How To Create A Function Calling Gemini API With Company Token

July 29, 2025 by StackCamp Team 63 views

Creating a Function to Call Gemini with Your Company's Token

Hey guys! Today, we're diving into how to create a function that calls the Gemini API using your company's token. This is super useful for integrating Gemini's powerful capabilities into your applications while ensuring secure access with your organization's credentials. We'll break down the process step by step, making it easy to follow along, even if you're not a coding whiz. Let's get started!

Understanding the Basics of Gemini API

Before we jump into the code, let's quickly recap what the Gemini API is and why you might want to use it. Gemini is a cutting-edge AI model developed by Google, capable of a wide range of tasks, including text generation, translation, question answering, and more. The Gemini API allows you to access these capabilities programmatically, meaning you can integrate them into your own applications and workflows. Think of it as having a super-smart AI assistant ready to help with all sorts of tasks!

To access the Gemini API, you'll need an API key or token. This token acts as your company's unique identifier, ensuring that only authorized users can access the API and preventing unauthorized use. Keeping your API token secure is crucial, so we'll cover best practices for handling it in our function. You should treat your API token like a password and avoid hardcoding it directly into your script. Instead, use environment variables or a secure configuration file to store the token. This prevents accidental exposure of the token if your code is shared or committed to a public repository. The Gemini API is a powerful tool, but it's essential to use it responsibly and ethically. Ensure that your applications comply with the Gemini API's terms of service and any relevant regulations. Consider the potential impact of your application and take steps to mitigate any negative consequences. For example, if you're using Gemini to generate text, ensure that the output is accurate and does not promote misinformation or harmful content. Regularly review your application's usage of the Gemini API to identify and address any potential issues. This includes monitoring API usage limits and optimizing your code to minimize unnecessary calls. You can also use monitoring tools to track the performance of your application and identify any bottlenecks or errors. By taking a proactive approach to monitoring, you can ensure that your application continues to function smoothly and efficiently.

Setting Up Your Environment

Okay, before we start writing any code, let's set up our environment. First, you'll need to have Python installed on your system. If you don't already have it, head over to the Python website and download the latest version. Once you have Python installed, you'll need to install the google-generativeai library, which provides the necessary tools for interacting with the Gemini API. You can install it using pip, Python's package installer. Open your terminal or command prompt and run the following command:

pip install google-generativeai

This command will download and install the google-generativeai library and its dependencies. You'll also need to obtain your company's Gemini API token. This token is usually provided by your organization's IT department or the administrator responsible for managing API access. Once you have the token, you'll want to store it securely. As we mentioned earlier, it's best practice to avoid hardcoding the token directly into your script. Instead, we'll use an environment variable. An environment variable is a variable that is set outside of your script and can be accessed by your code. This allows you to keep sensitive information like API tokens separate from your code, making it more secure. To set an environment variable, you'll need to follow the instructions for your operating system. On most systems, you can set environment variables in your shell configuration file (e.g., .bashrc or .zshrc on Linux/macOS) or in the system settings (on Windows). For example, to set an environment variable named GEMINI_API_TOKEN on Linux/macOS, you can add the following line to your shell configuration file:

export GEMINI_API_TOKEN="YOUR_API_TOKEN"

Replace YOUR_API_TOKEN with your actual API token. After setting the environment variable, you'll need to reload your shell configuration or restart your terminal for the changes to take effect. Now that we've got our environment set up, we're ready to start writing the function to call the Gemini API!

Writing the Function to Call Gemini

Alright, let's get to the fun part – writing the function! We'll use Python for this example, but the general principles apply to other programming languages as well. Here's a basic outline of what our function will do:

Import necessary libraries.
Get the API token from the environment variable.
Initialize the Gemini API client.
Define the function to call the API.
Handle any errors that may occur.

Here's the Python code for the function:

import os
import google.generativeai as genai

def call_gemini(prompt):
    """Calls the Gemini API with the given prompt and returns the response."""
    try:
        # Get the API token from the environment variable
        api_token = os.environ.get("GEMINI_API_TOKEN")
        if not api_token:
            raise ValueError("GEMINI_API_TOKEN environment variable not set.")

