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Groq

Usage​

First, create an API key at the Groq Console. Then save it in your environment:

export GROQ_API_KEY=<your-api-key>

The initialize the Groq module.

import { Groq, Settings } from "llamaindex";

Settings.llm = new Groq({
  // If you do not wish to set your API key in the environment, you may
  // configure your API key when you initialize the Groq class.
  // apiKey: "<your-api-key>",
});

Load and index documents​

For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.

const document = new Document({ text: essay, id_: "essay" });

const index = await VectorStoreIndex.fromDocuments([document]);

Query​

const queryEngine = index.asQueryEngine();

const query = "What is the meaning of life?";

const results = await queryEngine.query({
  query,
});

Full Example​

import fs from "node:fs/promises";

import {
  Document,
  Groq,
  HuggingFaceEmbedding,
  Settings,
  VectorStoreIndex,
} from "llamaindex";

// Update llm to use Groq
Settings.llm = new Groq({
  apiKey: process.env.GROQ_API_KEY,
});

// Use HuggingFace for embeddings
Settings.embedModel = new HuggingFaceEmbedding({
  modelType: "Xenova/all-mpnet-base-v2",
});

async function main() {
  // Load essay from abramov.txt in Node
  const path = "node_modules/llamaindex/examples/abramov.txt";
  const essay = await fs.readFile(path, "utf-8");
  const document = new Document({ text: essay, id_: "essay" });

  // Load and index documents
  const index = await VectorStoreIndex.fromDocuments([document]);

  // get retriever
  const retriever = index.asRetriever();

  // Create a query engine
  const queryEngine = index.asQueryEngine({
    retriever,
  });

  const query = "What is the meaning of life?";

  // Query
  const response = await queryEngine.query({
    query,
  });

  // Log the response
  console.log(response.response);
}

main().catch(console.error);

API Reference​