# Add a Cohere Model

Prerequisites for using Cohere APIs

To get started with Cohere APIs, you need to meet the following prerequisites:

1. Cohere Account: You must create an account on the Cohere website. This will provide you with access to the API and your API key.

2. API Key: After creating your account, you will receive an API key. This key is required to authenticate your requests to the Cohere API.

3. Programming Environment: Set up a programming environment where you can make HTTP requests. This can be done using various programming languages such as Python, JavaScript, etc.

4. HTTP Client: Familiarity with an HTTP client or library (like requests in Python or axios in JavaScript) to make API calls.

5. Basic Understanding of APIs: A basic understanding of how to interact with RESTful APIs, including making requests and handling responses.

For more detailed information, you can refer to the official Cohere API documentation

Post the Prerequisites are met, you can go ahead and proceed to configure. The following details need to be added to use a model by Cohere

Model ID

The specific identifier for the model you want to use within Cohere’s ecosystem.

Display Name

A user-defined, human-readable name shown in the internal model catalog

API Key

A secret token provided by Cohere to authorize access to their API. Where to Get It: The Cohere dashboard under the API Keys section.

Gemini

Prerequisites to Host Google Gemini Models

1. Google Cloud Account - Ensure you have an active Google Cloud account.

2. Project Setup - Create a new project in the Google Cloud Console or use an existing one.

3. Enable the necessary APIs:

• Vertex AI API

• Vertex AI in Firebase API (if integrating with Firebase)

4. Billing Configuration - Upgrade your project to the Blaze (pay-as-you-go) pricing plan to access Vertex AI services.

5. Authentication and Permission- Set up appropriate authentication methods:

For server-side applications, configure a Google Cloud service account with the necessary permissions.

For mobile or web applications, utilize the Vertex AI in Firebase SDKs, which offer built-in security features like Firebase App Check.

6. SDK Integration

Depending on your development environment, integrate the relevant SDKs

7. Model Deployment - Use Vertex AI to deploy and manage your Gemini models. Vertex AI offers a suite of MLOps tools to streamline usage, deployment, and monitoring, ensuring efficiency and reliability

Post the Prerequisites are met, you can proceed to configure, you will need to add the following details for adding a model by Gemini:

Model ID

The exact identifier of the Gemini model variant you are using. Specifies which model your requests are sent to for inference tasks.

Display Name

A custom, user-defined name shown within the platform's UI.

API Key

A secure credential from Google Cloud is used to authorize API requests.