pocketgroq 0.2.0

A library for easy integration with Groq API, including image handling

PocketGroq

PocketGroq provides a simpler interface to interact with the Groq API, aiding in rapid development by abstracting complex API calls into simple functions.

Installation

Option 1: Install from PyPI (Recommended)

The easiest way to install PocketGroq is directly from PyPI using pip:

pip install pocketgroq

This will install the latest stable version of PocketGroq and its dependencies.

Option 2: Install from Source

If you want to use the latest development version or contribute to PocketGroq, you can install it from the source:

1. Clone the repository:

git clone https://github.com/jgravelle/pocketgroq.git
cd pocketgroq

2. Install the package and its dependencies:

pip install -e .

This will install PocketGroq in editable mode, allowing you to make changes to the source code and immediately see the effects.

Basic Usage

Initializing GroqProvider

from pocketgroq import GroqProvider

# Initialize the GroqProvider
groq = GroqProvider()

Simple Text Generation

response = groq.generate("Tell me a joke about programming.")
print(response)

Retrieving Available Models

models = groq.get_available_models()
print("Available Models:", models)

Overriding the Default Model

selected_model = 'llama3-groq-8b-8192-tool-use-preview'
response = groq.generate("Explain quantum computing", model=selected_model)
print("Response with Selected Model:", response)

Advanced Features

Tool Usage

PocketGroq allows you to define tools (functions) that the model can use during the conversation:

def reverse_string(input_string: str) -> dict:
    return {"reversed_string": input_string[::-1]}

tools = [
    {
        "type": "function",
        "function": {
            "name": "reverse_string",
            "description": "Reverse the given string",
            "parameters": {
                "type": "object",
                "properties": {
                    "input_string": {
                        "type": "string",
                        "description": "The string to be reversed",
                    }
                },
                "required": ["input_string"],
            },
            "implementation": reverse_string
        }
    }
]

response = groq.generate("Please reverse the string 'hello world'", tools=tools)
print("Response:", response)

Streaming Responses

For long responses, you can use streaming:

for chunk in groq.generate("Write a short story about AI", stream=True):
    print(chunk, end='', flush=True)

Asynchronous Generation

For asynchronous operations:

import asyncio

async def main():
    response = await groq.generate("Explain the theory of relativity", async_mode=True)
    print(response)

asyncio.run(main())

JSON Mode

To get responses in JSON format:

response = groq.generate("List 3 programming languages and their main uses", json_mode=True)
print(response)

Image Handling

PocketGroq supports image analysis using compatible models:

image_url = "https://example.com/image.jpg"
response = groq.generate("Describe this image in detail", image_path=image_url)
print(response)

# For local images
local_image_path = "path/to/local/image.jpg"
response = groq.generate("What objects do you see in this image?", image_path=local_image_path)
print(response)

Use Case Scenarios

1. Content Generation: Use PocketGroq for automated blog post writing, social media content creation, or product descriptions.

blog_topic = "The Future of Artificial Intelligence"
blog_post = groq.generate(f"Write a 500-word blog post about {blog_topic}")
print(blog_post)

2. Code Assistant: Leverage PocketGroq for code explanation, debugging, or generation.

code_snippet = """
def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)
"""
explanation = groq.generate(f"Explain this Python code and suggest any improvements:\n\n{code_snippet}")
print(explanation)

3. Data Analysis: Use PocketGroq to interpret data or generate data analysis reports.

data = {
    "sales": [100, 150, 200, 180, 220],
    "expenses": [80, 90, 110, 100, 130]
}
analysis = groq.generate(f"Analyze this sales and expenses data and provide insights:\n\n{data}", json_mode=True)
print(analysis)

4. Image Analysis: Utilize PocketGroq's image handling capabilities for various visual tasks.

image_url = "https://example.com/chart.jpg"
chart_analysis = groq.generate("Analyze this chart image and provide key insights", image_path=image_url)
print(chart_analysis)

5. Automated Customer Support: Implement PocketGroq in a chatbot for handling customer inquiries.

user_query = "How do I reset my password?"
response = groq.generate(f"Provide a step-by-step guide to answer this customer query: {user_query}")
print(response)

Configuration

PocketGroq uses environment variables for configuration. Set GROQ_API_KEY in your environment or in a .env file in your project root.

Error Handling

PocketGroq raises custom exceptions:

• GroqAPIKeyMissingError: Raised when the Groq API key is missing.
• GroqAPIError: Raised when there's an error with the Groq API.

Handle these exceptions in your code for robust error management.

Contributing

Feel free to open issues or submit pull requests on the GitHub repository if you encounter any problems or have feature suggestions.

License

This project is licensed under the MIT License. Mention J. Gravelle in your code and/or docs. He's kinda full of himself...