Getting Started with the Gemini API using Python

This tutorial will guide you through the process of setting up and making your first request to the Gemini API using the Google's Generative AI Python library.

Prerequisites
Configure Your API Key
Initialize the Model
Make Your First Request
Using the Chat Interface
Exploring Further
Troubleshooting
Conclusion

Prerequisites

Before you begin, make sure you have the following:

Python 3.11+ installed
You can check your Python version by running:
```
python --version
```
or:
```
python3 --version
```
A Google Cloud Project
If you don't have one, create a new project in the Google Cloud Console.
A Gemini API Key
1. Go to Google AI Studio.
2. Click on "Get API Key".
3. Select your Google Cloud Project where you want to enable the API.
4. Click on "Create API Key". Copy this key; you'll need it later.
The google-generativeai Python library
You can install it using pip:
```
pip install google-generativeai
```

Configure Your API Key

The google-generativeai library needs your API key to authenticate your requests to the Gemini API. There are a couple of ways to provide it:

Method 1: Environment Variable (Recommended)

Set an environment variable named GOOGLE_API_KEY with your API key as the value.
- Linux/macOS:
```
export GOOGLE_API_KEY="YOUR_API_KEY"
```
- Windows:
```
setx GOOGLE_API_KEY "YOUR_API_KEY"
```
  (You might need to restart your console or IDE for it to take effect.)

Then in your Python code, configure like this:

import google.generativeai as genai
import os

genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))

Method 2: Directly in Your Code (Less Secure)

You can set your API key directly in your Python code. However, this is less secure, especially if you are sharing or versioning your code.

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")  # Replace "YOUR_API_KEY" with your actual key

Initialize the Model

Now, let's initialize the GenerativeModel with the gemini-pro model:

import google.generativeai as genai

# ... API key configuration (see "Configure Your API Key") ...

model = genai.GenerativeModel("gemini-pro")

Make Your First Request

Let's send a simple prompt to the Gemini API and get a response:

import google.generativeai as genai

# ... API key configuration and model initialization (see "Configure Your API Key" & "Initialize the Model") ...

prompt = "What is the capital of France?"

response = model.generate_content(prompt)

print(response.text)

Expected Output:

The capital of France is Paris.

(Or a similar, more elaborate response.)

Explanation:

prompt: This variable holds the text prompt you are sending to the model.
model.generate_content(prompt): This calls the API, sending the prompt and receiving the generated content.
response.text: This accesses the text part of the response from the model.

Using the Chat Interface

The Gemini API also supports a chat interface where you can have back-and-forth conversations.

import google.generativeai as genai

# ... API key configuration (see "Configure Your API Key") ...

model = genai.GenerativeModel("gemini-pro")

messages = []

messages.append({
    "role": "user",
    "parts": ["What is the capital of France?"]
})

response = model.generate_content(messages)
print(response.text)

# Add model's response back into the conversation history
messages.append({
    "role": "model",
    "parts": [response.text]
})

# User asks another question
messages.append({
    "role": "user",
    "parts": ["And what is its population?"]
})

response = model.generate_content(messages)
print(response.text)

Explanation

messages: This list stores the history of your conversation.
Adding messages: Each item in messages is a dictionary with:
- role: Either "user" or "model".
- parts: A list of strings (or other content parts) representing the message content.
model.generate_content(messages): Takes the entire message history to provide context for the model.
Appending the model's response: To maintain conversation flow, you append the AI's response to messages so it “remembers” earlier turns.

Exploring Further

More Model Parameters
Check out the Google AI for Developers documentation to learn about additional parameters (e.g., temperature, top_k, top_p) for controlling the model's generation.
Safety Settings
You can configure safety settings to control what type of content the model generates. See the Safety Settings documentation.
Other Models
Explore other available models, such as gemini-pro-vision for multimodal input (text + images).

Troubleshooting

API key not found error
Make sure your API key is correctly configured as an environment variable or in your code.
PermissionDenied error
Verify that the API key is associated with a Google Cloud project that has the Gemini API enabled.
Other errors
Refer to the Gemini API documentation for more detailed error messages and troubleshooting steps.

Conclusion

Congratulations! You've now successfully made your first request to the Gemini API and even tried out a simple conversation. This is just the beginning of what you can do with this powerful API. Explore the documentation and experiment with different prompts and model parameters to unlock the full potential of Google's Generative AI models.

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