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Gemini

npm i llamaindex @llamaindex/google
import { gemini, GEMINI_MODEL } from "@llamaindex/google";
import { Settings } from "llamaindex";
Settings.llm = gemini({
model: GEMINI_MODEL.GEMINI_2_0_FLASH,
});

To use Gemini via Vertex AI, you can specify the vertex configuration:

import { gemini, GEMINI_MODEL } from "@llamaindex/google";
const llm = gemini({
model: GEMINI_MODEL.GEMINI_2_0_FLASH,
vertex: {
project: "your-cloud-project", // required for Vertex AI
location: "us-central1", // required for Vertex AI
},
});

To authenticate for local development:

Terminal window
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.

Gemini supports multimodal inputs including text, images, audio, and video:

import { gemini, GEMINI_MODEL } from "@llamaindex/google";
import fs from "fs";
const llm = gemini({ model: GEMINI_MODEL.GEMINI_2_0_FLASH });
const result = await llm.chat({
messages: [
{
role: "user",
content: [
{
type: "text",
text: "What's in this image?",
},
{
type: "image",
data: fs.readFileSync("./image.jpg").toString("base64"),
mimeType: "image/jpeg",
},
],
},
],
});

Gemini supports function calling with tools:

import { gemini, GEMINI_MODEL } from "@llamaindex/google";
import { tool } from "llamaindex";
import { z } from "zod";
const llm = gemini({ model: GEMINI_MODEL.GEMINI_2_0_FLASH });
const result = await llm.chat({
messages: [
{
content: "What's the weather in Tokyo?",
role: "user",
},
],
tools: [
tool({
name: "weather",
description: "Get the weather",
parameters: z.object({
location: z.string().describe("The location to get the weather for"),
}),
execute: ({ location }) => {
return `The weather in ${location} is sunny and hot`;
},
}),
],
});

For real-time audio/video conversations using Gemini Live API.

The Live API is running directly in the frontend. That’s why you have to generate an ephemeral key first on the server side and pass it to the frontend.

To use the Live API, make sure to pass apiVersion: "v1alpha" to the httpOptions.

import { gemini, GEMINI_MODEL } from "@llamaindex/google";
// Server-side: Generate ephemeral key
const serverLlm = gemini({
model: GEMINI_MODEL.GEMINI_2_0_FLASH_LIVE,
httpOptions: { apiVersion: "v1alpha" },
});
const ephemeralKey = await serverLlm.live.getEphemeralKey();
// Client-side: Use ephemeral key for Live API
const llm = gemini({
apiKey: ephemeralKey,
model: GEMINI_MODEL.GEMINI_2_0_FLASH_LIVE,
voiceName: "Zephyr",
httpOptions: { apiVersion: "v1alpha" },
});
const session = await llm.live.connect();

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]);
const queryEngine = index.asQueryEngine();
const query = "What is the meaning of life?";
const results = await queryEngine.query({
query,
});
import { gemini, GEMINI_MODEL } from "@llamaindex/google";
import { Document, VectorStoreIndex, Settings } from "llamaindex";
Settings.llm = gemini({
model: GEMINI_MODEL.GEMINI_2_0_FLASH,
});
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();
const query = "What is the meaning of life?";
// Query
const response = await queryEngine.query({
query,
});
// Log the response
console.log(response.response);
}
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/