How to use LangChain with MultiLLMs

langChain@main.ts

import {OpenAI} from "langchain/llms/openai";
import {PromptTemplate} from "langchain/prompts";
import {LLMChain} from "langchain";
import {GoogleVertexAI} from "langchain/dist/llms/googlevertexai";


const model = new OpenAI({
    openAIApiKey: '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',
    modelName: 'text-davinci-003'
});
const modelVertextAI = new GoogleVertexAI({
    // openAIApiKey: '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',
    // modelName: 'text-davinci-003'
});

const jobSummaryTemplate =
    `
     Summarize the key points in this job posting as JSON.
     Job posting:
     {job}
    `; //skills

const resumeFixTemplate: string = `
    You an expert resume couch that gives clear advice on improvement for tech roles.
    
    List all the possible improvement to write a resume for a {role} that has this base requirements:
    
    {jobReq}
`;

const getJobSummaryPrompt = new PromptTemplate({
    template: jobSummaryTemplate,
    inputVariables: ["job"],
});

const resumeFixPrompt = new PromptTemplate({
    template: resumeFixTemplate,
    inputVariables: ["role", "jobReq"],
});

// const promptFormatted = await prompt.format(
//     {
//         job: document.querySelector('#job-posting') //coming from the user somewhere
//     });

const chainOne = new LLMChain({
    llm: model,
    prompt: getJobSummaryPrompt
})

const {skills} = await chainOne.call({
    job: document.querySelector('#job-posting')
})

const chainTwo = new LLMChain({
    llm: model,
    prompt: resumeFixPrompt
})
const suggestion = await chainTwo.call({
    role: document.querySelector('#job-role'),
    jobReq: skills
})
// {  response['skill']}

// output to use and format nicely....
console.log(suggestion)

// console.log(response)

// we actually now send the prompt to the API
// const responseFromGPT = await model.call(promptFormatted)

// the GPT response
// console.log(responseFromGPT)

// const res = await model.call(
//     `
//     Summarize the key points in this job posting as JSON.
//
//     Job posting:
//     Assists the Government in developing requirements engineering, solutions engineering, scheduling, reliability, resiliency, services development, integration, test and evaluation, maintainability and analysis across NSG, ASG and Federal Agencies.
//     Develops system and programming specifications.
//     Designs data processing solutions based on business need and technical considerations. Researches and resolves application production problems. Monitors application performance and performs run time improvement functions.
//     Prepares system documentation.
//     Performs complex engineering analysis and design tasks. Prepares specifications and designs, and implements solutions.
//     Assists in design subsystems; assists in developing standards. May work as part of a project team.
//     Evaluates IT system problems of workflow, organization, and planning.
//     Assists in the design, development, analysis, installation, maintenance, operation and servicing of computer operating/hardware systems within a cybersecurity environment; provides customer support · Works under direct supervision; does related work as required.
//     `
// )

// console.log(res)
// openAiModel.call('deddededeeded')
const jobFormEl = document.querySelector('form')

// const jobPostingInputEl = document.querySelector('#job-posting')
// const jobExpInputEl = document.querySelector('#job-exp')


// jobFormEl.addEventListener('submit', (e) => {
//     e.preventDefault()
//
//     const formData = new FormData(e.target);
//
//     const jobPostingInput = formData.get('job-posting');
//     const jobExpInput = formData.get('job-exp');
//
//     console.log(jobPostingInput)
//
// })