index.js

const { default: Groq } = require("groq-sdk");
const { Ollama } = require("ollama");


const groq = new Groq({ apiKey: "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" });

async function getGroqChatCompletion(q) {
  data = await groq.chat.completions.create({
    messages: [
      {
        role: "user",
        content: q,
      },

    ],

    model: "llama-3.1-70b-versatile",
  });

  return data.choices[0]?.message?.content || "";
}


const ollama = new Ollama({ host: "http://localhost:11434" });

// Function to call the Ollama model through the library
async function callLLM(query) {
  try {
    return getGroqChatCompletion(query);
    console.log(`Prompt: ${query}`); // Log the prompt being sent
    const response = await ollama.generate({
      model: "qwen2.5-coder",
      prompt: query,
    });

    // console.log(response);

    const output = response.response.trim();
    console.log(`Response: ${output}`); // Log the response received
    return output;
  } catch (error) {
    console.error("Error:", error);
    throw error;
  }
}

// Function to create a chain of thought for the input question
async function chainOfThought(inputQuery) {
  let thoughtChain = [];

  console.log(`\nInput Question: ${inputQuery}\n`);

  const step1 = `Break down the following question into key points: "${inputQuery}"`;
  const understanding = await callLLM(step1);
  thoughtChain.push(understanding);

  const step2 = `Given the key points: "${understanding}", provide any relevant background information.`;
  const context = await callLLM(step2);
  thoughtChain.push(context);

  const step3 = `Analyze the following question based on its background information: "${inputQuery}". What are the different aspects to consider?`;
  const analysis = await callLLM(step3);
  thoughtChain.push(analysis);

  const step4 = `Based on the analysis: "${analysis}", generate possible solutions or insights.`;
  const solutions = await callLLM(step4);
  thoughtChain.push(solutions);

  const step5 = `Given the possible solutions: "${solutions}", evaluate the pros and cons, or refine the best approach.`;
  const evaluation = await callLLM(step5);
  thoughtChain.push(evaluation);

  const step6 = `Based on the evaluation: "${evaluation}", provide a concise and well-reasoned answer to the original question.`;
  const conclusion = await callLLM(step6);
  thoughtChain.push(conclusion);

  return {
    thoughtProcess: thoughtChain,
    finalAnswer: conclusion,
  };
}

// Test the function with an example question
const inputQuestion =
  "How can we improve network security in a large organization?";
chainOfThought(inputQuestion)
  .then((response) => {
    console.log("\nFinal Thought Process:", response.thoughtProcess);
    console.log("Final Answer:", response.finalAnswer);
  })
  .catch((error) => {
    console.error("Error:", error);
  });