Show HN: Magentic – Use LLMs as simple Python functions
📰 Hacker News · jackmpcollins
Use LLMs as simple Python functions with Magentic, a package that enables mixing regular code with LLM calls for enhanced creativity and reasoning
Action Steps
- Install Magentic using pip: 'pip install magentic'
- Define an LLM query using a Python function signature with Magentic
- Use the LLM output in your Python code, parsed according to the return type annotation
- Experiment with different LLM models and queries to find the best fit for your project
- Contribute to the Magentic project on GitHub to help improve its functionality
Who Needs to Know This
Data scientists and software engineers can benefit from Magentic to integrate LLMs into their Python workflows, making it easier to leverage AI capabilities in their projects
Key Insight
💡 Magentic allows for seamless integration of LLMs into Python code, enabling more flexible and creative AI applications
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🚀 Use LLMs as Python functions with Magentic! 🤖
Key Takeaways
Use LLMs as simple Python functions with Magentic, a package that enables mixing regular code with LLM calls for enhanced creativity and reasoning
Full Article
This is a Python package that allows you to write function signatures to define LLM queries. This makes it easy to mix regular code with calls to LLMs, which enables you to use the LLM for its creativity and reasoning while also enforcing structure/logic as necessary. LLM output is parsed for you according to the return type annotation of the function, including complex return types such as streaming an array of structured objects. I built this to show that we can think about using LLMs more fluidly than just chains and chats, i.e. more interchangeably with regular code, and to make it easy to do that. Please let me know what you think! Contributions welcome. https://github.com/jackmpcollins/magentic
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