOYMENT_ID>",
  model: "<MODEL_ID>",
  ...props,
});
Note:
  • You must provide spaceId, projectId or idOrName(deployment id) unless you use lighweight engine which works without specifying either (refer to watsonx.ai docs)
  • Depending on the region of your provisioned service instance, use correct serviceUrl.
  • You need to specify the model you want to use for inferencing through model_id.

​
Invocation and generation

const result = await instance.invoke("Print hello world.");
console.log(result);

const results = await instance.generate([
  "Print hello world.",
  "Print bye, bye world!",
]);
console.log(results);


print('Hello world.')<|endoftext|>
{
  generations: [ [ [Object] ], [ [Object] ] ],
  llmOutput: { tokenUsage: { generated_token_count: 28, input_token_count: 10 } }
}

​
Chaining

We can chain our completion model with a prompt template like so: 
import { PromptTemplate } from "@langchain/core/prompts"

const prompt = PromptTemplate.fromTemplate("How to say {input} in {output_language}:\n")

const chain = prompt.pipe(instance);
await chain.invoke(
  {
    output_language: "German",
    input: "I love programming.",
  }
)

Ich liebe Programmieren.

To express that you are passionate about programming in German,

​
Props overwriting

Passed props at initialization will last for the whole life cycle of the object, however you may overwrite them for a single method’s call by passing second argument as below 
const result2 = await instance.invoke("Print hello world.", {
  parameters: {
    maxNewTokens: 100,
  },
});
console.log(result2);

print('Hello world.')<|endoftext|>

​
Tokenization

This package has it’s custom getNumTokens implementation which returns exact amount of tokens that would be used. 
const tokens = await instance.getNumTokens("Print hello world.");
console.log(tokens);

4

​
API reference

For detailed documentation of all IBM watsonx.ai features and configurations head to the API reference: API docs