Kye Gomez
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# Documentation for GPT4o Module
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## Overview and Introduction
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The `GPT4o` module is a multi-modal conversational model based on OpenAI's GPT-4 architecture. It extends the functionality of the `BaseMultiModalModel` class, enabling it to handle both text and image inputs for generating diverse and contextually rich responses. This module leverages the power of the GPT-4 model to enhance interactions by integrating visual information with textual prompts, making it highly relevant for applications requiring multi-modal understanding and response generation.
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### Key Concepts
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- **Multi-Modal Model**: A model that can process and generate responses based on multiple types of inputs, such as text and images.
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- **System Prompt**: A predefined prompt to guide the conversation flow.
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- **Temperature**: A parameter that controls the randomness of the response generation.
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- **Max Tokens**: The maximum number of tokens (words or word pieces) in the generated response.
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## Class Definition
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### `GPT4o` Class
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### Parameters
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| Parameter | Type | Description |
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|-----------------|--------|--------------------------------------------------------------------------------------|
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| `system_prompt` | `str` | The system prompt to be used in the conversation. |
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| `temperature` | `float`| The temperature parameter for generating diverse responses. Default is `0.1`. |
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| `max_tokens` | `int` | The maximum number of tokens in the generated response. Default is `300`. |
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| `openai_api_key`| `str` | The API key for accessing the OpenAI GPT-4 API. |
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| `*args` | | Additional positional arguments. |
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| `**kwargs` | | Additional keyword arguments. |
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## Functionality and Usage
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### `encode_image` Function
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The `encode_image` function is used to encode an image file into a base64 string format, which can then be included in the request to the GPT-4 API.
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#### Parameters
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| Parameter | Type | Description |
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|---------------|--------|----------------------------------------------|
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| `image_path` | `str` | The local path to the image file to be encoded. |
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#### Returns
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| Return Type | Description |
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|-------------|---------------------------------|
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| `str` | The base64 encoded string of the image. |
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### `GPT4o.__init__` Method
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The constructor for the `GPT4o` class initializes the model with the specified parameters and sets up the OpenAI client.
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### `GPT4o.run` Method
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The `run` method executes the GPT-4o model to generate a response based on the provided task and optional image.
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#### Parameters
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| Parameter | Type | Description |
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|---------------|--------|----------------------------------------------------|
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| `task` | `str` | The task or user prompt for the conversation. |
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| `local_img` | `str` | The local path to the image file. |
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| `img` | `str` | The URL of the image. |
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| `*args` | | Additional positional arguments. |
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| `**kwargs` | | Additional keyword arguments. |
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#### Returns
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| Return Type | Description |
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|-------------|--------------------------------------------------|
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| `str` | The generated response from the GPT-4o model. |
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## Usage Examples
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### Example 1: Basic Text Prompt
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```python
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from swarms import GPT4o
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# Initialize the model
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model = GPT4o(
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system_prompt="You are a helpful assistant.",
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temperature=0.7,
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max_tokens=150,
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openai_api_key="your_openai_api_key"
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)
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# Define the task
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task = "What is the capital of France?"
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# Generate response
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response = model.run(task)
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print(response)
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```
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### Example 2: Text Prompt with Local Image
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```python
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from swarms import GPT4o
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# Initialize the model
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model = GPT4o(
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system_prompt="Describe the image content.",
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temperature=0.5,
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max_tokens=200,
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openai_api_key="your_openai_api_key"
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)
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# Define the task and image path
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task = "Describe the content of this image."
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local_img = "path/to/your/image.jpg"
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# Generate response
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response = model.run(task, local_img=local_img)
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print(response)
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```
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### Example 3: Text Prompt with Image URL
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```python
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from swarms import GPT4o
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# Initialize the model
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model = GPT4o(
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system_prompt="You are a visual assistant.",
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temperature=0.6,
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max_tokens=250,
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openai_api_key="your_openai_api_key"
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)
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# Define the task and image URL
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task = "What can you tell about the scenery in this image?"
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img_url = "http://example.com/image.jpg"
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# Generate response
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response = model.run(task, img=img_url)
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print(response)
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```
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## Additional Information and Tips
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- **API Key Management**: Ensure that your OpenAI API key is securely stored and managed. Do not hard-code it in your scripts. Use environment variables or secure storage solutions.
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- **Image Encoding**: The `encode_image` function is crucial for converting images to a base64 format suitable for API requests. Ensure that the images are accessible and properly formatted.
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- **Temperature Parameter**: Adjust the `temperature` parameter to control the creativity of the model's responses. Lower values make the output more deterministic, while higher values increase randomness.
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- **Token Limit**: Be mindful of the `max_tokens` parameter to avoid exceeding the API's token limits. This parameter controls the length of the generated responses.
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## References and Resources
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- [OpenAI API Documentation](https://beta.openai.com/docs/)
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- [Python Base64 Encoding](https://docs.python.org/3/library/base64.html)
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- [dotenv Documentation](https://saurabh-kumar.com/python-dotenv/)
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- [BaseMultiModalModel Documentation](https://swarms.apac.ai)
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