DeepInfra

Check out available LLMs here.

Installation

npm install llamaindex @llamaindex/deepinfra

import { DeepInfra } from "@llamaindex/deepinfra";
import { Settings } from "llamaindex";
 
// Get the API key from `DEEPINFRA_API_TOKEN` environment variable
import { config } from "dotenv";
config();
Settings.llm = new DeepInfra();
 
// Set the API key
apiKey = "YOUR_API_KEY";
Settings.llm = new DeepInfra({ apiKey });

You can setup the apiKey on the environment variables, like:

export DEEPINFRA_API_TOKEN="<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.

import { Document, VectorStoreIndex } from "llamaindex";
 
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 { DeepInfra } from "@llamaindex/deepinfra";
import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
// Use custom LLM
const model = "meta-llama/Meta-Llama-3-8B-Instruct";
Settings.llm = new DeepInfra({ model, temperature: 0 });
 
async function main() {
  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);
}

Feedback

If you have any feedback, please reach out to us at feedback@deepinfra.com

API Reference

DeepInfra