Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Simon Willison's Blog·Aug 22·5 min read
llm 0.33
TL;DR
A comprehensive fix for LLM 0.33 enhances embedding plugins by allowing them to use per-call keys without altering shared model state. This update improves compatibility and flexibility in model configuration.
✦ Why It Matters
Update your embedding plugins to utilize the new per-call key feature for improved flexibility.
Key Takeaways
- Utilize per-call keys in embedding methods for better state management.
- Combine multiple templates for flexible model configurations.
- Leverage the reasoning_summary option for detailed API responses.
- Create reusable templates to simplify model setup.
How It Works
The new embedding methods allow for a per-call key, which is passed to embedding plugins, ensuring that shared model state remains unchanged. This is particularly useful for applications requiring different configurations without affecting the overall model performance.
The template management feature enables users to stack multiple templates, allowing for a modular approach to model configuration, where options from one template can be applied to prompts from another.