Firebase Genkit <> Hugging Face Models Plugin

Hugging Face Models Community Plugin for Google Firebase Genkit

genkitx-huggingface is a community plugin for using Hugging Face Models APIs with Firebase Genkit. Built by Xavier Portilla Edo.

This Genkit plugin allows to use Hugging Face models through their official APIs.

Installation

Install the plugin in your project with your favorite package manager:

• npm install genkitx-huggingface
• pnpm add genkitx-huggingface

Usage

Configuration

To use the plugin, you need to configure it with your Hugging Face Token key. You can do this by calling the genkit function:

import { genkit, z } from 'genkit';
import { huggingface, openAIGpt4o } from "genkitx-huggingface";

const ai = genkit({
  plugins: [
    huggingface({
      huggingfaceToken: '<my-huggingface-token>',
    }),
    openAIGpt4o,
  ]
});

You can also initialize the plugin in this way if you have set the HUGGINGFACE_TOKEN environment variable:

import { genkit, z } from 'genkit';
import { huggingface, openAIGpt4o } from "genkitx-huggingface";

const ai = genkit({
  plugins: [
    huggingface({
      huggingfaceToken: '<my-huggingface-token>',
    }),
    openAIGpt4o,
  ]
});

Basic examples

The simplest way to call the text generation model is by using the helper function generate:

import { genkit, z } from 'genkit';
import { huggingface, openAIGpt4o } from "genkitx-huggingface";

// Basic usage of an LLM
const response = await ai.generate({
  prompt: 'Tell me a joke.',
});

console.log(await response.text);

Within a flow

// ...configure Genkit (as shown above)...

export const myFlow = ai.defineFlow(
  {
    name: 'menuSuggestionFlow',
    inputSchema: z.string(),
    outputSchema: z.string(),
  },
  async (subject) => {
    const llmResponse = await ai.generate({
      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
    });

    return llmResponse.text;
  }
);

Tool use

// ...configure Genkit (as shown above)...

const specialToolInputSchema = z.object({ meal: z.enum(["breakfast", "lunch", "dinner"]) });
const specialTool = ai.defineTool(
  {
    name: "specialTool",
    description: "Retrieves today's special for the given meal",
    inputSchema: specialToolInputSchema,
    outputSchema: z.string(),
  },
  async ({ meal }): Promise<string> => {
    // Retrieve up-to-date information and return it. Here, we just return a
    // fixed value.
    return "Baked beans on toast";
  }
);

const result = ai.generate({
  tools: [specialTool],
  prompt: "What's for breakfast?",
});

console.log(result.then((res) => res.text));

For more detailed examples and the explanation of other functionalities, refer to the official Genkit documentation.

Supported models

This plugin supports all currently available Chat/Completion and Embeddings models from Hugging Face Models. This plugin supports image input and multimodal models.

API Reference

This plugin supports all Hugging Face models available in the Inference Providers on the Hub.

Contributing

Want to contribute to the project? That's awesome! Head over to our Contribution Guidelines.

Need support?

Note

This repository depends on Google's Firebase Genkit. For issues and questions related to Genkit, please refer to instructions available in Genkit's repository.

Reach out by opening a discussion on GitHub Discussions.

Credits

This plugin is proudly maintained by Xavier Portilla Edo Xavier Portilla Edo.

I got the inspiration, structure and patterns to create this plugin from the Genkit Community Plugins repository built by the Fire Compnay as well as the ollama plugin.

License

This project is licensed under the Apache 2.0 License.