Beta: Firebase Genkit is in Beta, which means that it is not subject to any SLA or deprecation policy and could change in backwards-incompatible ways. Throughout the Beta period, Firebase Genkit and its documentation will be updated and improved.

Google Generative AI plugin

The Google Generative AI plugin provides interfaces to Google's Gemini models through the Gemini API.

Installation

npm i --save @genkit-ai/googleai

Configuration

To use this plugin, specify it when you initialize Genkit:

import { genkit } from 'genkit';
import { googleAI } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
});

The plugin requires an API key for the Gemini API, which you can get from Google AI Studio.

Configure the plugin to use your API key by doing one of the following:

Set the GOOGLE_GENAI_API_KEY environment variable to your API key.
Specify the API key when you initialize the plugin:
```
googleAI({ apiKey: yourKey });
```
However, don't embed your API key directly in code! Use this feature only in conjunction with a service like Cloud Secret Manager or similar.

Usage

This plugin statically exports references to its supported models:

import {
  gemini15Flash,
  gemini15Pro,
  textEmbedding004,
} from '@genkit-ai/googleai';

You can use these references to specify which model generate() uses:

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

const llmResponse = await ai.generate('Tell me a joke.');

or use embedders (ex. textEmbedding004) with embed or retrievers:

const ai = genkit({
  plugins: [googleAI()],
});

const embedding = await ai.embed({
  embedder: textEmbedding004,
  content: input,
});

Gemini Files API

You can use files uploaded to the Gemini Files API with Genkit:

import { GoogleAIFileManager } from '@google/generative-ai/server';
import { genkit } from 'genkit';
import { googleAI } from '@genkit-ai/googleai';

const ai = genkit({
  plugins: [googleAI()],
});

const fileManager = new GoogleAIFileManager(process.env.GOOGLE_GENAI_API_KEY);
const uploadResult = await fileManager.uploadFile(
  'path/to/file.jpg',
  {
    mimeType: 'image/jpeg',
    displayName: 'Your Image',
  }
);

const response = await ai.generate({
  model: gemini15Flash,
  prompt: [
    {text: 'Describe this image:'},
    {media: {contentType: uploadResult.file.mimeType, url: uploadResult.file.uri}}
  ]
});

Fine-tuned models

You can use models fine-tuned with the Google Gemini API. Follow the instructions from the Gemini API or fine-tune a model using AI Studio.

The tuning process uses a base model—for example, Gemini 1.5 Flash—and your provided examples to create a new tuned model. Remember the base model you used, and copy the new model's ID.

When calling the tuned model in Genkit, use the base model as the model parameter, and pass the tuned model's ID as part of the config block. For example, if you used Gemini 1.5 Flash as the base model, and got the model ID tunedModels/my-example-model-apbm8oqbvuv2 you can call it with:

const ai = genkit({
  plugins: [googleAI()],
});

const llmResponse = await ai.generate({
  prompt: `Suggest an item for the menu of fish themed restruant`,
  model: gemini15Flash.withConfig({
    version: "tunedModels/my-example-model-apbm8oqbvuv2",
  }),
});

Context Caching

The Google Generative AI plugin supports context caching, which allows models to reuse previously cached content to optimize performance and reduce latency for repetitive tasks. This feature is especially useful for conversational flows or scenarios where the model references a large body of text consistently across multiple requests.

How to Use Context Caching

To enable context caching, ensure your model supports it. For example, gemini15Flash and gemini15Pro are models that support context caching.

You can define a caching mechanism in your application like this:

const ai = genkit({
  plugins: [googleAI()],
});

const llmResponse = await ai.generate({
  messages: [
    {
      role: 'user',
      content: [{ text: 'Here is the relevant text from War and Peace.' }],
    },
    {
      role: 'model',
      content: [
        {
          text: 'Based on War and Peace, here is some analysis of Pierre Bezukhov’s character.',
        },
      ],
      metadata: {
        cache: {
          ttlSeconds: 300, // Cache this message for 5 minutes
        },
      },
    },
  ],
  model: gemini15Flash,
  config: {
    version: 'gemini-1.5-flash-001', // Only 001 currently supports context caching
  },
  prompt: 'Describe Pierre’s transformation throughout the novel.',
});

In this setup: - messages: Allows you to pass conversation history. - metadata.cache.ttlSeconds: Specifies the time-to-live (TTL) for caching a specific response.

Example: Leveraging Large Texts with Context

For applications referencing long documents, such as War and Peace or Lord of the Rings, you can structure your queries to reuse cached contexts:

const fs = require('fs/promises');

const textContent = await fs.readFile('path/to/war_and_peace.txt', 'utf-8');

const llmResponse = await ai.generate({
  messages: [
    {
      role: 'user',
      content: [{ text: textContent }], // Include the large text as context
    },
    {
      role: 'model',
      content: [
        {
          text: 'This analysis is based on the provided text from War and Peace.',
        },
      ],
      metadata: {
        cache: {
          ttlSeconds: 300, // Cache the response to avoid reloading the full text
        },
      },
    },
  ],
  model: gemini15Flash,
  config: {
    version: 'gemini-1.5-flash-001', // Only 001 currently supports context caching
  },
  prompt: 'Analyze the relationship between Pierre and Natasha.',
});

Caching other modes of content

The Gemini models are multi-modal, and other modes of content are allowed to be cached as well.

For example, to cache a long piece of video content, you must first upload using the file manager from the Google AI SDK:

import { GoogleAIFileManager } from '@google/generative-ai/server';

const fileManager = new GoogleAIFileManager(
  process.env.GOOGLE_GENAI_API_KEY
);

// Upload video to Google AI using the Gemini Files API
const uploadResult = await fileManager.uploadFile(videoFilePath, {
  mimeType: 'video/mp4', // Adjust according to the video format
  displayName: 'Uploaded Video for Analysis',
});

const fileUri = uploadResult.file.uri;

Now you may configure the cache in your calls to ai.generate: ts const analyzeVideoResponse = await ai.generate({ messages: [ { role: 'user', content: [ { media: { url: fileUri, // Use the uploaded file URL contentType: 'video/mp4', }, }, ], }, { role: 'model', content: [ { text: 'This video seems to contain several key moments. I will analyze it now and prepare to answer your questions.', }, ], // Everything up to (including) this message will be cached. metadata: { cache: true, }, }, ], config: { version: 'gemini-1.5-flash-001', // Only 001 versions support context caches }, model: gemini15Flash, prompt: query, });

Supported Models for Context Caching

Only specific models, such as gemini15Flash and gemini15Pro, support context caching. If an unsupported model is used, an error will be raised, indicating that caching cannot be applied.