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vlt Launches "reproduce": A New Tool Challenging the Limits of Package Provenance
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genkitx-azure-openai
Advanced tools
genkitx-azure-openai
is a community plugin for using Azure OpenAI APIs with
Firebase Genkit. Built by The Fire Company. 🔥
Install the plugin in your project with your favorite package manager:
npm install genkitx-azure-openai
yarn add genkitx-azure-openai
pnpm add genkitx-azure-openai
The interface to the models of this plugin is the same as for the OpenAI plugin.
You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this guide.
Once you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the "Keys and Endpoint" section of your instance.
You can then define the following environment variables to use the service:
AZURE_OPENAI_ENDPOINT=<YOUR_ENDPOINT>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
OPENAI_API_VERSION=<YOUR_API_VERSION>
Alternatively, you can pass the values directly to the azureOpenAI
constructor:
import { azureOpenAI, gpt4o } from 'genkitx-azure-openai';
import { genkit } from 'genkit';
const apiVersion = '2024-10-21';
const ai = genkit({
plugins: [
azureOpenAI({
apiKey: '<your_key>',
endpoint: '<your_endpoint>',
deployment: '<your_embedding_deployment_name',
apiVersion,
}),
// other plugins
],
model: gpt4o,
});
If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor:
import { azureOpenAI, gpt4o } from 'genkitx-azure-openai';
import { genkit } from 'genkit';
import {
DefaultAzureCredential,
getBearerTokenProvider,
} from '@azure/identity';
const apiVersion = '2024-10-21';
const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);
const ai = genkit({
plugins: [
azureOpenAI({
azureADTokenProvider,
endpoint: '<your_endpoint>',
deployment: '<your_embedding_deployment_name',
apiVersion,
}),
// other plugins
],
model: gpt4o,
});
The simplest way to call the text generation model is by using the helper function generate
:
// Basic usage of an LLM
const response = await ai.generate({
prompt: 'Tell me a joke.',
});
console.log(await response.text);
Using the same interface, you can prompt a multimodal model:
const response = await ai.generate({
model: gpt4o,
prompt: [
{ text: 'What animal is in the photo?' },
{ media: { url: imageUrl } },
],
config: {
// control of the level of visual detail when processing image embeddings
// Low detail level also decreases the token usage
visualDetailLevel: 'low',
},
});
console.log(await response.text);
For more detailed examples and the explanation of other functionalities, refer to the examples in the official Github repo of the plugin or in the official Genkit documentation.
Want to contribute to the project? That's awesome! Head over to our Contribution Guidelines.
[!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.
This plugin is proudly maintained by the team at The Fire Company. 🔥
This project is licensed under the Apache 2.0 License.
FAQs
Firebase Genkit AI framework plugin for Azure OpenAI APIs.
We found that genkitx-azure-openai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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