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Gemini

Usage​

import { Gemini, Settings, GEMINI_MODEL } from "llamaindex";

Settings.llm = new Gemini({
  model: GEMINI_MODEL.GEMINI_PRO,
});

Usage with Vertex AI​

To use Gemini via Vertex AI you can use GeminiVertexSession.

GeminiVertexSession accepts the env variables: GOOGLE_VERTEX_LOCATION and GOOGLE_VERTEX_PROJECT

import { Gemini, GEMINI_MODEL, GeminiVertexSession } from "llamaindex";

const gemini = new Gemini({
  model: GEMINI_MODEL.GEMINI_PRO,
  session: new GeminiVertexSession({
    location: "us-central1",      // optional if provided by GOOGLE_VERTEX_LOCATION env variable
    project: "project1",          // optional if provided by GOOGLE_VERTEX_PROJECT env variable
    googleAuthOptions: {...},     // optional, but useful for production. It accepts all values from `GoogleAuthOptions`
  }),
});

GoogleAuthOptions

To authenticate for local development:

npm install @google-cloud/vertexai
gcloud auth application-default login

To authenticate for production you'll have to use a service account. googleAuthOptions has credentials which might be useful for you.

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 {
  Gemini,
  Document,
  VectorStoreIndex,
  Settings,
  GEMINI_MODEL,
} from "llamaindex";

Settings.llm = new Gemini({
  model: GEMINI_MODEL.GEMINI_PRO,
});

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

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

  // 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);
}