import { ChatOllama } from "@langchain/ollama";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { z } from "zod";
//import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

// const llm = new ChatGoogleGenerativeAI({
//   model: "gemini-2.0-flash",
//   temperature: 0,
//   apiKey: "Use your own key",
// });

const llm = new ChatOllama({
  model: "llama3.2:3b",
  temperature: 0,
  baseUrl: "http://localhost:11434"
})



const classificationSchema2 = z.object({
  sentiment: z
    .enum(["happy", "neutral", "sad"])
    .describe("The sentiment of the text"),
  aggressiveness: z
    .number()
    .int()
    .describe(
      "describes how aggressive the statement is on a scale from 1 to 5. The higher the number the more aggressive"
    ),
  language: z
    .enum(["spanish", "english", "french", "german", "italian"])
    .describe("The language the text is written in"),
});

const taggingPrompt2 = ChatPromptTemplate.fromTemplate(
  `Extract the desired information from the following passage.

Passage:
{input}
`
);

const llmWithStructuredOutput2 = llm.withStructuredOutput(
  classificationSchema2,
  { name: "extractor" }
);

const prompt2 = await taggingPrompt2.invoke({
  input:
    "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
});

const result = await llmWithStructuredOutput2.invoke(prompt2);

console.log("Result:", result);