app.ts

//Import the OpenAPI Large Language Model (you can import other models here eg. Cohere)
import { ChatOpenAI } from "@langchain/openai";
import { ChatVertexAI } from "@langchain/google-vertexai";

import { ChatPromptTemplate } from "@langchain/core/prompts";
import { z } from "zod";
import * as dotenv from "dotenv";
import { BaseChatModel } from "@langchain/core/language_models/chat_models";

  //Load environment variables (populate process.env from .env file)
  dotenv.config();

  const systemTemplate = "You are an expert in {language}, you have to detect grammar problems sentences";

  const classificationSchema = z.object({
    sentiment: z
    .enum(["happy", "neutral", "sad", "angry", "frustrated"])
    .describe("The sentiment of the text"),
    aggressiveness: z
      .number()
      .int()
      .min(1)
      .max(10)
      .describe("How aggressive the text is on a scale from 1 to 10"),
      correctness: z
      .number()
      .int()
      .min(1)
      .max(10)
      .describe("How the sentece is correct grammarly the text is on a scale from 1 to 10"),
      errors: z.
      array(z.string())
      .describe("The errors in the text. Specify the proper way to write the text and where it is wrong. Explain it in a human-readable way. Write each errror in a separate string"),
      solution: z.string().describe("The solution to the errors in the text. Write the solution in {language}"),
    language: z.string().describe("The language the text is written in"),
  });


  const promptTemplate = ChatPromptTemplate.fromMessages([
    ["system", systemTemplate],
    ["user", "{text}"],
  ]);

  let model: BaseChatModel;
  if (process.env.LLM_PROVIDER == "OPENAI") {
    model = new ChatOpenAI({ 
      model: "gpt-4",
      temperature: 0,
    });
  } else {
    model = new ChatVertexAI({ 
      model: "gemini-1.5-pro-001",
      temperature: 0,
    });
  }

  export const run = async () => {

    // Name is optional, but gives the models more clues as to what your schema represents
    const llmWihStructuredOutput = model.withStructuredOutput(classificationSchema, {
        name: "extractor",
    });

    //Calls out to the model's (OpenAI's) endpoint passing the prompt. This call returns a string
    const chain = await promptTemplate.pipe(llmWihStructuredOutput);

    const result = await chain.invoke({ language: "Spanish", text: "Yo soy enfadado" });

    console.log({ result });
  };

  run();