        # Configure the Gemini API
        genai.configure(api_key=api_token)

        # Set up the model
        model = genai.GenerativeModel('gemini-pro')

        # Generate content with safety settings
        safety_settings = [
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "threshold": "BLOCK_MEDIUM_AND_ABOVE"
            },
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "threshold": "BLOCK_MEDIUM_AND_ABOVE"
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "threshold": "BLOCK_MEDIUM_AND_ABOVE"
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_MEDIUM_AND_ABOVE"
            }
        ]

        # Generate content
        response = model.generate_content(prompt, safety_settings=safety_settings)

        # Return the response
        return response.text

    except Exception as e:
        print(f"Error calling Gemini API: {e}")
        return None

Let's break down this code step by step:

First, we import the necessary libraries: os for accessing environment variables and google.generativeai for interacting with the Gemini API.
Next, we define the call_gemini function, which takes a prompt as input. The prompt is the text that you want to send to the Gemini API to generate a response.
Inside the function, we first try to get the API token from the GEMINI_API_TOKEN environment variable using os.environ.get(). If the environment variable is not set, we raise a ValueError to indicate that the token is missing.
Then, we configure the Gemini API using genai.configure(), passing in the API token. This tells the library which token to use for authentication.
We set up the model using genai.GenerativeModel('gemini-pro'). This specifies which Gemini model we want to use. In this case, we're using the gemini-pro model, which is a powerful general-purpose model. There might be other models available depending on your needs and the Gemini API's offerings.
We then generate content using model.generate_content(), passing in the prompt and safety_settings. The safety_settings parameter allows us to control the types of content that the API generates. In this example, we're blocking content that falls into the categories of harassment, hate speech, sexually explicit content, and dangerous content. You can adjust these settings to suit your needs.
Finally, we return the generated text using response.text. We also include a try...except block to catch any errors that may occur during the API call. If an error occurs, we print an error message and return None.

Testing the Function

Now that we've written the function, let's test it out! Here's an example of how you can use the call_gemini function:

prompt = "Write a short story about a cat who goes on an adventure."
response = call_gemini(prompt)
if response:
    print(response)

This code first defines a prompt, which is the text we want Gemini to use as a starting point for generating a story. Then, we call the call_gemini function with the prompt and store the response in the response variable. Finally, we check if the response is not None (meaning that the API call was successful) and print the generated text. When you run this code, you should see a short story generated by Gemini printed to your console. The story will vary depending on the prompt you provide and the specific version of the Gemini model you're using. You can experiment with different prompts to see what kinds of content Gemini can generate. Remember to keep your prompts clear and specific to get the best results. You can also try adjusting the safety settings to see how they affect the generated content. For example, you could lower the threshold for blocking harmful content or remove certain categories from the safety settings altogether.

Best Practices and Security Considerations

Before we wrap up, let's talk about some best practices and security considerations for using the Gemini API in your applications. We've already touched on the importance of storing your API token securely, but let's reiterate that point. Never hardcode your API token directly into your script or commit it to a public repository. Always use environment variables or a secure configuration file to store the token. Another important best practice is to handle errors gracefully. The Gemini API may return errors for various reasons, such as rate limits, invalid requests, or server issues. Your application should be able to handle these errors gracefully and provide informative messages to the user. Use the try...except block in our call_gemini function as a starting point for error handling. You can add more specific error handling logic to catch different types of exceptions and take appropriate actions. For example, you might want to implement retry logic for rate limit errors or log errors for debugging purposes. It's also important to be mindful of the Gemini API's rate limits. Rate limits are restrictions on the number of API calls you can make within a certain time period. If you exceed the rate limits, your API calls may be throttled or rejected. Check the Gemini API's documentation for the specific rate limits that apply to your account. You can optimize your code to minimize the number of API calls you make. For example, you can cache the results of API calls and reuse them when appropriate. You can also use batching techniques to send multiple requests in a single API call. By following these best practices and security considerations, you can ensure that your applications use the Gemini API safely and efficiently.

Conclusion

So, there you have it, guys! We've walked through the process of creating a function to call the Gemini API using your company's token. We covered everything from setting up your environment to writing the function, testing it out, and discussing best practices and security considerations. By following these steps, you can seamlessly integrate Gemini's powerful AI capabilities into your applications and workflows. Remember to always prioritize security and handle your API token with care. Happy coding!